Analysis of Distributed Green Power Transaction Optimization Based on Nash Bargaining Theory

doi:10.11930/j.issn.1004-9649.202209094

Abstract

Abstract: In order to promote the market-oriented consumption of distributed green power and improve the operation efficiency of distributed green power units, this paper studies the cooperative trading mode of distributed power generation operators, energy storage operators and flexible load aggregators that aggregate distributed green power by using virtual power plant joint technology. Firstly, the optimal operation model of each subject is established under the premise of energy transaction between each subject, and then the multi-agent cooperative game model is established based on Nash bargaining theory. In order to better solve the cooperative game model, the model is transformed into a sub-problem of maximizing the total benefit of cooperative operation and a sub-problem of asymmetric income distribution. The alternating direction multiplier method is used to solve the two sub-problems in a distributed manner. Finally, the effectiveness of the proposed model is further verified by simulation. The conclusion shows that participating in power cooperative trading can effectively improve the operating efficiency of distributed green power units.

Key words: distributed power supply, green electricity transaction, Nash bargaining, cooperative game, alternating direction multiplier method

ZHANG Yan, QIAO Songbo, XU Qifeng, YU Jing. Analysis of Distributed Green Power Transaction Optimization Based on Nash Bargaining Theory[J]. Electric Power, 2022, 55(12): 168-178.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202209094

https://www.electricpower.com.cn/EN/Y2022/V55/I12/168

References

[1] 张运洲, 张宁, 代红才, 等. 中国电力系统低碳发展分析模型构建与转型路径比较[J]. 中国电力, 2021, 54(3): 1–11
ZHANG Yunzhou, ZHANG Ning, DAI Hongcai, et al. Model construction and pathways of low-carbon transition of China's power system[J]. Electric Power, 2021, 54(3): 1–11
[2] 程林, 张靖, 黄仁乐, 等. 基于多能互补的综合能源系统多场景规划案例分析[J]. 电力自动化设备, 2017, 37(6): 282–287
CHENG Lin, ZHANG Jing, HUANG Rele, et al. Case analysis of multi-scenario planning based on multi-energy complementation for integrated energy system[J]. Electric Power Automation Equipment, 2017, 37(6): 282–287
[3] 丁明, 姚宇亮, 李林, 等. 分布式光伏并网装置的研制[J]. 电力自动化设备, 2018, 38(3): 1–6, 15
DING Ming, YAO Yuliang, LI Lin, et al. Development of distributed photovoltaic grid-connection device[J]. Electric Power Automation Equipment, 2018, 38(3): 1–6, 15
[4] 曹子健, 林今, 宋永华. 含分布式电源及灵活负荷的配电网电量合约市场[J]. 电网技术, 2019, 43(7): 2430–2440
CAO Zijian, LIN Jin, SONG Yonghua. Electricity contract market for distribution network with distributed generations and flexible loads[J]. Power System Technology, 2019, 43(7): 2430–2440
[5] 王深, 黄国和, 刘政平. 多重不确定条件下的乌鲁木齐分布式绿色电力规划研究[J]. 太阳能学报, 2018, 39(11): 3090–3096
WANG Shen, HUANG Guohe, LIU Zhengping. Urumqi's distributed power generation planning of clean power constrained by mult-uncertainty[J]. Acta Energiae Solaris Sinica, 2018, 39(11): 3090–3096
[6] 魏利屾, 冯宇昂, 方家琨, 等. 现货市场环境下新能源并网接入对市场出清的影响[J]. 上海交通大学学报, 2021, 55(12): 1631–1639
WEI Lishen, FENG Yuang, FANG Jiakun, et al. Impact of renewable energy integration on market-clearing results in spot market environment[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1631–1639
[7] 韩国栋, 吉培荣, 王飞. 考虑负荷调节的分布式市场交易模式[J]. 中国电力, 2022, 55(10): 178–184
HAN Guodong, JI Peirong, WANG Fei. Research on distributed transaction mode considering load adjustment[J]. Electric Power, 2022, 55(10): 178–184
[8] 姜明军, 任明远, 徐兰兰, 等. 计及分布式电源不确定性的多微网鲁棒博弈研究[J]. 可再生能源, 2021, 39(4): 533–540
JIANG Mingjun, REN Mingyuan, XU Lanlan, et al. Research on robust game in multi-microgrid considering distributed generation uncertainty[J]. Renewable Energy Resources, 2021, 39(4): 533–540
[9] 李彬, 覃秋悦, 祁兵, 等. 基于区块链的分布式能源交易方案设计综述[J]. 电网技术, 2019, 43(3): 961–972
LI Bin, QIN Qiuyue, QI Bing, et al. Design of distributed energy trading scheme based on blockchain[J]. Power System Technology, 2019, 43(3): 961–972
[10] 龚诚嘉锐, 林顺富, 边晓燕, 等. 基于多主体主从博弈的负荷聚合商经济优化模型[J]. 电力系统保护与控制, 2022, 50(2): 30–40
GONG Chengjiarui, LIN Shunfu, BIAN Xiaoyan, et al. Economic optimization model of a load aggregator based on the multi-agent Stackelberg game[J]. Power System Protection and Control, 2022, 50(2): 30–40
[11] 王帅, 帅轩越, 王智冬, 等. 基于纳什议价方法的虚拟电厂分布式多运营主体电能交易机制[J]. 电力建设, 2022, 43(3): 141–148
WANG Shuai, SHUAI Xuanyue, WANG Zhidong, et al. Distributed electricity trading mechanism of multi-operator virtual power plant based on Nash bargaining method[J]. Electric Power Construction, 2022, 43(3): 141–148
[12] MASHHOUR E, MOGHADDAS-TAFRESHI S M. Bidding strategy of virtual power plant for participating in energy and spinning reserve markets-part II: numerical analysis[J]. IEEE Transactions on Power Systems, 2011, 26(2): 957–964.
[13] RAHIMIYAN M, BARINGO L. Strategic bidding for a virtual power plant in the day-ahead and real-time markets: a price-taker robust optimization approach[J]. IEEE Transactions on Power Systems, 2016, 31(4): 2676–2687.
[14] 马腾飞, 裴玮, 肖浩, 等. 基于纳什谈判理论的风–光–氢多主体能源系统合作运行方法[J]. 中国电机工程学报, 2021, 41(1): 25–39,395
MA Tengfei, PEI Wei, XIAO Hao, et al. Cooperative operation method for wind-solar-hydrogen multi-agent energy system based on Nash bargaining theory[J]. Proceedings of the CSEE, 2021, 41(1): 25–39,395
[15] 方燕琼, 甘霖, 艾芊, 等. 基于主从博弈的虚拟电厂双层竞标策略[J]. 电力系统自动化, 2017, 41(14): 61–69
FANG Yanqiong, GAN Lin, AI Qian, et al. Stackelberg game based Bi-level bidding strategy for virtual power plant[J]. Automation of Electric Power Systems, 2017, 41(14): 61–69
[16] 赵力航, 常伟光, 杨敏, 等. 电力市场环境下虚拟电厂两阶段能量经济优化调度[J]. 中国电力, 2022, 55(10): 14–22
ZHAO Lihang, CHANG Weiguang, YANG Min, et al. Two-stage energy economic optimal dispatch of virtual power plant in deregulated electricity market[J]. Electric Power, 2022, 55(10): 14–22
[17] 周博, 吕林, 高红均, 等. 多虚拟电厂日前鲁棒交易策略研究[J]. 电网技术, 2018, 42(8): 2694–2703
ZHOU Bo, Lü Lin, GAO Hongjun, et al. Robust day-ahead trading strategy for multiple virtual power plants[J]. Power System Technology, 2018, 42(8): 2694–2703
[18] 顾欣, 王琦, 胡云龙, 等. 基于纳什议价的多微网综合能源系统分布式低碳优化运行策略[J]. 电网技术, 2022, 46(4): 1464–1482
GU Xin, WANG Qi, HU Yunlong, et al. Distributed low-carbon optimal operation strategy of multi-microgrids integrated energy system based on Nash bargaining[J]. Power System Technology, 2022, 46(4): 1464–1482
[19] SHUAI X Y, WANG X L, GUO H, et al. Cooperative operation mechanism of multi-energy microgrids based on Nash bargaining method[J]. IOP Conference Series:Earth and Environmental Science, 2021, 769(4): 042121.
[20] 吴静. 分布式资源聚合虚拟电厂多维交易优化模型研究[D]. 北京: 华北电力大学(北京), 2021
WU Jing. Research on multi-dimensional transaction optimization model of virtual power plant aggregated by distributed resources[D]. Beijing: North China Electric Power University, 2021.
[21] WANG H, HUANG J W. Incentivizing energy trading for interconnected microgrids[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 2647–2657.
[22] FAN S L, AI Q, PIAO L J. Bargaining-based cooperative energy trading for distribution company and demand response[J]. Applied Energy, 2018, 226: 469–482.
[23] BOYD S. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2010, 3(1): 1–122.