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- Literature Review on the Influence of Wind Power on System Frequency and Frequency Regulation Technologies of Wind Power
- Current Development and Research Prospect of VSC-MTDC
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- Impact of Large-scale Electric Vehicle Charging on Voltage Quality of Distribution Network and Relevant Countermeasures
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The "Latest Accepted" column displays the articles officially accepted by the journal after peer review. These articles are currently in the process of editing, typesetting and author revision, and have not yet been finalized.
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, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0136
Abstract:
With increasing penetration of wind power, how to improve the operational stability of active distribution network while reducing operating costs has become an urgent problem to be solved. A robust coordinated active-reactive power optimization for the distribution network is established in this paper, aiming at reducing the operation costs and voltage deviation of the distribution network. The decision variables include the operation strategy of energy storage system, output of gas turbine and active management measures such as adjustment of transformer tap, capacitor banks. The MVEE (Minimum Volume Enclosing Ellipsoid) algorithm is used to transform the correlated wind power output scenarios into elliptic uncertain set constraints considering the correlation of wind turbine output, which improves the conservative property of the traditional interval uncertainty set of robust optimization. The conic duality theorem is used to transform the proposed robust model into bi-level mixed-integer second-order cone programming problem. The column constrained generation algorithm is used to solve the two-layer optimal scheduling problem. The simulation analysis is carried out on the improved IEEE 33-bus distribution network. The influence of wind power output correlation on the distribution network optimal scheduling is studied, and the economic and robustness of the proposed method are verified.
With increasing penetration of wind power, how to improve the operational stability of active distribution network while reducing operating costs has become an urgent problem to be solved. A robust coordinated active-reactive power optimization for the distribution network is established in this paper, aiming at reducing the operation costs and voltage deviation of the distribution network. The decision variables include the operation strategy of energy storage system, output of gas turbine and active management measures such as adjustment of transformer tap, capacitor banks. The MVEE (Minimum Volume Enclosing Ellipsoid) algorithm is used to transform the correlated wind power output scenarios into elliptic uncertain set constraints considering the correlation of wind turbine output, which improves the conservative property of the traditional interval uncertainty set of robust optimization. The conic duality theorem is used to transform the proposed robust model into bi-level mixed-integer second-order cone programming problem. The column constrained generation algorithm is used to solve the two-layer optimal scheduling problem. The simulation analysis is carried out on the improved IEEE 33-bus distribution network. The influence of wind power output correlation on the distribution network optimal scheduling is studied, and the economic and robustness of the proposed method are verified.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0135
Abstract:
The scale of DC microgrid systems continues to increase, and a large number of converters are integrated into the power grid, resulting in severe stability problems, and the traditional impedance analysis method has certain limitations in establishing an equivalent model of complex topological networks. To this end, this paper proposes an improved impedance analysis method based on the generalized impedance ratio, first popularizes the concept of traditional impedance ratio, establishes a system equivalent model according to the port characterristics, and derives the concept of generalized impedance ratio to characterize system stability. The system is further modeled with small signals, and a stability criterion based on the generalized impedance ratio is obtained for stability analysis. Finally, the correctness and effectiveness of the stability analysis of the proposed method in the multivariate DC microgrid system are verified by example analysis and time domain simulation.
The scale of DC microgrid systems continues to increase, and a large number of converters are integrated into the power grid, resulting in severe stability problems, and the traditional impedance analysis method has certain limitations in establishing an equivalent model of complex topological networks. To this end, this paper proposes an improved impedance analysis method based on the generalized impedance ratio, first popularizes the concept of traditional impedance ratio, establishes a system equivalent model according to the port characterristics, and derives the concept of generalized impedance ratio to characterize system stability. The system is further modeled with small signals, and a stability criterion based on the generalized impedance ratio is obtained for stability analysis. Finally, the correctness and effectiveness of the stability analysis of the proposed method in the multivariate DC microgrid system are verified by example analysis and time domain simulation.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0144
Abstract:
With the continuous promotion of China's electricity market-oriented reform, the on-grid price of coal-fired power generation is formed by the market, and the power grid companies organize power purchase as the agency, which indicates that the market-oriented reform has entered a new stage. In provinces that do not actually operate the spot market, due to the cancellation of the base electricity, the power market lacks a supplementary mechanism to deal with the monthly deviation electricity. In this paper, the electricity composition of medium and long term power generation and the consumption is analyzed based on the situation that the power gird companies organize power purchase as the agency. Then the causes of medium and long term monthly electricity deviation is given. A market equilibrium model and three monthly balance mechanisms are proposed. An example simulation of a provincial power grid in East China is given to analyze their characteristics and adaptability. Finally, corresponding suggestions are put forward for China’s market design based on analysis of problems and development needs.
With the continuous promotion of China's electricity market-oriented reform, the on-grid price of coal-fired power generation is formed by the market, and the power grid companies organize power purchase as the agency, which indicates that the market-oriented reform has entered a new stage. In provinces that do not actually operate the spot market, due to the cancellation of the base electricity, the power market lacks a supplementary mechanism to deal with the monthly deviation electricity. In this paper, the electricity composition of medium and long term power generation and the consumption is analyzed based on the situation that the power gird companies organize power purchase as the agency. Then the causes of medium and long term monthly electricity deviation is given. A market equilibrium model and three monthly balance mechanisms are proposed. An example simulation of a provincial power grid in East China is given to analyze their characteristics and adaptability. Finally, corresponding suggestions are put forward for China’s market design based on analysis of problems and development needs.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0130
Abstract:
By means of aggregation operation the demand side resource represented by electric vehicle (abbr. EV) can provide a lot of adjustable resource for power grid to mitigate the contradiction of balanced operation of the system. However, its business operation mode is not yet mature and one of the key reasons is that the aggregate value of EV is difficult to evaluate, so it is hard to support the construction of relevant market trading mechanism. Firstly, by means of bringing in the modeling method of value network, the value creation mode of EV aggregation operation was established to construct the value network model of EV aggregation operation. Secondly, from the perspective of EV aggregators the multi-agent value exchange relationship within the aggregation system was analyzed, and the path to obtain multi-agent value acquisition under aggregation mode was proposed. Finally, combining with a certain domestic regional market environment, the benefits of each agent in the market with/without aggregator were analyzed. Simulation results show that the EV aggregation operation makes each agent enable to generate incremental revenue, and under existing mode the aggregators obtain the most benefit. The market compensation coefficient and the increment of accommodation proportion of new energy is the key factor impacting the benefits of all parties.
By means of aggregation operation the demand side resource represented by electric vehicle (abbr. EV) can provide a lot of adjustable resource for power grid to mitigate the contradiction of balanced operation of the system. However, its business operation mode is not yet mature and one of the key reasons is that the aggregate value of EV is difficult to evaluate, so it is hard to support the construction of relevant market trading mechanism. Firstly, by means of bringing in the modeling method of value network, the value creation mode of EV aggregation operation was established to construct the value network model of EV aggregation operation. Secondly, from the perspective of EV aggregators the multi-agent value exchange relationship within the aggregation system was analyzed, and the path to obtain multi-agent value acquisition under aggregation mode was proposed. Finally, combining with a certain domestic regional market environment, the benefits of each agent in the market with/without aggregator were analyzed. Simulation results show that the EV aggregation operation makes each agent enable to generate incremental revenue, and under existing mode the aggregators obtain the most benefit. The market compensation coefficient and the increment of accommodation proportion of new energy is the key factor impacting the benefits of all parties.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0138
Abstract:
Based on the active participation of the distributed generation (DG) and distribution static synchronous compensator in voltage regulation, this paper proposes a voltage support strategy based on zone-division control in the active distribution network. First, the concept of DG voltage support capability distribution is proposed. Second, a specific DG configuration function is designed based on the distribution of voltage support capability, and the configuration scheme of each voltage regulating equipment is given. Then, by analyzing the voltage support capability of each piece of equipment, the voltage support region and joint support region of each piece of equipment is divided, and a voltage support operation strategy based on zone-division control is proposed. Finally, the effectiveness of the proposed method is verified in the IEEE BUS-33 case. The result shows that the proposed strategy can make full use of the potential of DG voltage support, avoid the conflicting actions of various voltage regulation equipment, ensure that the reactive power compensation equipment maintains a certain capacity reserve, and achieve a good voltage regulation effect.
Based on the active participation of the distributed generation (DG) and distribution static synchronous compensator in voltage regulation, this paper proposes a voltage support strategy based on zone-division control in the active distribution network. First, the concept of DG voltage support capability distribution is proposed. Second, a specific DG configuration function is designed based on the distribution of voltage support capability, and the configuration scheme of each voltage regulating equipment is given. Then, by analyzing the voltage support capability of each piece of equipment, the voltage support region and joint support region of each piece of equipment is divided, and a voltage support operation strategy based on zone-division control is proposed. Finally, the effectiveness of the proposed method is verified in the IEEE BUS-33 case. The result shows that the proposed strategy can make full use of the potential of DG voltage support, avoid the conflicting actions of various voltage regulation equipment, ensure that the reactive power compensation equipment maintains a certain capacity reserve, and achieve a good voltage regulation effect.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0358
Abstract:
Frequency fluctuations will inevitably occur when the microgrid with hybrid DC power supply of line commutated converter (abbr. LCC)-voltage source converter (abbr. VSC) is disturbed. In order to keep the frequency within the specified range under the different kinds of disturbances, a DC frequency regulation method with self-adapt disturbance changes was proposed in the LCC-VSC supplying microgrid system. Firstly, the critical droop coefficient required to pull the system frequency into the specified range after disturbance was theoretically deduced, and based on this, a primary frequency regulation method of variable droop coefficient with self-adapt disturbance changes was proposed to rapidly control the disturbed system frequency within the specified range by means of the rapidity of DC reaction. Secondly, as the primary frequency regulation is still a differential regulation, the secondary frequency regulation strategy of variable reference power was proposed. Finally, a simulation model was established on the PSCAD/EMTDC platform. The results show that when the load changes within 12.5% and the light changes within 10%, the primary frequency regulation can restore the frequency deviation to within 0.2 Hz, and when the load changes within 15%, the secondary frequency regulation can achieve zero-error frequency regulation, which verifies the effectiveness of the proposed strategy.
Frequency fluctuations will inevitably occur when the microgrid with hybrid DC power supply of line commutated converter (abbr. LCC)-voltage source converter (abbr. VSC) is disturbed. In order to keep the frequency within the specified range under the different kinds of disturbances, a DC frequency regulation method with self-adapt disturbance changes was proposed in the LCC-VSC supplying microgrid system. Firstly, the critical droop coefficient required to pull the system frequency into the specified range after disturbance was theoretically deduced, and based on this, a primary frequency regulation method of variable droop coefficient with self-adapt disturbance changes was proposed to rapidly control the disturbed system frequency within the specified range by means of the rapidity of DC reaction. Secondly, as the primary frequency regulation is still a differential regulation, the secondary frequency regulation strategy of variable reference power was proposed. Finally, a simulation model was established on the PSCAD/EMTDC platform. The results show that when the load changes within 12.5% and the light changes within 10%, the primary frequency regulation can restore the frequency deviation to within 0.2 Hz, and when the load changes within 15%, the secondary frequency regulation can achieve zero-error frequency regulation, which verifies the effectiveness of the proposed strategy.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0210
Abstract:
The power load sequence is complicated and easily affected by multiple external factors, making it difficult to anticipate with accuracy. A parallel forecasting method of short-term power load combining variational modal decomposition (VMD) and convolutional neural network and long short-term memory network (CNN-LSTM) is proposed to address the problem. Firstly, VMD is adopted to decompose the load data into various intrinsic mode functions (IMF) with strong regularity and residual error; Secondly, the obtained components are input into the corresponding CNN-LSTM hybrid prediction network to obtain each initial prediction value, and combine this value with the correlation factor feature set obtained by combining climate, date type, etc. to further obtain the revised prediction value; Finally, the revised prediction values of each component are superimposed to obtain a complete prediction result. According to the simulation on the actual load data, the average relative error of daily load forecasting can be reduced by 2.18% after taking into about the relevant external factor features set. In addition, compared with several conventional load forecasting methods, the effectiveness and feasibility of the proposed method can be verified.
The power load sequence is complicated and easily affected by multiple external factors, making it difficult to anticipate with accuracy. A parallel forecasting method of short-term power load combining variational modal decomposition (VMD) and convolutional neural network and long short-term memory network (CNN-LSTM) is proposed to address the problem. Firstly, VMD is adopted to decompose the load data into various intrinsic mode functions (IMF) with strong regularity and residual error; Secondly, the obtained components are input into the corresponding CNN-LSTM hybrid prediction network to obtain each initial prediction value, and combine this value with the correlation factor feature set obtained by combining climate, date type, etc. to further obtain the revised prediction value; Finally, the revised prediction values of each component are superimposed to obtain a complete prediction result. According to the simulation on the actual load data, the average relative error of daily load forecasting can be reduced by 2.18% after taking into about the relevant external factor features set. In addition, compared with several conventional load forecasting methods, the effectiveness and feasibility of the proposed method can be verified.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0331
Abstract:
In allusion to the problem that it is difficult to meet the growing demand for hydrogen energy due to the low economy of hydrogen energy production, an energy optimization management strategy model for a photovoltaic hydrogen production system based on the Stackelberg game mechanism was proposed. Firstly, in the game model, the hydrogen energy producer, as the upper leader, set the hydrogen energy price traded with the lower users with the goal of maximizing the net income; As the follower of the lower layer, the users adjusted their energy consumption strategy and fed the required load back to the upper layer with the goal of maximizing consumer surplus and taking into account the hydrogen selling price set by the hydrogen energy producer of the upper layer. The two sides play a game and finally reach the game equilibrium. Secondly, the energy management mode of the photovoltaic hydrogen production system was established to optimize the output of each piece of equipment of the hydrogen production system to achieve the goal of improving the economy of hydrogen energy manufacturers and meeting the hydrogen energy demand of users. Finally, by analyzing the game objectives and strategies of hydrogen producers and users, a unique equilibrium solution in the game being exist was proved theoretically, which the model was solved by calling the Cplex, The results show that the proposed game model can effectively improve the system economy and meet the needs of users, and achieve a win-win effect for both players.
In allusion to the problem that it is difficult to meet the growing demand for hydrogen energy due to the low economy of hydrogen energy production, an energy optimization management strategy model for a photovoltaic hydrogen production system based on the Stackelberg game mechanism was proposed. Firstly, in the game model, the hydrogen energy producer, as the upper leader, set the hydrogen energy price traded with the lower users with the goal of maximizing the net income; As the follower of the lower layer, the users adjusted their energy consumption strategy and fed the required load back to the upper layer with the goal of maximizing consumer surplus and taking into account the hydrogen selling price set by the hydrogen energy producer of the upper layer. The two sides play a game and finally reach the game equilibrium. Secondly, the energy management mode of the photovoltaic hydrogen production system was established to optimize the output of each piece of equipment of the hydrogen production system to achieve the goal of improving the economy of hydrogen energy manufacturers and meeting the hydrogen energy demand of users. Finally, by analyzing the game objectives and strategies of hydrogen producers and users, a unique equilibrium solution in the game being exist was proved theoretically, which the model was solved by calling the Cplex, The results show that the proposed game model can effectively improve the system economy and meet the needs of users, and achieve a win-win effect for both players.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0265
Abstract:
This paper proposes an active distribution network reliability assessment method that takes into account the failure of distribution network automation equipment in multiple scenarios, in view of the "double carbon" target that will promote the development of clean energy access and intelligent distribution network equipment in power systems. Firstly, Latin hypercube sampling method with time series segmentation clustering algorithm is used to generate scenarios. Then, based on the theory of equipment health index to establish a fault model of automation equipment under multiple scenarios, as well as a comprehensive consideration of the source-load model under multiple scenarios, the real-time formation probability of islands and the operation of automation equipment under that time period are calculated, and then the reliability of the distribution network is evaluated and the system reliability index is calculated, and finally the effectiveness of the method and model is proved by the IEEE-RBTS BUS6 F4 arithmetic example.
This paper proposes an active distribution network reliability assessment method that takes into account the failure of distribution network automation equipment in multiple scenarios, in view of the "double carbon" target that will promote the development of clean energy access and intelligent distribution network equipment in power systems. Firstly, Latin hypercube sampling method with time series segmentation clustering algorithm is used to generate scenarios. Then, based on the theory of equipment health index to establish a fault model of automation equipment under multiple scenarios, as well as a comprehensive consideration of the source-load model under multiple scenarios, the real-time formation probability of islands and the operation of automation equipment under that time period are calculated, and then the reliability of the distribution network is evaluated and the system reliability index is calculated, and finally the effectiveness of the method and model is proved by the IEEE-RBTS BUS6 F4 arithmetic example.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0296
Abstract:
In allusion to the uncertainty of renewable energy and load in integrated energy system, an optimal dispatch method based on deep reinforcement learning was proposed. Firstly, the methodology of the deep reinforcement learning was expounded, and an optimal dispatch model based on the deep reinforcement learning, in which the state space, action space and reward function were designed, was proposed. Secondly, the model solving process based on asynchronous advantage actor-critic (abbr. A3C) algorithm was designed. Finally, the results of example simulation show that the proposed method can adaptively respond to the uncertainty of source and loads, and its optimization effect is similar to that of traditional mathematical programming method.
In allusion to the uncertainty of renewable energy and load in integrated energy system, an optimal dispatch method based on deep reinforcement learning was proposed. Firstly, the methodology of the deep reinforcement learning was expounded, and an optimal dispatch model based on the deep reinforcement learning, in which the state space, action space and reward function were designed, was proposed. Secondly, the model solving process based on asynchronous advantage actor-critic (abbr. A3C) algorithm was designed. Finally, the results of example simulation show that the proposed method can adaptively respond to the uncertainty of source and loads, and its optimization effect is similar to that of traditional mathematical programming method.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0343
Abstract:
To improve the operation efficiency of active distribution network with battery energy storage system(abbr. BESS), a hierarchical architecture including economic scheduling and collaborative optimization method for active distribution networks with BESS was proposed. Firstly, the logical operation relationship between different levels in the model was overviewed. Secondly, a two-stage economic dispatching and optimization model of the active distribution network with photovoltaics-BESS-charging was established, which the first stage was to minimize the system cost, and the second stage was to minimize the system network loss. Finally, based on the simulation results under different operating scenarios, the variation rules of photovoltaic-BESS-charging were analyzed to obtain the optimal configuration scheme of BESS.
To improve the operation efficiency of active distribution network with battery energy storage system(abbr. BESS), a hierarchical architecture including economic scheduling and collaborative optimization method for active distribution networks with BESS was proposed. Firstly, the logical operation relationship between different levels in the model was overviewed. Secondly, a two-stage economic dispatching and optimization model of the active distribution network with photovoltaics-BESS-charging was established, which the first stage was to minimize the system cost, and the second stage was to minimize the system network loss. Finally, based on the simulation results under different operating scenarios, the variation rules of photovoltaic-BESS-charging were analyzed to obtain the optimal configuration scheme of BESS.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0332
Abstract:
As one of the most potential demand side response resources, air conditioning thermostatically controlled load (abbr. TCL) cluster will play an important role in peak shifting, distributed photovoltaic consumption and power grid regulation. An adjustable potential assessment and interaction framework of air conditioning thermostatically controlled load cluster participating in photovoltaic consumption based on data-driven and Deep Belief Nets (abbr. DBN) is proposed in the power market environment. Firstly, a data-driven adjustable potential assessment model based on Deep Belief Nets is constructed to output the adjustable potential of thermostatically controlled load cluster in real time. Secondly, within the certain range of power adjustment, a demand interaction framework based on Deep Belief Nets is also constructed to regulate the real-time temperature setting of thermostatically controlled load cluster. Finally, taking a 10KV feeder in Northern Hebei as an example, the results show that the proposed framework can make full use of the adjustable potential of the air conditioning thermostatically controlled load cluster, participate in the consumption of distributed photovoltaic, and have high engineering application value.
As one of the most potential demand side response resources, air conditioning thermostatically controlled load (abbr. TCL) cluster will play an important role in peak shifting, distributed photovoltaic consumption and power grid regulation. An adjustable potential assessment and interaction framework of air conditioning thermostatically controlled load cluster participating in photovoltaic consumption based on data-driven and Deep Belief Nets (abbr. DBN) is proposed in the power market environment. Firstly, a data-driven adjustable potential assessment model based on Deep Belief Nets is constructed to output the adjustable potential of thermostatically controlled load cluster in real time. Secondly, within the certain range of power adjustment, a demand interaction framework based on Deep Belief Nets is also constructed to regulate the real-time temperature setting of thermostatically controlled load cluster. Finally, taking a 10KV feeder in Northern Hebei as an example, the results show that the proposed framework can make full use of the adjustable potential of the air conditioning thermostatically controlled load cluster, participate in the consumption of distributed photovoltaic, and have high engineering application value.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0297
Abstract:
Hybrid power flow controller (abbr. HPFC) is effective in branch power flow overload of wind power integrated system with lower cost compared with unified power flow controller (abbr. UPFC). Since the existing research of HPFC power flow optimization has not considered the branch power flow maximum constraint and wind power uncertainty, a new power flow optimization model based on scene reduction was proposed for wind power integrated system with HPFC. Power injection model of HPFC was established and corresponding injection power was derived. Then, K-means algorithm was used to reduce the probability scenes of wind power and load, and the optimal scene is selected by the CH(+) index. Besides, a multi-objective optimization model was established, which considers the generator operation cost, power loss of the system, branch load rate in normal operation and after N-1 contingencies. Multi-objective particle swarm optimization (MOPSO) algorithm was used to solve the model, and the selection of compromise solution in Pareto solution was realized by the fuzzy satisfaction function. The effectiveness of the proposed method was verified in MATLAB, and the results show that the method can fully consider the uncertainty of wind power and ensure the safe and economic operation of a power grid in different scenes.
Hybrid power flow controller (abbr. HPFC) is effective in branch power flow overload of wind power integrated system with lower cost compared with unified power flow controller (abbr. UPFC). Since the existing research of HPFC power flow optimization has not considered the branch power flow maximum constraint and wind power uncertainty, a new power flow optimization model based on scene reduction was proposed for wind power integrated system with HPFC. Power injection model of HPFC was established and corresponding injection power was derived. Then, K-means algorithm was used to reduce the probability scenes of wind power and load, and the optimal scene is selected by the CH(+) index. Besides, a multi-objective optimization model was established, which considers the generator operation cost, power loss of the system, branch load rate in normal operation and after N-1 contingencies. Multi-objective particle swarm optimization (MOPSO) algorithm was used to solve the model, and the selection of compromise solution in Pareto solution was realized by the fuzzy satisfaction function. The effectiveness of the proposed method was verified in MATLAB, and the results show that the method can fully consider the uncertainty of wind power and ensure the safe and economic operation of a power grid in different scenes.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0286
Abstract:
DC microgrids often contain constant power loads (abbr. CPLs), whose negative impedance characteristics can reduce the stability of the system, causing DC bus voltages to fluctuate and even collapse. Therefore, firstly, a small-signal model of DC microgrid is established, and the influence of constant power load on system stability is analyzed using the root trajectory method; secondly, a hybrid sensitivity optimization-based voltage control strategy is proposed to improve the stability of the DC microgrid system, and an improved particle swarm optimization(abbr. PSO) algorithm is used to optimize the weight function and further improve the performance of the robust controller; finally, a Matlab/Simulnik simulation is used to verify the performance of the robust controller, and the simulation results show that the proposed robust controller reduces the bus voltage fluctuation and effectively improves the stability of the DC microgrid system.
DC microgrids often contain constant power loads (abbr. CPLs), whose negative impedance characteristics can reduce the stability of the system, causing DC bus voltages to fluctuate and even collapse. Therefore, firstly, a small-signal model of DC microgrid is established, and the influence of constant power load on system stability is analyzed using the root trajectory method; secondly, a hybrid sensitivity optimization-based voltage control strategy is proposed to improve the stability of the DC microgrid system, and an improved particle swarm optimization(abbr. PSO) algorithm is used to optimize the weight function and further improve the performance of the robust controller; finally, a Matlab/Simulnik simulation is used to verify the performance of the robust controller, and the simulation results show that the proposed robust controller reduces the bus voltage fluctuation and effectively improves the stability of the DC microgrid system.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0269
Abstract:
To accurately identify the abnormal line loss in the substation area and ensure the economic and stable operation of the distribution network, in allusion to the abnormal line loss in the substation area, based on second-order clustering and robust random cut forest (abbr. RRCF) algorithm a method to detect the abnormal line loss in the substation area was proposed. Firstly, by means of second order clustering the different operating conditions of the substation area were clustered and the line loss nodes under the same operating conditions were merged. Secondly, the nodal line loss data of all kinds of operating conditions was led into RRCF algorithm to conduct the analysis. By means of deleting and inserting sample nodes and computing the complexity of the evaluation model after inserting nodes, the score values of abnormal line loss nodes could be obtained, and further the nodes with abnormal line loss could be found out. Finally, the effectiveness and accuracy of the proposed method are verified by related examples.
To accurately identify the abnormal line loss in the substation area and ensure the economic and stable operation of the distribution network, in allusion to the abnormal line loss in the substation area, based on second-order clustering and robust random cut forest (abbr. RRCF) algorithm a method to detect the abnormal line loss in the substation area was proposed. Firstly, by means of second order clustering the different operating conditions of the substation area were clustered and the line loss nodes under the same operating conditions were merged. Secondly, the nodal line loss data of all kinds of operating conditions was led into RRCF algorithm to conduct the analysis. By means of deleting and inserting sample nodes and computing the complexity of the evaluation model after inserting nodes, the score values of abnormal line loss nodes could be obtained, and further the nodes with abnormal line loss could be found out. Finally, the effectiveness and accuracy of the proposed method are verified by related examples.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0336
Abstract:
With the continuous development of new power systems in China, the problem of sub-synchronous oscillation in power systems has become prominent, seriously affecting the safe and stable operation of the power grid, and the level of oscillation damping has an important impact on the sub-synchronous oscillation of wind farms. As the system damping changes with the operation mode of the power system, a sub-synchronous oscillation suppression strategy for wind farms based on the deep Q network optimization operation mode was proposed. Firstly, the influence of pitch angle and series compensation capacitor on sub-synchronous oscillation damping of wind farms was analyzed by time domain simulation, and on this basis, a joint optimization mathematical model of sub-synchronous oscillation with adjusting doubly fed induction generator (abbr. DFIG) output by pitch angle and adjusting line series compensation by parallel capacitor was established. Secondly, the deep Q-learning network algorithm was applied to the optimization solution of system oscillation damping to obtain the optimization strategy of wind turbine sub-synchronous oscillation suppression, and the results are compared with the results of sub-synchronous oscillation suppression based on the genetic algorithm, The results show that this method effectively reduces the oscillation amplitude and improves the damping of the system, which verifies the rationality and superiority of this method.
With the continuous development of new power systems in China, the problem of sub-synchronous oscillation in power systems has become prominent, seriously affecting the safe and stable operation of the power grid, and the level of oscillation damping has an important impact on the sub-synchronous oscillation of wind farms. As the system damping changes with the operation mode of the power system, a sub-synchronous oscillation suppression strategy for wind farms based on the deep Q network optimization operation mode was proposed. Firstly, the influence of pitch angle and series compensation capacitor on sub-synchronous oscillation damping of wind farms was analyzed by time domain simulation, and on this basis, a joint optimization mathematical model of sub-synchronous oscillation with adjusting doubly fed induction generator (abbr. DFIG) output by pitch angle and adjusting line series compensation by parallel capacitor was established. Secondly, the deep Q-learning network algorithm was applied to the optimization solution of system oscillation damping to obtain the optimization strategy of wind turbine sub-synchronous oscillation suppression, and the results are compared with the results of sub-synchronous oscillation suppression based on the genetic algorithm, The results show that this method effectively reduces the oscillation amplitude and improves the damping of the system, which verifies the rationality and superiority of this method.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0378
Abstract:
Under the background of uncertainty of renewable energy uncertainty, in order to coordinate the interest relationship among comprehensive energy operators, electric heating load aggregators and shared energy storage power stations, a comprehensive dynamic pricing mechanism for electricity and heat was proposed, which guides electric heating load aggregators based on the characteristics of integrated demand response to participate in the optimization of integrated energy system operators, promote efficient application of energy storage and local consumption of new energy, and achieve mutual benefit and win-win results for multiple stakeholders. With the integrated energy system operator as the main body, and the electric heating load aggregators and the shared energy storage operators as the secondary body, an integrated energy multi-operator system optimization model with one master and multiple slaves was constructed. Fully explore the operation mode of shared energy storage operators, including charging, discharging and providing reserve modes. CPLEX was used to iteratively solve the master-slave game model, and the results of the example verify the validity of the model and method.
Under the background of uncertainty of renewable energy uncertainty, in order to coordinate the interest relationship among comprehensive energy operators, electric heating load aggregators and shared energy storage power stations, a comprehensive dynamic pricing mechanism for electricity and heat was proposed, which guides electric heating load aggregators based on the characteristics of integrated demand response to participate in the optimization of integrated energy system operators, promote efficient application of energy storage and local consumption of new energy, and achieve mutual benefit and win-win results for multiple stakeholders. With the integrated energy system operator as the main body, and the electric heating load aggregators and the shared energy storage operators as the secondary body, an integrated energy multi-operator system optimization model with one master and multiple slaves was constructed. Fully explore the operation mode of shared energy storage operators, including charging, discharging and providing reserve modes. CPLEX was used to iteratively solve the master-slave game model, and the results of the example verify the validity of the model and method.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0294
Abstract:
In allusion to the problem that the centralized control of photovoltaic inverter takes into account the short-term fluctuations of source and load deficiently, a robust centralized-local control strategy was proposed. Firstly, taking minimizing network loss and light rejection as an object, centralized active and reactive power control model was established; In view of the voltage out of limit and power flow fluctuation caused by the short-term fluctuation in the control interval, a three parameter centralized-local control model of reactive power output-active power reduction-slope parameter was proposed based on the centralized control results. Secondly, the interval model was used to model short-term fluctuations, and the robust optimization model of local control strategy was established to minimize the voltage deviation in extreme fluctuation scenarios. Finally, solution method of control strategy based on second-order cone programming and sensitivity analysis was proposed. The validity of proposed model was verified by a numerical example, and the influence of fluctuation, permeability and inverter capacity on the control was analyzed to improve the operation safety and economy of the active distribution system.
In allusion to the problem that the centralized control of photovoltaic inverter takes into account the short-term fluctuations of source and load deficiently, a robust centralized-local control strategy was proposed. Firstly, taking minimizing network loss and light rejection as an object, centralized active and reactive power control model was established; In view of the voltage out of limit and power flow fluctuation caused by the short-term fluctuation in the control interval, a three parameter centralized-local control model of reactive power output-active power reduction-slope parameter was proposed based on the centralized control results. Secondly, the interval model was used to model short-term fluctuations, and the robust optimization model of local control strategy was established to minimize the voltage deviation in extreme fluctuation scenarios. Finally, solution method of control strategy based on second-order cone programming and sensitivity analysis was proposed. The validity of proposed model was verified by a numerical example, and the influence of fluctuation, permeability and inverter capacity on the control was analyzed to improve the operation safety and economy of the active distribution system.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0298
Abstract:
As an important link to ensure the grid connection of offshore wind farms, the distributed capacitance of the submarine cable is much higher than that of overhead lines, so the generated capacitive current cannot be ignored. The existing current differential protection criterion does not consider the distributed capacitance and is affected by the load current, besides, the anti-transition resistance ability is poor. Therefore, it is not highly applicable on ultra-long submarine cable transmission lines. In allusion to the defects in existing protection criterions, an improved current differential protection criterion was proposed, in the improved criterion the larger distributed capacitance current in the transmission line was compensated, so the protection malfunction during both normal operation and external short circuit was resolved, and the impact of load current on the action characteristic of differential protection was reduced, thus, the ability of anti-transition resistance was improved. A 220kV long distance cable line model was built on PSCAD/EMTDC platform to conduct the simulation analysis on the proposed improved protection scheme, simulation results show that on the basis of ensuring inaction under external faults the proposed new current differential protection criterion for ultra long submarine cable significantly improves the ability to resist transition resistance of faults in the area, so it is an ideal current differential protection scheme for the long high-voltage cable line.
As an important link to ensure the grid connection of offshore wind farms, the distributed capacitance of the submarine cable is much higher than that of overhead lines, so the generated capacitive current cannot be ignored. The existing current differential protection criterion does not consider the distributed capacitance and is affected by the load current, besides, the anti-transition resistance ability is poor. Therefore, it is not highly applicable on ultra-long submarine cable transmission lines. In allusion to the defects in existing protection criterions, an improved current differential protection criterion was proposed, in the improved criterion the larger distributed capacitance current in the transmission line was compensated, so the protection malfunction during both normal operation and external short circuit was resolved, and the impact of load current on the action characteristic of differential protection was reduced, thus, the ability of anti-transition resistance was improved. A 220kV long distance cable line model was built on PSCAD/EMTDC platform to conduct the simulation analysis on the proposed improved protection scheme, simulation results show that on the basis of ensuring inaction under external faults the proposed new current differential protection criterion for ultra long submarine cable significantly improves the ability to resist transition resistance of faults in the area, so it is an ideal current differential protection scheme for the long high-voltage cable line.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0276
Abstract:
In allusion to the problem that the supply-demand imbalance of synchronous phasor measurement units (abbr. PMUs) due to large number of nodes in the distribution network but low investment cost, a multi-objective optimization configuration model of PMU, in which the number of PMU configuration and the state estimation error were considered, was established to minimize both the number of required PMU and the state estimation error. An improved whale algorithm was utilized to solve the established model. Firstly, non-dominated ordering and congestion calculation were led in to select and order the Pareto non-dominated solutions to ensure the ability of the algorithm to solve the global optimum. Secondly, the Levy flight strategy was led in to perturb the spiral update position of the whale algorithm in a variational manner to make the algorithm not easy to fall into local optimal. Finally, the simulation calculation on IEEE 33 standard node system by the optimal allocation model was conducted. Simulation results show that comparing with genetic algorithm and particle swam optimization, there are higher feasibility and effectiveness when the PMU multi-objective optimal configuration model is solved by the improved whale algorithm.
In allusion to the problem that the supply-demand imbalance of synchronous phasor measurement units (abbr. PMUs) due to large number of nodes in the distribution network but low investment cost, a multi-objective optimization configuration model of PMU, in which the number of PMU configuration and the state estimation error were considered, was established to minimize both the number of required PMU and the state estimation error. An improved whale algorithm was utilized to solve the established model. Firstly, non-dominated ordering and congestion calculation were led in to select and order the Pareto non-dominated solutions to ensure the ability of the algorithm to solve the global optimum. Secondly, the Levy flight strategy was led in to perturb the spiral update position of the whale algorithm in a variational manner to make the algorithm not easy to fall into local optimal. Finally, the simulation calculation on IEEE 33 standard node system by the optimal allocation model was conducted. Simulation results show that comparing with genetic algorithm and particle swam optimization, there are higher feasibility and effectiveness when the PMU multi-objective optimal configuration model is solved by the improved whale algorithm.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0391
Abstract:
To address the problem of slow tracking speed of existing differential evolutionary algorithm in the case of local shading, a new maximum power point tracking(abbr.MPPT) control strategy for photovoltaic(abbr.PV) power generation system with improved differential evolutionary algorithm was proposed. Firstly, the triangular mutation strategy was introduced and embedded in the crossover formula of the traditional differential evolutionary algorithm to avoid duplicate sampled power due to the same duty cycle of the output before and after the iteration. Secondly, an adaptive scaling factor strategy was introduced to enhance individual convergence. Finally, the global exploitation and local exploration capabilities of the proposed algorithm were balanced by a reward-penalty mechanism. The simulation results show that the improved algorithm has significant advantages in tracking speed and stability compared with the differential evolution algorithm of the comparative literature.
To address the problem of slow tracking speed of existing differential evolutionary algorithm in the case of local shading, a new maximum power point tracking(abbr.MPPT) control strategy for photovoltaic(abbr.PV) power generation system with improved differential evolutionary algorithm was proposed. Firstly, the triangular mutation strategy was introduced and embedded in the crossover formula of the traditional differential evolutionary algorithm to avoid duplicate sampled power due to the same duty cycle of the output before and after the iteration. Secondly, an adaptive scaling factor strategy was introduced to enhance individual convergence. Finally, the global exploitation and local exploration capabilities of the proposed algorithm were balanced by a reward-penalty mechanism. The simulation results show that the improved algorithm has significant advantages in tracking speed and stability compared with the differential evolution algorithm of the comparative literature.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0344
Abstract:
In allusion to the practical problems of limited measurement information and insufficient computing resources in low-voltage distribution area, a collaborative voltage regulation strategy for active power and reactive power of photovoltaic inverters was proposed based on voltage sensitivity matrices derived from actual measurement. Firstly, voltage sensitivity matrices of active power and reactive power regulation were obtained by adjusting the photovoltaic output and observing the node voltage shifting. Secondly, considering the capacity and power factor constraint of inverters, a linear optimization model for collaborative voltage regulation of multiple photovoltaic inverters was proposed to maximize the active output and minimize the reactive output under the premise of ensuring the voltage quality. Finally, a rural distribution area is selected as the study case to verify the effectiveness, control accuracy and calculation speed of the proposed strategy. The results show that by fully utilizing inverter’s reactive power regulation capacity, the proposed strategy can reduce the waste of photovoltaic energy on the basis of accurate voltage regulation of the distribution network with relatively fast solving speed, which make it feasible for limited computing resources in real distribution area.
In allusion to the practical problems of limited measurement information and insufficient computing resources in low-voltage distribution area, a collaborative voltage regulation strategy for active power and reactive power of photovoltaic inverters was proposed based on voltage sensitivity matrices derived from actual measurement. Firstly, voltage sensitivity matrices of active power and reactive power regulation were obtained by adjusting the photovoltaic output and observing the node voltage shifting. Secondly, considering the capacity and power factor constraint of inverters, a linear optimization model for collaborative voltage regulation of multiple photovoltaic inverters was proposed to maximize the active output and minimize the reactive output under the premise of ensuring the voltage quality. Finally, a rural distribution area is selected as the study case to verify the effectiveness, control accuracy and calculation speed of the proposed strategy. The results show that by fully utilizing inverter’s reactive power regulation capacity, the proposed strategy can reduce the waste of photovoltaic energy on the basis of accurate voltage regulation of the distribution network with relatively fast solving speed, which make it feasible for limited computing resources in real distribution area.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0270
Abstract:
When a fault occurs in the high-voltage power grid, the existing fault diagnosis analytical model will misjudge when the key protection or circuit breaker components mis-operate or the information is falsely reported or missed. In order to solve this problem, this paper proposes a power grid fault diagnosis analytical model and alarm method considering protection start-up information. Fristly, on the basis of the traditional fault diagnosis analytical model, the difference between the expected start-up state and the actual state of the protection is added, and the fitness function of the power grid fault diagnosis considering the start-up information of the protection is constructed. Based on the particle swarm optimization algorithm, the fault components are obtained by optimization calculation, which solves the problem that the key protection or circuit breaker components have mis-operation or information false alarm and missed alarm. Secondly, based on the obtained fault component, the protection start-up information is used to generate the protection start-up level diagram to alarm the abnormal protection. Finally, the whole fault process is analyzed by the method of inference chain. The sensitivity and accuracy of the new analytical model are greatly improved compared with the traditional method, and the protection alarm method considering the protection start-up information can warn the protection with abnormal start-up information in advance, which has a good application prospect.
When a fault occurs in the high-voltage power grid, the existing fault diagnosis analytical model will misjudge when the key protection or circuit breaker components mis-operate or the information is falsely reported or missed. In order to solve this problem, this paper proposes a power grid fault diagnosis analytical model and alarm method considering protection start-up information. Fristly, on the basis of the traditional fault diagnosis analytical model, the difference between the expected start-up state and the actual state of the protection is added, and the fitness function of the power grid fault diagnosis considering the start-up information of the protection is constructed. Based on the particle swarm optimization algorithm, the fault components are obtained by optimization calculation, which solves the problem that the key protection or circuit breaker components have mis-operation or information false alarm and missed alarm. Secondly, based on the obtained fault component, the protection start-up information is used to generate the protection start-up level diagram to alarm the abnormal protection. Finally, the whole fault process is analyzed by the method of inference chain. The sensitivity and accuracy of the new analytical model are greatly improved compared with the traditional method, and the protection alarm method considering the protection start-up information can warn the protection with abnormal start-up information in advance, which has a good application prospect.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0318
Abstract:
To address the issue of ultra-short-term wind power prediction, a novel prediction model is proposed based on multivariate variational mode decomposition (abbr. MVMD), multi-objective crisscross optimization (abbr. MOCSO) algorithm and blending ensemble learning. In the data processing stage, to maintain synchronization correlation and ensure matching of IMF number and frequency, the MVMD method is used to decompose the multi-channel original data synchronously. Considering the insufficient comprehensiveness, inaccuracy, and low robustness of the single machine learning model, a blending ensemble learning model is proposed to combine multiple deep learning networks using MOCSO dynamic weighting. The prediction results of RNN, CNN and LSTM are dynamically weighted, integrated, and then optimized by MOCSO to improve the prediction accuracy and stability. Experimental results show that the proposed model is not only effective, but also significantly superior to other prediction models.
To address the issue of ultra-short-term wind power prediction, a novel prediction model is proposed based on multivariate variational mode decomposition (abbr. MVMD), multi-objective crisscross optimization (abbr. MOCSO) algorithm and blending ensemble learning. In the data processing stage, to maintain synchronization correlation and ensure matching of IMF number and frequency, the MVMD method is used to decompose the multi-channel original data synchronously. Considering the insufficient comprehensiveness, inaccuracy, and low robustness of the single machine learning model, a blending ensemble learning model is proposed to combine multiple deep learning networks using MOCSO dynamic weighting. The prediction results of RNN, CNN and LSTM are dynamically weighted, integrated, and then optimized by MOCSO to improve the prediction accuracy and stability. Experimental results show that the proposed model is not only effective, but also significantly superior to other prediction models.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0175
Abstract:
The selection of energy storage is the key content for planning and design of energy storage to participate in the new energy primary frequency regulation. It involves many indicators such as safety, primary frequency regulation adaptability, economy and environmental performance, and is a complex multi-objective decision-making problem. This paper proposed a new energy primary frequency regulation selection scheme based on the combination of AHP (analytic hierarchy process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). This paper analyzed the characteristics of each index and built a two-tier decision-making system. According to expert scores and practical application requirements, the AHP method was used to obtain the weight of each decision-making index, and the TOPSIS method was further used to select the energy storage according to the weight of each index and related parameters. Finally, this paper selected 12 types of energy storage, such as compressed air, flywheel energy storage, lithium iron phosphate batteries and super capacitors, as cases for analysis. The results show that lithium iron phosphate batteries have advantages in participating in new energy primary frequency regulation. The selection scheme can provide certain theoretical support for energy storage to participate in the planning and design of new energy primary frequency regulation.
The selection of energy storage is the key content for planning and design of energy storage to participate in the new energy primary frequency regulation. It involves many indicators such as safety, primary frequency regulation adaptability, economy and environmental performance, and is a complex multi-objective decision-making problem. This paper proposed a new energy primary frequency regulation selection scheme based on the combination of AHP (analytic hierarchy process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). This paper analyzed the characteristics of each index and built a two-tier decision-making system. According to expert scores and practical application requirements, the AHP method was used to obtain the weight of each decision-making index, and the TOPSIS method was further used to select the energy storage according to the weight of each index and related parameters. Finally, this paper selected 12 types of energy storage, such as compressed air, flywheel energy storage, lithium iron phosphate batteries and super capacitors, as cases for analysis. The results show that lithium iron phosphate batteries have advantages in participating in new energy primary frequency regulation. The selection scheme can provide certain theoretical support for energy storage to participate in the planning and design of new energy primary frequency regulation.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0159
Abstract:
In order to alleviated the problem effectively that large-scale wind curtailment caused by the high proportion of renewable energy connected to the grid in the three-north area, the thermal power units, combined heat and power units (CHP), concentrating solar power plant (CSPP), wind farm and electric-heat load were aggregated into virtual power plants (VPP). Firstly, the scene analysis method was used to optimized the day ahead wind power and solar scene randomly to reduce the prediction error; Made full use of the integrated demand response mechanism to dispatch the electric-heat flexible load resources. And then based on the wind curtailment cost, the operation cost and demand response cost, the day-ahead scheduling optimal model was constructed, got the optimal day-ahead scheduling planning through solving model in different mode. Finally, according to the example verified that the proposed scheme could promote wind power consumption while improving the system economy effectively.
In order to alleviated the problem effectively that large-scale wind curtailment caused by the high proportion of renewable energy connected to the grid in the three-north area, the thermal power units, combined heat and power units (CHP), concentrating solar power plant (CSPP), wind farm and electric-heat load were aggregated into virtual power plants (VPP). Firstly, the scene analysis method was used to optimized the day ahead wind power and solar scene randomly to reduce the prediction error; Made full use of the integrated demand response mechanism to dispatch the electric-heat flexible load resources. And then based on the wind curtailment cost, the operation cost and demand response cost, the day-ahead scheduling optimal model was constructed, got the optimal day-ahead scheduling planning through solving model in different mode. Finally, according to the example verified that the proposed scheme could promote wind power consumption while improving the system economy effectively.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0179
Abstract:
To overcome the disadvantages of wind power on dispatching such as the randomness and intermittentness, and exert the flexible adjustment ability of high-proportion hydropower. The optimization goal is set to promote full consumption of wind power, lowest operating cost of thermal power and minimum output fluctuation, then a multi-objective optimal dispatch model for wind-hydro-thermal power systems is established to evaluate the results of dispatch. We considered a variety of scenarios such as start-stop and low-load operation of thermal power generator and whether wind power has abandoned wind, some quantitative indicators such as hydropower utilization rate and thermal power fluctuations are proposed. And hierarchical optimization strategy is designed to ensure the efficiency in solving model, first layer optimizes output of hydropower generators, ensures minimum total fluctuation of thermal power, and promotes full use of hydropower adjustment ability, second layer optimizes output of each thermal power generator to make the system operation cost the lowest, and uses particle swarm optimization (PSO) algorithm to solve each layer optimization problem. Through experimental test, the scheduling results before and after optimization, the scheduling results considering the start-stop of power unit, and the scheduling results under different wind power installed capacity are compared, and the effectiveness of the proposed model and hierarchical optimization strategy is verified.
To overcome the disadvantages of wind power on dispatching such as the randomness and intermittentness, and exert the flexible adjustment ability of high-proportion hydropower. The optimization goal is set to promote full consumption of wind power, lowest operating cost of thermal power and minimum output fluctuation, then a multi-objective optimal dispatch model for wind-hydro-thermal power systems is established to evaluate the results of dispatch. We considered a variety of scenarios such as start-stop and low-load operation of thermal power generator and whether wind power has abandoned wind, some quantitative indicators such as hydropower utilization rate and thermal power fluctuations are proposed. And hierarchical optimization strategy is designed to ensure the efficiency in solving model, first layer optimizes output of hydropower generators, ensures minimum total fluctuation of thermal power, and promotes full use of hydropower adjustment ability, second layer optimizes output of each thermal power generator to make the system operation cost the lowest, and uses particle swarm optimization (PSO) algorithm to solve each layer optimization problem. Through experimental test, the scheduling results before and after optimization, the scheduling results considering the start-stop of power unit, and the scheduling results under different wind power installed capacity are compared, and the effectiveness of the proposed model and hierarchical optimization strategy is verified.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0164
Abstract:
The regenerative electric heating load has time shifts and continuous adjustability, which can be used as a demand-side resource to solve the problem of renewable energy consumption and clean heating in rural areas. The current peak-valley time-of-use electricity price cannot effectively guide the operation of electric heating to absorb the blocked scenery according to the change of source and load. Therefore, the scenario method is proposed to describe the uncertainty of wind-solar-load, and the time-of-use electricity price is determined according to the change of peak-valley period of source-load. Considering the problem of a high simultaneous rate of electricity consumption when the electric heating responds to the time-of-use electricity price, the demand-price elasticity coefficient is introduced. A day-ahead and daily endogenous load coordination optimization model with the minimum expectation of the operating cost of regenerative electric heating and the peak-valley difference of the system load is established, and the flexible resource output of each period is adjusted rollingly. Finally, the validity of the model is verified by simulation.
The regenerative electric heating load has time shifts and continuous adjustability, which can be used as a demand-side resource to solve the problem of renewable energy consumption and clean heating in rural areas. The current peak-valley time-of-use electricity price cannot effectively guide the operation of electric heating to absorb the blocked scenery according to the change of source and load. Therefore, the scenario method is proposed to describe the uncertainty of wind-solar-load, and the time-of-use electricity price is determined according to the change of peak-valley period of source-load. Considering the problem of a high simultaneous rate of electricity consumption when the electric heating responds to the time-of-use electricity price, the demand-price elasticity coefficient is introduced. A day-ahead and daily endogenous load coordination optimization model with the minimum expectation of the operating cost of regenerative electric heating and the peak-valley difference of the system load is established, and the flexible resource output of each period is adjusted rollingly. Finally, the validity of the model is verified by simulation.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0163
Abstract:
To improve the accuracy of transformer fault diagnosis, this paper proposes a GWO-ResNet fault diagnosis method based on data augmentation and feature attention mechanism. Aimed at the effect produced by imbalanced dataset on the power transformer fault diagnosis model, the Wasserstein generative adversarial network with gradient penalty (WGANGP) is utilized to make data augmentation for the power transformer dataset. Secondly, to enhance the sensitivity of the model to the key features in the augmented dataset, a feature attention mechanism is introduced into the input side of the model. Thirdly, in order to accelerate the convergence of the model, the grey wolf optimization algorithm (GWO) was used to optimize the residual neural network(ResNet) in the early stage of training. Finally, the validity of the proposed WGANGP-ATT-GWO-ResNet fault diagnosis model is verified based on a measured power transformer dataset.
To improve the accuracy of transformer fault diagnosis, this paper proposes a GWO-ResNet fault diagnosis method based on data augmentation and feature attention mechanism. Aimed at the effect produced by imbalanced dataset on the power transformer fault diagnosis model, the Wasserstein generative adversarial network with gradient penalty (WGANGP) is utilized to make data augmentation for the power transformer dataset. Secondly, to enhance the sensitivity of the model to the key features in the augmented dataset, a feature attention mechanism is introduced into the input side of the model. Thirdly, in order to accelerate the convergence of the model, the grey wolf optimization algorithm (GWO) was used to optimize the residual neural network(ResNet) in the early stage of training. Finally, the validity of the proposed WGANGP-ATT-GWO-ResNet fault diagnosis model is verified based on a measured power transformer dataset.
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0225
Abstract:
Microgrid can sell surplus resources in peak load to promote renewable energy usage and to get maximum benefit from them. The auction mechanism is adopted in the trading process, however the change of electricity purchase demand in the trading period also may affect the renewable energy usage and income of the microgrid.Accordingly, the paper has put forward a micro-grid energy auction transaction model that takes into consideration of the uncertain requirement of the power supply. The model adopts the unified price synchronous increasing auction mechanism for the price of electricity. It also puts forward an improved auction mechanism that balances the requirement of an energy purchaser in a different time period;Based on the uncertainty of energy demand, a linear optimization algorithm was used. As a result, it was found that a robust linear optimal solution was obtained, which can maximize the saleable renewable energy quantity and income of microgrids by using MATLAB simulation analysis.
Microgrid can sell surplus resources in peak load to promote renewable energy usage and to get maximum benefit from them. The auction mechanism is adopted in the trading process, however the change of electricity purchase demand in the trading period also may affect the renewable energy usage and income of the microgrid.Accordingly, the paper has put forward a micro-grid energy auction transaction model that takes into consideration of the uncertain requirement of the power supply. The model adopts the unified price synchronous increasing auction mechanism for the price of electricity. It also puts forward an improved auction mechanism that balances the requirement of an energy purchaser in a different time period;Based on the uncertainty of energy demand, a linear optimization algorithm was used. As a result, it was found that a robust linear optimal solution was obtained, which can maximize the saleable renewable energy quantity and income of microgrids by using MATLAB simulation analysis.
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Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0249
Abstract:
To ensure the stable operation of power system during large-scale new energy grid connection and reduce the instances of wind or solar energy curtailment, firstly, an analysis was made on methods for power fluctuation suppression in the new energy grid. Additionally, the generalized short circuit ratio of operation, a parameter for AC power grid strength evaluation after new energy being connected to the grid with arbitrary power, was introduced. Secondly, a dispatch model for new energy grid connection considering three time scales: day-ahead, intra-day and real-time, was presented. Furthermore, a multi-time scale new energy grid connection capacity optimization models was established with the objective functions of minimum operation dispatch cost, maximum operation generalized short circuit ratio, and the minimum fluctuation of joint output power. Finally, taking the system consisting of thermal power units, new energy stations, energy storage power stations and high energy loads as an instance, the effectiveness of proposed strategy and model was verified through simulation.
To ensure the stable operation of power system during large-scale new energy grid connection and reduce the instances of wind or solar energy curtailment, firstly, an analysis was made on methods for power fluctuation suppression in the new energy grid. Additionally, the generalized short circuit ratio of operation, a parameter for AC power grid strength evaluation after new energy being connected to the grid with arbitrary power, was introduced. Secondly, a dispatch model for new energy grid connection considering three time scales: day-ahead, intra-day and real-time, was presented. Furthermore, a multi-time scale new energy grid connection capacity optimization models was established with the objective functions of minimum operation dispatch cost, maximum operation generalized short circuit ratio, and the minimum fluctuation of joint output power. Finally, taking the system consisting of thermal power units, new energy stations, energy storage power stations and high energy loads as an instance, the effectiveness of proposed strategy and model was verified through simulation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0099
Abstract:
As a complex energy system with multi inputs and outputs, the design, planning and operation management of integrated energy system are current research difficulties. In allusion to the integrated energy park system with coupled hydrogen energy storage, a comprehensive evaluation index considering energy efficiency, reliability, environmental protection and economy was proposed. Under specified operation strategy, an optimization method combining particle swarm optimization (abbr. PSO) with maximum rectangle method (abbr. MRM) was utilized to perform system capacity allocation. To verify the reliability of capacity allocation results, taking a certain comprehensive energy park system in Xi’an for example, the cooling, heating and electrical loads in this park area within a whole year were taken as load demand, on the basis of traditional operation strategy of determining electricity by heat and determining heat by electricity a comprehensive operation strategy was proposed. The system capacity allocation for three operation strategies was researched, and the characteristics of their comprehensive operation strategies were analyzed. Computing results show that comparing with determining electricity by heat and determining heat by electricity, the comprehensive evaluation index of the proposed comprehensive operation strategy were improved by 1.66% and 0.13% respectively.
As a complex energy system with multi inputs and outputs, the design, planning and operation management of integrated energy system are current research difficulties. In allusion to the integrated energy park system with coupled hydrogen energy storage, a comprehensive evaluation index considering energy efficiency, reliability, environmental protection and economy was proposed. Under specified operation strategy, an optimization method combining particle swarm optimization (abbr. PSO) with maximum rectangle method (abbr. MRM) was utilized to perform system capacity allocation. To verify the reliability of capacity allocation results, taking a certain comprehensive energy park system in Xi’an for example, the cooling, heating and electrical loads in this park area within a whole year were taken as load demand, on the basis of traditional operation strategy of determining electricity by heat and determining heat by electricity a comprehensive operation strategy was proposed. The system capacity allocation for three operation strategies was researched, and the characteristics of their comprehensive operation strategies were analyzed. Computing results show that comparing with determining electricity by heat and determining heat by electricity, the comprehensive evaluation index of the proposed comprehensive operation strategy were improved by 1.66% and 0.13% respectively.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0158
Abstract:
In traditional model to assess power system transient voltage stability there are defects in two aspects, i.e., difficult to capture the key information during the failure process and the imbalance between transient stability samples and unstability samples leads to the tendentiousness of the model for majority class samples. For this reason, a voltage stability forewarning model based on improved deep residual network was proposed. Firstly, to capture the key information during the failure process, a convolutional attention module was embedded in the residual network, and through the dual attention of the time channel and the space channel the potential spatiotemporal relationship in the dynamic trajectory of the power system was dug. Secondly, in allusion to problem that during the training process the model was tended to majority class samples, the gradient harmonizing mechanism-based loss function was led in to reduce the influence of imbalance samples on assessment results. Thirdly, to intensify the model’s ability of extracting data features, traditional convolution kernel was replaced with asymmetric convolution block. Finally, by means of connecting two different wind power ratios to IEEE 39-bus system, the performance of the proposed method in the assessment on transient voltage stability was verified.
In traditional model to assess power system transient voltage stability there are defects in two aspects, i.e., difficult to capture the key information during the failure process and the imbalance between transient stability samples and unstability samples leads to the tendentiousness of the model for majority class samples. For this reason, a voltage stability forewarning model based on improved deep residual network was proposed. Firstly, to capture the key information during the failure process, a convolutional attention module was embedded in the residual network, and through the dual attention of the time channel and the space channel the potential spatiotemporal relationship in the dynamic trajectory of the power system was dug. Secondly, in allusion to problem that during the training process the model was tended to majority class samples, the gradient harmonizing mechanism-based loss function was led in to reduce the influence of imbalance samples on assessment results. Thirdly, to intensify the model’s ability of extracting data features, traditional convolution kernel was replaced with asymmetric convolution block. Finally, by means of connecting two different wind power ratios to IEEE 39-bus system, the performance of the proposed method in the assessment on transient voltage stability was verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0068
Abstract:
Grid-connection of high proportion renewable energy is an important feature of new-type power grid. To promote the accommodation of renewable energy in new-type power grid and to improve flexible and stable operation of distribution networks, a bi-level planning model for distribution network was proposed. In the planning level of the proposed model the minimized annualized cost of distribution network operator was taken as the objective function and taking carbon emission target into account the planning scheme was determined. In the operation level the minimized flexible operation cost was taken as the objective function to determine the interruptible and transferable electric quantity in each node. The proposed model wad solved by simulated annealing particle swarm optimization, to improve the global search ability of the algorithm. Finally, by means of computing example based on IEEE 33-bus distribution network system the proposed model was verified. Simulation results show that considering flexible demand in the phase of planning the system flexibility can be improved and distribution network operator’s cost of investment can be reduced, thus, the effectiveness of the proposed method is verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0187
Abstract:
To improve the flexibility of microgrid operation, the interruptible load was regarded as a schedulable resource that directly participated in the operation of microgrid, and according to the length of time of peak load and valley load the transmission power of the tie line was set in different interval. Considering the depreciation, operation and maintenance cost of microgrid and its environmental benefit cost as well as its tie-line deviation penalty cost, a multi-objective optimization model of microgrid operation was constructed, and by use of relative objective adjacent scale method the proposed multi-objective optimization model was transformed into single-objective optimization model, and the combination weighting was performed by the analytic hierarchy process (abbr. AHP) and entropy weight method. The Gurobi solver was utilized to solve the 24h output and the state of charge of battery of various distributed power sources in the microgrid to obtain the day-ahead dispatching model of microgrid. By means of a simulation example based on a microgrid in a certain region in the United States, it was verified that the proposed model could give consideration to the economy and environmental protection property, besides, it could mitigate the power fluctuation in the main network, meanwhile it was shown that such an operation mode, which took the interruptive load and tie-line power control into account, can effectively reduce the depreciation cost of operation and maintenance, environmental pollution and the burden of main network.
To improve the flexibility of microgrid operation, the interruptible load was regarded as a schedulable resource that directly participated in the operation of microgrid, and according to the length of time of peak load and valley load the transmission power of the tie line was set in different interval. Considering the depreciation, operation and maintenance cost of microgrid and its environmental benefit cost as well as its tie-line deviation penalty cost, a multi-objective optimization model of microgrid operation was constructed, and by use of relative objective adjacent scale method the proposed multi-objective optimization model was transformed into single-objective optimization model, and the combination weighting was performed by the analytic hierarchy process (abbr. AHP) and entropy weight method. The Gurobi solver was utilized to solve the 24h output and the state of charge of battery of various distributed power sources in the microgrid to obtain the day-ahead dispatching model of microgrid. By means of a simulation example based on a microgrid in a certain region in the United States, it was verified that the proposed model could give consideration to the economy and environmental protection property, besides, it could mitigate the power fluctuation in the main network, meanwhile it was shown that such an operation mode, which took the interruptive load and tie-line power control into account, can effectively reduce the depreciation cost of operation and maintenance, environmental pollution and the burden of main network.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0216
Abstract:
Reducing the cost of offshore wind power sending system is key to promote the development of offshore wind power sources. The technical solution based on transmission by diode rectifier possesses good economy. However there are defects in the system such as the wind farm cannot be black-started and the transmission system cannot provide AC voltage for the grid-connection of wind farm. To explore more feasible approaches based on the above mentioned technical solution, a design of improved diode rectifier based transmission system was proposed. On the basis of configuring auxiliary AC line, by adding reactive power compensator at the offshore end or making the inverter on mainland side to directly participate in system coordination, the black-start and the steady state power flow direction control could be realized. The theoretical analysis, system scheme design and control strategy design for the proposed technical scheme were carried out. In the simulation environment of PSCAD/EMTDC, a model of offshore wind power transmission system was constructed. Both methods were verified by simulation results and were proven to satisfy the reliable transmission of offshore wind power.
Reducing the cost of offshore wind power sending system is key to promote the development of offshore wind power sources. The technical solution based on transmission by diode rectifier possesses good economy. However there are defects in the system such as the wind farm cannot be black-started and the transmission system cannot provide AC voltage for the grid-connection of wind farm. To explore more feasible approaches based on the above mentioned technical solution, a design of improved diode rectifier based transmission system was proposed. On the basis of configuring auxiliary AC line, by adding reactive power compensator at the offshore end or making the inverter on mainland side to directly participate in system coordination, the black-start and the steady state power flow direction control could be realized. The theoretical analysis, system scheme design and control strategy design for the proposed technical scheme were carried out. In the simulation environment of PSCAD/EMTDC, a model of offshore wind power transmission system was constructed. Both methods were verified by simulation results and were proven to satisfy the reliable transmission of offshore wind power.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0189
Abstract:
In allusion to the fact that there are few incremental updates and layouts for the stock single-line diagram in the distribution network, for this reason, a method to automatically generate a distribution network single line diagram, which supported the incremental updates, was proposed. Firstly, based on connected relation of device topology a connection relation mapping tree, in which the root was the power supply, was constructed. Secondly, according to the coordinates of the stock single-line diagram the assignment of layout direction was conducted for each device to obtain the device connected relation mapping tree with layout direction. Finally, according to the reverse stitching of the device layout direction the initial layout and partial contraction of the device was completed. Based on the connection relation among devices the wiring was completed, thus a distribution network single-line diagram, which inherited the original layout, could be obtained. The proposed method to automatically generate distribution network single-line diagram supporting incremental updating was applied to the development of automatic generation system for distribution network thematic map, and its effectiveness was verified by engineering projects.
In allusion to the fact that there are few incremental updates and layouts for the stock single-line diagram in the distribution network, for this reason, a method to automatically generate a distribution network single line diagram, which supported the incremental updates, was proposed. Firstly, based on connected relation of device topology a connection relation mapping tree, in which the root was the power supply, was constructed. Secondly, according to the coordinates of the stock single-line diagram the assignment of layout direction was conducted for each device to obtain the device connected relation mapping tree with layout direction. Finally, according to the reverse stitching of the device layout direction the initial layout and partial contraction of the device was completed. Based on the connection relation among devices the wiring was completed, thus a distribution network single-line diagram, which inherited the original layout, could be obtained. The proposed method to automatically generate distribution network single-line diagram supporting incremental updating was applied to the development of automatic generation system for distribution network thematic map, and its effectiveness was verified by engineering projects.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0064
Abstract:
Comparing with traditional contact switches, the soft open point (SOP) possesses more functions and faster response speed, so it is one of important device for the realization of power grid flexible interconnection. The existence of the switching element in the SOP made system operation states switched back and forth between continuous system and discrete system, for this reason, it was put forward for SOP to adopt a more suitable hybrid system model to conduct modeling control. Firstly, eight switching modes of each port were analyzed, and then one power frequency cycle was divided into twelve subintervals and through the relation between the system continuous state quantity and each switching mode the switching rule of the switching mode in each subinterval was derived. According to the switching rule the discrete controller was designed, and according to the conversional relation of continuous state quantities each other the continuous controller was designed, and the hybrid controller of each port under different operating modes was comprehensively obtained. Finally, by means of MATLAB/Simulink platform a simulation model was constructed, and through the simulation comparison with traditional modeling control method the effectiveness of the proposed scheme is verified.
Comparing with traditional contact switches, the soft open point (SOP) possesses more functions and faster response speed, so it is one of important device for the realization of power grid flexible interconnection. The existence of the switching element in the SOP made system operation states switched back and forth between continuous system and discrete system, for this reason, it was put forward for SOP to adopt a more suitable hybrid system model to conduct modeling control. Firstly, eight switching modes of each port were analyzed, and then one power frequency cycle was divided into twelve subintervals and through the relation between the system continuous state quantity and each switching mode the switching rule of the switching mode in each subinterval was derived. According to the switching rule the discrete controller was designed, and according to the conversional relation of continuous state quantities each other the continuous controller was designed, and the hybrid controller of each port under different operating modes was comprehensively obtained. Finally, by means of MATLAB/Simulink platform a simulation model was constructed, and through the simulation comparison with traditional modeling control method the effectiveness of the proposed scheme is verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0439
Abstract:
The varying degrees of power outages caused by extreme disasters have posed challenges to the power system's resilience from failures. The participation of various types of distributed power sources and interconnection switches brings possibilities for improving the reliability of power supply in the distribution network after faults, meanwhile, bringing possibilities to enhance the resilience of the distribution network in the face of extreme disaster scenarios. For that reason, a fault recovery strategy for island reconstruction was proposed, which adopts mixed-integer second-order cone planning and utilizes the coordinated operation strategy of island operation and topology reconstruction for the recovery of critical loads. And a comprehensive evaluation index for the resilience of the distribution network after fault recovery was proposed, so as to quantitatively evaluate the resilience through these three aspects: resistance stage, recovery stage, and resilience evaluation. Finally, taking the improved IEEE 33 node distribution system as an example, the effectiveness of the proposed coordination strategy for fault recovery in distribution networks was verified, as well as the resilience evaluation index for resilience evaluation after fault distribution network recovery.
The varying degrees of power outages caused by extreme disasters have posed challenges to the power system's resilience from failures. The participation of various types of distributed power sources and interconnection switches brings possibilities for improving the reliability of power supply in the distribution network after faults, meanwhile, bringing possibilities to enhance the resilience of the distribution network in the face of extreme disaster scenarios. For that reason, a fault recovery strategy for island reconstruction was proposed, which adopts mixed-integer second-order cone planning and utilizes the coordinated operation strategy of island operation and topology reconstruction for the recovery of critical loads. And a comprehensive evaluation index for the resilience of the distribution network after fault recovery was proposed, so as to quantitatively evaluate the resilience through these three aspects: resistance stage, recovery stage, and resilience evaluation. Finally, taking the improved IEEE 33 node distribution system as an example, the effectiveness of the proposed coordination strategy for fault recovery in distribution networks was verified, as well as the resilience evaluation index for resilience evaluation after fault distribution network recovery.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0259
Abstract:
As the proportion of renewable energy in the distribution network continues to increase, As the proportion of renewable energy in the distribution network continues to increase, problems such as voltage exceeding limits that affect the stable and economic operation of post-disaster distribution network islands become more prominent. In order to effectively solve the voltage exceeding limit problem in isolated islands and consider the operational economy, a multi-objective isolated island operation strategy considering the assistance of mobile energy storage system for active distribution networks was proposed. Firstly, voltage control and economic operation of active distribution network islands can be theoretically proven to be achieved through the optimization of both active and reactive power. Secondly, using mobile energy storage as an auxiliary resource, taking minimizing node voltage deviation and total cost during islanding operation as an objective, a multi-objective islanding operation model for an active distribution network was established, and the Pareto optimal solution set was obtained by using the ε-constraint method. Finally, an IEEE 33 bus distribution system was taken as an example to verify the effectiveness of the proposed strategy. The results show that the proposed strategy can synergistically optimize the active and reactive power output of different power sources after island partition, allowing for the operational economy while eliminating the risk of voltage exceeding limits in post-disaster distribution network islands.
As the proportion of renewable energy in the distribution network continues to increase, As the proportion of renewable energy in the distribution network continues to increase, problems such as voltage exceeding limits that affect the stable and economic operation of post-disaster distribution network islands become more prominent. In order to effectively solve the voltage exceeding limit problem in isolated islands and consider the operational economy, a multi-objective isolated island operation strategy considering the assistance of mobile energy storage system for active distribution networks was proposed. Firstly, voltage control and economic operation of active distribution network islands can be theoretically proven to be achieved through the optimization of both active and reactive power. Secondly, using mobile energy storage as an auxiliary resource, taking minimizing node voltage deviation and total cost during islanding operation as an objective, a multi-objective islanding operation model for an active distribution network was established, and the Pareto optimal solution set was obtained by using the ε-constraint method. Finally, an IEEE 33 bus distribution system was taken as an example to verify the effectiveness of the proposed strategy. The results show that the proposed strategy can synergistically optimize the active and reactive power output of different power sources after island partition, allowing for the operational economy while eliminating the risk of voltage exceeding limits in post-disaster distribution network islands.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0271
Abstract:
AC/DC hybrid distribution mode is an important development direction of future distribution network. In order to overcome the limitation of power information transmission caused by different interest subjects of each subsystem, a distributed optimization operation method of AC/DC hybrid distribution network based on improved analytical target cascading method was proposed. Firstly, in allusion to DC, VSC and AC region of AC/DC hybrid distribution network, the optimization models including regional operation optimization and inter-regional interactive power operation optimization were established respectively. Secondly, using the idea of distributed optimization, the interaction power was used as a shared variable, the model objective function was decoupled, and a balance coefficient was introduced in the traditional analytical target cascading method to solve the problem of poor algorithm performance due to improper selection of the initial value of the penalty multiplier and unbalanced weights of subsystem function terms and penalty terms, then the distributed model was solved iteratively under the consistency constraint. Finally, the effectiveness of the improved algorithm and the universality of the distributed model are verified by example simulation.
AC/DC hybrid distribution mode is an important development direction of future distribution network. In order to overcome the limitation of power information transmission caused by different interest subjects of each subsystem, a distributed optimization operation method of AC/DC hybrid distribution network based on improved analytical target cascading method was proposed. Firstly, in allusion to DC, VSC and AC region of AC/DC hybrid distribution network, the optimization models including regional operation optimization and inter-regional interactive power operation optimization were established respectively. Secondly, using the idea of distributed optimization, the interaction power was used as a shared variable, the model objective function was decoupled, and a balance coefficient was introduced in the traditional analytical target cascading method to solve the problem of poor algorithm performance due to improper selection of the initial value of the penalty multiplier and unbalanced weights of subsystem function terms and penalty terms, then the distributed model was solved iteratively under the consistency constraint. Finally, the effectiveness of the improved algorithm and the universality of the distributed model are verified by example simulation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0383
Abstract:
Although the transient angle stability assessment of power systems based on data-driven can provide more accurate results, it is difficult to apply in engineering practice due to the lack of interpretability of the results. To solve this problem, an analysis method of transient angle stability assessment and interpretability based on gradient boosting enhanced with step-wise feature augmentation (abbr. AugBoost) was proposed. Firstly, the mapping relationship between the power system input features and transient angle stability index was established by training the AugBoost assessment model. Secondly, the real-time measurement data of phasor measurement units were transferred to the trained AugBoost assessment model to provide real-time assessment results. And the relationship between assessment results and input features was explained according to Shapley additive explanations (abbr. SHAP) interpretation model to improve the credibility of results. Finally, a model update process was designed to improve the robustness of the assessment model to the variation of power system operating conditions. The simulation results on the 23-bus power system and the 1648-bus power system provided by power system simulation software PSS/E verify the effectiveness of the proposed method.
Although the transient angle stability assessment of power systems based on data-driven can provide more accurate results, it is difficult to apply in engineering practice due to the lack of interpretability of the results. To solve this problem, an analysis method of transient angle stability assessment and interpretability based on gradient boosting enhanced with step-wise feature augmentation (abbr. AugBoost) was proposed. Firstly, the mapping relationship between the power system input features and transient angle stability index was established by training the AugBoost assessment model. Secondly, the real-time measurement data of phasor measurement units were transferred to the trained AugBoost assessment model to provide real-time assessment results. And the relationship between assessment results and input features was explained according to Shapley additive explanations (abbr. SHAP) interpretation model to improve the credibility of results. Finally, a model update process was designed to improve the robustness of the assessment model to the variation of power system operating conditions. The simulation results on the 23-bus power system and the 1648-bus power system provided by power system simulation software PSS/E verify the effectiveness of the proposed method.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0420
Abstract:
To promote wind and light power consumption of wind-light-electric load power uncertainty in the integrated energy system and reduce carbon emissions, firstly, a wind and light power uncertainty set was constructed considering the wind and light volatility and the phenomenon of wind and light abandonment; a price-based demand response uncertainty set of the electric load was constructed considering electricity price volatility and load demand response elasticity coefficient. Secondly, a sorting truncated apportionment method was proposed to convert an uncertain set of wind, light, and electricity load power into a linear corresponding formula that can be solved linearly. Thirdly, a combined heat and power (abbr. CHP) unit-carbon capture system was established, and derived an overall model of the system, which only focuses on the external energy interaction of the system. Fourthly, an integrated energy system (abbr. IES) optimization model for wind, light, and electric load power uncertainty was constructed, and their wind and light power consumption and carbon emissions in multiple scenarios were analyzed. Finally, the effectiveness of the proposed method is demonstrated using arithmetic examples.
To promote wind and light power consumption of wind-light-electric load power uncertainty in the integrated energy system and reduce carbon emissions, firstly, a wind and light power uncertainty set was constructed considering the wind and light volatility and the phenomenon of wind and light abandonment; a price-based demand response uncertainty set of the electric load was constructed considering electricity price volatility and load demand response elasticity coefficient. Secondly, a sorting truncated apportionment method was proposed to convert an uncertain set of wind, light, and electricity load power into a linear corresponding formula that can be solved linearly. Thirdly, a combined heat and power (abbr. CHP) unit-carbon capture system was established, and derived an overall model of the system, which only focuses on the external energy interaction of the system. Fourthly, an integrated energy system (abbr. IES) optimization model for wind, light, and electric load power uncertainty was constructed, and their wind and light power consumption and carbon emissions in multiple scenarios were analyzed. Finally, the effectiveness of the proposed method is demonstrated using arithmetic examples.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0347
Abstract:
The negative electrical damping is the direct reason for the sub-synchronous oscillation in the near area where a large number of power electronic devices are connected to the power system. It is very important to understand the mechanism of electrical negative damping and to formulate suppression measures that determine the key links and parameters that cause electrical negative damping. To address this problem, firstly, the complex torque coefficient transfer function was expanded; the extreme value of electrical damping was defined; the key oscillation modes and fractions causing negative damping were determined based on mode decoupling by the electrical damping analysis method with improved complex torque coefficients. Secondly, the electrical damping correlation factor and sensitivity were introduced to determine the key influencing links and parameters of electrical negative damping, and the parameter adjustment scheme was given to suppress sub-synchronous oscillation according to the sensitivity analysis results. Finally, the time domain simulation of this method is verified by the PSCAD model..
The negative electrical damping is the direct reason for the sub-synchronous oscillation in the near area where a large number of power electronic devices are connected to the power system. It is very important to understand the mechanism of electrical negative damping and to formulate suppression measures that determine the key links and parameters that cause electrical negative damping. To address this problem, firstly, the complex torque coefficient transfer function was expanded; the extreme value of electrical damping was defined; the key oscillation modes and fractions causing negative damping were determined based on mode decoupling by the electrical damping analysis method with improved complex torque coefficients. Secondly, the electrical damping correlation factor and sensitivity were introduced to determine the key influencing links and parameters of electrical negative damping, and the parameter adjustment scheme was given to suppress sub-synchronous oscillation according to the sensitivity analysis results. Finally, the time domain simulation of this method is verified by the PSCAD model..
Decision Model of Day Ahead Frequency Regulation for Electric Vehicle Users Based on Prospect Theory
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0282
Abstract:
To reduce the difficulty for electric vehicle (EV) users to participate in the day-ahead frequency regulation ancillary services market and improve their participation enthusiasm, by introducing the net income of EV when charging off the grid and the impact of battery power on user psychology into the value function of prospect theory, a day-ahead decision model for EV users to participate in frequency regulation auxiliary service market under bounded rationality was established. This model considers the battery aging cost of EVs participating in frequency regulation auxiliary service, studies the optimization strategy of EV reserve capacity, and the quotation strategy of the frequency regulation auxiliary service market. In addition, three feasible simplified EV frequency regulation auxiliary service participation modes were provided for users to choose from. The effectiveness and feasibility of EV users' day ahead FM decision model based on prospect theory was verified through the analysis of a numerical example, and it is more consistent with the actual EV users' decision-making behavior.
To reduce the difficulty for electric vehicle (EV) users to participate in the day-ahead frequency regulation ancillary services market and improve their participation enthusiasm, by introducing the net income of EV when charging off the grid and the impact of battery power on user psychology into the value function of prospect theory, a day-ahead decision model for EV users to participate in frequency regulation auxiliary service market under bounded rationality was established. This model considers the battery aging cost of EVs participating in frequency regulation auxiliary service, studies the optimization strategy of EV reserve capacity, and the quotation strategy of the frequency regulation auxiliary service market. In addition, three feasible simplified EV frequency regulation auxiliary service participation modes were provided for users to choose from. The effectiveness and feasibility of EV users' day ahead FM decision model based on prospect theory was verified through the analysis of a numerical example, and it is more consistent with the actual EV users' decision-making behavior.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0387
Abstract:
In allusion to the problems that the current research on power scheduling does not integrate the carbon emission flow with the power flow as well as the intelligence of the solution algorithm still needs to be explored, a low-carbon optimal learning and scheduling method of power systems that took into account the carbon capture device and the carbon emission flow theory was proposed. Firstly, the power system’s carbon emission flow model was constructed at the equipment and the system level respectively. Secondly, a bi-level alternating optimal scheduling model, which includes system day-ahead scheduling and load demand response adjustment, was established by considering each link of source-grid-load-storage of the power system, and a deep reinforcement learning algorithm was adopted to solve the model. Finally, the effectiveness and applicability of the proposed theoretical approach in reducing operating costs and carbon emissions were verified through actual example simulations.
In allusion to the problems that the current research on power scheduling does not integrate the carbon emission flow with the power flow as well as the intelligence of the solution algorithm still needs to be explored, a low-carbon optimal learning and scheduling method of power systems that took into account the carbon capture device and the carbon emission flow theory was proposed. Firstly, the power system’s carbon emission flow model was constructed at the equipment and the system level respectively. Secondly, a bi-level alternating optimal scheduling model, which includes system day-ahead scheduling and load demand response adjustment, was established by considering each link of source-grid-load-storage of the power system, and a deep reinforcement learning algorithm was adopted to solve the model. Finally, the effectiveness and applicability of the proposed theoretical approach in reducing operating costs and carbon emissions were verified through actual example simulations.
3D Static Reconstruction Method of Power Grid Facilities Based on Principle of Structure from Motion
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0402
Abstract:
Abstract : The digitization of grid equipment is a key step of power grid digital twinning. The traditional manual modeling method based on design data and ledger data is inefficient and far from reality. To address the above drawbacks, a 3D static model reconstruction method for digital twin power grid facilities was studied, considering the difficulty in extracting feature points due to the weak texture and single color of some power grid facilities, as well as the real-time and accuracy demands of 3D reconstruction. Firstly, based on the Structure from motion (abbr. SFM), a complete algorithm flow design was carried out for the steps of pose estimation, dense point cloud extraction, and surface reconstruction in this principle. Secondly, the accuracy of camera pose estimation was improved by improving the matching conditions of feature points and combining the five-spot method with the random sample consensus algorithm. Thirdly, the minimum eigenvalue algorithm and the Kanade-Lucas-Tomasi(abbr. KLT) optical flow method were combined to increase the selection number of dense point clouds, and ultimately effectively improve the accuracy and visual effect of point cloud reconstruction. Finally, the combined transformer box was taken as an example for experimental verification. The experimental results and precision comparison analysis show that the proposed method can quickly and accurately construct grid facilities’ virtual model, which effectively supports the digital transformation of the traditional power grid and the implementation of a digital twin power grid.
Abstract
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0217
Abstract:
The drive train is an important part of the doubly-fed induction generator (DFIG) wind turbine (WT), its model and parameters have vital influence on power system synchronous stability and frequency stability analysis. Therefore, an accurate drive train model is the prerequisite for studying the dynamic characteristics of new energy power systems. In order to solve the difficulty of identifying model parameters due to insufficient measurement information for large disturbances, a neural network model is proposed based on the rich historical response data under random small disturbances excitation during normal operation of the unit, and the corresponding relationship between the response data and model parameters is used to predict the driving system model parameters based on the current response data. Firstly, the BP neural network modelling principle is introduced. Secondly, the power spectrum characteristic data of response signal is extracted based on a simulation system with a DFIG wind farm integrated into an infinite system. Thirdly, the key parameters are selected based on the power spectrum sensitivity. Finally, the BP neural network model is built to reflect the nonlinear mapping between the response signal power spectrum and model parameters, then the model parameters are predicted based on trained neural network. The model error is also analyzed to validate the feasibility of data-driven modelling method for WTs.
The drive train is an important part of the doubly-fed induction generator (DFIG) wind turbine (WT), its model and parameters have vital influence on power system synchronous stability and frequency stability analysis. Therefore, an accurate drive train model is the prerequisite for studying the dynamic characteristics of new energy power systems. In order to solve the difficulty of identifying model parameters due to insufficient measurement information for large disturbances, a neural network model is proposed based on the rich historical response data under random small disturbances excitation during normal operation of the unit, and the corresponding relationship between the response data and model parameters is used to predict the driving system model parameters based on the current response data. Firstly, the BP neural network modelling principle is introduced. Secondly, the power spectrum characteristic data of response signal is extracted based on a simulation system with a DFIG wind farm integrated into an infinite system. Thirdly, the key parameters are selected based on the power spectrum sensitivity. Finally, the BP neural network model is built to reflect the nonlinear mapping between the response signal power spectrum and model parameters, then the model parameters are predicted based on trained neural network. The model error is also analyzed to validate the feasibility of data-driven modelling method for WTs.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0239
Abstract:
With the increasing penetration of renewable energy resources and flexible loads, the damping characteristics of critical electromechanical eigenmodes change with the time invariant operating conditions of power systems. When it comes to the dynamic stability evaluation, the QR method is accurate, but fails to satisfy the needs for online tracing. In order to calculate the time-varying eigenmodes, this paper proposes a method to reduce the state matrix order of the system via inertia equivalence. All state variables of an eigenmode are mapped to a new sub-space upon the modal clusters. However, the pattern matching and modal varying of eigenvalues in the calculation may lead to discontinuous results. To solve the problems, the relevant factor index of the dominant clusters is proposed and a framework is built to achieve fast calculation of critical eigenvalues in different conditions. The critical eigenmode calculation method is tested in the IEEE 145-bus system, and the simulation results demonstrate its accuracy and rapidity.
With the increasing penetration of renewable energy resources and flexible loads, the damping characteristics of critical electromechanical eigenmodes change with the time invariant operating conditions of power systems. When it comes to the dynamic stability evaluation, the QR method is accurate, but fails to satisfy the needs for online tracing. In order to calculate the time-varying eigenmodes, this paper proposes a method to reduce the state matrix order of the system via inertia equivalence. All state variables of an eigenmode are mapped to a new sub-space upon the modal clusters. However, the pattern matching and modal varying of eigenvalues in the calculation may lead to discontinuous results. To solve the problems, the relevant factor index of the dominant clusters is proposed and a framework is built to achieve fast calculation of critical eigenvalues in different conditions. The critical eigenmode calculation method is tested in the IEEE 145-bus system, and the simulation results demonstrate its accuracy and rapidity.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0398
Abstract:
To cope with the blackout risk of the new power system, it is an inevitable requirement to explore the potential and value of distributed resources participating in distribution network recovery from the multiple links of "source, load and storage", and work out a safe and fast recovery plan. For that reason, considering the different flexibility requirements of the black-start restoration process under multiple time scales, a self-restoration scheme of distribution network considering the participation of multiple heterogeneous distributed resources was proposed. Firstly, for the power distribution system with multiple types of distributed resources, an optimization model of the start-up sequence of distributed resources was constructed with the goal of maximizing available power generation and active load recovery. Secondly, a comprehensive path evaluation was constructed based on node importance and path recovery time to optimize the recovery path of the unit to be started, and then the outage load was put into operation step by step to reduce the outage loss. Thirdly, considering the black-start capacity and utilization of distributed resources in the recovery process, a black-start incentive mechanism including capacity compensation fee and utilization compensation fee was proposed to effectively promote the fair and reasonable distribution of service fees. Finally, taking the improved IEEE33 node system as an example, the analysis of multiple power outage scenarios shows that the proposed method can effectively allocate restoration resources and improve the speed and stability of power distribution system restoration.
To cope with the blackout risk of the new power system, it is an inevitable requirement to explore the potential and value of distributed resources participating in distribution network recovery from the multiple links of "source, load and storage", and work out a safe and fast recovery plan. For that reason, considering the different flexibility requirements of the black-start restoration process under multiple time scales, a self-restoration scheme of distribution network considering the participation of multiple heterogeneous distributed resources was proposed. Firstly, for the power distribution system with multiple types of distributed resources, an optimization model of the start-up sequence of distributed resources was constructed with the goal of maximizing available power generation and active load recovery. Secondly, a comprehensive path evaluation was constructed based on node importance and path recovery time to optimize the recovery path of the unit to be started, and then the outage load was put into operation step by step to reduce the outage loss. Thirdly, considering the black-start capacity and utilization of distributed resources in the recovery process, a black-start incentive mechanism including capacity compensation fee and utilization compensation fee was proposed to effectively promote the fair and reasonable distribution of service fees. Finally, taking the improved IEEE33 node system as an example, the analysis of multiple power outage scenarios shows that the proposed method can effectively allocate restoration resources and improve the speed and stability of power distribution system restoration.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0227
Abstract:
To address the problems of large installed capacity of renewable power yet with relatively low output and inadequate peak shaving capacity of the power system, this article elaborates and analyzes how to maximize the potential of the user side in the power system through the emerging solid-state electric heating and heat storage technology. A system model of the solid-state electric heating and heat storage equipment with power demand response on the user side was designed and established, aiming at the maximum amount of wind power consumed by the system and the optimal economy of the user side in operation. The article adopted the improved multi-objective particle swarm operation (MOPSO) algorithm for model optimization and model solution. A comparative analysis of wind power accommodation capacity, load curve peak-to-valley difference and economic cost under diverse conditions was conducted in the source side, grid side and load side of the power side system. The simulation results show that the wind power consumption capacity and peak shaving capability have been significantly improved with the installation of the solid-state electric heating and heat storage equipment on the user side on the basis of demand response. In addition, this method has certain economic value.
To address the problems of large installed capacity of renewable power yet with relatively low output and inadequate peak shaving capacity of the power system, this article elaborates and analyzes how to maximize the potential of the user side in the power system through the emerging solid-state electric heating and heat storage technology. A system model of the solid-state electric heating and heat storage equipment with power demand response on the user side was designed and established, aiming at the maximum amount of wind power consumed by the system and the optimal economy of the user side in operation. The article adopted the improved multi-objective particle swarm operation (MOPSO) algorithm for model optimization and model solution. A comparative analysis of wind power accommodation capacity, load curve peak-to-valley difference and economic cost under diverse conditions was conducted in the source side, grid side and load side of the power side system. The simulation results show that the wind power consumption capacity and peak shaving capability have been significantly improved with the installation of the solid-state electric heating and heat storage equipment on the user side on the basis of demand response. In addition, this method has certain economic value.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0106
Abstract:
To promote carbon emission reduction of road transportation and realize carbon peak as soon as possible, firstly the carbon emission of road transportation was measured and calculated. In addition to the carbon emissions from gasoline and fuel oil consumed by road traffic, the carbon emissions from coal electricity in charge volume were also taken into account. Then, STIRPAT model was used to analyze the influencing factors of carbon emissions from road transportation. Six factors, namely road transport GDP, car ownership, coal power ratio of charging capacity, road freight turnover and road passenger turnover, were selected to study the change of road transport carbon emission, and the contribution rate of road transport carbon emission of these six influencing factors was calculated and analyzed. Finally, by setting the baseline scenario and the concentrated emission reduction scenario, the future carbon emissions of road transportation in China were predicted, which provided development ideas for achieving the carbon peak of road transportation. The results show that on the premise of vigorously promoting electric vehicles, the change rate of the proportion of electric vehicles should be 37.91% from 2019 to 2030, and 1.70% after 2030. In addition, the ratio of clean energy should be increased so that the ratio of coal power in EV charging can reach -23%, which is more clean and low-carbon in terms of EV charging carbon emissions. At the same time, it is necessary to maintain the downward trend of 4.14% of highway passenger turnover and the slow growth of 4.84% of highway freight volume. China's highway traffic will be expected to achieve carbon peak before 2030.
To promote carbon emission reduction of road transportation and realize carbon peak as soon as possible, firstly the carbon emission of road transportation was measured and calculated. In addition to the carbon emissions from gasoline and fuel oil consumed by road traffic, the carbon emissions from coal electricity in charge volume were also taken into account. Then, STIRPAT model was used to analyze the influencing factors of carbon emissions from road transportation. Six factors, namely road transport GDP, car ownership, coal power ratio of charging capacity, road freight turnover and road passenger turnover, were selected to study the change of road transport carbon emission, and the contribution rate of road transport carbon emission of these six influencing factors was calculated and analyzed. Finally, by setting the baseline scenario and the concentrated emission reduction scenario, the future carbon emissions of road transportation in China were predicted, which provided development ideas for achieving the carbon peak of road transportation. The results show that on the premise of vigorously promoting electric vehicles, the change rate of the proportion of electric vehicles should be 37.91% from 2019 to 2030, and 1.70% after 2030. In addition, the ratio of clean energy should be increased so that the ratio of coal power in EV charging can reach -23%, which is more clean and low-carbon in terms of EV charging carbon emissions. At the same time, it is necessary to maintain the downward trend of 4.14% of highway passenger turnover and the slow growth of 4.84% of highway freight volume. China's highway traffic will be expected to achieve carbon peak before 2030.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0027
Abstract:
The participation of new energy power producers (abbr. NEPP) in the market is the key path to realize the clean and low-carbon transformation of China’s energy system, but the uncertainty of its own output will lead to credit risk. therefore, it is necessary to evaluate NEPP’s credit risk and provide reference for promoting the classified management of enterprise credit risk. Based on the aspiration-ability-action framework, a credit risk evaluation index system of NEPP, in which three primary indices and 14 secondary indices were included, was designed. An index weighting method based on game theory combined with anti-entropy-weight (abbr. AEW) method and level based weight assessment (LBWA) method, and hierarchical weighting method, and a credit risk evaluation method based on difference and quotient grey correlation analysis and the credit risk evaluation method based on difference and quotient grey relation analysis improved technique for order preference by similarity to an ideal solution (DQGRA-TOPSIS) method were constructed. Seven NEPPs were analyzed as the example, and the results show that the contract electricity compliance rate, spot market declaration deviation rate and output prediction deviation rate are the key factors affecting the credit risk of NEEPs. In addition, unfair competition of NEEP and the tax evasion behavior are also the focus of market credit management institutions. Results of ranking consistency test and LOO (Leave-One-Out) analysis show that the proposed model possesses strong robustness.
The participation of new energy power producers (abbr. NEPP) in the market is the key path to realize the clean and low-carbon transformation of China’s energy system, but the uncertainty of its own output will lead to credit risk. therefore, it is necessary to evaluate NEPP’s credit risk and provide reference for promoting the classified management of enterprise credit risk. Based on the aspiration-ability-action framework, a credit risk evaluation index system of NEPP, in which three primary indices and 14 secondary indices were included, was designed. An index weighting method based on game theory combined with anti-entropy-weight (abbr. AEW) method and level based weight assessment (LBWA) method, and hierarchical weighting method, and a credit risk evaluation method based on difference and quotient grey correlation analysis and the credit risk evaluation method based on difference and quotient grey relation analysis improved technique for order preference by similarity to an ideal solution (DQGRA-TOPSIS) method were constructed. Seven NEPPs were analyzed as the example, and the results show that the contract electricity compliance rate, spot market declaration deviation rate and output prediction deviation rate are the key factors affecting the credit risk of NEEPs. In addition, unfair competition of NEEP and the tax evasion behavior are also the focus of market credit management institutions. Results of ranking consistency test and LOO (Leave-One-Out) analysis show that the proposed model possesses strong robustness.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0008
Abstract:
To improve the absorptive ability of new energy and realize low-carbon and economic operation of integrated energy system, a combined gas, heat and power (abbr. CGHP) system model and coordinated operation strategy of the source-load sides considering the substitution of energy on the load-side was considered. Firstly, according to the operating characteristics of power to gas (abbr. P2G) and carbon capture system, a basic frame of CGHP system model was built to research and analyze the thermoelectric operation characteristic of CGHP system and the quantitative relation of electricity-gas-heat. Secondly, according to the source-side energy price information, an comprehensive demand response model for the substitution and transformation among loads under the carbon trading mechanism was established. Finally, under the premise of ensuring the satisfaction with users’ energy consumption, taking the minimized daily operating cost of integrated energy system as the objective, a dispatching plan at the source-load side of the grid was reasonably arranged. The results of the computing example show that the proposed model and operation strategy are feasible and effective, and can provide some references for promoting the integrated energy system to absorb new energy, decreasing the emission of CO2 and reducing daily operating cost of the system.
To improve the absorptive ability of new energy and realize low-carbon and economic operation of integrated energy system, a combined gas, heat and power (abbr. CGHP) system model and coordinated operation strategy of the source-load sides considering the substitution of energy on the load-side was considered. Firstly, according to the operating characteristics of power to gas (abbr. P2G) and carbon capture system, a basic frame of CGHP system model was built to research and analyze the thermoelectric operation characteristic of CGHP system and the quantitative relation of electricity-gas-heat. Secondly, according to the source-side energy price information, an comprehensive demand response model for the substitution and transformation among loads under the carbon trading mechanism was established. Finally, under the premise of ensuring the satisfaction with users’ energy consumption, taking the minimized daily operating cost of integrated energy system as the objective, a dispatching plan at the source-load side of the grid was reasonably arranged. The results of the computing example show that the proposed model and operation strategy are feasible and effective, and can provide some references for promoting the integrated energy system to absorb new energy, decreasing the emission of CO2 and reducing daily operating cost of the system.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0111
Abstract:
The prediction of photovoltaic power generation is of great significance to the stability and safe operation of power grid. A digital twin prediction model based on long short term memory (abbr.LSTM) network was proposed, and by means of digital twin technology model the accurate prediction of photovoltaic (abbr. PV) power generation was realized. The digital Twin can be divided into physical space and data space. Firstly, according to the meteorological twin data obtained from the physical space the preliminary predicted power was obtained by LSTM algorithm, meanwhile the historical meteorological database was updated. Secondly, the similar day was found out in meteorological database, and comparing the predicted power on similar days with the actual power the error correction of the preliminary predicted power was conducted to obtain the predicted value of final PV power. Using the proposed digital twin the connection of the physical entity with the data-driven method was realized, simultaneously the self-studying and updating of the physical entity could be carried out. Therefore, compared with traditional PV prediction results the obtained result was more accurate. The accuracy of digital twin prediction is further verified by simulation example.
The prediction of photovoltaic power generation is of great significance to the stability and safe operation of power grid. A digital twin prediction model based on long short term memory (abbr.LSTM) network was proposed, and by means of digital twin technology model the accurate prediction of photovoltaic (abbr. PV) power generation was realized. The digital Twin can be divided into physical space and data space. Firstly, according to the meteorological twin data obtained from the physical space the preliminary predicted power was obtained by LSTM algorithm, meanwhile the historical meteorological database was updated. Secondly, the similar day was found out in meteorological database, and comparing the predicted power on similar days with the actual power the error correction of the preliminary predicted power was conducted to obtain the predicted value of final PV power. Using the proposed digital twin the connection of the physical entity with the data-driven method was realized, simultaneously the self-studying and updating of the physical entity could be carried out. Therefore, compared with traditional PV prediction results the obtained result was more accurate. The accuracy of digital twin prediction is further verified by simulation example.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0180
Abstract:
Edge computing is extensively concerned by the energy industry because of the advantages of fast data processing, low cost and high real-time, and the prediction on the edge server is helpful for the refined management and control of energy. For this reason, in allusion to the limitations of edge service resources, based on difference autoregressive integrated moving average (abbr. ARIMA) model and gated recurrent unit (abbr. GRU）neural network a combined prediction method of wind, light and load was proposed. Firstly, the ARIMA was used to extract the linear characteristics of source and load, and through fitting linear characteristics with the true value the residual with nonlinear features was obtained. Secondly, taking the residual as the training dataset of GRU a prediction model was established, and then leading in the pruning and quantification method the GRU model was optimized and compressed to reduce the size of the prediction model to suit the deploy of edge servers. Results of lots of simulation examples show that the constructed GRU compression model possesses the features of small scale and high prediction accuracy, so it is suitable to the deployment and application of edge servers.
Edge computing is extensively concerned by the energy industry because of the advantages of fast data processing, low cost and high real-time, and the prediction on the edge server is helpful for the refined management and control of energy. For this reason, in allusion to the limitations of edge service resources, based on difference autoregressive integrated moving average (abbr. ARIMA) model and gated recurrent unit (abbr. GRU）neural network a combined prediction method of wind, light and load was proposed. Firstly, the ARIMA was used to extract the linear characteristics of source and load, and through fitting linear characteristics with the true value the residual with nonlinear features was obtained. Secondly, taking the residual as the training dataset of GRU a prediction model was established, and then leading in the pruning and quantification method the GRU model was optimized and compressed to reduce the size of the prediction model to suit the deploy of edge servers. Results of lots of simulation examples show that the constructed GRU compression model possesses the features of small scale and high prediction accuracy, so it is suitable to the deployment and application of edge servers.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0098
Abstract:
Considering that the risk evaluation system and model of virtual power plant operation in China need to be established and improved, this paper mainly studies the comprehensive risk evaluation system of virtual power plant operation to provide support and guidance for the construction and operation of virtual power plant. Firstly, the comprehensive risk evaluation index library of virtual power plant is constructed by taking operation risk, economic risk, safety risk and management risk as first-level indexes, which provides support for safe and reliable operation evaluation of virtual power plant. Secondly, the comprehensive risk evaluation model of virtual power plant is proposed. AHP-OWA is used to assign weights to the indicators, and then the evaluation scores of all indicators are obtained through the cloud model evaluation method based on Bayesian feedback correction, which is used to evaluate the risk degree of virtual power plant and provide judgment basis for the operation improvement and optimization of virtual power plant. Finally, a virtual power plant is selected for example analysis to verify the effectiveness and superiority of the proposed virtual power plant risk evaluation index system based on Bayesian improved cloud model.
Considering that the risk evaluation system and model of virtual power plant operation in China need to be established and improved, this paper mainly studies the comprehensive risk evaluation system of virtual power plant operation to provide support and guidance for the construction and operation of virtual power plant. Firstly, the comprehensive risk evaluation index library of virtual power plant is constructed by taking operation risk, economic risk, safety risk and management risk as first-level indexes, which provides support for safe and reliable operation evaluation of virtual power plant. Secondly, the comprehensive risk evaluation model of virtual power plant is proposed. AHP-OWA is used to assign weights to the indicators, and then the evaluation scores of all indicators are obtained through the cloud model evaluation method based on Bayesian feedback correction, which is used to evaluate the risk degree of virtual power plant and provide judgment basis for the operation improvement and optimization of virtual power plant. Finally, a virtual power plant is selected for example analysis to verify the effectiveness and superiority of the proposed virtual power plant risk evaluation index system based on Bayesian improved cloud model.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0089
Abstract:
In allusion to the voltage ride through of wind power generating units, a voltage fault ride-through control method based on the coordinated control of energy storage and rotor of wind turbines was proposed. By use of rotor inertia response of the wind turbine generating unit cooperated with power response of energy storage the active power output of wind power generating units during the fault was regulated and then the compensation power and the capacity required by the energy storage device was reduced, meanwhile it was ensured that the grid-connected voltage of the wind turbine generating unit was maintained at the normal level, thus the voltage fault ride-through ability of wind turbine generating units was improved. Finally, by using of MATLAB/Simulink platform the model of doubly-fed induction generator (abbr. DFIG) and permanent magnet synchronous generator (abbr. PMSG) were constructed, and the simulation validation on symmetrical voltage sag, asymmetrical voltage sag and voltage rise was performed. Simulation results show that the proposed fault ride-through control method is effective.
In allusion to the voltage ride through of wind power generating units, a voltage fault ride-through control method based on the coordinated control of energy storage and rotor of wind turbines was proposed. By use of rotor inertia response of the wind turbine generating unit cooperated with power response of energy storage the active power output of wind power generating units during the fault was regulated and then the compensation power and the capacity required by the energy storage device was reduced, meanwhile it was ensured that the grid-connected voltage of the wind turbine generating unit was maintained at the normal level, thus the voltage fault ride-through ability of wind turbine generating units was improved. Finally, by using of MATLAB/Simulink platform the model of doubly-fed induction generator (abbr. DFIG) and permanent magnet synchronous generator (abbr. PMSG) were constructed, and the simulation validation on symmetrical voltage sag, asymmetrical voltage sag and voltage rise was performed. Simulation results show that the proposed fault ride-through control method is effective.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0076
Abstract:
The users' daily electricity consumption data can reflect their electricity consumption behavior characteristics, and the clustering task can extract users' representative daily load from a large amount of operating data, it can provide such a basis for tasks as power system planning and scheduling. In allusion to such defects in traditional clustering methods as low efficiency and the difficulty in extracting potential representations from daily load data scenarios with huge data size and high data dimensions, a clustering method based on convolutional-variational autoencoder (C-VAE) was proposed to cluster the load curves. Firstly, the potential characteristics of daily load data was extracted by dimensionality reduction of C-VAE. Secondly, cooperated with K-means the load clustering task was conducted. Finally, based on the distance between each load curve with the clustering center, each sort of clustering center was revised by weighted correction to obtain more representative typical day load curve. The actually collected data from Portuguese users in UCI dataset was utilized to conduct the examples validation, and the results show that the Davies-Bouldin Index (DBI) of this method decreased significantly than such traditional clustering methods as K-means, PCA+ K-means and so on, it also showed that the data within each type was more compact, and the distance among each type was further away, so the clustering quality was improved. Afterward, the Gaussian distance weighting was utilized to improve the clustering center, and a more typical daily load curve was extracted to make the analysis on the characteristics of users' electricity consumption behavior more accurate. Thus, the effectiveness of the convolutional variational autoencoder clustering method in daily load curve clustering task was verified.
The users' daily electricity consumption data can reflect their electricity consumption behavior characteristics, and the clustering task can extract users' representative daily load from a large amount of operating data, it can provide such a basis for tasks as power system planning and scheduling. In allusion to such defects in traditional clustering methods as low efficiency and the difficulty in extracting potential representations from daily load data scenarios with huge data size and high data dimensions, a clustering method based on convolutional-variational autoencoder (C-VAE) was proposed to cluster the load curves. Firstly, the potential characteristics of daily load data was extracted by dimensionality reduction of C-VAE. Secondly, cooperated with K-means the load clustering task was conducted. Finally, based on the distance between each load curve with the clustering center, each sort of clustering center was revised by weighted correction to obtain more representative typical day load curve. The actually collected data from Portuguese users in UCI dataset was utilized to conduct the examples validation, and the results show that the Davies-Bouldin Index (DBI) of this method decreased significantly than such traditional clustering methods as K-means, PCA+ K-means and so on, it also showed that the data within each type was more compact, and the distance among each type was further away, so the clustering quality was improved. Afterward, the Gaussian distance weighting was utilized to improve the clustering center, and a more typical daily load curve was extracted to make the analysis on the characteristics of users' electricity consumption behavior more accurate. Thus, the effectiveness of the convolutional variational autoencoder clustering method in daily load curve clustering task was verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0078
Abstract:
In order to improve the efficiency of coal-fired units participating in system regulation, promote the penetration rate of renewable energy and achieve the low-carbon transition of the power system, two cooperative operation optimization models were constructed from the perspective of coal-fired units system. One was aiming at low carbon for overall operation, and the other was aimming at economic for overall operation. In the two models, the cooperative effect of coal-fired units with different technical characteristics was explored under high penetration rate of renewable energy. Case study indicated that the cooperative operation of units based on low-carbon objectives was conducive to giving play to the advantages of high-performance units. Small-capacity, low-parameter units generated less power and mainly provided auxiliary services. It effectively reduced the total carbon emissions of coal-fired units while improving the penetration rate of renewable energy. However, the overall economics of the units would be affected. As the penetration rate of renewable energy increases, the effection of cooperative emission reduction would be significantly enhanced. The design of future power system should fully consider the cooperation between the units with different technical characteristics.
In order to improve the efficiency of coal-fired units participating in system regulation, promote the penetration rate of renewable energy and achieve the low-carbon transition of the power system, two cooperative operation optimization models were constructed from the perspective of coal-fired units system. One was aiming at low carbon for overall operation, and the other was aimming at economic for overall operation. In the two models, the cooperative effect of coal-fired units with different technical characteristics was explored under high penetration rate of renewable energy. Case study indicated that the cooperative operation of units based on low-carbon objectives was conducive to giving play to the advantages of high-performance units. Small-capacity, low-parameter units generated less power and mainly provided auxiliary services. It effectively reduced the total carbon emissions of coal-fired units while improving the penetration rate of renewable energy. However, the overall economics of the units would be affected. As the penetration rate of renewable energy increases, the effection of cooperative emission reduction would be significantly enhanced. The design of future power system should fully consider the cooperation between the units with different technical characteristics.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0252
Abstract:
Complementation among cascade hydropower stations and photovoltaic is of great significance for stabilizing photovoltaic fluctuations and improving photovoltaic consumption. For this reason, firstly, starting form the two objective functions, i.e., minimum fluctuation of generated power and maximum economic benefit of power generation, considering the participation of hydro-photovoltaic complementary system into power grid active power balance auxiliary service, by means of setting the design value of hydro-photovoltaic complementary system to make its intraday output trend enable to fallow the fluctuation of power network load in a short-term multi-objective certain range, a multi-objective optimal dispatching model of short-term cascade hydro-photovoltaic (abbr. PV) complementary system was established. Secondly, taking the water volume constraint of cascade hydropower stations and typical daily power generation characteristics of three typical intraday sunny, cloudy and rainy photovoltaic power stations into account, a multi-objective evolutionary algorithm based on decomposition was used to solve the problem. Finally, the intraday optimal scheduling of the cascaded hydro-photovoltaic complementary system under three typical scenarios is verified and analyzed. The results show that the cascaded hydro-photovoltaic complementary system can effectively suppress the intermittency and volatility of photovoltaic power generation, and the output of the complementary system can be adjusted in time during the peak load period of the grid. The optimal scheduling scheme of the complementary system takes into account both power fluctuation and economic benefits of power generation.
Complementation among cascade hydropower stations and photovoltaic is of great significance for stabilizing photovoltaic fluctuations and improving photovoltaic consumption. For this reason, firstly, starting form the two objective functions, i.e., minimum fluctuation of generated power and maximum economic benefit of power generation, considering the participation of hydro-photovoltaic complementary system into power grid active power balance auxiliary service, by means of setting the design value of hydro-photovoltaic complementary system to make its intraday output trend enable to fallow the fluctuation of power network load in a short-term multi-objective certain range, a multi-objective optimal dispatching model of short-term cascade hydro-photovoltaic (abbr. PV) complementary system was established. Secondly, taking the water volume constraint of cascade hydropower stations and typical daily power generation characteristics of three typical intraday sunny, cloudy and rainy photovoltaic power stations into account, a multi-objective evolutionary algorithm based on decomposition was used to solve the problem. Finally, the intraday optimal scheduling of the cascaded hydro-photovoltaic complementary system under three typical scenarios is verified and analyzed. The results show that the cascaded hydro-photovoltaic complementary system can effectively suppress the intermittency and volatility of photovoltaic power generation, and the output of the complementary system can be adjusted in time during the peak load period of the grid. The optimal scheduling scheme of the complementary system takes into account both power fluctuation and economic benefits of power generation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0063
Abstract:
To deal with the potential threat to the security and stability of power grid caused by high proportion of grid-connected new energy, a comprehensive evaluation method was utilized to realize the security evaluation and the identification of weak power grid to provide a more comprehensive reference information for the decision of system secure operation. Firstly, a security evaluation index system for the power grid containing new energy was established. Secondly, the decision-making trial and evaluation laboratory (abbr. DEMATEL) method and the anti-entropy weight (abbr. AEW) method were used to calculate the subjective and the objective weights of the index. Thirdly, based on the thought of relative entropy the comprehensive weight was calculated. Finally, taking the modified IEEE-118 bus new energy power system with different permeability as a reference, and by use of Više-Kriterijumska Optimizacija I Kompromisno Rešenje (abbr. VIKOR) method the comprehensive evaluation, the ranking analysis on the advantages and disadvantages as well as the verification of sensitivity were conducted. The security ranking of new energy power systems with different permeabilities is as following: 10% >40% >20% >30% >50%, and such an evaluation results can be available for reference and utilized in practical engineering.
To deal with the potential threat to the security and stability of power grid caused by high proportion of grid-connected new energy, a comprehensive evaluation method was utilized to realize the security evaluation and the identification of weak power grid to provide a more comprehensive reference information for the decision of system secure operation. Firstly, a security evaluation index system for the power grid containing new energy was established. Secondly, the decision-making trial and evaluation laboratory (abbr. DEMATEL) method and the anti-entropy weight (abbr. AEW) method were used to calculate the subjective and the objective weights of the index. Thirdly, based on the thought of relative entropy the comprehensive weight was calculated. Finally, taking the modified IEEE-118 bus new energy power system with different permeability as a reference, and by use of Više-Kriterijumska Optimizacija I Kompromisno Rešenje (abbr. VIKOR) method the comprehensive evaluation, the ranking analysis on the advantages and disadvantages as well as the verification of sensitivity were conducted. The security ranking of new energy power systems with different permeabilities is as following: 10% >40% >20% >30% >50%, and such an evaluation results can be available for reference and utilized in practical engineering.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0084
Abstract:
In allusion to the fact that the current gridding partition method system for integrated energy system is not yet mature and in which the complementarity of load characteristics is not taken into account, a grid partition method system for integrated energy system, in which the load characteristics was considered, was proposed. Firstly, using the improved k-means clustering algorithm the initial division of energy grid was performed, i.e., on the basis of traditional k-means clustering algorithm the dividing range of gridding number and the position of initial clustering center were determined. Secondly, a method to revise energy gridding, in which the load characteristics was considered, was proposed, and after the accomplish of initial division of energy gridding the load characteristics was utilized to modify the gridding to reduce the inner peak valley rate of load in each energy gridding and the peak load of total grid. Finally, by means of fuzzy ideal decision-making the optimal number of energy gridding division was chosen, and the horizontal comparison of the number of divided number of optimal gridding was conducted. Comparison results show that comparing with the grid division method without considering the load characteristics, using the proposed method the peak-to-valley rates of electrical, heating and cooling loads can be reduced by 7.1%, 0.5% and 4.5% respectively, and the overall peak load of electrical, heating and cooling can be reduced by 7.9%, 11.5% and 3.9% respectively.
In allusion to the fact that the current gridding partition method system for integrated energy system is not yet mature and in which the complementarity of load characteristics is not taken into account, a grid partition method system for integrated energy system, in which the load characteristics was considered, was proposed. Firstly, using the improved k-means clustering algorithm the initial division of energy grid was performed, i.e., on the basis of traditional k-means clustering algorithm the dividing range of gridding number and the position of initial clustering center were determined. Secondly, a method to revise energy gridding, in which the load characteristics was considered, was proposed, and after the accomplish of initial division of energy gridding the load characteristics was utilized to modify the gridding to reduce the inner peak valley rate of load in each energy gridding and the peak load of total grid. Finally, by means of fuzzy ideal decision-making the optimal number of energy gridding division was chosen, and the horizontal comparison of the number of divided number of optimal gridding was conducted. Comparison results show that comparing with the grid division method without considering the load characteristics, using the proposed method the peak-to-valley rates of electrical, heating and cooling loads can be reduced by 7.1%, 0.5% and 4.5% respectively, and the overall peak load of electrical, heating and cooling can be reduced by 7.9%, 11.5% and 3.9% respectively.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0067
Abstract:
The mission of "carbon peaking and carbon neutralization" is forcing China's energy industry transform to sustainable development. At the same time, the technology of new energy vehicles has been continuously upgraded, and the supporting facilities related to EV charging and battery swapping services have been gradually improved, which has jointly promoted the rapid development of new energy vehicle industry of China. The current situation and development tendency of new energy vehicles and their charging and battery swapping technology were presented, and in allusion to the impact of grid connection of large scale new energy vehicles on the operation of distribution network it was proposed that through the way of aggregation the new energy vehicles was taken as the flexible load participating the operation control mode of interaction of vehicle and grid and the situation of application was presented. The construction progress of standard system of charging and charging and battery swapping services of China’s new energy vehicles was expounded. Finally, some suggestions were put forward for the key work of industry development.
The mission of "carbon peaking and carbon neutralization" is forcing China's energy industry transform to sustainable development. At the same time, the technology of new energy vehicles has been continuously upgraded, and the supporting facilities related to EV charging and battery swapping services have been gradually improved, which has jointly promoted the rapid development of new energy vehicle industry of China. The current situation and development tendency of new energy vehicles and their charging and battery swapping technology were presented, and in allusion to the impact of grid connection of large scale new energy vehicles on the operation of distribution network it was proposed that through the way of aggregation the new energy vehicles was taken as the flexible load participating the operation control mode of interaction of vehicle and grid and the situation of application was presented. The construction progress of standard system of charging and charging and battery swapping services of China’s new energy vehicles was expounded. Finally, some suggestions were put forward for the key work of industry development.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0112
Abstract:
To remedy the shortage of wind turbine in wind power leveling and fault ride-through, in allusion to direct drive wind power generation system based on hybrid energy storage a compound power control strategy, which considered both wind power leveling and fault ride-through simultaneously, was proposed. On the one hand, an improved second-order filtered power allocation method with power error feedback loop was proposed to realtime revise the power response command of super capacitor and battery storage to increase the target power allocation accuracy meanwhile the tracking control effect could be improved, thus the wind power leveling was realized at the same time the service life of energy storage medium was prolonged. On the other hand, a compound power control strategy, in which the grid side convertor acted together with hybrid energy storage, was put forward to realtime revise the power response command of each control quantity and quickly remove the redundant power of the DC bus, so that the fault ride-through capability of wind turbines was improved to make the wind power system basically not affected by grid faults.
To remedy the shortage of wind turbine in wind power leveling and fault ride-through, in allusion to direct drive wind power generation system based on hybrid energy storage a compound power control strategy, which considered both wind power leveling and fault ride-through simultaneously, was proposed. On the one hand, an improved second-order filtered power allocation method with power error feedback loop was proposed to realtime revise the power response command of super capacitor and battery storage to increase the target power allocation accuracy meanwhile the tracking control effect could be improved, thus the wind power leveling was realized at the same time the service life of energy storage medium was prolonged. On the other hand, a compound power control strategy, in which the grid side convertor acted together with hybrid energy storage, was put forward to realtime revise the power response command of each control quantity and quickly remove the redundant power of the DC bus, so that the fault ride-through capability of wind turbines was improved to make the wind power system basically not affected by grid faults.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0065
Abstract:
To solve the problem of severe damage and easy failure of substations in coastal areas caused by typhoon disasters, a substation typhoon vulnerability assessment method based on improved lion swarm algorithm and fuzzy evaluation was proposed. Firstly, based on the danger of disaster causing factors, the sensitivity of disaster pregnant environment and the vulnerability of disaster bearing body a vulnerability assessment system was established. Secondly, a combined cloud model was led into the traditional fuzzy comprehensive evaluation method to construct a fuzzy comprehensive evaluation membership function, and based on the improved lion swarm algorithm (abbr. LSA) the clustering center was optimized to obtain the threshold partition rules of membership function. Thirdly, based on the subjective and objective combination weights, the disaster risk index was calculated by weight and the improved fuzzy comprehensive evaluation method, and based on the historical data the disaster risk probability was calculated. The vulnerability assessment results of substation typhoon disaster were obtained from the disaster risk probability and risk index. Finally, according to the evaluation results different targeted measures were adopted. Results of computing example show that using the proposed assessment method of substation typhoon vulnerability the assessment accuracy is improved and the effect of disaster prevention and reduction is achieved.
To solve the problem of severe damage and easy failure of substations in coastal areas caused by typhoon disasters, a substation typhoon vulnerability assessment method based on improved lion swarm algorithm and fuzzy evaluation was proposed. Firstly, based on the danger of disaster causing factors, the sensitivity of disaster pregnant environment and the vulnerability of disaster bearing body a vulnerability assessment system was established. Secondly, a combined cloud model was led into the traditional fuzzy comprehensive evaluation method to construct a fuzzy comprehensive evaluation membership function, and based on the improved lion swarm algorithm (abbr. LSA) the clustering center was optimized to obtain the threshold partition rules of membership function. Thirdly, based on the subjective and objective combination weights, the disaster risk index was calculated by weight and the improved fuzzy comprehensive evaluation method, and based on the historical data the disaster risk probability was calculated. The vulnerability assessment results of substation typhoon disaster were obtained from the disaster risk probability and risk index. Finally, according to the evaluation results different targeted measures were adopted. Results of computing example show that using the proposed assessment method of substation typhoon vulnerability the assessment accuracy is improved and the effect of disaster prevention and reduction is achieved.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0102
Abstract:
To promote the accommodation of new energy and realize the value maximization of virtual power plants, taking wind power generation companies, photovoltaic (abbr. PV) power generation companies and energy storage power generation companies as the mainbody of revenue the power transaction with participation of virtual power plants was constructed. Firstly, the architecture of a virtual power plant including wind, PV and energy storage, batteries and super capacitors under the blockchain was built. Secondly, based on such parameters as power generation data, energy consumption data, and operating data shared in blockchain a charging/discharging strategy of hybrid energy storage as well as a capacity optimal configuration method, which took the lowest cost of hybrid energy storage as the objective, were proposed and solved by particle swarm algorithm. Thirdly, taking the application level of blockchain, risk preference, cooperative willingness and degree of participation as the factors the Shapley value method was improved and a revenue modification model was established, and the smart contract-based revenue distribution process under the blockchain was analyzed. Finally, by means of example simulation and the analysis on the results, both feasibility and applicability of the established model and method are verified.
To promote the accommodation of new energy and realize the value maximization of virtual power plants, taking wind power generation companies, photovoltaic (abbr. PV) power generation companies and energy storage power generation companies as the mainbody of revenue the power transaction with participation of virtual power plants was constructed. Firstly, the architecture of a virtual power plant including wind, PV and energy storage, batteries and super capacitors under the blockchain was built. Secondly, based on such parameters as power generation data, energy consumption data, and operating data shared in blockchain a charging/discharging strategy of hybrid energy storage as well as a capacity optimal configuration method, which took the lowest cost of hybrid energy storage as the objective, were proposed and solved by particle swarm algorithm. Thirdly, taking the application level of blockchain, risk preference, cooperative willingness and degree of participation as the factors the Shapley value method was improved and a revenue modification model was established, and the smart contract-based revenue distribution process under the blockchain was analyzed. Finally, by means of example simulation and the analysis on the results, both feasibility and applicability of the established model and method are verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0083
Abstract:
The large-scale grid-connection of such renewable energy as wind power and photovoltaic (abbr. PV) power leads to the decreasing of power system inertial and the increasing of frequency instability risk, there is an urgent need for online assessment of equivalent inertia. However, most existing inertia estimation methods are based on the frequency change rate under large disturbance scenario so it is hard for them to meet the needs of equivalent inertia estimation under quasi-steady operating state scenario. For this reason, firstly, in allusion to the normal inertial estimation the mechanism of power system inertia response was analyzed, and the expression for the equivalent inertia under quasi-steady state operating condition was derived. Secondly, based on the theory of system identification a controlled autoregressive moving average with exogenous variable (abbr. ARMAX) model was constructed and by use of Akaike information criterion (abbr. AIC) the model order was determined, and by use of the method of least minimum square the equivalent inertia of system was estimated. Finally, the effectiveness of the proposed method was verified by WSCC 9-bus system. Verification results show that the proposed method possesses higher identification precision and can adapt to different load fluctuation forms.
The large-scale grid-connection of such renewable energy as wind power and photovoltaic (abbr. PV) power leads to the decreasing of power system inertial and the increasing of frequency instability risk, there is an urgent need for online assessment of equivalent inertia. However, most existing inertia estimation methods are based on the frequency change rate under large disturbance scenario so it is hard for them to meet the needs of equivalent inertia estimation under quasi-steady operating state scenario. For this reason, firstly, in allusion to the normal inertial estimation the mechanism of power system inertia response was analyzed, and the expression for the equivalent inertia under quasi-steady state operating condition was derived. Secondly, based on the theory of system identification a controlled autoregressive moving average with exogenous variable (abbr. ARMAX) model was constructed and by use of Akaike information criterion (abbr. AIC) the model order was determined, and by use of the method of least minimum square the equivalent inertia of system was estimated. Finally, the effectiveness of the proposed method was verified by WSCC 9-bus system. Verification results show that the proposed method possesses higher identification precision and can adapt to different load fluctuation forms.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0105
Abstract:
In allusion to the forecasting of impact load, an impact load forecasting model based on chaotic multi-objective antlion optimization algorithm (abbr. CMOALO) and kernel extreme learning machine (abbr. KELM) was proposed. Firstly, to decrease the difficulty of forecasting the ensemble empirical mode decomposition (abbr. EEMD) was utilized to decompose the original impact load into a series of smoother subseries. Secondly, to simultaneously improve the forecasting accuracy and stability of the proposed model, a multi-objective ant lion optimization algorithm (abbr. MOALO) was proposed. Thirdly, to further improve the solution search ability of the algorithm, the MOALO was integrated with chaotic operation to put forward CMOALO algorithm and applying the latter to optimize KELM. Finally, the put forward EEMD-CMOALO-KELM model was verified by true-collected impact load data in a certain region. It can be know by case study that the proposed impact load forecasting model possesses the best performance in both aspects of forecasting accuracy and stability of predicted results.
In allusion to the forecasting of impact load, an impact load forecasting model based on chaotic multi-objective antlion optimization algorithm (abbr. CMOALO) and kernel extreme learning machine (abbr. KELM) was proposed. Firstly, to decrease the difficulty of forecasting the ensemble empirical mode decomposition (abbr. EEMD) was utilized to decompose the original impact load into a series of smoother subseries. Secondly, to simultaneously improve the forecasting accuracy and stability of the proposed model, a multi-objective ant lion optimization algorithm (abbr. MOALO) was proposed. Thirdly, to further improve the solution search ability of the algorithm, the MOALO was integrated with chaotic operation to put forward CMOALO algorithm and applying the latter to optimize KELM. Finally, the put forward EEMD-CMOALO-KELM model was verified by true-collected impact load data in a certain region. It can be know by case study that the proposed impact load forecasting model possesses the best performance in both aspects of forecasting accuracy and stability of predicted results.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0128
Abstract:
In the UHVDC Hierarchical connection system, when the AC system on the inverter side fails, the commutation failure of the high and low end converters may occur at the same time due to the delay in starting the commutation failure prevention control of the non-faulty layer. Therefore, a coordinated control strategy of high and low side converters based on the commutation time and voltage integral area is proposed. Taking advantage of the fact that the commutation failure prevention control of the faulty layer can respond to faults more quickly, the proposed strategy introduces the output of the commutation failure prevention control of the faulty layer into the non-faulty layer, so that the start time of the commutation failure prevention control of the non-faulty layer is advanced, increase the turn-off angle of the non-faulty layer converter; at the same time, the voltage dependent current order limiter control is modified to reduce the DC current during the fault process. A simulation model was built in PSCAD/EMTDC to verify the proposed strategy under different working conditions. The results show that this strategy can significantly improve the sensitivity of non-faulty layer converters to faults and reduce the risk of simultaneous commutation failures.
In the UHVDC Hierarchical connection system, when the AC system on the inverter side fails, the commutation failure of the high and low end converters may occur at the same time due to the delay in starting the commutation failure prevention control of the non-faulty layer. Therefore, a coordinated control strategy of high and low side converters based on the commutation time and voltage integral area is proposed. Taking advantage of the fact that the commutation failure prevention control of the faulty layer can respond to faults more quickly, the proposed strategy introduces the output of the commutation failure prevention control of the faulty layer into the non-faulty layer, so that the start time of the commutation failure prevention control of the non-faulty layer is advanced, increase the turn-off angle of the non-faulty layer converter; at the same time, the voltage dependent current order limiter control is modified to reduce the DC current during the fault process. A simulation model was built in PSCAD/EMTDC to verify the proposed strategy under different working conditions. The results show that this strategy can significantly improve the sensitivity of non-faulty layer converters to faults and reduce the risk of simultaneous commutation failures.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0363
Abstract:
The correctness of the protection setting is of great importance to make full use of the relay protection system, but current setting comparison still uses manual methods, which requires heavy workload, and consumes long time, however the correctness of the results cannot be guaranteed. For this reason, the naming characteristics of the setting value name were sorted out, and a neural network-based intelligent comparison method for the setting value names of relay protection was proposed. Firstly, the text preprocessing was performed. Secondly, the preprocessed setting value text was vectorized. Finally, the bi-directional long short-term memory (Bi-LSTM) neural network was utilized to calculate the similarity of semantic feature vectors of the setting value name. Results of computing example show that the intelligent comparison method of setting value name based on neural network can effectively complete the matching of setting value list and running fixed value name, and comparing with fuzzy matching the neural network possesses higher accuracy and faster speed in handling setting value name matching problem.
The correctness of the protection setting is of great importance to make full use of the relay protection system, but current setting comparison still uses manual methods, which requires heavy workload, and consumes long time, however the correctness of the results cannot be guaranteed. For this reason, the naming characteristics of the setting value name were sorted out, and a neural network-based intelligent comparison method for the setting value names of relay protection was proposed. Firstly, the text preprocessing was performed. Secondly, the preprocessed setting value text was vectorized. Finally, the bi-directional long short-term memory (Bi-LSTM) neural network was utilized to calculate the similarity of semantic feature vectors of the setting value name. Results of computing example show that the intelligent comparison method of setting value name based on neural network can effectively complete the matching of setting value list and running fixed value name, and comparing with fuzzy matching the neural network possesses higher accuracy and faster speed in handling setting value name matching problem.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0364
Abstract:
In allusion to the problem that during the analysis on power quality disturbances the frequency band division of empirical Fourier decomposition (abbr. EFD) is not adaptive, an improved FED method was proposed. Firstly, the maximum envelope of normalized spectrum of disturbance signal was extracted by piecewise cubic Hermite interpolation. Secondly, the maximum value of the envelope was searched and it’s dynamic measurement was calculated. Thirdly, the frequency, whose dynamic measurement was greater than the set threshold, was taken as the characteristic frequency of disturbance signal, and the midpoint of adjacent characteristic frequency was taken as the boundary of frequency band segmentation of disturbance signal. Then, the inverse fast Fourier transform (abbr. IFFT) was performed on the divided frequency band to obtain the analytic Fourier intrinsic band function (abbr. AFIBF) of the corresponding frequency band, and then, Hilbert transform (abbr. HT) was applied to the decomposed AFIBF components to extract their disturbance parameters. Finally, through the analysis of simulated signals and real measured data, the correctness and effectiveness of the proposed method were verified.
In allusion to the problem that during the analysis on power quality disturbances the frequency band division of empirical Fourier decomposition (abbr. EFD) is not adaptive, an improved FED method was proposed. Firstly, the maximum envelope of normalized spectrum of disturbance signal was extracted by piecewise cubic Hermite interpolation. Secondly, the maximum value of the envelope was searched and it’s dynamic measurement was calculated. Thirdly, the frequency, whose dynamic measurement was greater than the set threshold, was taken as the characteristic frequency of disturbance signal, and the midpoint of adjacent characteristic frequency was taken as the boundary of frequency band segmentation of disturbance signal. Then, the inverse fast Fourier transform (abbr. IFFT) was performed on the divided frequency band to obtain the analytic Fourier intrinsic band function (abbr. AFIBF) of the corresponding frequency band, and then, Hilbert transform (abbr. HT) was applied to the decomposed AFIBF components to extract their disturbance parameters. Finally, through the analysis of simulated signals and real measured data, the correctness and effectiveness of the proposed method were verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0248
Abstract:
As the the main force of zero-carbon electricity and renewable energy generation, wind power plays a key role in assisting the comprehensive green and low-carbon transformation of society in the context of "dual carbon". It is crucial to maximize the utilization of wind energy and to increase the wind power generation system output while ensuring stable power generation. The maximum power point tracking (MPPT) problem of permanent-magnet synchronous wind power generation system was investigated. Firstly, a mechanism simulation model of permanent-magnet synchronous wind power generation system was established and a two-level dual-PWM full-power converter was used to connect the wind turbine to the grid. Secondly, based on the above model, an integer-order PI controller, fractional-order PI λ controller and fuzzy fractional-order PI λ controller were designed to implement MPPT control. Finally, simulation research was conducted on the above control strategies. The results show that the fuzzy fractional-order PI λ controller has better MPPT performance and better robustness than the other two types, regardless of whether it is in step or random wind speed.
As the the main force of zero-carbon electricity and renewable energy generation, wind power plays a key role in assisting the comprehensive green and low-carbon transformation of society in the context of "dual carbon". It is crucial to maximize the utilization of wind energy and to increase the wind power generation system output while ensuring stable power generation. The maximum power point tracking (MPPT) problem of permanent-magnet synchronous wind power generation system was investigated. Firstly, a mechanism simulation model of permanent-magnet synchronous wind power generation system was established and a two-level dual-PWM full-power converter was used to connect the wind turbine to the grid. Secondly, based on the above model, an integer-order PI controller, fractional-order PI λ controller and fuzzy fractional-order PI λ controller were designed to implement MPPT control. Finally, simulation research was conducted on the above control strategies. The results show that the fuzzy fractional-order PI λ controller has better MPPT performance and better robustness than the other two types, regardless of whether it is in step or random wind speed.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0240
Abstract:
In this paper, a two-stage robust optimization method is proposed to address the uncertainties in renewable energy output and carbon emissions in microgrids under the low-carbon back-ground. Our method is a two-phase robust optimal method incorporating both the step carbon trading mechanism and the flexible loads which participate in the regulation. A step carbon trading mechanism was introduced into the dispatch model to limit the carbon emissions of the system and compensate for the power shortage caused by the renewable energy output fluctuations through the regulation of flexible loads, further reducing the carbon emissions and dispatch costs of the microgrid. In addition, the uncertain parameter was incorporated into the model to adjust the conservativeness of the system. In the day-ahead stage, a scheduling plan was formulated based on the forecast data and worst scenarios that the system may suffer from within a day. In the intra-day stage, the sub-optimization was provided based on the day-ahead scheduling plan; an intra-day control scheme of the system was further presented for worst-case scenario. The scheduling model was solved using the nested C&CG algorithm. The simulation results of the example demonstrate that, on average, the total dispatch cost of the robust optimization method was reduced by 3.1% compared with that of the deterministic optimization method. The inclusion of the carbon trading mechanism resulted in a 17.7% reduction in carbon emissions and a 3.7% decrease in the total operating cost of the system in a single day.. With the consideration of the flexible loads which participate in the dispatch, the carbon emissions and total dispatch cost of the system in a single day operation were reduced by 26% and 24.3%, respectively, compared to those without flexible loads. The effectiveness of the optimization model proposed in this paper is thus verified.
In this paper, a two-stage robust optimization method is proposed to address the uncertainties in renewable energy output and carbon emissions in microgrids under the low-carbon back-ground. Our method is a two-phase robust optimal method incorporating both the step carbon trading mechanism and the flexible loads which participate in the regulation. A step carbon trading mechanism was introduced into the dispatch model to limit the carbon emissions of the system and compensate for the power shortage caused by the renewable energy output fluctuations through the regulation of flexible loads, further reducing the carbon emissions and dispatch costs of the microgrid. In addition, the uncertain parameter was incorporated into the model to adjust the conservativeness of the system. In the day-ahead stage, a scheduling plan was formulated based on the forecast data and worst scenarios that the system may suffer from within a day. In the intra-day stage, the sub-optimization was provided based on the day-ahead scheduling plan; an intra-day control scheme of the system was further presented for worst-case scenario. The scheduling model was solved using the nested C&CG algorithm. The simulation results of the example demonstrate that, on average, the total dispatch cost of the robust optimization method was reduced by 3.1% compared with that of the deterministic optimization method. The inclusion of the carbon trading mechanism resulted in a 17.7% reduction in carbon emissions and a 3.7% decrease in the total operating cost of the system in a single day.. With the consideration of the flexible loads which participate in the dispatch, the carbon emissions and total dispatch cost of the system in a single day operation were reduced by 26% and 24.3%, respectively, compared to those without flexible loads. The effectiveness of the optimization model proposed in this paper is thus verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0318
Abstract:
In allusion to such problems as excessive regulation pressure of automatic generation control (abbr. AGC) units in traditional two-stage dispatching after grid-connection of wind power and higher unit regulation expenses because of not considering control performance standard (abbr. CPS) assessment index in common multi time-scale dispatching, by means of analyzing actual frequency data of power grid it was found that on longer time scales the positive and negative change of the mean value of system frequency deviation possessed the regularity. Therefore, under the CPS index a multi-time scale optimization scheduling model, in which the characteristic of the system frequency deviation was considered, was proposed. The validity of the proposed model was verified by the simulation on Matlab platform. Simulation results show that by means of predicting that the symbol of system frequency deviation will be positive or negative under longer time scales in the future, the proposed new dispatching model decreases the regulated output of non-AGC units at the critical moment of intraday and realtime dispatching phase, and comparing with existing models, the proposed model can make the power grid satisfying CPS assessment criteria and at the same time the regulating stress of AGC unit can be relieved and the comprehensive operating cost can also be reduced.
In allusion to such problems as excessive regulation pressure of automatic generation control (abbr. AGC) units in traditional two-stage dispatching after grid-connection of wind power and higher unit regulation expenses because of not considering control performance standard (abbr. CPS) assessment index in common multi time-scale dispatching, by means of analyzing actual frequency data of power grid it was found that on longer time scales the positive and negative change of the mean value of system frequency deviation possessed the regularity. Therefore, under the CPS index a multi-time scale optimization scheduling model, in which the characteristic of the system frequency deviation was considered, was proposed. The validity of the proposed model was verified by the simulation on Matlab platform. Simulation results show that by means of predicting that the symbol of system frequency deviation will be positive or negative under longer time scales in the future, the proposed new dispatching model decreases the regulated output of non-AGC units at the critical moment of intraday and realtime dispatching phase, and comparing with existing models, the proposed model can make the power grid satisfying CPS assessment criteria and at the same time the regulating stress of AGC unit can be relieved and the comprehensive operating cost can also be reduced.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0295
Abstract:
With the rapid development of large-scale wind power, the demand for peak shaving service in power system is increasing, and the cost of peak shaving service is also increasing. Reasonable calculation of peak shaving service cost caused by wind power is the premise to encourage conventional units to provide peak shaving service. Proposes a quantitative method of peak shaving cost caused by wind power, which is called wind power mean reference method. In this method, the output of wind power in a certain period is evenly distributed to each dispatching period to obtain the reference scenario for peak shaving cost calculation by wind power average output profile. Then the peak shaving cost in specific scenarios can be calculated and expressed clearly based on this average output profile. The proposed method is verified on a 5 thermal power units system with wind farm, and the results demonstrate the correctness and effectiveness of the proposed method.
With the rapid development of large-scale wind power, the demand for peak shaving service in power system is increasing, and the cost of peak shaving service is also increasing. Reasonable calculation of peak shaving service cost caused by wind power is the premise to encourage conventional units to provide peak shaving service. Proposes a quantitative method of peak shaving cost caused by wind power, which is called wind power mean reference method. In this method, the output of wind power in a certain period is evenly distributed to each dispatching period to obtain the reference scenario for peak shaving cost calculation by wind power average output profile. Then the peak shaving cost in specific scenarios can be calculated and expressed clearly based on this average output profile. The proposed method is verified on a 5 thermal power units system with wind farm, and the results demonstrate the correctness and effectiveness of the proposed method.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0097
Abstract:
The nonlinear characteristics in power grid can extremely easy lead to power quality disturbance and destroy the stability of the power grid operation. Most of existing power quality disturbance analysis methods transmit the collected electrical signals to the central server to extract the statistical characteristic and then the models are built by machine learning. However, in real environment there exist such defects as weak privacy protection, complicated facility environment and over-reliance on artificial experience. For this reason, a power quality disturbance analysis method based on blockchain and secure computation was proposed. Firstly, a private chain based on the smart contract and the federated learning was constructed to protect data privacy and by means of certificateless encryption the identity creditability was ensured. Secondly, by use of the model parameters based on Paillier cryptosystem the gradient security during the deep learning process was protected by homomorphic encryption, and an anomaly analysis model of power quality disturbance based on variational mode decomposition and long short-term memory network was established to cover the shortage of traditional statistical feature modeling in coverage rate and accuracy. The experimental results of the actually set up microgrid show that using the proposed model the privacy, the usability, the security and the accuracy can be taken into account.
The nonlinear characteristics in power grid can extremely easy lead to power quality disturbance and destroy the stability of the power grid operation. Most of existing power quality disturbance analysis methods transmit the collected electrical signals to the central server to extract the statistical characteristic and then the models are built by machine learning. However, in real environment there exist such defects as weak privacy protection, complicated facility environment and over-reliance on artificial experience. For this reason, a power quality disturbance analysis method based on blockchain and secure computation was proposed. Firstly, a private chain based on the smart contract and the federated learning was constructed to protect data privacy and by means of certificateless encryption the identity creditability was ensured. Secondly, by use of the model parameters based on Paillier cryptosystem the gradient security during the deep learning process was protected by homomorphic encryption, and an anomaly analysis model of power quality disturbance based on variational mode decomposition and long short-term memory network was established to cover the shortage of traditional statistical feature modeling in coverage rate and accuracy. The experimental results of the actually set up microgrid show that using the proposed model the privacy, the usability, the security and the accuracy can be taken into account.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0368
Abstract:
Under the background of realizing the goal of "double carbon", vigorously developing new energy represented by wind and solar and building a new power system that adapts to the large-scale development of new energy have become the inevitable path of China's energy transformation. However, in the peak period of power load, new energy has lower active power, it is difficult to provide reliable support for the system. Energy storage has the ability of two-way fast and flexible adjustment, and can provide power, capacity and security support services for the system. However, energy storage is a resource with limited capacity, so it is becoming more and more important to accurately evaluate the contribution of energy storage to system adequacy. Therefore, this paper outlined the assessment framework of energy storage capacity value, and based on the concept of equal reliable capacity ratio, a capacity credit definition considering marginal value was proposed to characterize the capacity value of energy storage, and a specific calculation method for capacity credit of energy storage was given. The power planning model considering the carbon trading market and the renewable energy quota system was established for market simulation, and the impact of the introduction of different low-carbon policies on the capacity credit of energy storage was analyzed. The calculation example shows that the capacity credit of energy storage is affected by the carbon trading market and the renewable energy quota system to varying degrees.
Under the background of realizing the goal of "double carbon", vigorously developing new energy represented by wind and solar and building a new power system that adapts to the large-scale development of new energy have become the inevitable path of China's energy transformation. However, in the peak period of power load, new energy has lower active power, it is difficult to provide reliable support for the system. Energy storage has the ability of two-way fast and flexible adjustment, and can provide power, capacity and security support services for the system. However, energy storage is a resource with limited capacity, so it is becoming more and more important to accurately evaluate the contribution of energy storage to system adequacy. Therefore, this paper outlined the assessment framework of energy storage capacity value, and based on the concept of equal reliable capacity ratio, a capacity credit definition considering marginal value was proposed to characterize the capacity value of energy storage, and a specific calculation method for capacity credit of energy storage was given. The power planning model considering the carbon trading market and the renewable energy quota system was established for market simulation, and the impact of the introduction of different low-carbon policies on the capacity credit of energy storage was analyzed. The calculation example shows that the capacity credit of energy storage is affected by the carbon trading market and the renewable energy quota system to varying degrees.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0110
Abstract:
In allusion to the low nodal charging efficiency due to the sensitivity of energy transfer efficiency on the charging distance, a high efficient wireless charging dispatching strategy based on dynamic gravitational field was constructed. Firstly, considering the energy state of the sensing node itself, the nodal residual energy during the arrival of the charging equipment was predicted and a set of value nodes was established to estimate the dead node. Secondly, through leading in the theory of dynamic gravitational field, a multi-node charging algorithm based on the fusion of dynamic gravitational field was proposed, and considering the surplus energy, the consumed power and the distance to the base station of value nodes, the attractive range of the node was defined, and a better charging position was chosen according to the fusion principle of gravitational field. Finally, the simulation of the proposed charging algorithm was conducted, and by means of comparing the proposed charging algorithm with the multi-node rechargeable algorithm and the grid node clustering algorithm it is verified that using the proposed charging algorithm the energy efficiency of the sensor network can be effectively improved, and when reasonable charging delay was ensured the excessive loss of nodal energy can be avoided, so the network sustainability can be evidently enhanced.
In allusion to the low nodal charging efficiency due to the sensitivity of energy transfer efficiency on the charging distance, a high efficient wireless charging dispatching strategy based on dynamic gravitational field was constructed. Firstly, considering the energy state of the sensing node itself, the nodal residual energy during the arrival of the charging equipment was predicted and a set of value nodes was established to estimate the dead node. Secondly, through leading in the theory of dynamic gravitational field, a multi-node charging algorithm based on the fusion of dynamic gravitational field was proposed, and considering the surplus energy, the consumed power and the distance to the base station of value nodes, the attractive range of the node was defined, and a better charging position was chosen according to the fusion principle of gravitational field. Finally, the simulation of the proposed charging algorithm was conducted, and by means of comparing the proposed charging algorithm with the multi-node rechargeable algorithm and the grid node clustering algorithm it is verified that using the proposed charging algorithm the energy efficiency of the sensor network can be effectively improved, and when reasonable charging delay was ensured the excessive loss of nodal energy can be avoided, so the network sustainability can be evidently enhanced.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0080
Abstract:
In allusion to the defect of traditional extreme learning machine easily falling into local optimal solutions and the characteristic of environment variation leading to the fluctuation of photovoltaic (abbr. PV) output, based on complete ensemble empirical mode decomposition with adaptive noise (abbr. CEEMDAN) algorithm and combining with extreme learning machine neural network optimized by chimp optimization algorithm a short-term PV output prediction model was constructed. Firstly, by use of CEEMDAN algorithm the key environment factor series impacting PV output was decomposed to obtain the local feature of data signals in different time-scales to reduce the non-stationary of environment factor series. Secondly, taking each decomposed subseries and PV historical data series as the input of extreme learning machine prediction model optimized by the chimp algorithm the prediction was performed. Finally, the data set of DKASC Solar Centre PV station was chosen to conduct the contrast and verifying for different prediction models. Results of simulation example show that the prediction effect of each index of the constructed improved PV output prediction combined model is better and suitable to the prediction of PV generation in different environments.
In allusion to the defect of traditional extreme learning machine easily falling into local optimal solutions and the characteristic of environment variation leading to the fluctuation of photovoltaic (abbr. PV) output, based on complete ensemble empirical mode decomposition with adaptive noise (abbr. CEEMDAN) algorithm and combining with extreme learning machine neural network optimized by chimp optimization algorithm a short-term PV output prediction model was constructed. Firstly, by use of CEEMDAN algorithm the key environment factor series impacting PV output was decomposed to obtain the local feature of data signals in different time-scales to reduce the non-stationary of environment factor series. Secondly, taking each decomposed subseries and PV historical data series as the input of extreme learning machine prediction model optimized by the chimp algorithm the prediction was performed. Finally, the data set of DKASC Solar Centre PV station was chosen to conduct the contrast and verifying for different prediction models. Results of simulation example show that the prediction effect of each index of the constructed improved PV output prediction combined model is better and suitable to the prediction of PV generation in different environments.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0427
Abstract:
Soft open point (SOP) can effectively improve the power flow distribution and voltage level, which has great potential in improving the photovoltaic (PV) acceptance capacity of the distribution network. In order to tap the potential of SOP in improving the PV acceptance capacity of distribution networks, the PV acceptance capacity improving method of distribution networks based on SOP is proposed. Firstly, the principle of SOP to improve the PV capacity of distribution network is analyzed from the aspects of restraining overvoltage and improving power flow distribution. Secondly, an optimization model for improving the PV acceptance capacity of distribution network is proposed, which comprehensively considers the SOP and network reconfiguration. Taking the power flow control parameters of SOP, network topology and PV access capacity as variables, the PV acceptance capacity is calculated by genetic algorithm; Finally, the proposed method of improving the PV acceptance capacity based on SOP is simulated and analyzed. The simulation results show that the PV acceptance capacity of distribution network can be effectively improved through SOP.
Soft open point (SOP) can effectively improve the power flow distribution and voltage level, which has great potential in improving the photovoltaic (PV) acceptance capacity of the distribution network. In order to tap the potential of SOP in improving the PV acceptance capacity of distribution networks, the PV acceptance capacity improving method of distribution networks based on SOP is proposed. Firstly, the principle of SOP to improve the PV capacity of distribution network is analyzed from the aspects of restraining overvoltage and improving power flow distribution. Secondly, an optimization model for improving the PV acceptance capacity of distribution network is proposed, which comprehensively considers the SOP and network reconfiguration. Taking the power flow control parameters of SOP, network topology and PV access capacity as variables, the PV acceptance capacity is calculated by genetic algorithm; Finally, the proposed method of improving the PV acceptance capacity based on SOP is simulated and analyzed. The simulation results show that the PV acceptance capacity of distribution network can be effectively improved through SOP.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0036
Abstract:
With connecting a lot of flexible loads and distributed generations into distribution network the operation reliability of the latter is affected by multi uncertain factors. For this reason, a method to analyze the reliability of active distribution network considering the response strategy of flexible loads was put forward. Firstly, by use of Ito stochastic process the timing uncertainty of the output of distributed generation was described. Secondly, according to the feature of flexible loads and the constraint of transfer capacity based on the first order stochastic dominance theory a rolling response strategy of flexible load was proposed. Finally, by use of Markov chain and Monte Carlo method the reliability analysis for the improved RTBS6 system was performed. By this computing example, the response strategy of different flexible loads and the impact of switched-in capacity of the distributed generation on the output of flexible loads and reliability were analyzed to provide reference for the analysis on the response of flexible load.
With connecting a lot of flexible loads and distributed generations into distribution network the operation reliability of the latter is affected by multi uncertain factors. For this reason, a method to analyze the reliability of active distribution network considering the response strategy of flexible loads was put forward. Firstly, by use of Ito stochastic process the timing uncertainty of the output of distributed generation was described. Secondly, according to the feature of flexible loads and the constraint of transfer capacity based on the first order stochastic dominance theory a rolling response strategy of flexible load was proposed. Finally, by use of Markov chain and Monte Carlo method the reliability analysis for the improved RTBS6 system was performed. By this computing example, the response strategy of different flexible loads and the impact of switched-in capacity of the distributed generation on the output of flexible loads and reliability were analyzed to provide reference for the analysis on the response of flexible load.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0003
Abstract:
In view of the coordination of thermal power with wind power in multi energy power system, from the perspective of the combined thermoelectric power system operator, the complementary characteristics of thermal energy and electric energy were deeply explored, and the active capacity generated by the horizontal coordination and optimization of thermal power was quantitatively analyzed, which runs through the whole vertical power balance process so as to participate in the electricity market with uncertain wind power and formulate reasonable operation strategies, and enhance the schedule ability and controllability of the system. Firstly, on the basis of analyzing influencing factors of the operation strategy the coupling drag among heat loads was decoupled by heat storage devices, and taking the maximized total operation earning as the objective a dispatching model for combined heat and power system including wind power was established to optimize the output power of thermoelectric generating units and heat storage devices. Secondly, considering the impacts of predicted deviation of wind power, the operating flexibility of heat-storage devices and the time-of-use price under market environment on system operation strategy, taking the capacity of heat-storage for example, the factors restricting the operation flexibility of heat-storage devices were explored. Finally, the validity and rationality of the proposed model are verified by the computing example in the environment of Visual Studio software.
In view of the coordination of thermal power with wind power in multi energy power system, from the perspective of the combined thermoelectric power system operator, the complementary characteristics of thermal energy and electric energy were deeply explored, and the active capacity generated by the horizontal coordination and optimization of thermal power was quantitatively analyzed, which runs through the whole vertical power balance process so as to participate in the electricity market with uncertain wind power and formulate reasonable operation strategies, and enhance the schedule ability and controllability of the system. Firstly, on the basis of analyzing influencing factors of the operation strategy the coupling drag among heat loads was decoupled by heat storage devices, and taking the maximized total operation earning as the objective a dispatching model for combined heat and power system including wind power was established to optimize the output power of thermoelectric generating units and heat storage devices. Secondly, considering the impacts of predicted deviation of wind power, the operating flexibility of heat-storage devices and the time-of-use price under market environment on system operation strategy, taking the capacity of heat-storage for example, the factors restricting the operation flexibility of heat-storage devices were explored. Finally, the validity and rationality of the proposed model are verified by the computing example in the environment of Visual Studio software.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0377
Abstract:
To cope with the conflict between the goal of “carbon neutral and carbon dioxide peaking” and the development goal of electricity price, as well as the problem of excessive power cross subsidies in China, firstly, from the perspective of region and users the cross subsidy was classified. Secondly, a calculating model of cross subsidy scale was constructed. Thirdly, taking the maximized social welfare as the objective and the considering the carbon emission as the constraint, an optimal electricity price mechanism was designed. Finally, the results of the computing example based on actual data of a certain provincial power grid show that overall social welfare can be increased by adopting the electricity price considering carbon constraints.
To cope with the conflict between the goal of “carbon neutral and carbon dioxide peaking” and the development goal of electricity price, as well as the problem of excessive power cross subsidies in China, firstly, from the perspective of region and users the cross subsidy was classified. Secondly, a calculating model of cross subsidy scale was constructed. Thirdly, taking the maximized social welfare as the objective and the considering the carbon emission as the constraint, an optimal electricity price mechanism was designed. Finally, the results of the computing example based on actual data of a certain provincial power grid show that overall social welfare can be increased by adopting the electricity price considering carbon constraints.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0366
Abstract:
Along with the rapid development of power industry, modular multilevel converter (abbr. MMC) has been increasingly used in DC transmission, distribution, grid-connection of new energy, and so on. However, when voltage fluctuation occurs at the DC side or AC side of MMC, circulating current will be generated on the bridge arm. To reduce the influence of circulation on MMC, firstly, the capacitance voltage fluctuation of the sub-module was derived by coupling the upper and lower bridge arm currents with the switching function, and the transfer law of the circulating current in phase sequence and frequency was obtained. Secondly, based on quasi fast repetitive control (abbr. Q-FRC) and combining with quasi proportional resonance (abbr. QPR) controller, a circulating current suppression controller was designed to achieve the effects of wide-band suppression of circulating current and key suppression of specific frequency. Finally, the analysis on the PSCAD simulation platform for three abnormal conditions, i.e. DC side bus voltage fluctuation and AC side voltage fluctuation were simulated by the PSCAD simulation platform, thus, the correctness of the theoretical derivation and the effectiveness of the proposed suppression measure are verified.
Along with the rapid development of power industry, modular multilevel converter (abbr. MMC) has been increasingly used in DC transmission, distribution, grid-connection of new energy, and so on. However, when voltage fluctuation occurs at the DC side or AC side of MMC, circulating current will be generated on the bridge arm. To reduce the influence of circulation on MMC, firstly, the capacitance voltage fluctuation of the sub-module was derived by coupling the upper and lower bridge arm currents with the switching function, and the transfer law of the circulating current in phase sequence and frequency was obtained. Secondly, based on quasi fast repetitive control (abbr. Q-FRC) and combining with quasi proportional resonance (abbr. QPR) controller, a circulating current suppression controller was designed to achieve the effects of wide-band suppression of circulating current and key suppression of specific frequency. Finally, the analysis on the PSCAD simulation platform for three abnormal conditions, i.e. DC side bus voltage fluctuation and AC side voltage fluctuation were simulated by the PSCAD simulation platform, thus, the correctness of the theoretical derivation and the effectiveness of the proposed suppression measure are verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0032
Abstract:
In allusion to such troubles as difficulty of uncertainty modeling and difficult to solve complex system efficiently in optimal dispatching of AC/DC hybrid microgrid, an artificial assisted deep reinforcement learning algorithm, which could improve the learning efficiency of intelligent agent through artificial strategy guidance, was proposed. Firstly, combining with the characteristic of demand side response of hybrid microgrid under grid-connected state a cost-minimized optimal dispatching model was constructed. Based on Markov decision process the modeling of optimal dispatching process was conducted and based on optimal dispatching model the reward function was designed. Secondly, the designed model was solved by artificially assisted deep deterministic policy gradient algorithm, and by means of continuous interaction between intelligent agent and environment the parameter of neural network was continually updated and then the optimal decision was obtained. Finally, it was verified by computing example that using the proposed algorithm the learning efficiency of intelligent agent could be effectively improved and while the training time of the model was decreased the operating cost of the subsystem could be effectively reduced.
In allusion to such troubles as difficulty of uncertainty modeling and difficult to solve complex system efficiently in optimal dispatching of AC/DC hybrid microgrid, an artificial assisted deep reinforcement learning algorithm, which could improve the learning efficiency of intelligent agent through artificial strategy guidance, was proposed. Firstly, combining with the characteristic of demand side response of hybrid microgrid under grid-connected state a cost-minimized optimal dispatching model was constructed. Based on Markov decision process the modeling of optimal dispatching process was conducted and based on optimal dispatching model the reward function was designed. Secondly, the designed model was solved by artificially assisted deep deterministic policy gradient algorithm, and by means of continuous interaction between intelligent agent and environment the parameter of neural network was continually updated and then the optimal decision was obtained. Finally, it was verified by computing example that using the proposed algorithm the learning efficiency of intelligent agent could be effectively improved and while the training time of the model was decreased the operating cost of the subsystem could be effectively reduced.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0359
Abstract:
In order to identify fault causes of distribution networks, currently used artificial patrol inspection not only consumes a lot of manpower and material but also prolongs the power outage time. For this reason, a data driven based fault cause identifying for distribution network was proposed. Firstly, by means of analyzing a lot of spot-recorded fault waveform data the mechanism of different fault causes and the wave characteristics were obtained, and a fault feature extraction method based on empirical mode decomposition (abbr. EMD) and principal component analysis (abbr. PCA) was proposed. Secondly, through EMD the time domain waveform of the fault was decomposed according to different time scales to obtain intrinsic mode function (abbr. IMF) components possessing local features of the signal. Thirdly, by use of PCA the dimensionality reduction of multi-IMF components were conducted and the principal characteristic components in IMF series were extracted to compose them into eigenvectors. Finally, a fault cause classification model based on long-short term memory (abbr. LSTM) network was put forward to extract dynamic time-scale feature and to realize the classification of fault causes. The experiment results, which utilizes practical field data, show that the proposed fault cause classification model possesses a higher accuracy.
In order to identify fault causes of distribution networks, currently used artificial patrol inspection not only consumes a lot of manpower and material but also prolongs the power outage time. For this reason, a data driven based fault cause identifying for distribution network was proposed. Firstly, by means of analyzing a lot of spot-recorded fault waveform data the mechanism of different fault causes and the wave characteristics were obtained, and a fault feature extraction method based on empirical mode decomposition (abbr. EMD) and principal component analysis (abbr. PCA) was proposed. Secondly, through EMD the time domain waveform of the fault was decomposed according to different time scales to obtain intrinsic mode function (abbr. IMF) components possessing local features of the signal. Thirdly, by use of PCA the dimensionality reduction of multi-IMF components were conducted and the principal characteristic components in IMF series were extracted to compose them into eigenvectors. Finally, a fault cause classification model based on long-short term memory (abbr. LSTM) network was put forward to extract dynamic time-scale feature and to realize the classification of fault causes. The experiment results, which utilizes practical field data, show that the proposed fault cause classification model possesses a higher accuracy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0375
Abstract:
To cope with the significant challenge to the power source planning and operation stability of the receiving-end system in southeast coastal provinces and cities in China due to the constantly increasing proportion of uncontrollable power such as extrinsic DC power and offshore wind power, a power planning model considering grid-connection of offshore wind power and its rotational inertia constraints was proposed. Firstly, considering the seasonal characteristics of historical output data of offshore wind power, the uncertainty of offshore wind power was described by scenario analysis method. Secondly, taking the lowest annual comprehensive cost as objective, a bi-layer power planning model, in which the planning was optimized by the outer-layer and the operation was optimized by the inner-layer, was constructed. Thirdly, the quantitative relation between rotary inertia level and frequency response index was analyzed, by means of applying the constraint of rate of change of frequency (abbr. RoCoF) in the inner-layer model the adequate inertial support capacity of the planned structure of power source was ensured. By use of genetic algorithm and Cplex solver the bi-layer planning model was solved. Finally, the feasibility and the effectiveness of the proposed model are verified by the simulation results of computing example, meanwhile, the impacts of the offshore wind power permeability and proportion of extrinsic DC power on power planning results were analyzed and induced.
To cope with the significant challenge to the power source planning and operation stability of the receiving-end system in southeast coastal provinces and cities in China due to the constantly increasing proportion of uncontrollable power such as extrinsic DC power and offshore wind power, a power planning model considering grid-connection of offshore wind power and its rotational inertia constraints was proposed. Firstly, considering the seasonal characteristics of historical output data of offshore wind power, the uncertainty of offshore wind power was described by scenario analysis method. Secondly, taking the lowest annual comprehensive cost as objective, a bi-layer power planning model, in which the planning was optimized by the outer-layer and the operation was optimized by the inner-layer, was constructed. Thirdly, the quantitative relation between rotary inertia level and frequency response index was analyzed, by means of applying the constraint of rate of change of frequency (abbr. RoCoF) in the inner-layer model the adequate inertial support capacity of the planned structure of power source was ensured. By use of genetic algorithm and Cplex solver the bi-layer planning model was solved. Finally, the feasibility and the effectiveness of the proposed model are verified by the simulation results of computing example, meanwhile, the impacts of the offshore wind power permeability and proportion of extrinsic DC power on power planning results were analyzed and induced.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0015
Abstract:
Grid-connection of large-scale renewable energy lead to such problems as reduction of equivalent rotating inertia of power grid, insufficient reserve capacity of traditional thermal power plants, and so on. Although the participation of energy storage provides an effective way to solve this problem, however the relevance between the degree of participation of energy storage and the cost of frequency regulation needs in-depth exploration. For this reason, a control scheme, in which according to the demand difference of regional frequency regulation the participation degree of energy storage was adjusted, was proposed. Firstly, by means of regional disturbance identification a drive link was designed to enhance the output of energy storage. Secondly, considering the impact of the depth of charging/discharging of energy storage on its lifetime of cycle use, an optimization problem, which took the effect of frequency regulation and the cost of energy storage frequency regulation as objectives, was designed and solved by multi-objective genetic algorithm to obtain the optimal responsibility assignment of energy storage participating frequency regulation. Simulation results show that comparing with participating way of traditional load frequency control, using the proposed control scheme the potential of auxiliary frequency modulation of battery energy storage can be fully tapped, and a better balance between the frequency regulation effect and the cost of energy storage participating frequency regulation could be obtained.
Grid-connection of large-scale renewable energy lead to such problems as reduction of equivalent rotating inertia of power grid, insufficient reserve capacity of traditional thermal power plants, and so on. Although the participation of energy storage provides an effective way to solve this problem, however the relevance between the degree of participation of energy storage and the cost of frequency regulation needs in-depth exploration. For this reason, a control scheme, in which according to the demand difference of regional frequency regulation the participation degree of energy storage was adjusted, was proposed. Firstly, by means of regional disturbance identification a drive link was designed to enhance the output of energy storage. Secondly, considering the impact of the depth of charging/discharging of energy storage on its lifetime of cycle use, an optimization problem, which took the effect of frequency regulation and the cost of energy storage frequency regulation as objectives, was designed and solved by multi-objective genetic algorithm to obtain the optimal responsibility assignment of energy storage participating frequency regulation. Simulation results show that comparing with participating way of traditional load frequency control, using the proposed control scheme the potential of auxiliary frequency modulation of battery energy storage can be fully tapped, and a better balance between the frequency regulation effect and the cost of energy storage participating frequency regulation could be obtained.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0369
Abstract:
Along with the scale expansion of power grid traditional operation risk assessment methods gradually can not satisfy real-time requirement, and in existing risk assessment methods based on machine learning technology the imbalanced sample in the real system has not been considered. For this reason, based on improved multi-layer perceptron (MLP) a method to assess power grid operation risk was proposed. Based on IEEE-RTS7 the risk data sample was generated, and according to four aspects, i.e., voltage out-of-limit, power flow overload, loss of load probability and power flow transferring, an index system, which could characterize current operating state of power grid and the influence of relative state change, was established to quantize the risk of power grid operation and according to the value-at-risk the risk data sample was labeled to construct power grid risk data set. Considering the sample unbalance in real power grid, multi sample balance methods were led in and by means of feature selection and principal component analysis (PCA) the data dimension reduction was performed. Finally, the sample was trained by the improved MLP model to obtain power grid operating risk assessment and calculation model. Using the obtained model, while the training speed was accelerated, the representational ability for the nolinear rule in power data was intensified, thus, the result of risk assessment could be obtained rapidly.
Along with the scale expansion of power grid traditional operation risk assessment methods gradually can not satisfy real-time requirement, and in existing risk assessment methods based on machine learning technology the imbalanced sample in the real system has not been considered. For this reason, based on improved multi-layer perceptron (MLP) a method to assess power grid operation risk was proposed. Based on IEEE-RTS7 the risk data sample was generated, and according to four aspects, i.e., voltage out-of-limit, power flow overload, loss of load probability and power flow transferring, an index system, which could characterize current operating state of power grid and the influence of relative state change, was established to quantize the risk of power grid operation and according to the value-at-risk the risk data sample was labeled to construct power grid risk data set. Considering the sample unbalance in real power grid, multi sample balance methods were led in and by means of feature selection and principal component analysis (PCA) the data dimension reduction was performed. Finally, the sample was trained by the improved MLP model to obtain power grid operating risk assessment and calculation model. Using the obtained model, while the training speed was accelerated, the representational ability for the nolinear rule in power data was intensified, thus, the result of risk assessment could be obtained rapidly.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0005
Abstract:
In allusion to the operation of grid-connected photovoltaic (abbr. PV) energy storage microgrid, a two-stage day-ahead energy management strategy containing centralized control and distributed control was proposed. In the first stage, all distributed PV storage units in the microgrid were equalized to one cluster and taking the minimized interaction cost between microgrid and large power grid was as the objective, an economic optimal dispatching model was established and solved by beetle swarm optimization (abbr. BSO) to obtain total discharging power of energy storage units and the power purchasing and selling planning of microgrid. In the second stage, taking the minimized charging loss of energy storage units as the objective a distributed control model was constructed, and by use of incremental cost consensus algorithm utilizing Leader-Follower mode the total energy storage power obtained from the calculation in the first stage was optimally allocated in real time. Finally, the day-ahead economic optimal dispatching of PV energy storage microgrid was realized. The effectiveness of the proposed strategy is verified by the results of analysis on computing example.
In allusion to the operation of grid-connected photovoltaic (abbr. PV) energy storage microgrid, a two-stage day-ahead energy management strategy containing centralized control and distributed control was proposed. In the first stage, all distributed PV storage units in the microgrid were equalized to one cluster and taking the minimized interaction cost between microgrid and large power grid was as the objective, an economic optimal dispatching model was established and solved by beetle swarm optimization (abbr. BSO) to obtain total discharging power of energy storage units and the power purchasing and selling planning of microgrid. In the second stage, taking the minimized charging loss of energy storage units as the objective a distributed control model was constructed, and by use of incremental cost consensus algorithm utilizing Leader-Follower mode the total energy storage power obtained from the calculation in the first stage was optimally allocated in real time. Finally, the day-ahead economic optimal dispatching of PV energy storage microgrid was realized. The effectiveness of the proposed strategy is verified by the results of analysis on computing example.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0236
Abstract:
With the development of power system containing high percentage of both renewable energy and power electronic devices the trouble caused by sub-synchronous oscillation becomes increasingly prominent, so it has to be settled urgently to research the key influencing factors on the propagation of sub-synchronous oscillation in AC and DC transmission lines. For that reason, based on the system response measurement time series data, a quantitative analysis method for the key influencing factors of sub-synchronous oscillation propagation was proposed. Firstly, by use of the improved wavelet threshold noise reduction based on the adaptive noise complete ensemble empirical mode decomposition (abbr. CEEMDAN) the noise suppression of measured data was performed to reduce the affection of noise on the Prony analysis. Secondly, based on the correlation coefficient and mutual information of each influencing factor of subsynchronous oscillation propagation a combination model for correlation evaluation was established. Finally, by means of computing evaluation indices of different AC and DC parameters in comprehensive model, the key impacting factors of subsynchronous oscillation propagated in AC and DC transmission lines were obtained. Analysis results of a 2-region 4-machine system constructed in PSCAD show that the highly correlated parameter impacting the propagation of subsynchronous oscillation in AC transmission lines is the power flow in AC transmission lines and the highly correlated parameter impacting the propagation of subsynchronous oscillation in DC transmission lines is the impedance characteristics of AC transmission line under the subsynchronous oscillation frequency.
With the development of power system containing high percentage of both renewable energy and power electronic devices the trouble caused by sub-synchronous oscillation becomes increasingly prominent, so it has to be settled urgently to research the key influencing factors on the propagation of sub-synchronous oscillation in AC and DC transmission lines. For that reason, based on the system response measurement time series data, a quantitative analysis method for the key influencing factors of sub-synchronous oscillation propagation was proposed. Firstly, by use of the improved wavelet threshold noise reduction based on the adaptive noise complete ensemble empirical mode decomposition (abbr. CEEMDAN) the noise suppression of measured data was performed to reduce the affection of noise on the Prony analysis. Secondly, based on the correlation coefficient and mutual information of each influencing factor of subsynchronous oscillation propagation a combination model for correlation evaluation was established. Finally, by means of computing evaluation indices of different AC and DC parameters in comprehensive model, the key impacting factors of subsynchronous oscillation propagated in AC and DC transmission lines were obtained. Analysis results of a 2-region 4-machine system constructed in PSCAD show that the highly correlated parameter impacting the propagation of subsynchronous oscillation in AC transmission lines is the power flow in AC transmission lines and the highly correlated parameter impacting the propagation of subsynchronous oscillation in DC transmission lines is the impedance characteristics of AC transmission line under the subsynchronous oscillation frequency.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0333
Abstract:
Based on consumer behavior theory and evolutionary game, taking consumer utility function, profit function of power generating enterprises, power generation company profit function and carbon emission reduction as constraints, a consumer utility function was constructed. The impacts of consumer's preference, the product network externality, the reward and punishment mechanism of local government, the regulatory cost and the central government restraint mechanism on evolutionary stability strategy were mainly analyzed, and a fixed-effect model was constructed to carry out empirical analysis on the conclusion. Results of the analysis showed that under static reward-penalty mechanism there was no stable strategy of evolutionary; under the dynamic reward-penalty mechanism the evolutionary trajectory of local government and power generating enterprises was constantly and spirally propelled around the unique equilibrium strategy. The probability of strict supervision of local government was positively correlated with consumer’s low-carbon preference, the cost of undemanding supervision and the constraint strength of central government were negatively correlated with consumer’s high-carbon preference, product network externality, the lower limit of the punish and the cost of strict supervision. The lower limit of punishment, the cost of undemanding supervision and the constraint strength of central government played positive role in promoting low-carbon production of enterprises, and strict regulation of cost played a negative role in carbon emission reduction of power generating enterprises.
Based on consumer behavior theory and evolutionary game, taking consumer utility function, profit function of power generating enterprises, power generation company profit function and carbon emission reduction as constraints, a consumer utility function was constructed. The impacts of consumer's preference, the product network externality, the reward and punishment mechanism of local government, the regulatory cost and the central government restraint mechanism on evolutionary stability strategy were mainly analyzed, and a fixed-effect model was constructed to carry out empirical analysis on the conclusion. Results of the analysis showed that under static reward-penalty mechanism there was no stable strategy of evolutionary; under the dynamic reward-penalty mechanism the evolutionary trajectory of local government and power generating enterprises was constantly and spirally propelled around the unique equilibrium strategy. The probability of strict supervision of local government was positively correlated with consumer’s low-carbon preference, the cost of undemanding supervision and the constraint strength of central government were negatively correlated with consumer’s high-carbon preference, product network externality, the lower limit of the punish and the cost of strict supervision. The lower limit of punishment, the cost of undemanding supervision and the constraint strength of central government played positive role in promoting low-carbon production of enterprises, and strict regulation of cost played a negative role in carbon emission reduction of power generating enterprises.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0226
Abstract:
To improve the reliability of fault recovery for active distribution networks with high proportions of new energy sources, a two-stage fault recovery strategy based on an improved firefly algorithm is proposed. First, construct a wind and solar power storage system model to reduce the impact of wind and solar power generation uncertainties on fault recovery. In the first stage, use a combination algorithm that considers load variability and user demand to divide the distribution network into islands after a fault occurs to restore essential loads for the first time. In the second stage, use an improved firefly algorithm to solve the distribution network reconstruction problem, which maximizes the restoration of the power supply while minimizing network losses. Finally, use the IEEE33 node distribution network as an example, and the results show that the proposed fault recovery strategy can obtain the optimal solution for distribution network fault recovery while meeting load user demand, ensuring uninterrupted power supply to essential loads, increasing power supply recovery rate, and reducing network losses after distribution network faults. The results verify the effectiveness and superiority of the proposed method.
To improve the reliability of fault recovery for active distribution networks with high proportions of new energy sources, a two-stage fault recovery strategy based on an improved firefly algorithm is proposed. First, construct a wind and solar power storage system model to reduce the impact of wind and solar power generation uncertainties on fault recovery. In the first stage, use a combination algorithm that considers load variability and user demand to divide the distribution network into islands after a fault occurs to restore essential loads for the first time. In the second stage, use an improved firefly algorithm to solve the distribution network reconstruction problem, which maximizes the restoration of the power supply while minimizing network losses. Finally, use the IEEE33 node distribution network as an example, and the results show that the proposed fault recovery strategy can obtain the optimal solution for distribution network fault recovery while meeting load user demand, ensuring uninterrupted power supply to essential loads, increasing power supply recovery rate, and reducing network losses after distribution network faults. The results verify the effectiveness and superiority of the proposed method.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0184
Abstract:
Due to the new energy resources output represented by wind and photovoltaic has uncertainty and fluctuation , thus it is difficult to avoid the deviation between the actual power output of virtual power plant （VPP） and the scheduling plan, which leads to the increase of VPP operation risk and decrease of revenue. In this paper, a bi-level scheduling strategy for VPP considering the risk of new energy output uncertainty is proposed.Firstly, an upper-level model considering the aggregation benefits of resources within VPP is established; secondly, a lower-level multi-objective scheduling model considering VPP benefits and risks is developed by quantifying VPP scheduling risks using conditional value-at-risk for new energy output uncertainty; finally, the effectiveness of the dispatching model is verified by an example, which can provide VPP with a dispatching aid decision considering both benefits and risks.
Due to the new energy resources output represented by wind and photovoltaic has uncertainty and fluctuation , thus it is difficult to avoid the deviation between the actual power output of virtual power plant （VPP） and the scheduling plan, which leads to the increase of VPP operation risk and decrease of revenue. In this paper, a bi-level scheduling strategy for VPP considering the risk of new energy output uncertainty is proposed.Firstly, an upper-level model considering the aggregation benefits of resources within VPP is established; secondly, a lower-level multi-objective scheduling model considering VPP benefits and risks is developed by quantifying VPP scheduling risks using conditional value-at-risk for new energy output uncertainty; finally, the effectiveness of the dispatching model is verified by an example, which can provide VPP with a dispatching aid decision considering both benefits and risks.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0355
Abstract:
To ensure the normal interaction of demand response information to carry out demand response safely in various regions, a method to identify and detect the distribute denial of service attack by improved long- short-term memory (abbr. LSTM) network under the interaction of power demand response information, which was suitable for the detection and classification of distributed denial of service (abbr. DDoS) attack in demand response interaction traffic under the form of multiple categories and multiple characteristics, was designed. Firstly, a classification and selection mechanism of power demand response traffic characteristics supported by demand response information exchange specification was presented. Secondly, to recognize the bidirectional traffic within the demand response interaction system, the linear element of Gussian error was led in and based on the improved long- short-term memory network a model to detect distributed denial of service attack was established. Finally, by means of selecting the traffic data set under the demand response, a method of setting up different attack rates under different states of power grid was established to verify the established model, and it was proved that the proposed method possessed high recognition rate for multi-class of distributed denial of service attack in the demand response information interaction and the accurate classification of distributed denial of service attacks could be accurately classified.
To ensure the normal interaction of demand response information to carry out demand response safely in various regions, a method to identify and detect the distribute denial of service attack by improved long- short-term memory (abbr. LSTM) network under the interaction of power demand response information, which was suitable for the detection and classification of distributed denial of service (abbr. DDoS) attack in demand response interaction traffic under the form of multiple categories and multiple characteristics, was designed. Firstly, a classification and selection mechanism of power demand response traffic characteristics supported by demand response information exchange specification was presented. Secondly, to recognize the bidirectional traffic within the demand response interaction system, the linear element of Gussian error was led in and based on the improved long- short-term memory network a model to detect distributed denial of service attack was established. Finally, by means of selecting the traffic data set under the demand response, a method of setting up different attack rates under different states of power grid was established to verify the established model, and it was proved that the proposed method possessed high recognition rate for multi-class of distributed denial of service attack in the demand response information interaction and the accurate classification of distributed denial of service attacks could be accurately classified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0215
Abstract:
The digital twin technology has the potential to break the boundaries of time and space for the key scenarios of safety and cost, but no-depth research has been conducted on this. To carry out the exploration on the practical application of digital twin in the operation and inspection of transformer substation, the Xiong'an Digital Twin Transformer Substation is taken as a case, and the pain points are summarized by combining with the actual operation and maintenance of transformer substation repair. Besides, it is proposed that the practical abilities of fast scanning modeling, physical engine base, and virtual and real two-way interaction should be possessed by the mature digital twin. The purposes of standardization and practicality should be closely followed by the relevant technical research, in which the applicable applications such as migration and extension should be done on the basis of existing technologies.The great potential of digital twin technology has been embodied in the operation and inspection of transformer substation. Although the relevant theories have been applied in various fields, the further systematic and standardized research is still required for the implementation of practical demonstration projects.
The digital twin technology has the potential to break the boundaries of time and space for the key scenarios of safety and cost, but no-depth research has been conducted on this. To carry out the exploration on the practical application of digital twin in the operation and inspection of transformer substation, the Xiong'an Digital Twin Transformer Substation is taken as a case, and the pain points are summarized by combining with the actual operation and maintenance of transformer substation repair. Besides, it is proposed that the practical abilities of fast scanning modeling, physical engine base, and virtual and real two-way interaction should be possessed by the mature digital twin. The purposes of standardization and practicality should be closely followed by the relevant technical research, in which the applicable applications such as migration and extension should be done on the basis of existing technologies.The great potential of digital twin technology has been embodied in the operation and inspection of transformer substation. Although the relevant theories have been applied in various fields, the further systematic and standardized research is still required for the implementation of practical demonstration projects.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0042
Abstract:
Aiming at the problem of evaluating the access scale of regenerative electric heating under the existing distribution network structure, a method for evaluating the carrying capacity of distribution network with regenerative electric heating was proposed. Firstly, the main influencing factors of heat load and operation characteristics of regenerative electric heating were analyzed, and the regenerative electric heating system model was established. At the same time, the distribution network available valley power model was established. Then, with the maximum heating area as the goal, combined with the available low-valley electricity constraint and chance constraint of the distribution network, the evaluation model of the distribution network carrying capacity of the regenerative electric heating was constructed. Finally, the evaluation model was applied to a rural distribution network in Hebei Province, and the impacts of different evaluation models, confidence of chance constraints and access points on the carrying capacity of distribution network were compared. The results show that the proposed evaluation model can effectively increase the heating area that the distribution network can carry, improve the utilization rate of distribution network resources, and realize the reasonable evaluation of the access scale of regenerative electric heating in the current distribution network structure.
Aiming at the problem of evaluating the access scale of regenerative electric heating under the existing distribution network structure, a method for evaluating the carrying capacity of distribution network with regenerative electric heating was proposed. Firstly, the main influencing factors of heat load and operation characteristics of regenerative electric heating were analyzed, and the regenerative electric heating system model was established. At the same time, the distribution network available valley power model was established. Then, with the maximum heating area as the goal, combined with the available low-valley electricity constraint and chance constraint of the distribution network, the evaluation model of the distribution network carrying capacity of the regenerative electric heating was constructed. Finally, the evaluation model was applied to a rural distribution network in Hebei Province, and the impacts of different evaluation models, confidence of chance constraints and access points on the carrying capacity of distribution network were compared. The results show that the proposed evaluation model can effectively increase the heating area that the distribution network can carry, improve the utilization rate of distribution network resources, and realize the reasonable evaluation of the access scale of regenerative electric heating in the current distribution network structure.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0195
Abstract:
In order to meet the needs of planning electric vehicle charging stations in mountainous cities, the characteristics of roads in mountainous cities are studied in this paper to improve the methods charging load forecasting and charging station planning. The main contributions of this study are as follows. Firstly, the spatial characteristics of roads in mountainous cities were explored, and a single-vehicle power consumption model was established for electric vehicles. Secondly, an analysis was conducted as to the impact caused by the power consumption characteristics of single vehicles in mountainous cities on the temporal and spatial distributions of charging load. Also, the improved Floyd shortest path algorithm was applied to establish a group charging load prediction model. Thirdly, an iterative calculation method is developed for load forecasting and charging station planning by taking into account the influence exerted by the temporal and spatial distributions of charging load on the location of charging stations. Lastly, a novel model of charging station planning in mountainous cities is proposed to reduce the temporal fluctuation of charging load and further balance its spatial distribution. According to the solution obtained through genetic algorithm MATLAB simulation, the above modeling method is capable to achieve a more reasonable planning of charging stations in mountainous cities. On the one hand, it significantly reduces the fluctuation of charging load for electric vehicles. On the other hand, it makes the charging load more balanced between different charging stations.
In order to meet the needs of planning electric vehicle charging stations in mountainous cities, the characteristics of roads in mountainous cities are studied in this paper to improve the methods charging load forecasting and charging station planning. The main contributions of this study are as follows. Firstly, the spatial characteristics of roads in mountainous cities were explored, and a single-vehicle power consumption model was established for electric vehicles. Secondly, an analysis was conducted as to the impact caused by the power consumption characteristics of single vehicles in mountainous cities on the temporal and spatial distributions of charging load. Also, the improved Floyd shortest path algorithm was applied to establish a group charging load prediction model. Thirdly, an iterative calculation method is developed for load forecasting and charging station planning by taking into account the influence exerted by the temporal and spatial distributions of charging load on the location of charging stations. Lastly, a novel model of charging station planning in mountainous cities is proposed to reduce the temporal fluctuation of charging load and further balance its spatial distribution. According to the solution obtained through genetic algorithm MATLAB simulation, the above modeling method is capable to achieve a more reasonable planning of charging stations in mountainous cities. On the one hand, it significantly reduces the fluctuation of charging load for electric vehicles. On the other hand, it makes the charging load more balanced between different charging stations.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0297
Abstract:
To improve both fault diagnosis accuracy and fault tolerance of protective relaying for distribution network under the lost of protection action information, a distribution network fault diagnosis method based on improved directional-weighted fuzzy Petri net was proposed. Firstly, considering the impact of near-backup protection on the diagnosis of suspected fault components during the occurance of the fault, the modeling for each fault spreading direction of suspected fault components was conducted. Secondly, on the basis of expert experience the random number was utilized to perform the random assignment of the input weight of the model. Finally, when protection action information was lost, the confidence value of not-acted suspected fault components was tracked and changed, and the adaptability and fault tolerance of the fault diagnosis method were analyzed. Simulation results show that the proposed distribution network fault diagnosis model based on improved directionality weighted fuzzy Petri net possesses higher fault diagnosis accuracy under incomplete information or rejection of protection and circuit breakers.
To improve both fault diagnosis accuracy and fault tolerance of protective relaying for distribution network under the lost of protection action information, a distribution network fault diagnosis method based on improved directional-weighted fuzzy Petri net was proposed. Firstly, considering the impact of near-backup protection on the diagnosis of suspected fault components during the occurance of the fault, the modeling for each fault spreading direction of suspected fault components was conducted. Secondly, on the basis of expert experience the random number was utilized to perform the random assignment of the input weight of the model. Finally, when protection action information was lost, the confidence value of not-acted suspected fault components was tracked and changed, and the adaptability and fault tolerance of the fault diagnosis method were analyzed. Simulation results show that the proposed distribution network fault diagnosis model based on improved directionality weighted fuzzy Petri net possesses higher fault diagnosis accuracy under incomplete information or rejection of protection and circuit breakers.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0125
Abstract:
Driven by the goal of carbon neutrality and carbon peaking, demand-side management has gradually become an important way to reduce carbon emissions. In order to better manage the demand side of the power grid, this paper proposes a two-stage power consumption green responsibility certificate allocation model that considers carbon flow tracking and user carbon emission level rating to encourage users to better assume the responsibility of carbon emission reduction. In the first stage of the model, the minimum system power generation cost is selected as the objective function to optimize the power system dispatch, determine the optimal combined output and system power flow of the unit, and then build a carbon flow tracking model to obtain user carbon emissions related indicators; the second stage comprehensively considers user carbon Emissions and Carbon Potential Establish a user carbon emission rating system, and distribute green responsibility certificates with the goal of minimizing the difference in carbon emissions across the entire network, so as to meet the common and different allocation of responsibility for carbon emission reduction entities. The CPLEX solver is used to solve the improved IEEE14 node example. The simulation results show that the method can effectively evaluate the carbon emission level of users, realize the effective distribution of green responsibility certificates, and reduce the equivalent carbon emission status of high-carbon users.
Driven by the goal of carbon neutrality and carbon peaking, demand-side management has gradually become an important way to reduce carbon emissions. In order to better manage the demand side of the power grid, this paper proposes a two-stage power consumption green responsibility certificate allocation model that considers carbon flow tracking and user carbon emission level rating to encourage users to better assume the responsibility of carbon emission reduction. In the first stage of the model, the minimum system power generation cost is selected as the objective function to optimize the power system dispatch, determine the optimal combined output and system power flow of the unit, and then build a carbon flow tracking model to obtain user carbon emissions related indicators; the second stage comprehensively considers user carbon Emissions and Carbon Potential Establish a user carbon emission rating system, and distribute green responsibility certificates with the goal of minimizing the difference in carbon emissions across the entire network, so as to meet the common and different allocation of responsibility for carbon emission reduction entities. The CPLEX solver is used to solve the improved IEEE14 node example. The simulation results show that the method can effectively evaluate the carbon emission level of users, realize the effective distribution of green responsibility certificates, and reduce the equivalent carbon emission status of high-carbon users.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0367
Abstract:
In allusion to virtual power plants composed of distributed generators and energy storages, a two-stage robust optimal scheduling model, in which the risk was taken into account, was proposed to deal with the impact of distributed renewable energy uncertainty on the operation of virtual power plants. In the first stage of this model the uncertainty set of the robust model was optimized, meanwhile the risk of renewable energy curtailment and that of loss of load were evaluated. In the second stage, the worst operation scenario was searched within the optimized uncertainty set, and the operation cost of the virtual power plant under this scenario was optimized. Cooperatively solving the two stage models, an appropriate robust model uncertainty set could be determined and a dispatching scheme to cope with the worst operation scenario could be enacted to ensure both economy and security of virtual power plant operation. By means of the linearization of the two kinds of risks, the proposed model could be solved by the state of the art column-and-constraint generation algorithm. Finally, the effectiveness of the proposed model is verified by case study.
In allusion to virtual power plants composed of distributed generators and energy storages, a two-stage robust optimal scheduling model, in which the risk was taken into account, was proposed to deal with the impact of distributed renewable energy uncertainty on the operation of virtual power plants. In the first stage of this model the uncertainty set of the robust model was optimized, meanwhile the risk of renewable energy curtailment and that of loss of load were evaluated. In the second stage, the worst operation scenario was searched within the optimized uncertainty set, and the operation cost of the virtual power plant under this scenario was optimized. Cooperatively solving the two stage models, an appropriate robust model uncertainty set could be determined and a dispatching scheme to cope with the worst operation scenario could be enacted to ensure both economy and security of virtual power plant operation. By means of the linearization of the two kinds of risks, the proposed model could be solved by the state of the art column-and-constraint generation algorithm. Finally, the effectiveness of the proposed model is verified by case study.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0313
Abstract:
Along with rapid growth of the electric vehicles(abbr. EV) and the fast charging station, the coupling between power network and transportation network is increasing. Charging price, as an incentive means, is an effective measure to guide the routing and charging choices of EV. To dig the spatial flexibility of electric vehicle charging loads, a coordinated optimization framework of power system vs. charging station operator was proposed. Firstly, the modeling of coupled power-transportation network was based on the discrete choice model to fulfill the charging load calculation considering the heterogeneity and incomplete rationality of vehicle driver's decision-making. Secondly, by means of the cooperation between power system operator and charging station operator, the charging price and charging capacity were coordinately optimized, and the behavior pattern of vehicle drivers was guided to dig the spatial transfer potential of charging loads. Finally, considering the income loss of charging station operators from the cooperation, the net income generated by cooperation was reasonably distributed based on the Nash bargaining mechanism. Results of computing example show that the proposed framework effectively reduces the power supply cost of power system and ensures the stability of cooperative alliance from the perspective of individual rationality.
Along with rapid growth of the electric vehicles(abbr. EV) and the fast charging station, the coupling between power network and transportation network is increasing. Charging price, as an incentive means, is an effective measure to guide the routing and charging choices of EV. To dig the spatial flexibility of electric vehicle charging loads, a coordinated optimization framework of power system vs. charging station operator was proposed. Firstly, the modeling of coupled power-transportation network was based on the discrete choice model to fulfill the charging load calculation considering the heterogeneity and incomplete rationality of vehicle driver's decision-making. Secondly, by means of the cooperation between power system operator and charging station operator, the charging price and charging capacity were coordinately optimized, and the behavior pattern of vehicle drivers was guided to dig the spatial transfer potential of charging loads. Finally, considering the income loss of charging station operators from the cooperation, the net income generated by cooperation was reasonably distributed based on the Nash bargaining mechanism. Results of computing example show that the proposed framework effectively reduces the power supply cost of power system and ensures the stability of cooperative alliance from the perspective of individual rationality.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0071
Abstract:
By means of injecting a certain amount of second harmonic current, the submodule capacitor voltage ripples of modular multilevel converter (abbr. MMC) can be suppressed to reduce the demand on the submodule capacitance, thus, the volume and cost of MMC converter valve can be reduced. Besides, under the unbalanced grid voltage the voltage ripple of the submodule capacitance increases, if adequate strategy is not adopted the overvoltage of submodule might be caused. In allusion to above-mentioned problems, a second harmonic current injection strategy suitable to hybrid MMC under imbalance grid voltage was proposed. Firstly, a mathematical model for MMC under the unbalanced grid voltage was established. Secondly, the ripple characteristic of the voltage of the submodule capacitance was analyzed to reveal the relation between the voltage of the submodule capacitance and the power fluctuation of bridge arms. Thirdly, based on the instantaneous power model of bridge arms the optimizing strategy and computing process of harmonic injection parameters was put forward. Finally, on the platform of PSCAD/EMTDC, a simulation model of hybrid MMC was constructed to verify the effectiveness of the proposed strategy. Simulation results show that the capacitor voltage ripple under several typical unbalanced grid voltages can be effectively suppressed by the proposed strategy.
By means of injecting a certain amount of second harmonic current, the submodule capacitor voltage ripples of modular multilevel converter (abbr. MMC) can be suppressed to reduce the demand on the submodule capacitance, thus, the volume and cost of MMC converter valve can be reduced. Besides, under the unbalanced grid voltage the voltage ripple of the submodule capacitance increases, if adequate strategy is not adopted the overvoltage of submodule might be caused. In allusion to above-mentioned problems, a second harmonic current injection strategy suitable to hybrid MMC under imbalance grid voltage was proposed. Firstly, a mathematical model for MMC under the unbalanced grid voltage was established. Secondly, the ripple characteristic of the voltage of the submodule capacitance was analyzed to reveal the relation between the voltage of the submodule capacitance and the power fluctuation of bridge arms. Thirdly, based on the instantaneous power model of bridge arms the optimizing strategy and computing process of harmonic injection parameters was put forward. Finally, on the platform of PSCAD/EMTDC, a simulation model of hybrid MMC was constructed to verify the effectiveness of the proposed strategy. Simulation results show that the capacitor voltage ripple under several typical unbalanced grid voltages can be effectively suppressed by the proposed strategy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0047
Abstract:
The credible capacity is an important index to measure the contribution of renewable energy to the generation capacity of the power grid and is extremely important to the planning and regulation of power grid containing new energy system. Considering the fact that there exists a certain correlation between the new energy output and load in the same region, a method to assess credible capacity of new energy, in which the source-load matching characteristics was taken into account, was proposed. Based on the kernel density estimation the new energy output probability model was acquired, and the Copula function was used to describe the correlation between renewable energy and load in seasonal scenarios, and a source-load joint probability density distribution model was constructed to generate source-load correlation output scenarios. Based on reliability index and credible capacity characteristic index the assessment on credible capacity was performed. Finally, the effectiveness of the proposed method is verified by the analysis on practical example of a certain region.
The credible capacity is an important index to measure the contribution of renewable energy to the generation capacity of the power grid and is extremely important to the planning and regulation of power grid containing new energy system. Considering the fact that there exists a certain correlation between the new energy output and load in the same region, a method to assess credible capacity of new energy, in which the source-load matching characteristics was taken into account, was proposed. Based on the kernel density estimation the new energy output probability model was acquired, and the Copula function was used to describe the correlation between renewable energy and load in seasonal scenarios, and a source-load joint probability density distribution model was constructed to generate source-load correlation output scenarios. Based on reliability index and credible capacity characteristic index the assessment on credible capacity was performed. Finally, the effectiveness of the proposed method is verified by the analysis on practical example of a certain region.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0075
Abstract:
With the gradual liberalization of the electricity market, emerging market subject such as wind power, photovoltaics (abbr. PV) power and energy storage system are allowed to participate in the electricity market. Firstly, starting from the participation of emerging market subject in the value system of electricity market, the interest flow relationship and game relationship of its participation in market transactions was analyzed, and the electric energy value, auxiliary service value and green energy value of its participation in the electricity market were quantified. On this basis, from the perspective of standalone type, aggregation type and hybrid type the business modes, by which the emerging market subject participated in electricity spot market, were put forward. The standalone type allowed the emerging market subject with larger capacity or under specific scenario and other market subjects entering the market in equal status bidding. The aggregation type allowed the emerging market subject to be aggregated into virtual power plants to participate in market bidding. The hybrid type focused on diversification and free participation, and allowed partial electric quantity of emerging market subject individually participating in and partial electric quantity aggregated into virtual power plant to participate. Secondly, a clearing model, in which the game theory-based emerging market subject participated in combined spot market, i.e., day-ahead electric energy market and auxiliary service market, was constructed, and the constrain conditions under different participation ways were given. Finally, taking typical application scenario for example, the feasibility and effectiveness of the proposed commercial mode are verified.
With the gradual liberalization of the electricity market, emerging market subject such as wind power, photovoltaics (abbr. PV) power and energy storage system are allowed to participate in the electricity market. Firstly, starting from the participation of emerging market subject in the value system of electricity market, the interest flow relationship and game relationship of its participation in market transactions was analyzed, and the electric energy value, auxiliary service value and green energy value of its participation in the electricity market were quantified. On this basis, from the perspective of standalone type, aggregation type and hybrid type the business modes, by which the emerging market subject participated in electricity spot market, were put forward. The standalone type allowed the emerging market subject with larger capacity or under specific scenario and other market subjects entering the market in equal status bidding. The aggregation type allowed the emerging market subject to be aggregated into virtual power plants to participate in market bidding. The hybrid type focused on diversification and free participation, and allowed partial electric quantity of emerging market subject individually participating in and partial electric quantity aggregated into virtual power plant to participate. Secondly, a clearing model, in which the game theory-based emerging market subject participated in combined spot market, i.e., day-ahead electric energy market and auxiliary service market, was constructed, and the constrain conditions under different participation ways were given. Finally, taking typical application scenario for example, the feasibility and effectiveness of the proposed commercial mode are verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0030
Abstract:
To cope with the defect during the enaction of current post-disaster emergency repair strategy for distribution network, namely such problems in the urgent repair as the blocked roads and traffic delay that had not been fully considered, a post-disaster rolling emergency repair strategy for distribution network, in which the traffic network delay was considered, was proposed. Firstly, a post-disaster rolling emergency repair strategy framework, in which the traffic network delay was taken into account, was established, and by use of road delay function and intersection delay function a real-time traffic network delay model was constructed, and by means of Floyd algorithm the shortest transit time matrix among fault points was obtained. Secondly, a two-layer optimization model of post-disaster emergency repair strategy, in which the traffic network delay was considered, was established, in the outer layer the minimum weighted power loss load and the shortest total emergency repair time were taken as the objectives and the emergency repair chain of each emergency repair team was obtained; in the inner layer the maximized recovery of the power loss load was taken as the objective and the emergency recovery plan was obtained. During the emergency repair process the shortest transit time matrix was updated according to the traffic network information, and the follow-up emergency repair strategy was rolled out. Finally, the effectiveness of the post-disaster rolling emergency repair strategy is verified by example simulation.
To cope with the defect during the enaction of current post-disaster emergency repair strategy for distribution network, namely such problems in the urgent repair as the blocked roads and traffic delay that had not been fully considered, a post-disaster rolling emergency repair strategy for distribution network, in which the traffic network delay was considered, was proposed. Firstly, a post-disaster rolling emergency repair strategy framework, in which the traffic network delay was taken into account, was established, and by use of road delay function and intersection delay function a real-time traffic network delay model was constructed, and by means of Floyd algorithm the shortest transit time matrix among fault points was obtained. Secondly, a two-layer optimization model of post-disaster emergency repair strategy, in which the traffic network delay was considered, was established, in the outer layer the minimum weighted power loss load and the shortest total emergency repair time were taken as the objectives and the emergency repair chain of each emergency repair team was obtained; in the inner layer the maximized recovery of the power loss load was taken as the objective and the emergency recovery plan was obtained. During the emergency repair process the shortest transit time matrix was updated according to the traffic network information, and the follow-up emergency repair strategy was rolled out. Finally, the effectiveness of the post-disaster rolling emergency repair strategy is verified by example simulation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0327
Abstract:
At present the determinate reserve capacity of primary frequency control (abbr. PFC) cannot satisfy the requirement of the sufficiency under large disturbance of power grid because the grid-connection of large scale new energy generating units replaces partial of conventional thermal power generating units. Along with the development of smart power grid, the demand side response resource represented by the electric water heater (abbr. EWH) is an excellent frequency regulation reserve, and by use of compensation mechanism the EWH can be stimulated to participate in the PFC. A dynamic response model of primary frequency regulation, in which the system inertia, the ramp rate of governor, the dead zone of frequency regulation and the load of EWH were taken into account, was constructed to form the constraint of the minimum PFC capacity for the unit. In the premise of considering the security constraint of PFC capacity the cost of ancillary services of PFC was brought into the operating cost of the system, and the objective function was constructed by the minimum operating cost and solved by Matlab-Yalmip-Cplex. Finally, the effectiveness and security of the constructed model were verified by the computing example based on the modified IEEE 30-bus standard test system. It is also verified that the EWH load can quickly respond in the early stage of frequency accident and effectively reduce the frequency deviation.
At present the determinate reserve capacity of primary frequency control (abbr. PFC) cannot satisfy the requirement of the sufficiency under large disturbance of power grid because the grid-connection of large scale new energy generating units replaces partial of conventional thermal power generating units. Along with the development of smart power grid, the demand side response resource represented by the electric water heater (abbr. EWH) is an excellent frequency regulation reserve, and by use of compensation mechanism the EWH can be stimulated to participate in the PFC. A dynamic response model of primary frequency regulation, in which the system inertia, the ramp rate of governor, the dead zone of frequency regulation and the load of EWH were taken into account, was constructed to form the constraint of the minimum PFC capacity for the unit. In the premise of considering the security constraint of PFC capacity the cost of ancillary services of PFC was brought into the operating cost of the system, and the objective function was constructed by the minimum operating cost and solved by Matlab-Yalmip-Cplex. Finally, the effectiveness and security of the constructed model were verified by the computing example based on the modified IEEE 30-bus standard test system. It is also verified that the EWH load can quickly respond in the early stage of frequency accident and effectively reduce the frequency deviation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0046
Abstract:
Driven by achieving the target of carbon peak and carbon neutrality, such new energies as wind power and photovoltaic (abbr. PV) generation rapidly develop, and large-scale, clustered, partitioned and stratified grid connection become the key development direction. In allusion to power system power flow fluctuation and voltage excursion due to large-scale grid-connection of new energy, a layered and partitioned grid-connection planning method for wind and PV power station clusters was proposed. Considering the topology of power grid and the impedance of transmission lines, based on the graph theory the power grid was partitioned, and a multi-indices assessment system to connect new energy into power grid, in which the voltage excursion, power flow fluctuation and the indices of economic operation were included, was constructed. Taking the minimized curtailment of wind power and PV power as the objectives, a layered and partitioned grid-connection planning model for new energy was established. The established model was simplified by the second-order cone approximation method of AC power flow, thus each assessment index was solved, and combined with TOPSIS comprehensive evaluation method all feasible schemes were assessed to determine the optimal grid-connecting scheme. Finally, using IEEE 39-bus system as computing example, both effectiveness and practicability of the proposed planning method are verified.
Driven by achieving the target of carbon peak and carbon neutrality, such new energies as wind power and photovoltaic (abbr. PV) generation rapidly develop, and large-scale, clustered, partitioned and stratified grid connection become the key development direction. In allusion to power system power flow fluctuation and voltage excursion due to large-scale grid-connection of new energy, a layered and partitioned grid-connection planning method for wind and PV power station clusters was proposed. Considering the topology of power grid and the impedance of transmission lines, based on the graph theory the power grid was partitioned, and a multi-indices assessment system to connect new energy into power grid, in which the voltage excursion, power flow fluctuation and the indices of economic operation were included, was constructed. Taking the minimized curtailment of wind power and PV power as the objectives, a layered and partitioned grid-connection planning model for new energy was established. The established model was simplified by the second-order cone approximation method of AC power flow, thus each assessment index was solved, and combined with TOPSIS comprehensive evaluation method all feasible schemes were assessed to determine the optimal grid-connecting scheme. Finally, using IEEE 39-bus system as computing example, both effectiveness and practicability of the proposed planning method are verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2021.0361
[Abstract](49)