Operation optimization strategy of multi-microgrids energy sharing based on asymmetric Nash bargaining

Автор: Kang Chuanzhi, Zhang Zongnan, Kudashev Sergei, Liu Meinan, Zhang Qianwei, Pan Jiashuang

Журнал: Бюллетень науки и практики @bulletennauki

Рубрика: Технические науки

Статья в выпуске: 8 т.8, 2022 года.

Бесплатный доступ

The accommodation of renewable energy is driving the development of energy storage technology, and shared energy storage has gained widespread attention because of its decentralized nature. In the optimal scheduling of shared energy storage, the problem of benefit distribution among multiple subjects is faced, so a shared energy storage plant operation optimization method based on Nash bargaining theory is proposed. The article constructs a joint model of shared energy storage plants and industrial users, establishes the cooperative operation model of each operator based on Nash bargaining theory, equates this nonconvex nonlinear problem into two subproblems of system revenue maximization and power transaction payment bargaining according to the mean value inequality, and uses the alternating direction multiplier method to solve them in a distributed manner. The algorithm selects three typical industrial users to participate in the joint system of shared energy storage, and through comparative analysis before and after cooperative bargaining, it is concluded that the proposed optimization method can effectively improve the benefits of each subject, while promoting the accommodation of new energy.

Еще

Shared energy storage plant, nash bargaining, optimized operation, alternating direction multiplier method

Короткий адрес: https://sciup.org/14125300

IDR: 14125300   |   DOI: 10.33619/2414-2948/81/36

Список литературы Operation optimization strategy of multi-microgrids energy sharing based on asymmetric Nash bargaining

  • Zhang, X., Zhang, Y., Zhao, Y., Chen, D., Li, H., & Xie, D. (2021, June). Shared energy storage market operation mechanism to promote new energy consumption. IOP Conference Series: Earth and Environmental Science, 766(1), 012002. IOP Publishing.
  • Jia, H., Wang, D., Xu, X., & Yu, X. D. (2015). Research on some key problems related to integrated energy systems. Automation of Electric Power Systems, 39(7), 198-207.
  • Cai, Q., Zheng, N., Chen, K., Chen, W., Li, Y., & Cai, J. (2021, October). Collaborative Planning of Distributed Generation and Distribution Network Considering Dynamic Reconstruction of Grid. In 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2) (pp. 55-59). IEEE. https://doi.org/10.1109/EI252483.2021.9712883
  • Kou, L., Zhang, Y., Ji, Y., Xiong, X., & Hu, C. (2020). Typical application scenario and operation mode analysis of distributed energy storage. Power System Protection and Control, 48(04), 177-187.
  • Liu, D., Zhao, N., Xu, X., Shao, P., Cao, X., & Feng, S. (2020, November). Market-Oriented Consumption Model Based on the Joint Tracking of Renewable Energy Generation Curve of "Shared Energy Storage & Demand Side Resources". In IOP Conference Series: Earth and Environmental Science (Vol. 571, No. 1, p. 012007). IOP Publishing.
  • Wu, S. J., Li, Q., Liu, J. K., Zhou, Q., & Wang, C. G. (2021). Bi-level optimal configuration for combined cooling heating and power multi-microgrids based on energy storage station service. Power Syst. Technol, 45, 3822-3832.
  • Zhong, X., Zhong, W., Liu, Y., Yang, C., & Xie, S. (2022). Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations. Energy, 246, 123428. https://doi.org/10.1016Zj.energy.2022.123428
  • Wang, S. J., Ping, C., & Xue, G. B. (2018). Synergic optimization of community energy internet considering the shared energy storage. Electric Power, 51(8), 77-84.
  • Sun, X., Chen, L., Qiu, X., Zheng, T., & Mei, S. (2019). Generation side shared energy storage planning model based on cooperative game. Journal of Global Energy Interconnection, 2(04), 360-366.
  • Fang, F., Wei, L., Liu, J., Zhang, J., & Hou, G. (2012). Complementary configuration and operation of a CCHP-ORC system. Energy, 46(1), 211-220. https://doi.org/10.1016Zj.energy.2012.08.030
  • Shuai, X. Y., Wang, X. L., & Huang, J. (2021). Optimal configuration of shared energy storage capacity under multiple regional integrated energy systems interconnection. Journal of Global Energy Interconnection, 4(4), 382-392.
  • Lin, Ling (2021). Research on multi-objective optimal scheduling of micro-energy grid and shared energy storage capacity allocation based on typical scenarios.
  • Walker, A., & Kwon, S. (2021). Analysis on impact of shared energy storage in residential community: Individual versus shared energy storage. Applied Energy, 282, 116172. https://doi.org/10.10167j.apenergy.2020.116172
  • Cui, S., Wang, Y. W., Shi, Y., & Xiao, J. W. (2020). Community energy cooperation with the presence of cheating behaviors. IEEE Transactions on Smart Grid, 12(1), 561-573. https://doi.org/10.1109/TSG.2020.3022792
  • Mei, S., Liu, F., & Wei, W. (2016). Engineering game theory and power system application. Beijing, the Science Publishing Compan.
  • Sun, Qian. (2020). Research on optimal dispatch of non-cooperative game in integrated energy system considering uncertainty. Xi'an: Xi'an University of Technology.
  • Ma, T., Pei, W., Xiao, H., Li, D., Lyu, X., & Hou, K. (2021). Cooperative operation method for wind-solar-hydrogen multi-agent energy system based on Nash bargaining theory. Proceeding CSEE, 41, 25-39.
  • Jin, Z., Cungang, H. U., & Tao, R. (2019). Nash bargaining model for direct electricity trading on distribution side with multi-microgrids participation. Energy Storage Science and Technology, 8(4), 645. https://esst.cip.com.cn/EN/Y2019/V8/I4/645
  • Kim, H., Lee, J., Bahrami, S., & Wong, V. W. (2019). Direct energy trading of microgrids in distribution energy market. IEEE Transactions on Power Systems, 35(1), 639-651. https://doi.org/10.1109/TPWRS.2019.2926305
  • Mi, Y., Song, Y., Fu, Y., & Wang, C. (2019). The adaptive sliding mode reactive power control strategy for wind-diesel power system based on sliding mode observer. IEEE Transactions on Sustainable Energy, 11(4), 2241-2251.
  • Wang, Y., Wang, X., Shao, C., & Gong, N. (2020). Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach. Renewable Energy, 155, 513-530. https://doi.Org/10.1016/j.renene.2020.03.006
  • Wang, H., & Huang, J. (2015, June). Bargaining-based energy trading market for interconnected microgrids. In 2015 IEEE International Conference on Communications (ICC) (pp. 776-781). IEEE. https://doi.org/10.1109/ICC.2015.7248416
  • Wang, C., & Liu, N. (2016). Distributed optimal dispatching of interconnected microgrid system based on alternating direction method of multipliers. Proc CSEE, 40(9), 2675-2681.
  • Wang, H., Ai, Q., Wu, J. H., Xie, Y. Z., & Zhou, X. Q. (2018). Bi-level distributed optimization for microgrid clusters based on alternating direction method of multipliers. Power System Technology, 42(6), 1718-1725.
  • Cong, O., Mingbo, L., Shunjiang, L., & Hanzhong, F. (2017). Decentralized dynamic economic dispatch algorithm of microgrids using synchronous alternating direction method of multipliers. Transactions of China Electrotechnical Society, 32(5), 134-142.
  • Ding, Y., Xu, Q., & Huang, Y. (2020). Optimal sizing of user-side energy storage considering demand management and scheduling cycle. Electric Power Systems Research, 184, 106284. https://doi.org/10.1016/j.epsr.2020.106284
  • Ding Y., Xu Q., Huang Y. Optimal sizing of user-side energy storage considering demand management and scheduling cycle //Electric Power Systems Research. - 2020. - Т. 184. - С. 106284. https://doi.org/10.1016/j.epsr.2020.106284
  • Gao, J., Ma, Z., Yang, Y., Gao, F., Guo, G., & Lang, Y. (2020). The impact of customers' demand response behaviors on power system with renewable energy sources. IEEE Transactions on Sustainable Energy, 11(4), 2581-2592.
  • Wang, M. Q., & Gooi, H. B. (2011). Spinning reserve estimation in microgrids. IEEE Transactions on Power Systems, 26(3), 1164-1174. https://doi.org/10.1109/TPWRS.2010.2100414
  • Li, Y., Yang, Z., Li, G., Zhao, D., & Tian, W. (2018). Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties. IEEE Transactions on Industrial Electronics, 66(2), 1565-1575. https://doi.org/10.1109/TIE.2018.2840498
  • Fan, S., Ai, Q., & Piao, L. (2018). Bargaining-based cooperative energy trading for distribution company and demand response. Applied energy, 226, 469-482. https://doi.org/10.1016/j.apenergy.2018.05.095
  • Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning, 3(1), 1-122. http://dx.doi.org/10.1561/2200000016
Еще
Статья научная