| Peer-Reviewed

Power Market Operation Efficiency Evaluation Based on a Hybrid BP Neural Network: A perspective from Market Regulator in China

Received: 30 October 2021    Accepted: 16 November 2021    Published: 23 November 2021
Views:       Downloads:
Abstract

The electricity market reform in China promotes the marketization process of the electricity market, but in this process, there are still some behaviors that disturb the market order. The operation of electricity market needs to be evaluated in order to evaluate its operation efficiency, and the regulatory agency is an important part of it, so it is very necessary to construct the evaluation index and method of the operation efficiency of electricity market from the perspective of the regulatory agency. This paper constructed an evaluation system of power market operation efficiency based on hybrid BP neural network. In the construction of methods, appropriate evaluation methods become very necessary when considering the uncertainty and ambiguity of the assessed things. Based on the above analysis, this paper constructed an evaluation index system of power market operation efficiency based on SCP model, and constructed evaluation methods based on fuzzy Delphi, fuzzy AHP and neural network. Finally, this paper selects the actual data of a certain region for example verification, and conducts sensitivity analysis to analyze the factors that have a key impact on the efficiency of market operation, and draws the conclusion of this paper. This paper hopes that the research results can be applied to the decision-making of regulators in order to make the electricity market run efficiently.

Published in American Journal of Electrical Power and Energy Systems (Volume 10, Issue 5)
DOI 10.11648/j.epes.20211005.12
Page(s) 82-99
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Power Market Monitoring, SCP Model, Fuzzy Set, BP Neural Network, Multi-criteria Decision Making

References
[1] Hu, C.; Du, S.; Su, J.; Tong, G.; Wang, M. Preliminary Research of Trading Approach and Management Modes of Chinese Electricity Retail Companies Under New Electricity Market Reform. Power Syst. Technol. 2016, 40, 3293–3299.
[2] Lai, F.; Xia, Q. Electricity Characteristics and Electricity Market. Autom. Electr. Power Syst. 2005, 29, 1–5, doi: 10.3321/j.issn:1000-1026.2005.22.001.
[3] Yan, L. Research on the Electricity Regulatory System Reform in China under the Theory of Regulation. Doctor thesis, Nanjing University, 2015.
[4] Miao, Y.; Luo, W.; Lei, W.; Zhang, P.; Jiang, R.; Deng, X. Power Supply Reliability Indices Computation with Consideration of Generation Systems, Transmission Systems and Sub-Transmission Systems’ Load Transfer Capabilities. In Proceedings of the 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC); IEEE: Xi’an, China, October 2016; pp. 1840–1844.
[5] Salarvand, A.; Mirzaeian, B.; Moallem, M. Obtaining a Quantitative Index for Power Quality Evaluation in Competitive Electricity Market. IET Gener. Transm. Distrib. 2010, 4, 810, doi: 10.1049/iet-gtd.2009.0479.
[6] Tianrui Zhou; Qixin Chen; Wentao Zhu; Li Zhang; Chongqing Kang A Comprehensive Low-Carbon Benefits Assessment Model for Power Systems. In Proceedings of the 2012 IEEE International Conference on Power System Technology (POWERCON); IEEE: Auckland, October 2012; pp. 1–5.
[7] Song, M.; Cui, L.-B. Economic Evaluation of Chinese Electricity Price Marketization Based on Dynamic Computational General Equilibrium Model. Comput. Ind. Eng. 2016, 101, 614–628, doi: 10.1016/j.cie.2016.05.035.
[8] Lee, Y.-Y.; Baldick, R.; Hur, J. Firm-Based Measurements of Market Power in Transmission-Constrained Electricity Markets. IEEE Trans. Power Syst. 2011, 26, 1962–1970, doi: 10.1109/TPWRS.2011.2157179.
[9] Xie, J.; Wang, S.; Zhou, X.; Sun, B.; Sun, X. Credit Evaluation Method of Generating Companies Considering the Market Behavior in China Electricity Market. Energy Sci. Eng. 2021, 9, 1554–1567, doi: 10.1002/ese3.929.
[10] Zhao, H.; Zhao, H.; Guo, S. Comprehensive Performance Evaluation of Electricity Grid Corporations Employing a Novel MCDM Model. Sustainability 2018, 10, 2130, doi: 10.3390/su10072130.
[11] Li, X. Research on Evaluation Indexes and Methods of Electricity Market in China. Master thesis, North China Electric Power University, 2017.
[12] Li, T.; Wang, S. Research on Evaluation System of Electricity Market Transaction in China Based on Gray Relational Grade Analysis and Fuzzy Analytic Hierarchy Process. Ind. Technol. Econ. 2018, 37, 130–137.
[13] Guo, L.; Wei, F.; Xia, Q.; Zhuang, Y.; Ma, L. Discussions on Evaluation Index Framework of Power Market in China. Electr. POWER Technol. Econ. 2008, 29–34.
[14] Xiao, L. Research on the Current Status of the Cultural Industry Organization in China and Development Countermeasures. In Proceedings of 2015 2nd International Conference on Industrial Economics System and Industrial Security Engineering; Li, M., Zhang, Q., Zhang, J., Li, Y., Eds.; Springer Singapore: Singapore, 2016; pp. 247–253 ISBN 978-981-287-654-6.
[15] Kening, C.; Qu, H.; Ma, Q.; Luo, Z.; Wenjun, Z. Design of China’s Electricity Market Evaluation Indicators Based on SCP Model. IOP Conf. Ser. Earth Environ. Sci. 2021, 657, 012092, doi: 10.1088/1755-1315/657/1/012092.
[16] Qi, S.; Wang, X.; Zhang, W.; Wu, X.; Wang, G.; Shi, X.; Wang, Y. Evaluation Index System for Clearing Models of Different Electricity Price Policies. In Proceedings of the 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2); IEEE: Beijing, China, October 2018; pp. 1–6.
[17] Chang, P.-T.; Huang, L.-C.; Lin, H.-J. The Fuzzy Delphi Method via Fuzzy Statistics and Membership Function Fitting and an Application to the Human Resources. Fuzzy Sets Syst. 2000, 112, 511–520, doi: 10.1016/S0165-0114(98)00067-0.
[18] Li, Y. Analysis and Improvement Applications Of BP Neutral Network. Master thesis, Anhui University of Science and Technology, 2012.
[19] Li, J. Research on Marketing Performance Evaluation of Urban Complex Based on AHP and BP Neural Network. Microcomput. Appl. 2021, 37, 158–162.
[20] Gao, X. Research on the Efficiency of the Listed Electric Power Enterprises in China Based on DEA Method. Master thesis, East China University of Political Science and Law, 2013.
[21] Wang, J.; Ding, D.; Liu, O.; Li, M. A Synthetic Method for Knowledge Management Performance Evaluation Based on Triangular Fuzzy Number and Group Support Systems. Appl. Soft Comput. 2016, 39, 11–20, doi: 10.1016/j.asoc.2015.09.041.
[22] Tseng, M.-L. Using Hybrid MCDM to Evaluate the Service Quality Expectation in Linguistic Preference. Appl. Soft Comput. 2011, 11, 4551–4562, doi: 10.1016/j.asoc.2011.08.011.
[23] Ebrahimi, S.; Bridgelall, R. A Fuzzy Delphi Analytic Hierarchy Model to Rank Factors Influencing Public Transit Mode Choice: A Case Study. Res. Transp. Bus. Manag. 2021, 39, 100496, doi: 10.1016/j.rtbm.2020.100496.
[24] Ilbahar, E.; Kahraman, C.; Cebi, S. Risk Assessment of Renewable Energy Investments: A Modified Failure Mode and Effect Analysis Based on Prospect Theory and Intuitionistic Fuzzy AHP. Energy 2022, 239, 121907, doi: 10.1016/j.energy.2021.121907.
[25] James, A. T.; Vaidya, D.; Sodawala, M.; Verma, S. Selection of Bus Chassis for Large Fleet Operators in India: An AHP-TOPSIS Approach. Expert Syst. Appl. 2021, 186, 115760, doi: 10.1016/j.eswa.2021.115760.
[26] Gogus, O.; Boucher, T. O. Strong Transitivity, Rationality and Weak Monotonicity in Fuzzy Pairwise Comparisons. Fuzzy Sets Syst. 1998, 94, 133–144, doi: 10.1016/S0165-0114(96)00184-4.
[27] Aragonés-Beltrán, P.; Chaparro-González, F.; Pastor-Ferrando, J.-P.; Pla-Rubio, A. An AHP (Analytic Hierarchy Process)/ANP (Analytic Network Process)-Based Multi-Criteria Decision Approach for the Selection of Solar-Thermal Power Plant Investment Projects. Energy 2014, 66, 222–238, doi: 10.1016/j.energy.2013.12.016.
[28] Zhou, J. Efficiency Evaluation of Container Terminal Based on AHP and BP Neutral Network. Master thesis, Jimei University, 2020.
[29] Yang, Y.; Zheng, X.; Sun, Z. Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network. Sustainability 2020, 12, 2294, doi: 10.3390/su12062294.
Cite This Article
  • APA Style

    Jun Dong, Yao Liu, Zhenjie Chen, Yaoyu Zhang, Yuzheng Jiang. (2021). Power Market Operation Efficiency Evaluation Based on a Hybrid BP Neural Network: A perspective from Market Regulator in China. American Journal of Electrical Power and Energy Systems, 10(5), 82-99. https://doi.org/10.11648/j.epes.20211005.12

    Copy | Download

    ACS Style

    Jun Dong; Yao Liu; Zhenjie Chen; Yaoyu Zhang; Yuzheng Jiang. Power Market Operation Efficiency Evaluation Based on a Hybrid BP Neural Network: A perspective from Market Regulator in China. Am. J. Electr. Power Energy Syst. 2021, 10(5), 82-99. doi: 10.11648/j.epes.20211005.12

    Copy | Download

    AMA Style

    Jun Dong, Yao Liu, Zhenjie Chen, Yaoyu Zhang, Yuzheng Jiang. Power Market Operation Efficiency Evaluation Based on a Hybrid BP Neural Network: A perspective from Market Regulator in China. Am J Electr Power Energy Syst. 2021;10(5):82-99. doi: 10.11648/j.epes.20211005.12

    Copy | Download

  • @article{10.11648/j.epes.20211005.12,
      author = {Jun Dong and Yao Liu and Zhenjie Chen and Yaoyu Zhang and Yuzheng Jiang},
      title = {Power Market Operation Efficiency Evaluation Based on a Hybrid BP Neural Network: A perspective from Market Regulator in China},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {10},
      number = {5},
      pages = {82-99},
      doi = {10.11648/j.epes.20211005.12},
      url = {https://doi.org/10.11648/j.epes.20211005.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20211005.12},
      abstract = {The electricity market reform in China promotes the marketization process of the electricity market, but in this process, there are still some behaviors that disturb the market order. The operation of electricity market needs to be evaluated in order to evaluate its operation efficiency, and the regulatory agency is an important part of it, so it is very necessary to construct the evaluation index and method of the operation efficiency of electricity market from the perspective of the regulatory agency. This paper constructed an evaluation system of power market operation efficiency based on hybrid BP neural network. In the construction of methods, appropriate evaluation methods become very necessary when considering the uncertainty and ambiguity of the assessed things. Based on the above analysis, this paper constructed an evaluation index system of power market operation efficiency based on SCP model, and constructed evaluation methods based on fuzzy Delphi, fuzzy AHP and neural network. Finally, this paper selects the actual data of a certain region for example verification, and conducts sensitivity analysis to analyze the factors that have a key impact on the efficiency of market operation, and draws the conclusion of this paper. This paper hopes that the research results can be applied to the decision-making of regulators in order to make the electricity market run efficiently.},
     year = {2021}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Power Market Operation Efficiency Evaluation Based on a Hybrid BP Neural Network: A perspective from Market Regulator in China
    AU  - Jun Dong
    AU  - Yao Liu
    AU  - Zhenjie Chen
    AU  - Yaoyu Zhang
    AU  - Yuzheng Jiang
    Y1  - 2021/11/23
    PY  - 2021
    N1  - https://doi.org/10.11648/j.epes.20211005.12
    DO  - 10.11648/j.epes.20211005.12
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 82
    EP  - 99
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20211005.12
    AB  - The electricity market reform in China promotes the marketization process of the electricity market, but in this process, there are still some behaviors that disturb the market order. The operation of electricity market needs to be evaluated in order to evaluate its operation efficiency, and the regulatory agency is an important part of it, so it is very necessary to construct the evaluation index and method of the operation efficiency of electricity market from the perspective of the regulatory agency. This paper constructed an evaluation system of power market operation efficiency based on hybrid BP neural network. In the construction of methods, appropriate evaluation methods become very necessary when considering the uncertainty and ambiguity of the assessed things. Based on the above analysis, this paper constructed an evaluation index system of power market operation efficiency based on SCP model, and constructed evaluation methods based on fuzzy Delphi, fuzzy AHP and neural network. Finally, this paper selects the actual data of a certain region for example verification, and conducts sensitivity analysis to analyze the factors that have a key impact on the efficiency of market operation, and draws the conclusion of this paper. This paper hopes that the research results can be applied to the decision-making of regulators in order to make the electricity market run efficiently.
    VL  - 10
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

  • Sections