Submission & Review
- Research on Integration Method of Load Characteristic Record
- Study on Inertia Control Mode of Doubly Fed Induction Generator Based on Virtual Inertia
- Overview on Research of Renewable Energy Microgrid
- Modeling Algorithm of Charging Station Planning for Regional Electric Vehicle
- Study on Hierarchical Control of Microgrid
- Accident Analysis and Prevention Measures of SF6 Low Pressure Abnormal Trip for the UHV Converter Transformer Bushing
- Research on Transient Equivalent Modelling of Doubly fed Induction Generator Based on PSCAD
- Dynamic Wind Power Probabilistic Forecasting Based on Wind Scenario Recognition
- Distributed Control Strategy for the Battery based DC Microgrid
- Design and Discussion of a Unified EV grid Integration Structure
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- Overview on Research of Renewable Energy Microgrid
- Review on the Application of Energy Storage Technology in Power System with Renewable Energy Sources
- A Review of Researches on Wind Power Forecasting Technology
- Research on Short-term Load Forecasting Method of Power Grid Based on Deep Learning
- A Review on Hybrid HVDC System
- Coordinated Charging of Peak valley Time period Optimization by Considering V2G User Reactivity
- 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
- Forecasting the Charging Load of Large-scale Electric Vehicle and Its Impact on the Power Grid
- Comprehensive Evaluation of Power Grid Security and Benefit Based on BWM Entropy Weight TOPSIS Method
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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.1007-2322.2022.0489
Abstract:
Most of the measurement of carbon dioxide in the electric power industry adopts the traditional static emission factor measurement method from the power supply side, while more and more research is carried out in the user side with the proposal of carbon flow theory. To address this issue, firstly, through the carbon flow tracking model, the carbon flow and carbon potential of each node were calculated, the principle of establishing the dynamic factor on the user side was formulated, and the dynamic carbon emission factor model on the substation side was established. Secondly, the definition of the dynamic carbon emission factor on the user side was given based on the spatial variability of the node's carbon potential. Finally, an empirical validation was carried out with a substation area in Shanxi Province under the guidance of a low-carbon demand response mechanism. The correctness of the proposed method was verified by comparing the differences in carbon reduction and carbon reduction benefits under static and dynamic carbon emission factors.
Most of the measurement of carbon dioxide in the electric power industry adopts the traditional static emission factor measurement method from the power supply side, while more and more research is carried out in the user side with the proposal of carbon flow theory. To address this issue, firstly, through the carbon flow tracking model, the carbon flow and carbon potential of each node were calculated, the principle of establishing the dynamic factor on the user side was formulated, and the dynamic carbon emission factor model on the substation side was established. Secondly, the definition of the dynamic carbon emission factor on the user side was given based on the spatial variability of the node's carbon potential. Finally, an empirical validation was carried out with a substation area in Shanxi Province under the guidance of a low-carbon demand response mechanism. The correctness of the proposed method was verified by comparing the differences in carbon reduction and carbon reduction benefits under static and dynamic carbon emission factors.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0028
Abstract:
To address the issue of the "peak-on-peak" phenomenon in the distribution network load in residential areas caused by the unregulated charging of electric vehicles (abbr. EVs) in the future, firstly, the corresponding relationship between charging/discharging probability and the initial state of charge was introduced to establish an EV load model considering "Many- days-a-charge". Secondly, based on the load characteristics of the region, the electricity price periods were divided; a fluctuating proportion relationship between peak and valley electricity prices was established by using the "cost compensation" method; the peak-valley time-of-use electricity prices for EV charging stations in residential areas were formulated and the EV loads after price guidance were obtained. Nevertheless, relying solely on price guidance will result in concentrated charging of EVs, leading to new peak load issues. Therefore, a dual-layer optimization scheduling model was established. Meanwhile, to improve the participation of vehicle owners, an evaluation coefficient for charging/discharging participation was introduced and a reward and punishment mechanism was determined. Finally, the proposed optimization model was solved by using the particle swarm algorithm. The results show that this optimization strategy reduces the peak-valley difference in the distribution network load and ensures the interests of vehicle owners.
To address the issue of the "peak-on-peak" phenomenon in the distribution network load in residential areas caused by the unregulated charging of electric vehicles (abbr. EVs) in the future, firstly, the corresponding relationship between charging/discharging probability and the initial state of charge was introduced to establish an EV load model considering "Many- days-a-charge". Secondly, based on the load characteristics of the region, the electricity price periods were divided; a fluctuating proportion relationship between peak and valley electricity prices was established by using the "cost compensation" method; the peak-valley time-of-use electricity prices for EV charging stations in residential areas were formulated and the EV loads after price guidance were obtained. Nevertheless, relying solely on price guidance will result in concentrated charging of EVs, leading to new peak load issues. Therefore, a dual-layer optimization scheduling model was established. Meanwhile, to improve the participation of vehicle owners, an evaluation coefficient for charging/discharging participation was introduced and a reward and punishment mechanism was determined. Finally, the proposed optimization model was solved by using the particle swarm algorithm. The results show that this optimization strategy reduces the peak-valley difference in the distribution network load and ensures the interests of vehicle owners.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0380
Abstract:
The service life of a transformer is directly related to its insulation performance. For UHV converter transformers, oil temperature forecasting can be used as an important basis for evaluating its insulation performance. In this paper, a method for top-oil temperature forecasting which combined long-short term memory networks (LSTM), self-attention mechanism (SA) and gated recurrent unit (GRU) was proposed, aiming to enhance temperature forecasting accuracy of converter transformers. Firstly, the original top-oil temperature series was preprocessed. Secondly, a parallel forecasting model was implemented by LSTM and SA, and fused the parallel forecasting features using GRU. Finally, the forecasting results were obtained after adjustment by the fully connected layer. The experimental results show that the proposed method is superior to other existing single forecasting models in UHV converters top-oil temperature forecasting.
The service life of a transformer is directly related to its insulation performance. For UHV converter transformers, oil temperature forecasting can be used as an important basis for evaluating its insulation performance. In this paper, a method for top-oil temperature forecasting which combined long-short term memory networks (LSTM), self-attention mechanism (SA) and gated recurrent unit (GRU) was proposed, aiming to enhance temperature forecasting accuracy of converter transformers. Firstly, the original top-oil temperature series was preprocessed. Secondly, a parallel forecasting model was implemented by LSTM and SA, and fused the parallel forecasting features using GRU. Finally, the forecasting results were obtained after adjustment by the fully connected layer. The experimental results show that the proposed method is superior to other existing single forecasting models in UHV converters top-oil temperature forecasting.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0308
Abstract:
hen the power system operates under non-ideal conditions, significant voltage fluctuations occur. The utilizatuion of a passivity-based control strategy in the shunt active power filter (SAPF) may not effectively and accurately regulate power quality . Besides, conventional sliding mode control is also prone to buffeting. In the light of this, a passive super-twisting second-order sliding mode control strategy is proposed in this paper, which is a combination of passivity-based control and super-twisting second-order sliding mode control with stronger resisting disturbance capacity. First of all, the Euler-Lagrange model is established based on positive and negative sequence separation, in accordance with the mathematical model of active power filter. Secondly, the passivity of model is analyzed, and a passivity-based controller is designed according to its passiveness. Meanwhile, the passivity-based controller is further optimized by using the super-twisting second-order sliding mode control, thereby enhancing the robustness and capacity of resisting disturbance. Finally, simulation experiments are carried out under both ideal condition and four non-ideal conditions, namely load mutation, load imbalance, grid voltage imbalance and single-phase voltage mutation. The simulation results verify the effectiveness and superiority of the passive super-twisting second-order sliding mode control strategy.
hen the power system operates under non-ideal conditions, significant voltage fluctuations occur. The utilizatuion of a passivity-based control strategy in the shunt active power filter (SAPF) may not effectively and accurately regulate power quality . Besides, conventional sliding mode control is also prone to buffeting. In the light of this, a passive super-twisting second-order sliding mode control strategy is proposed in this paper, which is a combination of passivity-based control and super-twisting second-order sliding mode control with stronger resisting disturbance capacity. First of all, the Euler-Lagrange model is established based on positive and negative sequence separation, in accordance with the mathematical model of active power filter. Secondly, the passivity of model is analyzed, and a passivity-based controller is designed according to its passiveness. Meanwhile, the passivity-based controller is further optimized by using the super-twisting second-order sliding mode control, thereby enhancing the robustness and capacity of resisting disturbance. Finally, simulation experiments are carried out under both ideal condition and four non-ideal conditions, namely load mutation, load imbalance, grid voltage imbalance and single-phase voltage mutation. The simulation results verify the effectiveness and superiority of the passive super-twisting second-order sliding mode control strategy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0017
Abstract:
Short-term load forecasting is of great significance to the safe and stable operation of power systems. For that reason, a short-term load forecasting model based on ensemble empirical mode decomposition (abbr. EEMD) and Q learning strategy optimization was proposed. Firstly, the original load series was decomposed by EEMD to reduce the difficulty of forecasting. Secondly, on this basis, four deep learning models, namely, convolution neural network (abbr. CNN), residual neural network (abbr. ResNet), long short-term memory (abbr. LSTM) neural network and gated recurrent unit (abbr. GRU) were respectively used for forecasting to obtain four forecasting results, of which weighted combination was used to obtain the final load forecasting value. Thirdly, the combination weight was optimized by Q learning algorithm to maximize the forecasting performance of the combination model. Finally, the experiment was conducted using real collected load data from a certain region, and the results showed that the proposed combined forecasting model is superior to other forecasting models, and the effectiveness of the proposed model was verified.
Short-term load forecasting is of great significance to the safe and stable operation of power systems. For that reason, a short-term load forecasting model based on ensemble empirical mode decomposition (abbr. EEMD) and Q learning strategy optimization was proposed. Firstly, the original load series was decomposed by EEMD to reduce the difficulty of forecasting. Secondly, on this basis, four deep learning models, namely, convolution neural network (abbr. CNN), residual neural network (abbr. ResNet), long short-term memory (abbr. LSTM) neural network and gated recurrent unit (abbr. GRU) were respectively used for forecasting to obtain four forecasting results, of which weighted combination was used to obtain the final load forecasting value. Thirdly, the combination weight was optimized by Q learning algorithm to maximize the forecasting performance of the combination model. Finally, the experiment was conducted using real collected load data from a certain region, and the results showed that the proposed combined forecasting model is superior to other forecasting models, and the effectiveness of the proposed model was verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0315
Abstract:
In this paper, we focus on the game process of virtual power plants participating in the electricity spot market competition, aiming to optimize purchasing and selling strategies that can maximize the total revenue of these virtual power plants. Based on the current market transaction volume, an optimized scheduling strategy is designed for virtual power plants to participate in the real-time market with the objective of minimizing the deviation assessment cost. The virtual power plant aggregation method is validated through a numerical example, indicating its ability to enhance flexibility in electricity purchasing and selling decisions by scheduling distributed energy resources. This not only improve their own operational profits for the companies but also provide technical support for resource integration.
In this paper, we focus on the game process of virtual power plants participating in the electricity spot market competition, aiming to optimize purchasing and selling strategies that can maximize the total revenue of these virtual power plants. Based on the current market transaction volume, an optimized scheduling strategy is designed for virtual power plants to participate in the real-time market with the objective of minimizing the deviation assessment cost. The virtual power plant aggregation method is validated through a numerical example, indicating its ability to enhance flexibility in electricity purchasing and selling decisions by scheduling distributed energy resources. This not only improve their own operational profits for the companies but also provide technical support for resource integration.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0096
Abstract:
To promote efficient utilization of energy storage and demand-side resources, improve the efficiency of new energy consumption, a multi-microgrid master-slave game optimization scheduling strategy that takes into account user demand response and cloud energy storage was proposed. Firstly, a novel probabilistic model integration method was proposed, which was applied to source and load power forecasting and electricity price forecasting. An evidence correction method was built based on kappa coefficient and accuracy rate weight to improve Dempster Shafer (D-S) evidence theory information integration framework, integrate multiple probabilistic model and generate distributed energy output scenario sets. Secondly, a dual layer optimized scheduling architecture for multi-microgrid systems based on demand response and cloud energy storage operation mode was designed; a two-layer master-slave game model with the optimal joint operation cost of multi-microgrids and the lowest user purchase cost was established: The lower level model determines the market quotation strategy and distributed power equipment output adjustment strategy based on the generation probability integration method, and feeds back to the upper level model. The optimization of multi-microgrid master-slave game equilibrium operation was achieved by iteratively solving the upper and lower layers. Finally, case analysis shows that cloud energy storage and user demand response in a multi microgrid system have a synergistic effect on improving the output of distributed power generation, effectively improving the economy of the multi microgrid system and reducing user energy purchasing costs.
To promote efficient utilization of energy storage and demand-side resources, improve the efficiency of new energy consumption, a multi-microgrid master-slave game optimization scheduling strategy that takes into account user demand response and cloud energy storage was proposed. Firstly, a novel probabilistic model integration method was proposed, which was applied to source and load power forecasting and electricity price forecasting. An evidence correction method was built based on kappa coefficient and accuracy rate weight to improve Dempster Shafer (D-S) evidence theory information integration framework, integrate multiple probabilistic model and generate distributed energy output scenario sets. Secondly, a dual layer optimized scheduling architecture for multi-microgrid systems based on demand response and cloud energy storage operation mode was designed; a two-layer master-slave game model with the optimal joint operation cost of multi-microgrids and the lowest user purchase cost was established: The lower level model determines the market quotation strategy and distributed power equipment output adjustment strategy based on the generation probability integration method, and feeds back to the upper level model. The optimization of multi-microgrid master-slave game equilibrium operation was achieved by iteratively solving the upper and lower layers. Finally, case analysis shows that cloud energy storage and user demand response in a multi microgrid system have a synergistic effect on improving the output of distributed power generation, effectively improving the economy of the multi microgrid system and reducing user energy purchasing costs.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0067
Abstract:
When a power grid composed of multiple virtual synchronous generators (abbr. VSGs) experiences external disturbances, traditional VSG control can lead to issues such as frequency and voltage deviations, low accuracy in reactive power sharing, and active power oscillations, which can easily cause system overload and instability. To address these problems, firstly, the droop characteristics and reasons for power oscillations of VSGs were analyzed. Secondly, a multi-objective coordinated secondary control strategy was proposed. In this strategy, the consistency frequency restoration control strategy for active power was used to adaptively adjust the reference value of active power to achieve frequency restoration and accurate proportional sharing of active power, the consistency and average voltage restoration control strategy for reactive power was used to adaptively compensate for voltage to achieve voltage restoration and accurate proportional sharing of reactive power. Finally, the effectiveness and robustness of this control strategy are verified through simulation.
When a power grid composed of multiple virtual synchronous generators (abbr. VSGs) experiences external disturbances, traditional VSG control can lead to issues such as frequency and voltage deviations, low accuracy in reactive power sharing, and active power oscillations, which can easily cause system overload and instability. To address these problems, firstly, the droop characteristics and reasons for power oscillations of VSGs were analyzed. Secondly, a multi-objective coordinated secondary control strategy was proposed. In this strategy, the consistency frequency restoration control strategy for active power was used to adaptively adjust the reference value of active power to achieve frequency restoration and accurate proportional sharing of active power, the consistency and average voltage restoration control strategy for reactive power was used to adaptively compensate for voltage to achieve voltage restoration and accurate proportional sharing of reactive power. Finally, the effectiveness and robustness of this control strategy are verified through simulation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0234
Abstract:
The multi-terminal flexible DC distribution network has drawn a lot of attention due to its major advantages in terms of power delivery dependability. However, the rapid and accurate detection of faults in DC line protection has become one of the key issues that need to be addressed in its rapid development. For that reason, a DC line pilot protection method utilizing the characteristics of current mode components was designed. This method uses the direction of the positive current aerial mode component during a fault to distinguish between internal and external faults, constructs the protection criteria, which distinguish the type of fault, and forms a protection strategy based on the difference in the average value of the positive current zero-mode component at the fault point under different fault conditions. Finally, a multi-terminal flexible DC distribution network model was built on PSCAD/EMTDC4.5, and simulation analysis was conducted on the protection criteria and key influencing factors. The results show that this protection method can selectively, quickly, and reliably identify faults and fault types inside and outside the zone, and has good resistance to transition resistance and noise.
The multi-terminal flexible DC distribution network has drawn a lot of attention due to its major advantages in terms of power delivery dependability. However, the rapid and accurate detection of faults in DC line protection has become one of the key issues that need to be addressed in its rapid development. For that reason, a DC line pilot protection method utilizing the characteristics of current mode components was designed. This method uses the direction of the positive current aerial mode component during a fault to distinguish between internal and external faults, constructs the protection criteria, which distinguish the type of fault, and forms a protection strategy based on the difference in the average value of the positive current zero-mode component at the fault point under different fault conditions. Finally, a multi-terminal flexible DC distribution network model was built on PSCAD/EMTDC4.5, and simulation analysis was conducted on the protection criteria and key influencing factors. The results show that this protection method can selectively, quickly, and reliably identify faults and fault types inside and outside the zone, and has good resistance to transition resistance and noise.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0428
Abstract:
To actively promote the construction of energy storage systems and improve the level of renewable energy utilization, a crowdfunding mode for the construction of energy storage power stations and a matching cost transmission mechanism were proposed to solve the problems of inefficient operation and difficult cost evacuation of self-built energy storage of renewable energy power plants. Firstly, the funding sources and business modes of crowdfunded energy storage stations were defined. Secondly, different cost evacuation mechanism models of crowdfunded energy storage power stations were designed based on the analysis of the business mode of energy storage power stations and the future development of the electricity market. Finally, based on the cost analysis method of the life cycle energy storage power station, the compensation price of the renewable energy power plant to the energy storage power station was measured under the different conditions of the rate of return, business model, and price of energy storage value under each mode, and sensitivity analysis of the main factors affecting the compensation price was performed.
To actively promote the construction of energy storage systems and improve the level of renewable energy utilization, a crowdfunding mode for the construction of energy storage power stations and a matching cost transmission mechanism were proposed to solve the problems of inefficient operation and difficult cost evacuation of self-built energy storage of renewable energy power plants. Firstly, the funding sources and business modes of crowdfunded energy storage stations were defined. Secondly, different cost evacuation mechanism models of crowdfunded energy storage power stations were designed based on the analysis of the business mode of energy storage power stations and the future development of the electricity market. Finally, based on the cost analysis method of the life cycle energy storage power station, the compensation price of the renewable energy power plant to the energy storage power station was measured under the different conditions of the rate of return, business model, and price of energy storage value under each mode, and sensitivity analysis of the main factors affecting the compensation price was performed.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0152
Abstract:
To improve the accuracy of power load monitoring, A hybrid nonintrusive load identification method based on density-based spatial clustering of applications with noise with principal component analysis (abbr. PCA- DBSCAN) was proposed. Firstly, in allusion to the problem of high dimensionality of the original load features, the PCA algorithm was used to reduce the dimensionality of the original feature data to construct a load feature template library, simultaneously, the load current waveform was obtained to construct the load current template library. Secondly, the DBSCAN clustering algorithm was used to unsupervised cluster the samples in the load feature template library to extract the centers of each cluster. Thirdly, the Euclidean distance between the load to be identified and the clustering centers of each feature template library was calculated for load feature matching; and the comprehensive correlation degree between the current waveform of the load to be identified and each of that in the current waveform template library was calculated for realizing load current waveform matching. Finally, by mixing the two matching results, load identification was taken synthetically for realizing high reliable identification. The simulation results of a testing dataset with electricity consumption data show that the indexes of the proposed method are over 96%.
To improve the accuracy of power load monitoring, A hybrid nonintrusive load identification method based on density-based spatial clustering of applications with noise with principal component analysis (abbr. PCA- DBSCAN) was proposed. Firstly, in allusion to the problem of high dimensionality of the original load features, the PCA algorithm was used to reduce the dimensionality of the original feature data to construct a load feature template library, simultaneously, the load current waveform was obtained to construct the load current template library. Secondly, the DBSCAN clustering algorithm was used to unsupervised cluster the samples in the load feature template library to extract the centers of each cluster. Thirdly, the Euclidean distance between the load to be identified and the clustering centers of each feature template library was calculated for load feature matching; and the comprehensive correlation degree between the current waveform of the load to be identified and each of that in the current waveform template library was calculated for realizing load current waveform matching. Finally, by mixing the two matching results, load identification was taken synthetically for realizing high reliable identification. The simulation results of a testing dataset with electricity consumption data show that the indexes of the proposed method are over 96%.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0207
Abstract:
After the power outage caused by extreme events, the damaged traffic roads will affect the progress of the grid repair team to the fault line area, thus delaying the restoration of the distribution networks. For this reason, a method of distribution network emergency repair and restoration combined with the repair of damaged roads was proposed. Firstly, the constraints of rush repair vehicles in the traffic network were constructed based on the analysis of the impact of different damaged roads on rush repair vehicles. Secondly, considering the impact of damaged road emergency repair on power grid emergency repair, taking minimizing power loss of distribution network as an objective, line emergency repair and road repair was coordinated to establish a distribution network emergency repair strategy model, which was solved by ant colony algorithm. Finally, a system coupled with IEEE33-node distribution network and a 12-node transportation network was taken as an example for analysis. The simulation results show that the proposed method effectively improves the speed of distribution network emergency repair and reduces the power loss of distribution network load after a major power outage. The proposed scheme is more suitable for the actual situation of disasters, and can provide reference for the post disaster recovery of distribution network.
After the power outage caused by extreme events, the damaged traffic roads will affect the progress of the grid repair team to the fault line area, thus delaying the restoration of the distribution networks. For this reason, a method of distribution network emergency repair and restoration combined with the repair of damaged roads was proposed. Firstly, the constraints of rush repair vehicles in the traffic network were constructed based on the analysis of the impact of different damaged roads on rush repair vehicles. Secondly, considering the impact of damaged road emergency repair on power grid emergency repair, taking minimizing power loss of distribution network as an objective, line emergency repair and road repair was coordinated to establish a distribution network emergency repair strategy model, which was solved by ant colony algorithm. Finally, a system coupled with IEEE33-node distribution network and a 12-node transportation network was taken as an example for analysis. The simulation results show that the proposed method effectively improves the speed of distribution network emergency repair and reduces the power loss of distribution network load after a major power outage. The proposed scheme is more suitable for the actual situation of disasters, and can provide reference for the post disaster recovery of distribution network.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0324
Abstract:
In this paper, the random scenario simulation method was employed to describe the stochastic outage accidents of units and transmission lines as well as load prediction errors. Additionally, a stochastic expansion planning method of AC/DC hybrid transmission network was proposed based on Benders decomposition. Firstly, the comprehensive cost objectives of AC/DC transmission line investment cost, loss of load cost and system operation cost were established. The mixed integer linear programming model of AC/DC hybrid transmission network was decomposed into the main planning problem and the subproblems of reliability verification and economic scheduling through Benders decomposition. The simulation results of the six-bus system and the modified IEEE-118 bus system demonstrate that, in comparison to the AC transmission system, expanding the DC transmission line yields greater cost savings. The selection of an AC/DC transmission line can enhance the controllability and economy of the transmission power flow, thereby reducing load loss in the DC line.
In this paper, the random scenario simulation method was employed to describe the stochastic outage accidents of units and transmission lines as well as load prediction errors. Additionally, a stochastic expansion planning method of AC/DC hybrid transmission network was proposed based on Benders decomposition. Firstly, the comprehensive cost objectives of AC/DC transmission line investment cost, loss of load cost and system operation cost were established. The mixed integer linear programming model of AC/DC hybrid transmission network was decomposed into the main planning problem and the subproblems of reliability verification and economic scheduling through Benders decomposition. The simulation results of the six-bus system and the modified IEEE-118 bus system demonstrate that, in comparison to the AC transmission system, expanding the DC transmission line yields greater cost savings. The selection of an AC/DC transmission line can enhance the controllability and economy of the transmission power flow, thereby reducing load loss in the DC line.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0423
Abstract:
Due to the continuous growth of the power load and the higher penetration rate of distributed generation, the power flow overload of the distribution network will appear and cause congestion.An optimization combination method of distribution network congestion management measures based on the optimization and classification of various congestion management resources was proposed to solve the problem of distribution network congestion. Firstly, the resources that can alleviate congestion in the distribution network were defined as congestion management resources; and corresponding mathematical models were established for six types of congestion management resources on the source, network, load, and storage sides of the distribution network. Secondly, the index to measure the level of resource congestion management was defined, and the optimal classification of congestion management resources was carried out by a grey fixed-weight cluster. Thirdly, on the basis of considering the life cycle cost of dynamic capacity enhancing equipment, the optimal combination model of distribution network congestion management measures was established with the objective of minimizing the total cost of congestion management; and was solved by the harmonic search algorithm. Finally, through a specific distribution network example, it is verified that the proposed congestion management scheme can reduce the action amount of congestion management measures and has better economic efficiency.
Due to the continuous growth of the power load and the higher penetration rate of distributed generation, the power flow overload of the distribution network will appear and cause congestion.An optimization combination method of distribution network congestion management measures based on the optimization and classification of various congestion management resources was proposed to solve the problem of distribution network congestion. Firstly, the resources that can alleviate congestion in the distribution network were defined as congestion management resources; and corresponding mathematical models were established for six types of congestion management resources on the source, network, load, and storage sides of the distribution network. Secondly, the index to measure the level of resource congestion management was defined, and the optimal classification of congestion management resources was carried out by a grey fixed-weight cluster. Thirdly, on the basis of considering the life cycle cost of dynamic capacity enhancing equipment, the optimal combination model of distribution network congestion management measures was established with the objective of minimizing the total cost of congestion management; and was solved by the harmonic search algorithm. Finally, through a specific distribution network example, it is verified that the proposed congestion management scheme can reduce the action amount of congestion management measures and has better economic efficiency.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0429
Abstract:
Given its fluctuation and stochasticity, renewable energy generation’s high proportion to connect poses challenges to the flexibility of the power system. For that reason, a quantitative assessment method of power system flexibility based on probabilistic optimal power flow was proposed. Firstly, the flexible resource models of the power system were constructed. Historical data was processed to generate scenarios by using the k-means method, and the probabilistic models of wind power, photovoltaic power, and load fluctuation were constructed considering the time correlation based on the Markov chain model and the Copula function. Secondly, associating economic costs with system flexibility, quantitative assessment indicators that take into account the operational economy were established considering system flexibility margin expectations, vacancy expectations, and probability of deficiencies. Thirdly, the probabilistic optimal power flow model with flexibility resources was constructed, and the system state and index were estimated by the Monte Carlo simulation method and tracking center trajectory interior point method. Finally, analyzing the case of the IEEE RTS-24 system show that the appropriate allocation of renewable energy and energy storage systems could improve system flexibility and operating economy.
Given its fluctuation and stochasticity, renewable energy generation’s high proportion to connect poses challenges to the flexibility of the power system. For that reason, a quantitative assessment method of power system flexibility based on probabilistic optimal power flow was proposed. Firstly, the flexible resource models of the power system were constructed. Historical data was processed to generate scenarios by using the k-means method, and the probabilistic models of wind power, photovoltaic power, and load fluctuation were constructed considering the time correlation based on the Markov chain model and the Copula function. Secondly, associating economic costs with system flexibility, quantitative assessment indicators that take into account the operational economy were established considering system flexibility margin expectations, vacancy expectations, and probability of deficiencies. Thirdly, the probabilistic optimal power flow model with flexibility resources was constructed, and the system state and index were estimated by the Monte Carlo simulation method and tracking center trajectory interior point method. Finally, analyzing the case of the IEEE RTS-24 system show that the appropriate allocation of renewable energy and energy storage systems could improve system flexibility and operating economy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0422
Abstract:
To solve the problem of incomplete protection in power system security protection, a power system security protection method based on edge node technology was designed. Firstly, based on the edge node technology, the edge multi-access computing model was established to implement data migration for the power system and realize distributed data deployment. Secondly, the data transmission encryption algorithm was designed. And based on the GCForest, the power system network intrusion detection model, which was composed of a sample input layer, multi-granularity scanning layer, and training layer, was established to implement system intrusion detection. The test results show that the average service delay of the proposed method is only 456.32ms, the network robustness has no significant fluctuations, and the maximum false detection rate and missed detection rate of intrusion detection are 0.13% and 0.14%, respectively.
To solve the problem of incomplete protection in power system security protection, a power system security protection method based on edge node technology was designed. Firstly, based on the edge node technology, the edge multi-access computing model was established to implement data migration for the power system and realize distributed data deployment. Secondly, the data transmission encryption algorithm was designed. And based on the GCForest, the power system network intrusion detection model, which was composed of a sample input layer, multi-granularity scanning layer, and training layer, was established to implement system intrusion detection. The test results show that the average service delay of the proposed method is only 456.32ms, the network robustness has no significant fluctuations, and the maximum false detection rate and missed detection rate of intrusion detection are 0.13% and 0.14%, respectively.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0431
Abstract:
To estimate the state of charge (abbr. SOC) of the li-ion battery accurately, considering the temperature effect on the basis of the reconfigurable battery module, the temperature-corrected SOC of the reconfigurable battery could be estimated by combining with graph calculation. Firstly,the open circuit voltage (abbr. OCV) and SOC reference relationships of the battery at different temperatures were obtained through experiments, and a thermal model of the reconfigurable battery pack was built to analyze the effect of temperature. Secondly, a reconfigurable battery network graph model was built, the depth-first graph was used to traverse and access battery topology, the optimal connection method after temperature correction was found, and the SOC of the individual battery was estimated using the OCV method. Finally, simulation results demonstrate that temperature correction can reduce the temperature error in reconfigurable battery topology and improve the SOC estimation accuracy of the reconfigurable energy storage system.
To estimate the state of charge (abbr. SOC) of the li-ion battery accurately, considering the temperature effect on the basis of the reconfigurable battery module, the temperature-corrected SOC of the reconfigurable battery could be estimated by combining with graph calculation. Firstly,the open circuit voltage (abbr. OCV) and SOC reference relationships of the battery at different temperatures were obtained through experiments, and a thermal model of the reconfigurable battery pack was built to analyze the effect of temperature. Secondly, a reconfigurable battery network graph model was built, the depth-first graph was used to traverse and access battery topology, the optimal connection method after temperature correction was found, and the SOC of the individual battery was estimated using the OCV method. Finally, simulation results demonstrate that temperature correction can reduce the temperature error in reconfigurable battery topology and improve the SOC estimation accuracy of the reconfigurable energy storage system.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0268
Abstract:
To cope with the enormous challenges brought by distributed energy storage terminals to the management and operation of the power grid, a battery energy storage system (abbr. BESS) terminal information exchange mechanism based on a universal plug-and-play protocol was proposed. Firstly, based on the modeling method of electric power Internet of Things(abbr. IoT) standard, a model pattern specifications with three dimensions of BESS attributes, services, and events were defined to construct an IoT terminal model applicable to BESS on the distribution side, which eliminates the heterogeneity problem between cloud master and terminal model in the traditional system. Secondly, a registration/identification mechanism between the BESS terminal and cloud master station based on a common plug-and-play protocol was proposed: based on the self-describing configuration of the BESS terminal, identity verification was completed by obtaining certificate or authentication of the cloud master station to ensure data security, so as to achieve fast identification and connection. Finally, a cloud edge-based BESS information exchange mechanism was constructed taking the model and protocol mapping as the core. And taking the energy storage system in a certain distribution network feeder as an example, the feasibility and effectiveness of the information interaction between the BESS terminal and the cloud master station’s various business function information are verified by consistency testing.
To cope with the enormous challenges brought by distributed energy storage terminals to the management and operation of the power grid, a battery energy storage system (abbr. BESS) terminal information exchange mechanism based on a universal plug-and-play protocol was proposed. Firstly, based on the modeling method of electric power Internet of Things(abbr. IoT) standard, a model pattern specifications with three dimensions of BESS attributes, services, and events were defined to construct an IoT terminal model applicable to BESS on the distribution side, which eliminates the heterogeneity problem between cloud master and terminal model in the traditional system. Secondly, a registration/identification mechanism between the BESS terminal and cloud master station based on a common plug-and-play protocol was proposed: based on the self-describing configuration of the BESS terminal, identity verification was completed by obtaining certificate or authentication of the cloud master station to ensure data security, so as to achieve fast identification and connection. Finally, a cloud edge-based BESS information exchange mechanism was constructed taking the model and protocol mapping as the core. And taking the energy storage system in a certain distribution network feeder as an example, the feasibility and effectiveness of the information interaction between the BESS terminal and the cloud master station’s various business function information are verified by consistency testing.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0408
Abstract:
Shared energy storage, which has shown great application potential and value in all aspects of the power supply chain, is regarded as an effective means to solve the problem of energy storage cost dredging. Nevertheless, energy storage sharing will not only involve multiple types of application scenarios; but its own operation mode is the coupling of multidimensional parameters such as power and capacity. It is urgent to explore an efficient energy storage sharing mechanism to maximize the flexibility potential of the system. For that reason, shared energy storage matching transaction mechanism considering multidimensional parameter coupling was studied. Firstly, a shared energy storage matching transaction framework considering multi-dimensional parameter coupling was proposed. On this basis, a shared energy storage matching transaction model with multidimensional parameter coupling was constructed based on the combinatorial auction theory. Secondly, in allusion to the problem of the high computational complexity of traditional methods for solving combinatorial auctions, the concept of bid density, which evaluates and sorts the bid information based on the value of the unit subject matter so as to realize the fast solution of the problem of shared energy storage combinatorial auctions, was proposed. In addition, a shared energy storage matching transaction settlement mechanism based on bid density was proposed. Finally, the effectiveness of the proposed method was demonstrated by numerical examples.
Shared energy storage, which has shown great application potential and value in all aspects of the power supply chain, is regarded as an effective means to solve the problem of energy storage cost dredging. Nevertheless, energy storage sharing will not only involve multiple types of application scenarios; but its own operation mode is the coupling of multidimensional parameters such as power and capacity. It is urgent to explore an efficient energy storage sharing mechanism to maximize the flexibility potential of the system. For that reason, shared energy storage matching transaction mechanism considering multidimensional parameter coupling was studied. Firstly, a shared energy storage matching transaction framework considering multi-dimensional parameter coupling was proposed. On this basis, a shared energy storage matching transaction model with multidimensional parameter coupling was constructed based on the combinatorial auction theory. Secondly, in allusion to the problem of the high computational complexity of traditional methods for solving combinatorial auctions, the concept of bid density, which evaluates and sorts the bid information based on the value of the unit subject matter so as to realize the fast solution of the problem of shared energy storage combinatorial auctions, was proposed. In addition, a shared energy storage matching transaction settlement mechanism based on bid density was proposed. Finally, the effectiveness of the proposed method was demonstrated by numerical examples.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0456
Abstract:
To improve the overall operational efficiency of multi-microgrids in the same region, as well as the local consumption capacity of distributed new energy, fully utilize the generation and load differences between microgrids and the resource sharing of shared energy storage,a method of multi-microgrids power interaction and shared energy storage optimization was proposed. Firstly, a comprehensive curve shape and distance-matching index based on microgrid's generation and load data was constructed, microgrid groups and combining pairs were divided, efficient power interaction among multi-microgrids was achieved, and economic benefits and new energy consumption capacity were improved. Secondly, a multi-microgrids shared energy storage cost allocation method based on the improved Shapley value method was proposed to achieve energy storage cost allocation. The example results show that the proposed method can effectively improve the economic benefits of the multi-microgrids operation and the local consumption capacity of distributed new energy, and significantly improve the energy storage utilization rate.
To improve the overall operational efficiency of multi-microgrids in the same region, as well as the local consumption capacity of distributed new energy, fully utilize the generation and load differences between microgrids and the resource sharing of shared energy storage,a method of multi-microgrids power interaction and shared energy storage optimization was proposed. Firstly, a comprehensive curve shape and distance-matching index based on microgrid's generation and load data was constructed, microgrid groups and combining pairs were divided, efficient power interaction among multi-microgrids was achieved, and economic benefits and new energy consumption capacity were improved. Secondly, a multi-microgrids shared energy storage cost allocation method based on the improved Shapley value method was proposed to achieve energy storage cost allocation. The example results show that the proposed method can effectively improve the economic benefits of the multi-microgrids operation and the local consumption capacity of distributed new energy, and significantly improve the energy storage utilization rate.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0444
Abstract:
In allusion to the problems of grid voltage fluctuation after a grid-connected doubly-fed wind turbine, a large amount of the modeling calculation, and the complex frequency coupling relationship of traditional simulation analysis method, a grid voltage stability cooperative control method based on equivalent doubly-fed wind farm cooperative static reactive power generator (abbr. SVG) was proposed. Firstly, a two-step equivalent modeling method based on K-means clustering was proposed according to the clustering phenomenon of wind farm power characteristics, and the maximum reactive power compensation capabilities of stator side and grid side converters in double-fed induction generators (abbr. DFIG) were analyzed and defined. Secondly, to address the issue of insufficient support capability of SVG to the voltage at wind farm connecting points in the case of a power grid disturbance, a DFIG collaborative SVG power grid voltage fluctuation suppression strategy for different voltage fluctuation levels, which makes full use of the reactive power reserve of DFIG inside the wind farm station, and adds a delay link in the voltage recovery process to ensure voltage stability, was proposed. Finally, the effectiveness of the proposed collaborative control strategy in improving the voltage fluctuation at the wind farm connecting points was verified through simulation.
In allusion to the problems of grid voltage fluctuation after a grid-connected doubly-fed wind turbine, a large amount of the modeling calculation, and the complex frequency coupling relationship of traditional simulation analysis method, a grid voltage stability cooperative control method based on equivalent doubly-fed wind farm cooperative static reactive power generator (abbr. SVG) was proposed. Firstly, a two-step equivalent modeling method based on K-means clustering was proposed according to the clustering phenomenon of wind farm power characteristics, and the maximum reactive power compensation capabilities of stator side and grid side converters in double-fed induction generators (abbr. DFIG) were analyzed and defined. Secondly, to address the issue of insufficient support capability of SVG to the voltage at wind farm connecting points in the case of a power grid disturbance, a DFIG collaborative SVG power grid voltage fluctuation suppression strategy for different voltage fluctuation levels, which makes full use of the reactive power reserve of DFIG inside the wind farm station, and adds a delay link in the voltage recovery process to ensure voltage stability, was proposed. Finally, the effectiveness of the proposed collaborative control strategy in improving the voltage fluctuation at the wind farm connecting points was verified through simulation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0473
Abstract:
In allusion to the difficulty of DC current prediction in the process of commutation failure recovery, firstly, the equivalent model of DC line was analyzed, the quantitative relationship between DC voltage on the rectifier side and the electrical quantity on the inverter side was established, and a method that can accurately predict the DC current change was proposed based on DC control analysis. Secondly, based on the prediction method of minimum off-area discrimination and DC current change, a subsequent commutation failure risk assessment model was constructed, and the trigger angle was quantitatively controlled according to the risk assessment results to suppress the occurrence of subsequent commutation failure. Taking the standard model of Conseil International Des Grands Reseaux Elecctriques (abbr. CIGRE) in the PSCAD/EMTDC platform as the test system, the simulation results under different fault types, severity and duration verify the accuracy of the proposed DC current prediction, subsequent commutation failure risk assessment and the effectiveness of the subsequent commutation failure suppression strategy.
In allusion to the difficulty of DC current prediction in the process of commutation failure recovery, firstly, the equivalent model of DC line was analyzed, the quantitative relationship between DC voltage on the rectifier side and the electrical quantity on the inverter side was established, and a method that can accurately predict the DC current change was proposed based on DC control analysis. Secondly, based on the prediction method of minimum off-area discrimination and DC current change, a subsequent commutation failure risk assessment model was constructed, and the trigger angle was quantitatively controlled according to the risk assessment results to suppress the occurrence of subsequent commutation failure. Taking the standard model of Conseil International Des Grands Reseaux Elecctriques (abbr. CIGRE) in the PSCAD/EMTDC platform as the test system, the simulation results under different fault types, severity and duration verify the accuracy of the proposed DC current prediction, subsequent commutation failure risk assessment and the effectiveness of the subsequent commutation failure suppression strategy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0476
Abstract:
In the application background of single large-capacity doubly-fed wind generators (abbr. DFIG) connected to the medium voltage DC grid through a voltage source converter in all-DC wind farm, a DC low voltage ride-through control method was proposed based on voltage-frequency coordinated control at both ends of the machine and grid, to ensure the continuous controllable grid-connected operation of the DFIG after the voltage drop of the DC grid. Firstly, through theoretical analysis, it was explained that the rigid and non adjustable frequency after voltage drop during AC grid connection is the main reason for the transient DC component of the stator flux and the overcurrent generated by the stator and rotor windings, Furthermore, fully utilizing the flexible and adjustable characteristics of AC voltage and frequency within the DFIG system independently linked to flexible DC grid, the generation of transient DC components of stator flux was avoided through coordinated control of stator voltage and frequency between the machine and the grid, thereby eliminating the difficult to solve transient overcurrent phenomenon of stator and rotor during AC grid connection, and ensuring the continuous and controllable operation of the DFIG system. Simultaneously, the problem of DC overvoltage of rotor side converter was solved by adding DC unloading circuit. Finally, the effectiveness of the DC low voltage ride-through method based on voltage-frequency coordinated control was verified by the comparison of simulation of an AC grid-connected and flexible DC grid-connected single 5MW DFIG system.
In the application background of single large-capacity doubly-fed wind generators (abbr. DFIG) connected to the medium voltage DC grid through a voltage source converter in all-DC wind farm, a DC low voltage ride-through control method was proposed based on voltage-frequency coordinated control at both ends of the machine and grid, to ensure the continuous controllable grid-connected operation of the DFIG after the voltage drop of the DC grid. Firstly, through theoretical analysis, it was explained that the rigid and non adjustable frequency after voltage drop during AC grid connection is the main reason for the transient DC component of the stator flux and the overcurrent generated by the stator and rotor windings, Furthermore, fully utilizing the flexible and adjustable characteristics of AC voltage and frequency within the DFIG system independently linked to flexible DC grid, the generation of transient DC components of stator flux was avoided through coordinated control of stator voltage and frequency between the machine and the grid, thereby eliminating the difficult to solve transient overcurrent phenomenon of stator and rotor during AC grid connection, and ensuring the continuous and controllable operation of the DFIG system. Simultaneously, the problem of DC overvoltage of rotor side converter was solved by adding DC unloading circuit. Finally, the effectiveness of the DC low voltage ride-through method based on voltage-frequency coordinated control was verified by the comparison of simulation of an AC grid-connected and flexible DC grid-connected single 5MW DFIG system.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0491
Abstract:
In allusion to the problem that uncertainty of distributed resources makes the day-ahead dispatching plan for virtual power plant (abbr. VPP) generates power deviation in the real-time, which affects the performance of VPP and system, a VPP cloud edge collaborative real-time regulation method that takes into account daily bias and power grid demand was proposed. Firstly, based on the characteristics of the distribution network structure, cloud-edge collaborative optimal schedule architecture of VPP was constructed. And the real-time optimal schedule strategy of VPP considering real-time market demand was proposed. Secondly, to improve the speed of resource regulation of VPP in real-time phase, an edge-side coordinated consistency regulation strategy was proposed to calculate the distributed optimization for regulation power in VPP station area. Finally, an example was given to prove the rationality and effectiveness of the proposed strategy.
In allusion to the problem that uncertainty of distributed resources makes the day-ahead dispatching plan for virtual power plant (abbr. VPP) generates power deviation in the real-time, which affects the performance of VPP and system, a VPP cloud edge collaborative real-time regulation method that takes into account daily bias and power grid demand was proposed. Firstly, based on the characteristics of the distribution network structure, cloud-edge collaborative optimal schedule architecture of VPP was constructed. And the real-time optimal schedule strategy of VPP considering real-time market demand was proposed. Secondly, to improve the speed of resource regulation of VPP in real-time phase, an edge-side coordinated consistency regulation strategy was proposed to calculate the distributed optimization for regulation power in VPP station area. Finally, an example was given to prove the rationality and effectiveness of the proposed strategy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0099
Abstract:
With rapid development of new power system dominated by new energy, how to ensure system frequency safety has become a problem needed to be solved urgently. Therefore, firstly, frequency dynamic response regulation process and characteristics of multiple types regulation methods in the power system were analyzed. Secondly, operation generalized short circuit ratio that reflects relative strength of AC system when new energy is connected to the grid at any power was studied and introduced into the given new power system source-load-storage coordination optimization strategy along with frequency safety constraints. On this basis, a source-load-storage coordination optimization combination model was established based on frequency safety, and particle swarm optimization algorithm ws used to solve global optimal solution. Finally, the proposed strategy was validated through an improved IEEE-39 node system on the Matlab/Simulink platform. The results show that the proposed strategy is flexible and cost-effective, can quickly achieve frequency safety regulation, ensures strength of power grid and resists violent frequency fluctuations when new energy is connected to the grid.
With rapid development of new power system dominated by new energy, how to ensure system frequency safety has become a problem needed to be solved urgently. Therefore, firstly, frequency dynamic response regulation process and characteristics of multiple types regulation methods in the power system were analyzed. Secondly, operation generalized short circuit ratio that reflects relative strength of AC system when new energy is connected to the grid at any power was studied and introduced into the given new power system source-load-storage coordination optimization strategy along with frequency safety constraints. On this basis, a source-load-storage coordination optimization combination model was established based on frequency safety, and particle swarm optimization algorithm ws used to solve global optimal solution. Finally, the proposed strategy was validated through an improved IEEE-39 node system on the Matlab/Simulink platform. The results show that the proposed strategy is flexible and cost-effective, can quickly achieve frequency safety regulation, ensures strength of power grid and resists violent frequency fluctuations when new energy is connected to the grid.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0036
Abstract:
To improve the accuracy of short-term load forecasting in power system, fully exploit the multi-dimensional information in the historical data to better overcome the adverse effects caused by the lack of the historical data, a short-term load forecasting method based on the self- organizing maps and back propagation (abbr. SOM-BP) and Prophet hybrid model was proposed. Firstly, the similar day set was obtained by clustering the historical non-power data through SOM neural network, and then the BP neural network was trained with the similar day data to obtain the single point load value prediction results. Secondly, focusing on the periodicity and temporal trends of historical data, the Prophet temporal model was used to perform periodic nonlinear fitting on historical load data. Through historical data fitting error feedback, the key hyperparameters of the optimization model was adjust, and finally the short-term load forecasting results based on the combination of error reciprocal method were obtained. Finally, Taking the power load data of a certain region as an example for verification, the results show that the proposed improved prediction model has higher prediction accuracy and advantages in overcoming historical data deficiencies and fitting non- working day load curves.
To improve the accuracy of short-term load forecasting in power system, fully exploit the multi-dimensional information in the historical data to better overcome the adverse effects caused by the lack of the historical data, a short-term load forecasting method based on the self- organizing maps and back propagation (abbr. SOM-BP) and Prophet hybrid model was proposed. Firstly, the similar day set was obtained by clustering the historical non-power data through SOM neural network, and then the BP neural network was trained with the similar day data to obtain the single point load value prediction results. Secondly, focusing on the periodicity and temporal trends of historical data, the Prophet temporal model was used to perform periodic nonlinear fitting on historical load data. Through historical data fitting error feedback, the key hyperparameters of the optimization model was adjust, and finally the short-term load forecasting results based on the combination of error reciprocal method were obtained. Finally, Taking the power load data of a certain region as an example for verification, the results show that the proposed improved prediction model has higher prediction accuracy and advantages in overcoming historical data deficiencies and fitting non- working day load curves.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0128
Abstract:
To effectively reduce the cost of comprehensive energy system capacity allocation, a double-layer optimal allocation algorithm considering reliability constraints was proposed. Firstly, based on the fundamental architecture of the integrated energy system, a double-layer capacity allocation optimization model was carried out, of which the upper layer was for capacity allocation and the lower layer was for optimal operation. Secondly, a Markov two-state fault transition model using sequential Monte Carlo method to extract equipment faults was added to this double-layer capacity allocation model to evaluate the reliability of the system, and the obtained results were taken as the reliability constraints of the system. Ultimately, the equipment capacity configuration of the comprehensive energy system satisfying the reliability constraint interval was output. The outcome demonstrates that the comprehensive energy system with multiple energy equipment not only facilitates the utilization of new energy units and reduces the use of fossil energy, but also improves the reliability of the system. Synchronously, the study case compares the economy of system capacity allocation under different energy supply reliability constraints and verifies the effectiveness of the proposed model.
To effectively reduce the cost of comprehensive energy system capacity allocation, a double-layer optimal allocation algorithm considering reliability constraints was proposed. Firstly, based on the fundamental architecture of the integrated energy system, a double-layer capacity allocation optimization model was carried out, of which the upper layer was for capacity allocation and the lower layer was for optimal operation. Secondly, a Markov two-state fault transition model using sequential Monte Carlo method to extract equipment faults was added to this double-layer capacity allocation model to evaluate the reliability of the system, and the obtained results were taken as the reliability constraints of the system. Ultimately, the equipment capacity configuration of the comprehensive energy system satisfying the reliability constraint interval was output. The outcome demonstrates that the comprehensive energy system with multiple energy equipment not only facilitates the utilization of new energy units and reduces the use of fossil energy, but also improves the reliability of the system. Synchronously, the study case compares the economy of system capacity allocation under different energy supply reliability constraints and verifies the effectiveness of the proposed model.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0112
Abstract:
Accurately predicting the power load in a station area can encourage power companies to arrange dispatching plans reasonably, ensuring the safety and stable economic operation of the station area. To fully exploit the characteristics of power load data and improve the accuracy of forecasting, a power load prediction method based on adaptive symplectic geometry mode decomposition (abbr. ASGMD), multiple linear regression (abbr. MLR) and convolutional long short-term memory (abbr. CLSTM) was proposed. Firstly, ASGMD was applied to decompose the station load data into two components: weakly correlated and strongly correlated. Secondly, MLR and LSTM were adopted to forecast the above two components, respectively. Finally, the load forecast value was obtained by combining the results of each model. The experiments show that the proposed method obtains higher forecasting accuracy than other models.
Accurately predicting the power load in a station area can encourage power companies to arrange dispatching plans reasonably, ensuring the safety and stable economic operation of the station area. To fully exploit the characteristics of power load data and improve the accuracy of forecasting, a power load prediction method based on adaptive symplectic geometry mode decomposition (abbr. ASGMD), multiple linear regression (abbr. MLR) and convolutional long short-term memory (abbr. CLSTM) was proposed. Firstly, ASGMD was applied to decompose the station load data into two components: weakly correlated and strongly correlated. Secondly, MLR and LSTM were adopted to forecast the above two components, respectively. Finally, the load forecast value was obtained by combining the results of each model. The experiments show that the proposed method obtains higher forecasting accuracy than other models.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0401
Abstract:
Considering the extreme disaster risk faced by the distribution network, a disaster prediction-based resilience enhancement power supply strategy for the distribution network was proposed. Firstly, the effect of disaster prediction on improving the flexible power supply of the distribution network was analyzed. Secondly, considering the optimal deployment of emergency power vehicles and the transfer of electric energy, a three-stage power supply elasticity improvement model for distribution network disaster prevention, resistance, and recovery was proposed based on the disaster prediction information. Thirdly, the constructed non-convex nonlinear model was transformed into a mixed integer second-order cone programming model and solved by using the second-order cone relaxation method. Finally, the effectiveness and superiority of the proposed strategy are verified through the example simulation of the improved 69-node power distribution system.
Considering the extreme disaster risk faced by the distribution network, a disaster prediction-based resilience enhancement power supply strategy for the distribution network was proposed. Firstly, the effect of disaster prediction on improving the flexible power supply of the distribution network was analyzed. Secondly, considering the optimal deployment of emergency power vehicles and the transfer of electric energy, a three-stage power supply elasticity improvement model for distribution network disaster prevention, resistance, and recovery was proposed based on the disaster prediction information. Thirdly, the constructed non-convex nonlinear model was transformed into a mixed integer second-order cone programming model and solved by using the second-order cone relaxation method. Finally, the effectiveness and superiority of the proposed strategy are verified through the example simulation of the improved 69-node power distribution system.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0182
Abstract:
In order to solve the problems of multiple classification and subclassification, a feature extraction strategy combining adaptive white noise complete set empirical Mode decomposition and wavelet packet energy entropy was proposed. The parameters of least squares support vector machine are optimized by the improved Sparrow search algorithm. Firstly, the phase voltage signal was processed by CEEMDAN to obtain the modal component, and IMF was screened jointly according to the correlation coefficient and variance contribution rate. The IMF with noise was denoised and reconstructed, and the IMF without noise was formed into the pure IMF group. Then, WPEE with obvious fault characteristics was decomposed by wavelet packet analysis. Secondly, LSSVM parameters are optimized by using Iterative chaotic mapping and the improved SSA based on random walk strategy, and the diagnostic model is established. Finally, Z-source inverter is taken as an example for verification. The results show that the proposed method can extract capacitor aging fault features quickly and effectively, and the diagnosis method is faster and the fault recognition rate is higher.
In order to solve the problems of multiple classification and subclassification, a feature extraction strategy combining adaptive white noise complete set empirical Mode decomposition and wavelet packet energy entropy was proposed. The parameters of least squares support vector machine are optimized by the improved Sparrow search algorithm. Firstly, the phase voltage signal was processed by CEEMDAN to obtain the modal component, and IMF was screened jointly according to the correlation coefficient and variance contribution rate. The IMF with noise was denoised and reconstructed, and the IMF without noise was formed into the pure IMF group. Then, WPEE with obvious fault characteristics was decomposed by wavelet packet analysis. Secondly, LSSVM parameters are optimized by using Iterative chaotic mapping and the improved SSA based on random walk strategy, and the diagnostic model is established. Finally, Z-source inverter is taken as an example for verification. The results show that the proposed method can extract capacitor aging fault features quickly and effectively, and the diagnosis method is faster and the fault recognition rate is higher.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0309
Abstract:
Aiming to address the impact of renewable energy output uncertainty on optimal scheduling of microgrid, a multi-time scale optimal scheduling strategy for wind and PV power scenario generation in microgrid based on C-WGAN (conditional Wasserstein generative adversarial networks) is proposed. Firstly, the specified wind and PV power scenarios are generated through C-WGAN. Then, during the day-ahead optimization scheduling stage, a comprehensive consideration is given to the cost of the connecting line and the battery degradation, while introducing an optimized coefficient to optimize the interactive power of the connecting line. The primary objective during the intraday optimization scheduling phase is to track the results of day-ahead optimization scheduling, so as to effectively mitigate the impact of inaccuracies in day-ahead renewable energy and load prediction data. The results indicate that the proposed method not only exhibits strong robustness, butalso effectively guarantees the economic operation of microgrid.
Aiming to address the impact of renewable energy output uncertainty on optimal scheduling of microgrid, a multi-time scale optimal scheduling strategy for wind and PV power scenario generation in microgrid based on C-WGAN (conditional Wasserstein generative adversarial networks) is proposed. Firstly, the specified wind and PV power scenarios are generated through C-WGAN. Then, during the day-ahead optimization scheduling stage, a comprehensive consideration is given to the cost of the connecting line and the battery degradation, while introducing an optimized coefficient to optimize the interactive power of the connecting line. The primary objective during the intraday optimization scheduling phase is to track the results of day-ahead optimization scheduling, so as to effectively mitigate the impact of inaccuracies in day-ahead renewable energy and load prediction data. The results indicate that the proposed method not only exhibits strong robustness, butalso effectively guarantees the economic operation of microgrid.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0289
Abstract:
Under the interactive mode of "Generation-Grid-Load-Storage", different types of power resources have been integrated, breaking through the barriers in power production and consumption circulation. As a result, there have been changes in the transaction object, transaction variety, organization mode and clearing method of electricity market transactions. The interaction between “Generation-Grid-Load-Storage” requires the participation of multiple energy resources and entities on the same platform, thereby presenting great challenges to the existing trading mechanism in terms of physical and economic characteristics as well as transactional requirements. Meanwhile, the traditional time-sharing settlement model struggles to sufficiently address the demands of all parties involved. The transaction varieties and declaration methods lack flexibility, hindering the full utilization of their flexible and interactive characteristics. Therefore, it is urgent to accquire a more diversified and flexible market-oriented competition mechanism. In this paper, starting from the concept of "Generation-Grid-Load- Storage" interaction mode, we examine the key factors of its development, analyze the characteristics of power resources under this interactive mode, and summarize the trading characteristics of multiple entities. Based on this, the trading mechanism is designed, providing a flexible trading mechanism to cater to the needs of multiple agents, and a market clearing model is presented. Finally, the effectiveness of the proposed market mechanism is verified through an example, which provides reference for the development of the "Generation-Grid-Load-Storage" mode.
Under the interactive mode of "Generation-Grid-Load-Storage", different types of power resources have been integrated, breaking through the barriers in power production and consumption circulation. As a result, there have been changes in the transaction object, transaction variety, organization mode and clearing method of electricity market transactions. The interaction between “Generation-Grid-Load-Storage” requires the participation of multiple energy resources and entities on the same platform, thereby presenting great challenges to the existing trading mechanism in terms of physical and economic characteristics as well as transactional requirements. Meanwhile, the traditional time-sharing settlement model struggles to sufficiently address the demands of all parties involved. The transaction varieties and declaration methods lack flexibility, hindering the full utilization of their flexible and interactive characteristics. Therefore, it is urgent to accquire a more diversified and flexible market-oriented competition mechanism. In this paper, starting from the concept of "Generation-Grid-Load- Storage" interaction mode, we examine the key factors of its development, analyze the characteristics of power resources under this interactive mode, and summarize the trading characteristics of multiple entities. Based on this, the trading mechanism is designed, providing a flexible trading mechanism to cater to the needs of multiple agents, and a market clearing model is presented. Finally, the effectiveness of the proposed market mechanism is verified through an example, which provides reference for the development of the "Generation-Grid-Load-Storage" mode.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0302
Abstract:
The traditional way of regulating wind farm based on the voltage command from the main station often leads to frequent fluctuations of reactive power sources within the station as well as insufficient reactive power margin, which affects the regulation capacity of the wind farm. Therefore, a new active voltage regulation optimization control strategy at field level is proposed considering wind power operation trajectory and scene division. First, based on the power prediction data of the wind farm and the active voltage sensitivity curve, the voltage fluctuation trajectory of the parallel node is drawn. The coordination of reactive power output of the wind turbine and the SVG, in combination with the voltage command received by the AVC substation, effectively reduces the regulation frequency of reactive power equipment. The detailed operation scenarios are further categorized based on the amplitude of the voltage drop, and the effective suppression of large ge-fluctuations of grid voltage is realized by adjusting the control mode of SVG. Finally, a simulation test platform incorporating AVC power controller is constructed to verify the effectiveness of the proposed method in reducing reactive power regulation frequency, enhancing the reactive power margin, and providing active voltage support compared to the traditional AVC control strategy of wind farms. This offers a new solution for realizing active voltage regulation in wind farms.
The traditional way of regulating wind farm based on the voltage command from the main station often leads to frequent fluctuations of reactive power sources within the station as well as insufficient reactive power margin, which affects the regulation capacity of the wind farm. Therefore, a new active voltage regulation optimization control strategy at field level is proposed considering wind power operation trajectory and scene division. First, based on the power prediction data of the wind farm and the active voltage sensitivity curve, the voltage fluctuation trajectory of the parallel node is drawn. The coordination of reactive power output of the wind turbine and the SVG, in combination with the voltage command received by the AVC substation, effectively reduces the regulation frequency of reactive power equipment. The detailed operation scenarios are further categorized based on the amplitude of the voltage drop, and the effective suppression of large ge-fluctuations of grid voltage is realized by adjusting the control mode of SVG. Finally, a simulation test platform incorporating AVC power controller is constructed to verify the effectiveness of the proposed method in reducing reactive power regulation frequency, enhancing the reactive power margin, and providing active voltage support compared to the traditional AVC control strategy of wind farms. This offers a new solution for realizing active voltage regulation in wind farms.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0326
Abstract:
To improve energy efficiency and facilitate the advancement and utilization of renewable energy, in this paper we examine the aggregatable resource scale of virtual power plants and investigate the optimization strategy for their low-carbon trading. First of all, it is essential to consider both technological development volume and economic development volume on the resource side to analyze the upper limit of the aggregatable resources. On the other hand, the upper limit of the aggregatable resources on the load side is analyzed, with the adjustable capacity of the load side taken into account. A low-carbon virtual power plant operation transaction framework is designed after that, with each unit within the virtual power plant being individually modeled. Secondly, a low-carbon joint trading optimization strategy for virtual power plants to participate in the electric energy market, carbon trading market and peak shaving auxiliary service market is developed based on the carbon trading mechanism. Finally, an example from a specific area is taken to verify the effectiveness of the model
To improve energy efficiency and facilitate the advancement and utilization of renewable energy, in this paper we examine the aggregatable resource scale of virtual power plants and investigate the optimization strategy for their low-carbon trading. First of all, it is essential to consider both technological development volume and economic development volume on the resource side to analyze the upper limit of the aggregatable resources. On the other hand, the upper limit of the aggregatable resources on the load side is analyzed, with the adjustable capacity of the load side taken into account. A low-carbon virtual power plant operation transaction framework is designed after that, with each unit within the virtual power plant being individually modeled. Secondly, a low-carbon joint trading optimization strategy for virtual power plants to participate in the electric energy market, carbon trading market and peak shaving auxiliary service market is developed based on the carbon trading mechanism. Finally, an example from a specific area is taken to verify the effectiveness of the model
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0307
Abstract:
In view of the increasing trend of renewable energy generation and flexible resources in the power grid, a robust optimization method for day-ahead dispatching considering the flexible resources of both source and load sides is proposed to cope with the challenges brought by the uncertainty of renewable energy generation to the dispatching operation of the power grid. Firstly, according to the response characteristics of reducible loads and shiftable loads, the influence of compensation price on the maximum reduction capacity of the former and the acceptable translation period of the latter is analyzed, and the flexible resources model of load side is established. Secondly, considering the uncertainty of wind power output, based on the model of deep peak regulation and flexible resources on load side, a robust optimization model for day-ahead dispatching is established, in which the compensation price and adjustment quantity of flexible loads are jointly optimized. Finally, the effectiveness of the proposed model and method is verified by an example analysis. The results show that the dispatching method considering the schedulable potential of flexible resources of both source and load sides can effectively improve the robustness and economy of power grid operation.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0039
Abstract:
To solve the problem of increase of isolation transformer losses led by a large number of high harmonics existing in the AC side voltage and current of quasi two-level modulation strategy in modular multilevel DC/DC converter (abbr. MMDC), firstly, the relationship between the switching angle of the power device and the total harmonic distortion (abbr. THD) of the primary side line voltage of the transformer was obtained by analysis. Secondly, a THD optimization modulation strategy with variable duty cycle and amplitude was proposed. The THD of the primary side line voltage and current of the transformer were reduced by changing the switch angle to reduce the transformer losses. Finally, the feasibility of the proposed THD-based the optimal modulation control strategy was verified by simulation software PSCAD/EMTDC.
To solve the problem of increase of isolation transformer losses led by a large number of high harmonics existing in the AC side voltage and current of quasi two-level modulation strategy in modular multilevel DC/DC converter (abbr. MMDC), firstly, the relationship between the switching angle of the power device and the total harmonic distortion (abbr. THD) of the primary side line voltage of the transformer was obtained by analysis. Secondly, a THD optimization modulation strategy with variable duty cycle and amplitude was proposed. The THD of the primary side line voltage and current of the transformer were reduced by changing the switch angle to reduce the transformer losses. Finally, the feasibility of the proposed THD-based the optimal modulation control strategy was verified by simulation software PSCAD/EMTDC.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0468
Abstract:
Currently, due to the increasing data amount in the Internet of Things, it is challenging to meet the the requirements for quality of business data services and achieve high bandwidth utilization rate. To address this problem, a multi-priority queuing theory bandwidth allocation method is proposed. First, the business data transmission process from perception terminal to edge IoT was improved. The improved transmission process enables prioritization of data transmission according to the different requirements of various data, and facilitates the implementation of different service mechanisms for different priority levels. Then, the Markov process was analyzed during business data transmission. Based on the improved data transmission process, a multi-priority bandwidth allocation model with the packet loss rate and the delay time taken as constrains was developed to optimize bandwidth utilization . The multi-priority queuing theory bandwidth allocation method is compared with traditional methods, resulting in an improved QoS indicator and a 9.73% higher bandwidth utilization rate compared to traditional bandwidth allocation methods. Additionally, it achieved a 31.17% higher utilization rate than the elastic coefficient method. Finally, the dynamic performance of the multi-priority queuing theory bandwidth allocation method was also explored. The results indicate that the proper improvement of the bandwidth can improve the QoS indicator; however, it should be noted that increasing the bandwidth may lead to a reduction in its utilization rate. Therefore, reasonable bandwidth allocation can avoid waste of resources effectively.
Currently, due to the increasing data amount in the Internet of Things, it is challenging to meet the the requirements for quality of business data services and achieve high bandwidth utilization rate. To address this problem, a multi-priority queuing theory bandwidth allocation method is proposed. First, the business data transmission process from perception terminal to edge IoT was improved. The improved transmission process enables prioritization of data transmission according to the different requirements of various data, and facilitates the implementation of different service mechanisms for different priority levels. Then, the Markov process was analyzed during business data transmission. Based on the improved data transmission process, a multi-priority bandwidth allocation model with the packet loss rate and the delay time taken as constrains was developed to optimize bandwidth utilization . The multi-priority queuing theory bandwidth allocation method is compared with traditional methods, resulting in an improved QoS indicator and a 9.73% higher bandwidth utilization rate compared to traditional bandwidth allocation methods. Additionally, it achieved a 31.17% higher utilization rate than the elastic coefficient method. Finally, the dynamic performance of the multi-priority queuing theory bandwidth allocation method was also explored. The results indicate that the proper improvement of the bandwidth can improve the QoS indicator; however, it should be noted that increasing the bandwidth may lead to a reduction in its utilization rate. Therefore, reasonable bandwidth allocation can avoid waste of resources effectively.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0022
Abstract:
A reasonable islanding operation strategy is of great significance for power restoration as well as system resilience enhancement when major failures occur in the distribution network. Therefore, the problem of utilizing traditional contact lines and emerging distributed generation (abbr. DG) to restore integrated power to outage areas in the distribution network was profoundly studied. Firstly, the integrated power restoration problem was uniformly simplified as a generalized DG-based islanding problem by equating the contact lines to virtual DGs. Secondly, to adequately quantify the risk of islanding operation during failure hours, the islanding operation risk indicators were defined from the perspectives of power balance and voltage stability, and a dynamic constraint method for islanding operation risk was proposed based on the opportunity constrained optimization model. Subsequently, a generalized dynamic islanding strategy for distribution networks considering operational risks was proposed. This strategy can dynamically adjust the island range based on risk factors such as DG output, load timing changes, and node voltage levels, maximizing the utilization of DG resources during fault periods while ensuring the stability and sustainability of island operation. In allusion to the problem of high difficulty in solving the dynamic islanding strategy, the improved branch-and-bound method in combination with the heuristic algorithm was used to solve and dynamically modify the islanding strategy to ensure the rationality and timeliness of recovery decisions under emergencies. Finally, simulation experiments and analysis were carried out based on a 40 node distribution network example to verify the effectiveness of the proposed method.
A reasonable islanding operation strategy is of great significance for power restoration as well as system resilience enhancement when major failures occur in the distribution network. Therefore, the problem of utilizing traditional contact lines and emerging distributed generation (abbr. DG) to restore integrated power to outage areas in the distribution network was profoundly studied. Firstly, the integrated power restoration problem was uniformly simplified as a generalized DG-based islanding problem by equating the contact lines to virtual DGs. Secondly, to adequately quantify the risk of islanding operation during failure hours, the islanding operation risk indicators were defined from the perspectives of power balance and voltage stability, and a dynamic constraint method for islanding operation risk was proposed based on the opportunity constrained optimization model. Subsequently, a generalized dynamic islanding strategy for distribution networks considering operational risks was proposed. This strategy can dynamically adjust the island range based on risk factors such as DG output, load timing changes, and node voltage levels, maximizing the utilization of DG resources during fault periods while ensuring the stability and sustainability of island operation. In allusion to the problem of high difficulty in solving the dynamic islanding strategy, the improved branch-and-bound method in combination with the heuristic algorithm was used to solve and dynamically modify the islanding strategy to ensure the rationality and timeliness of recovery decisions under emergencies. Finally, simulation experiments and analysis were carried out based on a 40 node distribution network example to verify the effectiveness of the proposed method.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0032
Abstract:
To promote the low carbon development of the power system and fully consider the coordinated low carbon operation of the source and load sides, a virtual power plant of wind farm, carbon capture and battery charging and swapping station (abbr. BCSS), which is integrated by wind farm, carbon capture and BCSS, was proposed. Firstly, according to the relationship between the capture and energy consumption of the carbon capture system, a virtual power plant of wind power, carbon capture and BCSS was established, the charging and discharging arrangement of the BCSS was formulated. And based on this arrangement, the low carbon performance of the virtual power plant of wind power, carbon capture and BCSS is analyzed. Secondly, the virtual power plant system model containing wind power, carbon capture and BCSS was established, and taking the lowest comprehensive cost of the system as the objective function, Low carbon economy scheduling was performed. Finally, the improved IEEE-30 node system was used for example simulation, it was verified that the proposed model can reduce the system's carbon emissions while achieving full grid wind power consumption and promoting low-carbon operation of the system.
To promote the low carbon development of the power system and fully consider the coordinated low carbon operation of the source and load sides, a virtual power plant of wind farm, carbon capture and battery charging and swapping station (abbr. BCSS), which is integrated by wind farm, carbon capture and BCSS, was proposed. Firstly, according to the relationship between the capture and energy consumption of the carbon capture system, a virtual power plant of wind power, carbon capture and BCSS was established, the charging and discharging arrangement of the BCSS was formulated. And based on this arrangement, the low carbon performance of the virtual power plant of wind power, carbon capture and BCSS is analyzed. Secondly, the virtual power plant system model containing wind power, carbon capture and BCSS was established, and taking the lowest comprehensive cost of the system as the objective function, Low carbon economy scheduling was performed. Finally, the improved IEEE-30 node system was used for example simulation, it was verified that the proposed model can reduce the system's carbon emissions while achieving full grid wind power consumption and promoting low-carbon operation of the system.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0129
Abstract:
With the improvements of energy coupling and carbon market mechanism, the traditional power demand response no longer meets the current development status of the integrated energy system (IES). In order to improve the integrated demand response capability of IES, a bi-level model of IES-user considering carbon market and electric-hydrogen energy storage is established. The upper-level is IES model that considers the investment cost of electricity-hydrogen energy storage, carbon cost, and multi-energy coupling, and the lower-level is users model that includes the transferable and reduced loads. Then, the operator is introduced as the park manager. A framework of stackelberg game between operators and users is constructed, and the objective function is to maximize the operator's profit and minimize the user's cost. Finally, the case study analyzes the impact of electricity-hydrogen energy storage on the demand response effect under the carbon market, and the impact of the carbon market and electricity-hydrogen energy storage on IES carbon emissions. And the electric-hydrogen energy storage capacity under different conditions is configured. The results verify the effectiveness of the interactive method.
With the improvements of energy coupling and carbon market mechanism, the traditional power demand response no longer meets the current development status of the integrated energy system (IES). In order to improve the integrated demand response capability of IES, a bi-level model of IES-user considering carbon market and electric-hydrogen energy storage is established. The upper-level is IES model that considers the investment cost of electricity-hydrogen energy storage, carbon cost, and multi-energy coupling, and the lower-level is users model that includes the transferable and reduced loads. Then, the operator is introduced as the park manager. A framework of stackelberg game between operators and users is constructed, and the objective function is to maximize the operator's profit and minimize the user's cost. Finally, the case study analyzes the impact of electricity-hydrogen energy storage on the demand response effect under the carbon market, and the impact of the carbon market and electricity-hydrogen energy storage on IES carbon emissions. And the electric-hydrogen energy storage capacity under different conditions is configured. The results verify the effectiveness of the interactive method.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0263
Abstract:
To enhance the inertia of the distributed energy storage system in the DC microgrid, and achieve the state of charge (abbr. SOC) balance of each energy storage unit(abbr. ESU) in the case of line impedance mismatch, a dynamic energy allocation strategy of distributed energy storage system based on virtual DC machine（abbr. VDCM）was proposed. Firstly, the VDCM technology was applied to the energy storage side control using the output characteristics of VDC to enhance the anti-interference capability of the system, then, using the output characteristics of the armature loop equation to dynamically design the virtual armature resistance according to the energy distribution demand of the energy storage system, so that it can adaptively change with the SOC within the specified limits, and the output power of each converter in real-time, and the voltage drop adjustment factor was introduced to dynamically fine-tune the virtual voltage drop of each converter to be equal, so as to compensate for the mismatch degree of the line impedance and realize the SOC equalization of each ESU. Secondly, a consensus algorithm was used to obtain the required average value information between adjacent ESUs, which improves the scalability of the system. Finally, a photovoltaic multi-energy storage DC microgrid system model was built, and the simulation and experimental results verify the rationality and effectiveness of the proposed control strategy.
To enhance the inertia of the distributed energy storage system in the DC microgrid, and achieve the state of charge (abbr. SOC) balance of each energy storage unit(abbr. ESU) in the case of line impedance mismatch, a dynamic energy allocation strategy of distributed energy storage system based on virtual DC machine（abbr. VDCM）was proposed. Firstly, the VDCM technology was applied to the energy storage side control using the output characteristics of VDC to enhance the anti-interference capability of the system, then, using the output characteristics of the armature loop equation to dynamically design the virtual armature resistance according to the energy distribution demand of the energy storage system, so that it can adaptively change with the SOC within the specified limits, and the output power of each converter in real-time, and the voltage drop adjustment factor was introduced to dynamically fine-tune the virtual voltage drop of each converter to be equal, so as to compensate for the mismatch degree of the line impedance and realize the SOC equalization of each ESU. Secondly, a consensus algorithm was used to obtain the required average value information between adjacent ESUs, which improves the scalability of the system. Finally, a photovoltaic multi-energy storage DC microgrid system model was built, and the simulation and experimental results verify the rationality and effectiveness of the proposed control strategy.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0018
Abstract:
The randomness and volatility of wind power output after its high proportion is connected to the power grid increases the difficulty of decision-making for safe dispatching operation in the power grid. In order to promote wind power consumption and reduce the impact of wind power uncertainty on the stable operation of power grid, this paper proposes a robust optimal scheduling strategy based on flexible resources and wind power cooperative interaction feasible region. Firstly, the energy storage and electric vehicles are used as the adjustable flexible resource set to build the feasible region of the wind power fluctuation range and the energy storage and electric vehicle power regulation range, and provide guidance for the control strategy according to the current operating state of the system; Secondly, based on the feasible region, a two-stage robust optimization model considering the optimal wind power consumption capacity and scheduling cost is proposed, which fully exploits the regulatory capacity of flexible resources and optimizes the cooperative scheduling strategy of flexible resources and wind power to deal with the uncertain factors in system scheduling; Finally, according to the feasible region shape index, the scheduling strategy is modified to further improve the wind power resource absorptive capacity. The effectiveness of the proposed model and method is verified by an example of IEEE 39 bus system.
The randomness and volatility of wind power output after its high proportion is connected to the power grid increases the difficulty of decision-making for safe dispatching operation in the power grid. In order to promote wind power consumption and reduce the impact of wind power uncertainty on the stable operation of power grid, this paper proposes a robust optimal scheduling strategy based on flexible resources and wind power cooperative interaction feasible region. Firstly, the energy storage and electric vehicles are used as the adjustable flexible resource set to build the feasible region of the wind power fluctuation range and the energy storage and electric vehicle power regulation range, and provide guidance for the control strategy according to the current operating state of the system; Secondly, based on the feasible region, a two-stage robust optimization model considering the optimal wind power consumption capacity and scheduling cost is proposed, which fully exploits the regulatory capacity of flexible resources and optimizes the cooperative scheduling strategy of flexible resources and wind power to deal with the uncertain factors in system scheduling; Finally, according to the feasible region shape index, the scheduling strategy is modified to further improve the wind power resource absorptive capacity. The effectiveness of the proposed model and method is verified by an example of IEEE 39 bus system.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0314
Abstract:
With the promotion of the "coal to electricity" project, the rural weak distribution networks may face the problems such as line congestion due to the increase in heating loads, the long payback period of power grid investment as well as low utilization rate of power grid assets during the non-heating season in the traditional line expansion scheme. It is an effective means to solve these problems that establish a microgrid that has the ability of friendly interaction and demand response based on the local renewable energy resource. For that reason, a coordinated optimization method for microgrids and distribution networks based on a multi-agent game and reinforcement learning was proposed for a practical microgrid project in Hebei Province. Each microgrid reports the optimal bidding strategy based on the intra-day rolling dispatching plan, considering the impact of the auxiliary service strategy on the future operation economy in the finite time domain; Taking minimizing the operation cost as an object, the distribution network determines the clearing scheme of the auxiliary service market based on the power demand of the auxiliary service and the quotation of each microgrid. Finally, The win or learn fast - policy Hill-Clipping(abbr. WoLF-PHC) algorithm was used to realize the fast solution of multi-agent game problems, and the effectiveness of the proposed method was verified based on the actual case of the micro-electric network group demonstration project.
With the promotion of the "coal to electricity" project, the rural weak distribution networks may face the problems such as line congestion due to the increase in heating loads, the long payback period of power grid investment as well as low utilization rate of power grid assets during the non-heating season in the traditional line expansion scheme. It is an effective means to solve these problems that establish a microgrid that has the ability of friendly interaction and demand response based on the local renewable energy resource. For that reason, a coordinated optimization method for microgrids and distribution networks based on a multi-agent game and reinforcement learning was proposed for a practical microgrid project in Hebei Province. Each microgrid reports the optimal bidding strategy based on the intra-day rolling dispatching plan, considering the impact of the auxiliary service strategy on the future operation economy in the finite time domain; Taking minimizing the operation cost as an object, the distribution network determines the clearing scheme of the auxiliary service market based on the power demand of the auxiliary service and the quotation of each microgrid. Finally, The win or learn fast - policy Hill-Clipping(abbr. WoLF-PHC) algorithm was used to realize the fast solution of multi-agent game problems, and the effectiveness of the proposed method was verified based on the actual case of the micro-electric network group demonstration project.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0053
Abstract:
The wide access of new energy, represented by distributed photovoltaic, has brought about new demands such as real-time business responsiveness intelligent analysis and processing within the substations. The State Grid Corporation currently lacks a solution to address the issue of local autonomy of distributed new energy consumption. The key to optimizing new energy consumption lies in accurate prediction of photovoltaic power generation and rapid development of edge devices for Internet of Things. Based on the power grid dispatching Internet of Things, in this paper we propose a local autonomy solution for new energy consumption combined with the energy router of the substation. An energy controller is designed for distributed new energy consumption, and the energy produced by photovoltaic power generation is predicted based on hierarchical fuzzy neural network algorithm. The research results demonstrate that the energy controller developed in this paper, along with the hierarchical fuzzy neural network model, exhibit certain effects and advantages in enhancing the autonomy at edge-side stations.
The wide access of new energy, represented by distributed photovoltaic, has brought about new demands such as real-time business responsiveness intelligent analysis and processing within the substations. The State Grid Corporation currently lacks a solution to address the issue of local autonomy of distributed new energy consumption. The key to optimizing new energy consumption lies in accurate prediction of photovoltaic power generation and rapid development of edge devices for Internet of Things. Based on the power grid dispatching Internet of Things, in this paper we propose a local autonomy solution for new energy consumption combined with the energy router of the substation. An energy controller is designed for distributed new energy consumption, and the energy produced by photovoltaic power generation is predicted based on hierarchical fuzzy neural network algorithm. The research results demonstrate that the energy controller developed in this paper, along with the hierarchical fuzzy neural network model, exhibit certain effects and advantages in enhancing the autonomy at edge-side stations.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0287
Abstract:
To address the issue of identifying the causes of abnormal line loss rates in low-voltage transformer district, a new line loss rate analysis method based on statistical and technical deviations was proposed. Firstly, combined with MATLAB numerical simulation, the ideal line loss rate was introduced by analyzing the statistical line loss rate and theoretical line loss calculation model. On this basis, the relationship among statistical line loss rate, theoretical line loss rate and ideal line loss rate was compared and analyzed to obtain the indexes of statistical and technical deviations. Then by comparing the deviation index with the threshold, the cause of abnormal line loss rate can be determined and corresponding loss reduction measures can be formulated. Finally, engineering practice has confirmed the effectiveness of this scheme in identifying the causes of abnormal line loss rates. After implementing appropriate technical loss reduction measures, the technical deviation index of the transformer district decreased from 1.2 to 1.0, and the line loss rate decreased from 3.1% to 2.5% under the same power supply.
To address the issue of identifying the causes of abnormal line loss rates in low-voltage transformer district, a new line loss rate analysis method based on statistical and technical deviations was proposed. Firstly, combined with MATLAB numerical simulation, the ideal line loss rate was introduced by analyzing the statistical line loss rate and theoretical line loss calculation model. On this basis, the relationship among statistical line loss rate, theoretical line loss rate and ideal line loss rate was compared and analyzed to obtain the indexes of statistical and technical deviations. Then by comparing the deviation index with the threshold, the cause of abnormal line loss rate can be determined and corresponding loss reduction measures can be formulated. Finally, engineering practice has confirmed the effectiveness of this scheme in identifying the causes of abnormal line loss rates. After implementing appropriate technical loss reduction measures, the technical deviation index of the transformer district decreased from 1.2 to 1.0, and the line loss rate decreased from 3.1% to 2.5% under the same power supply.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0278
Abstract:
To realize the accurate fault diagnosis of on-load tap changer (OLTC) under compound faults, a fault diagnosis method for transformer OLTC based on multi-scale feature extraction and IAO-LSTM was proposed. Firstly, features of the time domain scale, frequency domain scale and energy entropy scale were extracted from OLTC vibration signals to form feature vectors. By incorporating the mixing initialization strategy and elite solution retention strategy, the aquila optimizer (AO) was improved to enhance the convergence. The improved aquila optimizer (IAO) was used to optimize the number of hidden layer nodes and learning rate of LSTM, and thus an optimal LSTM model was obtained. Taking the fusion eigenvector of the single fault and compound fault as the input and the fault state as the output, the optimal model was trained. After that, the fault diagnosis was carried out. The results indicate that the method yields an average accuracy of 97.2% and is appropriate for OLTC fault diagnosis.
To realize the accurate fault diagnosis of on-load tap changer (OLTC) under compound faults, a fault diagnosis method for transformer OLTC based on multi-scale feature extraction and IAO-LSTM was proposed. Firstly, features of the time domain scale, frequency domain scale and energy entropy scale were extracted from OLTC vibration signals to form feature vectors. By incorporating the mixing initialization strategy and elite solution retention strategy, the aquila optimizer (AO) was improved to enhance the convergence. The improved aquila optimizer (IAO) was used to optimize the number of hidden layer nodes and learning rate of LSTM, and thus an optimal LSTM model was obtained. Taking the fusion eigenvector of the single fault and compound fault as the input and the fault state as the output, the optimal model was trained. After that, the fault diagnosis was carried out. The results indicate that the method yields an average accuracy of 97.2% and is appropriate for OLTC fault diagnosis.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0266
Abstract:
The fault current level of a DC grid determines the cost of current limiting devices and the lifetime of converter components. The fault current can be effectively reduced by optimizing the DC grid topology. However, the existing fault current level evaluation index of DC grid topology is inaccurate and fails to represent the comprehensive fault current level under different operation modes. In view of this, an optimal design method for DC grid topology is proposed to restrain fault currents under different operation modes. First, the calculation method for short-circuit fault current in DC grids is presented. Then, a general representation of the grid topology is established based on graph theory, upon which the evaluation indices for fault current levels under normal operation mode and N-1 operation modes are proposed. Finally, an optimization method for DC grid topology is proposed considering the fault current levels under different operation modes. The effectiveness of our method is validated through the simulation using a six-terminal DC grid in PSCAD/EMTDC.
The fault current level of a DC grid determines the cost of current limiting devices and the lifetime of converter components. The fault current can be effectively reduced by optimizing the DC grid topology. However, the existing fault current level evaluation index of DC grid topology is inaccurate and fails to represent the comprehensive fault current level under different operation modes. In view of this, an optimal design method for DC grid topology is proposed to restrain fault currents under different operation modes. First, the calculation method for short-circuit fault current in DC grids is presented. Then, a general representation of the grid topology is established based on graph theory, upon which the evaluation indices for fault current levels under normal operation mode and N-1 operation modes are proposed. Finally, an optimization method for DC grid topology is proposed considering the fault current levels under different operation modes. The effectiveness of our method is validated through the simulation using a six-terminal DC grid in PSCAD/EMTDC.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0283
Abstract:
The probabilistic analysis on the output prediction error of renewable generations is of great significance to reserve energy decision making of the power system. Regular probabilistic analysis of renewable generations is usually based on specific analytical forms. It is not only inflexible but also prone to errors. To compensate for the influence of randomness, centralized renewable generations are typically equipped with sufficient energy storage, which may have impacts on their economic benefits. In view of this, from the perspective of stochastic process, according to a unique stochastic differential equation, that is, Vasicek model, we put forward a method for estimating the parameters of the renewable generation output model. This method can avoid analytical expression of the probability distributions. Later, an improved configuration method for the capacity of energy storage system is put forward. Case studies indicate that our method has good performance in terms of short term prediction, and the energy storage allocation decision made based on our method is more precise and economic.
The probabilistic analysis on the output prediction error of renewable generations is of great significance to reserve energy decision making of the power system. Regular probabilistic analysis of renewable generations is usually based on specific analytical forms. It is not only inflexible but also prone to errors. To compensate for the influence of randomness, centralized renewable generations are typically equipped with sufficient energy storage, which may have impacts on their economic benefits. In view of this, from the perspective of stochastic process, according to a unique stochastic differential equation, that is, Vasicek model, we put forward a method for estimating the parameters of the renewable generation output model. This method can avoid analytical expression of the probability distributions. Later, an improved configuration method for the capacity of energy storage system is put forward. Case studies indicate that our method has good performance in terms of short term prediction, and the energy storage allocation decision made based on our method is more precise and economic.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0352
Abstract:
Operating condition changes can cause system power frequency fluctuations when the wind/photovoltaics/storage system using virtual synchronous generator control technology is connected to the grid for operation. In allusion to this problem, an adaptive inertia-damping power frequency control strategy based on a virtual synchronous generator(abbr. VSG) was established. Firstly, the VSG mathematical model and Small signal model were established to analyze the impact of the model’s virtual parameter changes on system stability. Secondly, a segmented-linear adjustment function was designed and parameter selection principles were given based on the principle of inertia damping dynamic characteristics analyzed by the power angle characteristic curve when the system is disturbed. Finally, the simulation model was built on the Matlab/Simulink platform. The results show that the power and frequency response of wind/photovoltaics/storage grid-connected system are improved by using VSG adaptive control strategy in the case of operating condition changes, and this control strategy has better transient performance compared to traditional VSG control technology.
Operating condition changes can cause system power frequency fluctuations when the wind/photovoltaics/storage system using virtual synchronous generator control technology is connected to the grid for operation. In allusion to this problem, an adaptive inertia-damping power frequency control strategy based on a virtual synchronous generator(abbr. VSG) was established. Firstly, the VSG mathematical model and Small signal model were established to analyze the impact of the model’s virtual parameter changes on system stability. Secondly, a segmented-linear adjustment function was designed and parameter selection principles were given based on the principle of inertia damping dynamic characteristics analyzed by the power angle characteristic curve when the system is disturbed. Finally, the simulation model was built on the Matlab/Simulink platform. The results show that the power and frequency response of wind/photovoltaics/storage grid-connected system are improved by using VSG adaptive control strategy in the case of operating condition changes, and this control strategy has better transient performance compared to traditional VSG control technology.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0246
Abstract:
The large-scale development of renewable energy such as wind power and so on brings new challenges such as the difficulty of accommodation, etc., through the renovation of thermal power flexibility the flexible adjustment capability of power grid can be enhanced and the accommodation space of renewable energy can be elevated, meanwhile it needs to consider the economy of thermal power flexibility renovation and its influence on the accommodation of renewable energy. Therefore comprehensively considering conventional operating cost of thermal power units and all the increased costs after the flexibility renovation and combining with the carbon emission cost and wind curtailment cost, an optimal dispatching model before and after the renovation of thermal power flexibility, in which the minimized total cost was taken as the object, was established, and by use of comparison difference method the cost of the renewable energy accommodation system based on the renovation of thermal power flexibility was calculated. In the computing example, the established model was applied to such unit operating conditions as before and after the flexibility renovation, under different carbon emission intensities, whether considering the unit operating conditions under different schemes such as wind curtailment and so on and the cost size of accommodation system, and the relation curve of the renewable energy permeability with the cost of accommodation system was obtained by fitting, thus the effectiveness of the established model is verified.
The large-scale development of renewable energy such as wind power and so on brings new challenges such as the difficulty of accommodation, etc., through the renovation of thermal power flexibility the flexible adjustment capability of power grid can be enhanced and the accommodation space of renewable energy can be elevated, meanwhile it needs to consider the economy of thermal power flexibility renovation and its influence on the accommodation of renewable energy. Therefore comprehensively considering conventional operating cost of thermal power units and all the increased costs after the flexibility renovation and combining with the carbon emission cost and wind curtailment cost, an optimal dispatching model before and after the renovation of thermal power flexibility, in which the minimized total cost was taken as the object, was established, and by use of comparison difference method the cost of the renewable energy accommodation system based on the renovation of thermal power flexibility was calculated. In the computing example, the established model was applied to such unit operating conditions as before and after the flexibility renovation, under different carbon emission intensities, whether considering the unit operating conditions under different schemes such as wind curtailment and so on and the cost size of accommodation system, and the relation curve of the renewable energy permeability with the cost of accommodation system was obtained by fitting, thus the effectiveness of the established model is verified.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0372
Abstract:
In allusion to the two-way congestion problem of the distribution network caused by the temporal and spatial accumulation of electricity load and the peak of renewable energy output, in view of the response potential of thermostatically controlled loads under the market environment and the spatiotemporal flexibility of mobile energy storage system technology, a day-ahead and intra-day two-stage congestion management method for active distribution networks, taking into account market mechanism and mobile energy storage, was proposed. Firstly, the hierarchical structure and trading mechanism of the power market considering thermostatically controlled load response characteristics were constructed, so as to realize day-ahead management of distribution network congestion by adjusting the power consumption of thermostatically controlled load. Secondly, considering the uncertainty of load response and the prediction error of day-ahead renewable energy output, the mobile energy storage system which could adapt to the spatiotemporal change of distribution network congestion was introduced to build a day-ahead and intra-day congestion management framework based on market mechanism and mobile energy storage, ensuring that system congestion was eliminated, taking into account economy simultaneously. The simulation results show that the proposed method can effectively solve the congestion problem of active distribution networks and reduce the congestion management cost of distribution system operators..
In allusion to the two-way congestion problem of the distribution network caused by the temporal and spatial accumulation of electricity load and the peak of renewable energy output, in view of the response potential of thermostatically controlled loads under the market environment and the spatiotemporal flexibility of mobile energy storage system technology, a day-ahead and intra-day two-stage congestion management method for active distribution networks, taking into account market mechanism and mobile energy storage, was proposed. Firstly, the hierarchical structure and trading mechanism of the power market considering thermostatically controlled load response characteristics were constructed, so as to realize day-ahead management of distribution network congestion by adjusting the power consumption of thermostatically controlled load. Secondly, considering the uncertainty of load response and the prediction error of day-ahead renewable energy output, the mobile energy storage system which could adapt to the spatiotemporal change of distribution network congestion was introduced to build a day-ahead and intra-day congestion management framework based on market mechanism and mobile energy storage, ensuring that system congestion was eliminated, taking into account economy simultaneously. The simulation results show that the proposed method can effectively solve the congestion problem of active distribution networks and reduce the congestion management cost of distribution system operators..
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0258
Abstract:
The growing penetration of new energy sources in the power system has significantly increases the uncertainties in the grid, asking for higher requirements for the planning, operation and control of the distribution network. The distribution network planning is an important cornerstone for the safe and stable operation of the power system. The traditional distribution network planning, in which all parameters are determined in advance, lacks adaptability to uncertainties. In view of this, we proposed a method for distribution network planning based on probabilistic power flow analysis. The source-load output model was firstly established according to the quantitative modeling of uncertainties in the distribution network by using our method. Secondly, we utilized the rank correlation coefficient matrix to characterize the correlation between wind speed, light intensity and load, and developed a semi-invariant probabilistic power flow calculation method with correlation taken into account. Finally, with the objective function of reducing the comprehensive cost, we constructed a distribution network planning model with the constraints of feeder capacity, node voltage, tidal balance, and radial structure of the grid. And the particle swarm algorithm was improved by optimization of inertia parameters and incorporation of variational operations. The improved algorithm was employed to solve the planning model. Simulations were conducted taking a 33-node system as an instance, and the results confirms the effectiveness of our method in reducing network loss and network planning costs.
The growing penetration of new energy sources in the power system has significantly increases the uncertainties in the grid, asking for higher requirements for the planning, operation and control of the distribution network. The distribution network planning is an important cornerstone for the safe and stable operation of the power system. The traditional distribution network planning, in which all parameters are determined in advance, lacks adaptability to uncertainties. In view of this, we proposed a method for distribution network planning based on probabilistic power flow analysis. The source-load output model was firstly established according to the quantitative modeling of uncertainties in the distribution network by using our method. Secondly, we utilized the rank correlation coefficient matrix to characterize the correlation between wind speed, light intensity and load, and developed a semi-invariant probabilistic power flow calculation method with correlation taken into account. Finally, with the objective function of reducing the comprehensive cost, we constructed a distribution network planning model with the constraints of feeder capacity, node voltage, tidal balance, and radial structure of the grid. And the particle swarm algorithm was improved by optimization of inertia parameters and incorporation of variational operations. The improved algorithm was employed to solve the planning model. Simulations were conducted taking a 33-node system as an instance, and the results confirms the effectiveness of our method in reducing network loss and network planning costs.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2023.0095
Abstract:
With the continuous opening of China's multi-energy market, the integrated energy microgrids and their communities established by different market entities appear on the energy consumption side. Affected by multiple factors such as multi-energy coupling, complex game relationships, and source and load uncertainty, the operation mechanism of this type of integrated energy microgrid cluster is complex, and the collaborative optimization operation is difficult. Based on this, firstly, a social welfare function considering economic costs and environmental costs was established. Secondly, in allusion to the uncertainty of renewable energy output and electricity and heat load, an integrated energy microgrid trading model based on conditional value-at-risk was established to realize the risk measurement of source and load uncertainty in microgrids. Thirdly, based on Nash negotiation theory, a cooperative operation model of integrated energy microgrid clusters was established, which was then decomposed into the social welfare maximization sub-problem and cooperative benefit distribution sub-problem, and solved by alternating direction multiplier method. Finally, the effectiveness of the proposed method was verified by example analysis.
With the continuous opening of China's multi-energy market, the integrated energy microgrids and their communities established by different market entities appear on the energy consumption side. Affected by multiple factors such as multi-energy coupling, complex game relationships, and source and load uncertainty, the operation mechanism of this type of integrated energy microgrid cluster is complex, and the collaborative optimization operation is difficult. Based on this, firstly, a social welfare function considering economic costs and environmental costs was established. Secondly, in allusion to the uncertainty of renewable energy output and electricity and heat load, an integrated energy microgrid trading model based on conditional value-at-risk was established to realize the risk measurement of source and load uncertainty in microgrids. Thirdly, based on Nash negotiation theory, a cooperative operation model of integrated energy microgrid clusters was established, which was then decomposed into the social welfare maximization sub-problem and cooperative benefit distribution sub-problem, and solved by alternating direction multiplier method. Finally, the effectiveness of the proposed method was verified by example analysis.
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.1007-2322.2022.0281
Abstract:
Sensing the flexible supply capacity of thermal power units actively is of great significance in promoting new energy consumption and enabling flexible dispatching of power grid. In this paper, a comprehensive evaluation method for thermal power unit flexibility based on data-drive was proposed. Firstly, flexibility, responsiveness, economy and environmental protection were selected as evaluation indexes , and the evaluation factors of each index were defined. Secondly, a significant volume of operating data were utilized to obtain the characterization values of each evaluation factor within the daily operation interval of the unit through fuzzy c -means clustering algorithm and polynomial fitting. Meanwhile, the quantization of each evaluation factor was achieved by a weighted integral mean method. Finally, the entropy weight method and subjective assignment method were combined to determine the weight of each evaluation index, thereby realizing comprehensive evaluation of thermal power unit flexibility. An instance was taken to verify the effectiveness of the proposed method.
Sensing the flexible supply capacity of thermal power units actively is of great significance in promoting new energy consumption and enabling flexible dispatching of power grid. In this paper, a comprehensive evaluation method for thermal power unit flexibility based on data-drive was proposed. Firstly, flexibility, responsiveness, economy and environmental protection were selected as evaluation indexes , and the evaluation factors of each index were defined. Secondly, a significant volume of operating data were utilized to obtain the characterization values of each evaluation factor within the daily operation interval of the unit through fuzzy c -means clustering algorithm and polynomial fitting. Meanwhile, the quantization of each evaluation factor was achieved by a weighted integral mean method. Finally, the entropy weight method and subjective assignment method were combined to determine the weight of each evaluation index, thereby realizing comprehensive evaluation of thermal power unit flexibility. An instance was taken to verify the effectiveness of the proposed method.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0253
Abstract:
In recent years, as China's energy transition has deepened and photovoltaics (PV)power generation technology has gradually matured while the cost of power generation has decreased, the proportion of distributed PV in the distribution network is increasing. Consequently, the power quality issues in the distribution network are becoming increasingly significant, such as voltage beyond limits, voltage unbalance, line overload, flicker and harmonic overload, etc. Among them, the main constraint on the capacity of distributed PV access is voltage exceeding limits. In this paper we firstly provide an overview of the commonly employed voltage regulation methods for for distributed PV systems with high penetration levels, followed by a detailed exposition of each method. Finally, a comparison and analysis of the advantages and disadvantages of various voltage regulation methods areconducted, and relevant suggestions are made for future voltage regulation methods of distribution networks with high-permeability distributed PV.
In recent years, as China's energy transition has deepened and photovoltaics (PV)power generation technology has gradually matured while the cost of power generation has decreased, the proportion of distributed PV in the distribution network is increasing. Consequently, the power quality issues in the distribution network are becoming increasingly significant, such as voltage beyond limits, voltage unbalance, line overload, flicker and harmonic overload, etc. Among them, the main constraint on the capacity of distributed PV access is voltage exceeding limits. In this paper we firstly provide an overview of the commonly employed voltage regulation methods for for distributed PV systems with high penetration levels, followed by a detailed exposition of each method. Finally, a comparison and analysis of the advantages and disadvantages of various voltage regulation methods areconducted, and relevant suggestions are made for future voltage regulation methods of distribution networks with high-permeability distributed PV.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0367
Abstract:
To have power generation and consumption costs of high-proportion new energy power systems calculated, based on the traditional wind power levelized cost of energy(abbr. LCOE) model, firstly, a wind power system-levelized cost of energy model(abbr. S-LCOE) was constructed considering the auxiliary service costs caused by wind power integration and the low-carbon benefits of wind power, and a change relationship model of wind power installed capacity was established considering the permeability. Furthermore, the output sequence of wind power was used as the link to connect the two models. The minimum wind power installed capacity which meets the permeability requirement was set as the input parameter to quantify the cost of wind power generation in order to realize the power cost calculation of wind power systems under different permeability scenarios. Finally, by numerical analysis, the changes in the power consumption cost of wind power generation under four typical permeability scenarios were studied, and the curves of the S-LCOE of wind power considering different permeability were plotted. The results show that the change in wind power penetration has the greatest impact on peak shaving cost. The proposed model can be effectively used to calculate the cost of wind power generation under different permeability scenarios, and provides support for the reasonable planning and cost control of regional wind power integration.
To have power generation and consumption costs of high-proportion new energy power systems calculated, based on the traditional wind power levelized cost of energy(abbr. LCOE) model, firstly, a wind power system-levelized cost of energy model(abbr. S-LCOE) was constructed considering the auxiliary service costs caused by wind power integration and the low-carbon benefits of wind power, and a change relationship model of wind power installed capacity was established considering the permeability. Furthermore, the output sequence of wind power was used as the link to connect the two models. The minimum wind power installed capacity which meets the permeability requirement was set as the input parameter to quantify the cost of wind power generation in order to realize the power cost calculation of wind power systems under different permeability scenarios. Finally, by numerical analysis, the changes in the power consumption cost of wind power generation under four typical permeability scenarios were studied, and the curves of the S-LCOE of wind power considering different permeability were plotted. The results show that the change in wind power penetration has the greatest impact on peak shaving cost. The proposed model can be effectively used to calculate the cost of wind power generation under different permeability scenarios, and provides support for the reasonable planning and cost control of regional wind power integration.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0114
Abstract:
In allusion to the site selection and capacity determination of the electric vehicle (abbr. EV) charging station, a multi-agent economic benefit model for distribution network, charging station and users under multi-scenarios was proposed. Firstly, by means of comparing the operation economy and load loss cost of distribution network under normal operating environment and under extreme weather conditions, the site pre-selection of the site for EV charging station was performed. Secondly, taking the preselected scheme of the charging station and the traffic flow distribution difference on holidays and working days as the basis and overall considering the economy of the charging station and the economy at the user side, the site and the capacity of the charging station were optimized. The local optimization effect was enhanced by joint utilizing particle swarm optimization and Voronoi diagram, so the result of site selection and capacity determination of EV charging station were further optimized. Finally, based on an actual computing example of a certain region the simulation analysis was conducted. Simulation results show that the proposed planning scheme of EV charging station is feasible and effective.
In allusion to the site selection and capacity determination of the electric vehicle (abbr. EV) charging station, a multi-agent economic benefit model for distribution network, charging station and users under multi-scenarios was proposed. Firstly, by means of comparing the operation economy and load loss cost of distribution network under normal operating environment and under extreme weather conditions, the site pre-selection of the site for EV charging station was performed. Secondly, taking the preselected scheme of the charging station and the traffic flow distribution difference on holidays and working days as the basis and overall considering the economy of the charging station and the economy at the user side, the site and the capacity of the charging station were optimized. The local optimization effect was enhanced by joint utilizing particle swarm optimization and Voronoi diagram, so the result of site selection and capacity determination of EV charging station were further optimized. Finally, based on an actual computing example of a certain region the simulation analysis was conducted. Simulation results show that the proposed planning scheme of EV charging station is feasible and effective.
Accepted Manuscript
, Available online ,
doi: 10.19725/j.cnki.1007-2322.2022.0255
Abstract:
Offshore wind farms connected to the power grid via AC submarine cables may experience harmonic resonance and harmonic amplification due to distributed capacitance of cable lines, which is not commonly observed in onshore wind farms. This poses a serious threaten to the safe and reliable operation of offshore wind farms. To investigate the mechanism of harmonic resonance in offshore wind farms and the influencing factors of harmonic amplification, we first established the state space mathematical model of the transmission network and collector network of offshore wind farms. The calculation methods of harmonic amplification factor, harmonic content, and other indicators were provided. The mechanism and key influencing factors of harmonic resonance were then investigated by means of eigenvalues and participation factors. Additionally, the root locus method was utilized to investigate the impact of grid short-circuit capacity, cable parameters and the number of wind turbine generators (WTGs) on resonance modes. Finally, a model of an offshore wind power system in Jiangsu was constructed on the Matlab/Simulink simulation platform, and the analysis results have confirmed the correctness of the theoretical analysis.
Offshore wind farms connected to the power grid via AC submarine cables may experience harmonic resonance and harmonic amplification due to distributed capacitance of cable lines, which is not commonly observed in onshore wind farms. This poses a serious threaten to the safe and reliable operation of offshore wind farms. To investigate the mechanism of harmonic resonance in offshore wind farms and the influencing factors of harmonic amplification, we first established the state space mathematical model of the transmission network and collector network of offshore wind farms. The calculation methods of harmonic amplification factor, harmonic content, and other indicators were provided. The mechanism and key influencing factors of harmonic resonance were then investigated by means of eigenvalues and participation factors. Additionally, the root locus method was utilized to investigate the impact of grid short-circuit capacity, cable parameters and the number of wind turbine generators (WTGs) on resonance modes. Finally, a model of an offshore wind power system in Jiangsu was constructed on the Matlab/Simulink simulation platform, and the analysis results have confirmed the correctness of the theoretical analysis.
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.1007-2322.2022.0187
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.1007-2322.2022.0439
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.
