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New Energy Power System
Economic Analysis and Stress Test of Off-grid Photovoltaic Hydrogen Production Projects
XU Chuanbo, ZHANG Wenzuo, LI Xinying, LÜ Xiaoyan
2023, 40(1): 1-7.   doi: 10.19725/j.cnki.1007-2322.2021.0218
[Abstract](0) [FullText HTML](0) [PDF 2096KB](0)
To solve the problems in renewable energy hydrogen production project as lacking of comprehensive evaluation indicators, accurate cost calculation and so on, firstly, the systemical analysis on the cost of off-grid photovoltaic (abbr. PV) hydrogen production project (abbr. PHPP) was conducted, and the impacts of scale effects and the rates of technological progress on the costs of PHPP were discussed, and the total cost composition was analyzed in detail. Secondly, the stress test for three factors, i.e., the purchase cost of power generation assembly, the electrolytic efficiency and the rate of technological progress, was performed. Analysis result show that under the circumistance of power plant scale of 100 MW in 2020 the levelized cost of hydrogen (abbr. LOCH) was 44.96 CNY/kg, in which the fixed cost of the power plant was of the highest proportion, i.e., 30.39%. Result of the stress test shows that the rate of technical progress exerts a significant influence on LCOH, and under the benchmark scenario, assuming that the rate of technical progress of power plant is 10% and that of the electrolysis plant is 20%, so the LCOH can be reduced to 5.76 CNY/kg in 2050, i.e., lower than 9.5 CNY/kg of hydrogen production from coal gasification, thus, it is proved that the off-grid photovoltaic hydrogen production is competitive.
A Bi-level Programming of Multi Scenario Distributed Generation Considering Flexible Supply and Demand
GUO Ranlong, XING Haijun, XIE Baojiang, QIN Jian, LUO Yangfan, LOU Weiming, CHENG Haozhong
2023, 40(1): 8-17.   doi: 10.19725/j.cnki.1007-2322.2021.0231
[Abstract](0) [FullText HTML](0) [PDF 2700KB](0)
The grid-connection of high penetration renewable energy makes a higher request on the flexibility of power grid. During the planning stage of renewable energy system taking collaborative optimization of multiple flexible resources into consideration can effectively improve the system flexibility. For this reason, based on the analysis on flexible regulating ability, considering flexibility a bi-level planning model of distributed generation in distribution network was proposed. Taking economic goals and flexible goals as optimization objectives, a multi scenario coordinated optimization planning model was constructed. Considering the defect of low solution efficiency due to too large scenery scene set, on the basis of affinity propagation (abbr. AP) clustering algorithm an AP-Kmedoids-based bi-level scene reduction technology was put forward, and the reduced scene was verified. Finally, by use of the mixed solution strategy of integer adaptive particle swarm optimization (abbr. APSO) and chaos particle swarm optimization (abbr. CPSO) the simulation of the proposed bi-level programming model was implemented. Simulation results show that the proposed programming method is effective in improving economy and flexible regulation ability.
A Multi-stage Optimal Configuration Method for SPMU of Subsynchronous Oscillation Monitoring System Under High Proportion of Wind Power
YI Shanjun, XIANG Song, SU Peng, WANG Yang, SONG Zihong
2023, 40(1): 18-26.   doi: 10.19725/j.cnki.1007-2322.2021.0239
[Abstract](0) [FullText HTML](0) [PDF 2173KB](0)
With large-scale grid-connection of such new energy as wind power and so on, the subsynchronous oscillation will be even more prominent in the future, so it is urgently need to establish a new-type of subsynchronous oscillation monitoring system. For this reason, relevant research on the subsynchronous oscillation monitoring device, i.e., the subsynchronous phasor measurement unit (abbr. SPMU), has been conducted by Chinese scholars, and on this basis, how to economically and efficiently enact a configuration plan of SPMU becomes the first question in the construction of subsynchronous oscillation monitoring system. Based on the frequency-coupled impedance model of wind turbine units, a monitoring critical degree evaluation system, in which the differences among importance of evaluation indices and the probability of occurrence of oscillation under operating condition were taken into account, was proposed. Considering the situation that during the installation and configuration of SPMU there were often the multi-stage installation, a multi-stage optimal configuration model for SPMU, in which the nodal monitoring critical degree was taken into account, was constructed. Finally, the integral linear programming was utilized to solve the multi-stage optimal configuration scheme. Both accuracy and economy of the proposed indices and algorithm are verified by simulation results of ERCOT system in Texas, USA, and the modified New England 39 bus system.
A Real-time Subsynchronous Oscillation Monitoring Method Using Improved Intrinsic Time-scale Decomposition Algorithm
ZHOU Bo, SHI Peng, WEI Wei, CHEN Gang, XIAO Xianyong, YANG Hanlu
2023, 40(1): 27-34.   doi: 10.19725/j.cnki.1007-2322.2021.0304
[Abstract](0) [FullText HTML](0) [PDF 3376KB](0)
To cope with the frequently occurred subsynchronous oscillation (abbr. SSO) in large-scale grid-connected wind power system, it is necessary to develop a method to identify and detect SSO quickly and accurately. During the occurrence of SSO there are such features as time-varying characteristics and uncertainty and these bring the challenge to the realtime monitoring of SSO. In allusion to this problem, firstly, a solution based on leading in intrinsic time-scale decomposition (abbr. ITD) improved by algebraic estimation was proposed, the proposed solution did not need any priori information and its performance was not affected by the frequency constitution of the SSO. Secondly, by use of synthetic signals, simulation results of Electro-Magnetic Transient Program (abbr. EMTP) and the measured data of SSO the research of comprehensive comparison was conducted, and the research results showed that in the aspects of dynamic performance of signal check and the accuracy of parameter estimation the proposed solution achieved good results. Finally, by means of hardware-in-the-loop test the feasibility of the proposed solution is verified.
Short-term Wind Power Prediction Based on Principal Component Analysis and Spectral Clustering
MEI Rui, LÜ Zhiyong, GU Wen, YANG Hongyu, XIAO Peng
2023, 40(1): 35-41.   doi: 10.19725/j.cnki.1007-2322.2021.0269
[Abstract](0) [FullText HTML](0) [PDF 2709KB](0)
The large-scale construction of wind farms leads to evident increase of wind power penetration. To ensure secure and stable operation of power grid and the accommodation of wind power, it is necessary to predict the wind power. To cope with the defect of too high data dimension in traditional prediction method, a prediction method, in which the data dimension reduction was performed was based on principal component analysis (abbr. PCA) and spectral clustering (abbr. SC), was proposed. Firstly, on the basis of PCA the principal component of power sequence of each generating unit in the wind farm was extracted to implement the dimension reduction of power sample information and the predicted object. Secondly, considering the fluctuation characteristic of wind speed and spatial distribution characteristics of each generating unit, the spectral clustering of wind speed information was conducted to realize further dimension reduction of sample data. Finally, based on the principal component information of wind power and the result of wind speed clustering an Elmer neural network-based wind power principal component prediction model was established, and by means of the inverse transformation the power prediction result of each generating unit in the wind farm was finally obtained. By use of actual data from a certain offshore wind farm in Nantong, Jiangsu Province the established method was verified. Verification results show that using the proposed method the predicted accuracy of wind power can be improved.
Electricity Market
Design of Price Market Linkage Mechanism and Economic Benefit Evaluation of Pumped Storage Power Station Under the Power Market Environment
LIU Yang, HE Yongxiu, LI Moxing, ZHANG Yan
2023, 40(1): 42-49.   doi: 10.19725/j.cnki.1007-2322.2021.0237
[Abstract](0) [FullText HTML](0) [PDF 2343KB](0)
To cope with such problems existed in pumped storage power stations in China as the pressure of investment cost recovery, the lack of social investment willingness and the lack of connection with market development, a two-part electricity price market connection mechanism of pumped storage power station was designed, in addition, a life cycle benefit evaluation model of pumped storage under the market-oriented mode was established to calculate the profit space of pumped storage participating in the market. By means of the life cycle period simulation of pumped storage power stations by capacity electric price and electrical capacity charge, it was found that the approved capacity electricity price existed a downward trend and this trend tended to be stable, and the capacity electricity charge emerged the U-shaped changing trend. Research results show that the designed two-part electricity price market connection mechanism can make the pumped storage power stations obtaining reasonable income in the electricity market and stepwisely reducing the proportion of the approved capacity electricity price covering the power station capacity to help the pumped storage power stations smoothly converted to the identity of an independent market subject.
An Improved Power Load Forecasting Method Based on Transformer
HUANG Feihu, ZHAO Honglei, YI Peiyu, LI Peidong, PENG Jian
2023, 40(1): 50-58.   doi: 10.19725/j.cnki.1007-2322.2021.0209
[Abstract](0) [FullText HTML](0) [PDF 2305KB](0)
Load forecasting is a key part of many power grid applications and plays an important role. However, the nonlinearity, time-varying characteristics and uncertainty of grid load are the challenging to accurate load prediction, therefore, it plays a vital role to improve prediction accuracy by fully mine potential characteristics of load sequence. It is considered that the position information, trend, periodicity and time information of the load sequence should be fully utilized in feature extraction, and a neural network framework at a deeper level should be constructed for feature mining. For this reason, a load forecasting model based on feature embedding and Transformer framework was proposed, and the proposed model was composed by a feature embedding layer, a Transformer layer and a prediction layer. In the feature embedding layer, firstly, the location information, trend, periodicity and time information of the historical load were embedded into a characteristic vector, and then its output feature vector was blended with meteorology information to obtain the feature vector. The Transform layer accepted the feature vector of historical series and mined the temporal nonlinear dependence hidden in the sequence based on the obtained feature vectors of load sequence. Through the fully connected network the prediction layer implemented the load forecasting. Experimental results show that the forecasting performance of the proposed model is better than that of the comparative model, thus both feasibility and availability of the proposed model are verified.
The Data Service Mechanism and Benefit Calculation Model for Power Customer
YANG Xiaoxi, DU Xinhui
2023, 40(1): 59-66.   doi: 10.19725/j.cnki.1007-2322.2021.0274
[Abstract](0) [FullText HTML](0) [PDF 2349KB](0)
Along with the gradual promotion of electric power market-oriented reform, the market participants face with the increased trading risk, and as an important measure to offer strategic information the data service is of far-reaching significance in reducing risk and leading healthy development of electricity market. For this reason, a data services architecture, which was based on electricity sale company and faced with electricity consumers, was proposed. On the basis of value analysis a data service benefit calculating model, in which the maximized benefit index was taken as objective function, was established and the improved Non-dominated Sorting Genetic Algorithm hybrid with Beetle Antennae Search (abbr. BAS-NSGA) was used to simulate the optimization of electricity utilization, and the optimal solution was brought into the proposed model to conduct the analysis. By means of MATLAB platform the double auction of power consumption right transaction of clients was simulated, and the post-transaction earning of electricity sale company was compared with the earning of post-full authority deployment of electricity sale company while the transaction was not conducted. Finally, the operating strategy of the best data service was obtained. The result of this research could provide feasible reference for electric power companies to develop data service.
Short-term Household Load Forecasting Based on State Frequency Memory Network
BU Xiangguo, LAI Bo, ZHOU Houpan
2023, 40(1): 67-72.   doi: 10.19725/j.cnki.1007-2322.2021.0228
[Abstract](0) [FullText HTML](0) [PDF 2354KB](0)
Short-term household power load forecasting has played an increasing important role in smart grid. To further improve the accuracy of the forecasting, a state frequency memory network-based short-term household power load forecasting model was proposed. Firstly, the k-means clustering method was utilized to classify the families possessing the same electricity consumption mode into the same category. Secondly, the wavelet denoising technology was applied to the load data. Finally, a state frequency memory network model was constructed to perform batch of household power load forecasting. In the proposed model, the discrete Fourier transform was led in to decompose the memory state into multi frequency components, and by means of the combination of these frequency components the future electricity consumption was forecasted. The mean square error (abbr. MSE) , root mean square error (abbr. RMSE) and mean absolute error (abbr. MAE) were used to evaluate the proposed model. Taking the load forecasting of the next day for example, comparing the results obtained by LSTM, which behaves the best in this field, with those obtained by the proposed model, the error of forecasted results of three kinds of household power load has reduced by 21.6%, 11.4% and 15.4% respectively, thus, the effectiveness of the proposed model is fully verified.
Energy Internet
Islanded Microgrid Economic Dispatch Based on Segmented Quantization of Uncertainty Margin and Segmented Penalty of Curtailed Wind and Solar Power
YANG Yiqi, ZHENG Pengyuan, MAO Ran, QIN Haijie, WANG Yalin
2023, 40(1): 73-81.   doi: 10.19725/j.cnki.1007-2322.2021.0238
[Abstract](0) [FullText HTML](0) [PDF 2341KB](0)
In view of the fact that during its operation the islanded microgrid was affected by the output of new energy and the uncertainty of load power, an economic dispatching algorithm for island microgrid was proposed based on the segmented quantization of margin of uncertainty and the penalty of curtailed wind and photovoltaic (abbr. PV) power. Firstly, in the day-ahead planning stage, utilizing the uncertain set based on the segmented quantization of margin of uncertainty and adopting the column constraint generation algorithm, the primal problem was divided into the main problem and the sub-problem to conduct the interactive iterative solution. Thereby, the most optimal economic scheduling scheme under the "worst case scenario" was obtained. Secondly, in the intra-day scheduling stage, the output of energy storage system obtained from the day-ahead planning was kept, and the output of traditional energy, the demand response load and the curtailed quantity of wind and PV power in the new energy were adjusted and optimized. Finally, both economy and robustness of the proposed algorithm under different scenarios are verified by example simulation.
Management and Control Heterogeneous Thermal Control Load Cluster Based on State Space Coordination in Load Network System
WU Xin, YOU Lan
2023, 40(1): 82-91.   doi: 10.19725/j.cnki.1007-2322.2021.0202
[Abstract](0) [FullText HTML](0) [PDF 2776KB](0)
To perform the maximized scheduling of load resources that were effectively connected in the same regional substation, a framework for the load network system, in which the joint control of heterogeneous thermal control load cluster was considered, was proposed. Firstly, the physical perception layer of the network system was constituted by the end-side loads with the same electrical connection. Secondly, restricted by regional dispersibility and parameter heterogeneity, the load resources, in which the effective power transfer practically occurred, were limited, thus, the edge data centre was brought in to analyze the generality of the equipment layer of heterogeneous thermal control load and the physically dispersed loads of the same regional substation were virtually aggregated, and a joint control model of multi-load state space coordination was established. Thirdly, the adjustable capacity and operating state of the thermal control load were comprehensively considered in cloud-end power grid data centre, and after the secondary distribution the optimal task participating group and the task load of each group were determined. The fourth, taking the homogeneous aggregation group was taken as the basic control unit, and a collaborative control model of heterogeneous loads was developed on the edge side to issue the unified control instructions to coordinated the end load participating in energy services. Finally, by means of computing example, the effectiveness of both the proposed system and the heterogeneous load joint control model are verified.
Energy Storage
Optimal Configuration of Integrated Energy Storage for Wind Farms Based on Battery Characteristics
HAN Chenyang, ZHANG Peng, XU Jinhua
2023, 40(1): 92-99.   doi: 10.19725/j.cnki.1007-2322.2021.0230
[Abstract](0) [FullText HTML](0) [PDF 2842KB](0)
To cope with high wind power penetration, a higher requirement for the stability of the regional power grid, where the find farm locates, is put forward. As the key factor to cope with high wind power penetration, the configuration of energy storage is closely related to the final stability of power grid. To solve the problem that it would cause high investment cost because of improving power grid stability by relying on single type of energy storage equipments and it was necessary to consider both the economy and reliability of energy storage, based on the characteristics of different sorts of batteries a configuration scheme of energy storage equipments, which combined the strong power regulation performance of the lithium battery with the high energy storage capacity of the liquid flow battery, was proposed, Based on the technical indices established according to the actual demand and combining with the objective function, in which the average usage cost was minimized, the proposed configuration scheme was solved by immune algorithm, thus a comprehensive configuration scheme of energy storage power and capacity were obtained. Finally, a case-study based on the data of a certain wind farm located in Shandong province was conducted to evaluate the improvement of wind farm output under the proposed energy storage equipment configuration in the proposed scheme to verify the economy and the reliability of the proposed scheme.
Power Equipment Manufacturing Technology
A Coordination Control Method for Multi-terminal High Voltage DC System Based on Modular Multilevel Converter
ZHAO Yingping, PAN Huan
2023, 40(1): 100-107.   doi: 10.19725/j.cnki.1007-2322.2021.0241
[Abstract](0) [FullText HTML](0) [PDF 2760KB](0)
In modular multilevel converter-based multi-terminal DC (abbr. MMC-MTDC) transmission system the load fluctuation in AC area leads to the transitory power imbalance in this area, thus it causes the grid frequency fluctuation in this area. To cope with this problem, firstly, a virtual synchronous generator (abbr. VSG) based converter control method was proposed. Secondly, in allusion to fact that when VSG strategy was applied to frequency regulation the output power of converter station might be changed and the power balance at DC side might be broken so that too large deviation of DC voltage might occur, and such an impact would be especially evident under the existence of line impedance, for this reason, combining VSG control with improved droop control to form a comprehensive coordinated control strategy of MMC-MTDC system with multi-stations. Such a control strategy not only could provide frequency support for AC area but also could optimize the distribution of the unbalance power of DC bus to improve the stability of system DC voltage. Finally, based on Matlab/Simulink platform, a five-terminal MMC-MTDC system was constructed for the simulation. Simulation results show that the proposed control strategy is effective.
Smart Grid
Deep Application of Substation Logic Model Handover Based on Digital Twin
CHEN Chen, SONG Xiaofan, DONG Pingxian, BAI Pingping, LI Qi
2023, 40(1): 108-116.   doi: 10.19725/j.cnki.1007-2322.2021.0234
[Abstract](0) [FullText HTML](0) [PDF 2821KB](0)
To improve the intelligent and digital level of power grid, in allusion to the three aspects of modeling, handover and display of the logical model of substation, a complete set of application research scheme of substation digital handover was proposed. Firstly, the general framework of substation logical model modeling, the database file modeling format and the digital information interaction were researched, and by means of combining the abstract conception in the logical model with the entity concept a rational data structure was designed to realize the full digital expression of the logical model. Secondly, the handover format and import/export interface were expounded in detail, and a digital panoramic logic model information display platform was developed to associate the digital logical model information with the 3D model to display the digital information related to the substation. The proposed research on the handover of logical model of digital design is a supplement to the pure physical model handover of substation and provides the basis for the development of digital power grid.
A Load Regulation Method Considering Optimal Consumer Side Benefits in Smart Grid
WANG Shunjiang, REN Shoudong, YU Bo, LU Pukun, LI He, LI Zhiwei
2023, 40(1): 117-124.   doi: 10.19725/j.cnki.1007-2322.2021.0227
[Abstract](0) [FullText HTML](0) [PDF 2333KB](0)
To regulate and control the electricity consumption in smart power grid during peak-valley load period and improve the benefit of electricity consumers, a game theory-based method for controlling and regulating load was proposed. Taking the consumer side and the gird side as game-agents the load model, the load side benefit model and the grid side benefit model were constructed respectively. Based on game mechanism an electricity price regulation strategy was designed, and the earning of the two game sides was adjusted by multi-round dynamic regulation to coordinate the unbalanced relation between consumer side and grid side. Under the double restraints of load and electricity price, by means of CPLEX solver the earning of the consumer and the load regulation results of the grid were computed. Results of computing example show that under the condition of same data and comparing with the improved particle swarm optimization, by use of the proposed regulation method the expenditure at load side was reduced 0.95% and that at the grid side was reduced 6.14%. It is verified that using the proposed method not only the optimal benefit of user side can be ensured but also the load during peak-valley period can be effectively regulated and controlled.
A Method to Detect False Data Injection Based on Feature Mapping and Deep Learning
HU Cong, HONG Dehua, ZHANG Cuicui, WANG Haixin, XUE Xiaoru, LI Yunlu
2023, 40(1): 125-132.   doi: 10.19725/j.cnki.1007-2322.2021.0255
[Abstract](0) [FullText HTML](0) [PDF 2651KB](0)
The smart grid is gradually being developed into large power cyber physical system, however the interaction between cyber and physical system reduces its ability to withstand the false data injection attack (abbr. FDIA). In allusion to this issue, a method to detect the fake data injection for smart grid based on multi-layer hierarchical fusion fuzzy feature mapping (abbr. MLHFFFM) combined with deep belief network (abbr. DBN) was researched and proposed. Firstly, the principle of FDIA was analyzed and based on MLHFFFM and combining with principal component analysis (abbr. PCA) the smart grid load data was clustered to select the approximate day with daily load similar to that of the forecasted day. Secondly, the conditional DBN was used to analyze the daily grid load of approximate day, and in the meantime, by means of selecting different parameters the daily grid load characteristics was dynamically captured thereby the FDIA was detected. Finally, combining with actual load data of a certain province in China, IEEE 33-bus system was taken for computing example to verify the proposed method. Experimental results show that compared with other models the accuracy rate of the proposed model under different attack intensities keeps above 95%, and the error rate is lower than 5%, thus, the injection of fake data can be effectively detected.
An Improved Graph Convolutional Network-based State Estimation Method of Distribution Network
WANG Chunyi, LU Zhipeng, YANG Yang, ZHAO Ren, LIU Zhao, GE Xiaoning
2023, 40(1): 133-142.   doi: 10.19725/j.cnki.1007-2322.2021.0254
[Abstract](0) [FullText HTML](0) [PDF 2441KB](0)
In order to improve reliability of distribution network and ensure its stable operation, the realtime state estimation of distribution network plays an important role in improving the reliability of distribution network and ensuring its stable operation. To perform state estimation with high accuracy under insufficient measuring information of distribution network, a new state estimation method for distribution network based on improved physical data fusion of graph convolutional network (abbr. GCN) was proposed. Firstly, in the proposed method a small number of phasor measuring units were utilized to divide the distribution network into partitions, then according to the maximum diameter after the partitioning the number of convolution modules required by the convolution network was determined. Secondly, the expressing method of traditional adjacent matrix in GCN was modified, thus by use of the convolution network all the state variables in the partitioned subsystems of distribution network were expressed by the measured quantities. By means of typical computing example of IEEE 33 bus system, the effectiveness of the proposed method was verified. Meanwhile, through the comparison test with traditional Gauss-Newton optimization algorithm and that with traditional deep learning network, the results show that using the proposed method not only the computation complexity can be transferred to off-line phase, but also it can be independent of the pseudo measurement with high redundancy, so the proposed method possesses higher estimation accuracy and computing speed.


Competent Authorities:

Ministry of Education of the People's Republic of China

Sponsor: North China Electric Power University

Editor and Publisher:

Editorial Department of Modern Electric Power

Editor-in-Chief: LIU Jizhen

Deputy Editor: ZHAO Dongmei

Director of Editorial Department: SONG Shufang

Publication Number:

ISSN 1007-2322 CN 11-3818/TM CODEN XDIIC2

Address: Building D0539,North China Electric Power University,Beijing 102206, China

Tel: +86-10-61772300

Fax: 010-61772726



Agent of Subscriptions Abroad:

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