Research on Peer-to-peer Transaction Strategy of Cloud Energy Storage Based on Semi-distributed Structured Topology

被引:0
|
作者
Ma Y. [1 ]
Wu C. [1 ]
Lin X. [1 ]
Chen C. [1 ]
Li Z. [1 ]
Wei F. [1 ]
Sui Q. [1 ]
Zhou D. [1 ]
机构
[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Hubei Province, Wuhan
关键词
cloud energy storage; cooperative game; Nash bargaining model; peer-to-peer network;
D O I
10.13334/j.0258-8013.pcsee.211263
中图分类号
学科分类号
摘要
With the growing demand for energy storage resources and the gradual development of peer-to-peer (P2P) technology, cloud energy storage (CES) is expected to become a new business form of user-side energy storage. Aiming at the problems of poor reliability and unbalanced load of centralized topology P2P transactions, poor scalability of fully distributed topology P2P transactions and overload of low bandwidth nodes, this paper proposed a P2P transaction framework for CES based on a semi-distributed structured topology, which closely coupled energy flow, information flow, and business flow to achieve user privacy protection while taking communication efficiency into account. On this basis, in order to solve the problem of difficulty in achieving global optimization under the hierarchical and multi-subject transaction framework, the cooperative game theory was introduced to construct a two-layer P2P two-stage transaction optimization model of CES operator-community-user. In the first stage, with the goal of maximizing the benefits of the cooperative alliance, a CES operator-community-user two-layer energy storage capacity sharing model was established based on the nested alternating direction multiplier method. In the second stage, a Nash bargaining model that considered the differences in the participation of entities and market factors was proposed, and a pricing mechanism for CES leasing fees was formulated to realize the distribution of benefits. Finally, simulations verified the feasibility and superiority of the proposed CES trading strategy and the fairness of the benefit distribution mechanism, providing a new perspective for the development of CES business. © 2022 Chinese Society for Electrical Engineering. All rights reserved.
引用
收藏
页码:7731 / 7745
页数:14
相关论文
共 43 条
  • [31] BOYD S, PARIKH N, CHU E, Distributed optimization and statistical learning via the alternating direction method of multipliers[J], Foundations and Trends® in Machine Learning, 3, 1, pp. 1-122, (2010)
  • [32] BOYD S,, VANDENBERGHE L., Convex optimization [M], pp. 28-34, (2004)
  • [33] LEE J, Jun GUO, CHOI J K, Distributed energy trading in microgrids:a game-theoretic model and its equilibrium analysis[J], IEEE Transactions on Industrial Electronics, 62, 6, pp. 3524-3533, (2015)
  • [34] ZHANG Shizhong, PEI Wei, MA Tengfei, Nash bargaining for reactive power incentive and reward of photovoltaic in distribution network[J], Power System Technology, 45, 8, pp. 3079-3086, (2021)
  • [35] XUAN Mingyang, LU Zhigang, Operation optimization strategy of multi integrated energy service companies based on cooperative game theory [J/OL], Proceedings of the CSEE
  • [36] Shichang CUI, WANG Yanwu, Yang SHI, Community energy cooperation with the presence of cheating behaviors[J], IEEE Transactions on Smart Grid, 12, 1, pp. 561-573, (2021)
  • [37] MA Tengfei, Wei PEI, XIAO Hao, Cooperative operation method for wind-solar-hydrogen multi-agent energy system based on NASH bargaining theory[J], Proceedings of the CSEE, 41, 1, pp. 25-39, (2021)
  • [38] Hao WANG, HUANG Jianwei, Incentivizing energy trading for interconnected microgrids[J], IEEE Transactions on Smart Grid, 9, 4, pp. 2647-2657, (2018)
  • [39] Rui DAI, CHARKHGARD H,, CHEN Yang, Balancing benefit distribution for energy storage sharing based on NASH bargaining solution[C], Proceedings of 2019 IEEE Power & Energy Society General Meeting, pp. 1-5, (2019)
  • [40] MAHARJAN S, Quanyan ZHU, ZHANG Yan, Dependable demand response management in the smart grid:a stackelberg game approach[J], IEEE Transactions on Smart Grid, 4, 1, pp. 120-132, (2013)