Low-Carbon Operation Strategy for P2P Energy Trading Among Multiple Microgrids Considering Demand Response

doi:10.12204/j.issn.1000-7229.2023.12.005

Abstract

Abstract:

With the introduction of the “double carbon” target, microgrid is considered an effective form that integrates renewable power sources and improves energy utilization efficiency. In line with the increasing number of microgrids, peer-to-peer (P2P) energy trading among multiple microgrids is regarded as an effective solution for energy sharing and integration of distribution generation. This study proposes a P2P energy trading model that accounts for demand response and carbon emission characteristics to achieve supply and demand coordination while determining the optimal operation strategy. First, the microgrid in the model solves an economic optimization problem by participating in the demand response. Second, by considering carbon emission factors, the model offers a great opportunity to reduce operation and carbon emission costs, accounting for energy transactions and energy storage scheduling. To incentivize energy sharing among multiple microgrids, a P2P energy-trading model based on generalized Nash bargaining was developed that enables energy sharing and revenue allocation. In addition, optimal power flow constraints were incorporated into the model to enhance the security of power system operations. Finally, a simulation of the IEEE-33 system demonstrates that P2P energy trading among multiple microgrids effectively reduces operating costs and promotes the integration of renewable energy sources.

Key words: multiple microgrids, demand response, peer to peer (P2P) energy trading, generalized Nash bargaining

CLC Number:

TM74

ZHAO Jie, WANG Cong, LI Guanguan, WU Bin, LI Na, PENG Ke. Low-Carbon Operation Strategy for P2P Energy Trading Among Multiple Microgrids Considering Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(12): 54-65.

Figures/Tables 9

Fig.1 Structure of P2P energy trading among multiple microgrids

Fig.2 Flow chart of the decomposition of P2P energy trading among multiple microgrids

Fig.3 Predictive value of renewable generations and loads

Fig.4 Transaction power price between microgrids and distribution network

Table 1

Fig.5 Demand response of multiple microgrids

Fig.6 Results of energy storage scheduling

Fig.7 P2P energy transaction among multiple microgrids

Fig.8 Voltage of node connected with multiple microgrids

References 38

[1]	刘迎澍, 陈曦, 李斌, 等. 多微网系统关键技术综述[J]. 电网技术, 2020, 44(10): 3804-3820.
	LIU Yingshu, CHEN Xi, LI Bin, et al. State of art of the key technologies of multiple microgrids system[J]. Power System Technology, 2020, 44(10): 3804-3820.
[2]	王开艳, 梁岩, 贾嵘. 考虑共享储能的冷热电联供型微网低碳经济调度[J]. 电网与清洁能源, 2022, 38(11): 155-162.
	WANG Kaiyan, LIANG Yan, JIA Rong. Low-carbon economical dispatch of the combined cooling, heating and power microgrid considering shared energy storage[J]. Power System and Clean Energy, 2022, 38(11): 155-162.
[3]	陈玥, 刘锋, 魏韡, 等. 需求侧能量共享:概念、机制与展望[J]. 电力系统自动化, 2021, 45(2): 1-11.
	CHEN Yue, LIU Feng, WEI Wei, et al. Energy sharing at demand side: concept, mechanism and prospect[J]. Automation of Electric Power Systems, 2021, 45(2): 1-11.
[4]	高松, 何俊, 杨松坤, 等. 基于交替方向乘子法的多微电网能量共享方法研究[J]. 电网与清洁能源, 2022, 38(6): 113-120.
	GAO Song, HE Jun, YANG Songkun, et al. Energy management of interconnected microgrid based on alternating direction method of multipliers method[J]. Power System and Clean Energy, 2022, 38(6): 113-120.
[5]	ZHOU Y, WU J Z, LONG C, et al. State-of-the-art analysis and perspectives for peer-to-peer energy trading[J]. Engineering, 2020, 6(7): 739-753, 836. doi: 10.1016/j.eng.2020.06.002 URL
[6]	CHEN Y, PARK B, KOU X, et al. A comparison study on trading behavior and profit distribution in local energy transaction games[J]. Applied Energy, 2020, 280: 115941. doi: 10.1016/j.apenergy.2020.115941 URL
[7]	WAYES T, CHAU Y, HAMED M R, et al. Transforming energy networks via peer-to-peer energy trading: the potential of game-theoretic approaches[J]. IEEE Signal Processing Magazine, 2018, 35(4): 90-111. doi: 10.1109/MSP.2018.2818327 URL
[8]	刘晓峰, 高丙团, 李扬. 博弈论在电力需求侧的应用研究综述[J]. 电网技术, 2018, 42(8): 2704-2711.
	LIU Xiaofeng, GAO Bingtuan, LI Yang. Review on application of game theory in power demand side[J]. Power System Technology, 2018, 42(8): 2704-2711.
[9]	郭柠瑄. 基于需求响应的负荷资源管理及博弈策略研究[D]. 杭州: 浙江大学, 2022.
	GUO Ningxuan. Research on load resource management and game strategy based on demand response[D]. Hangzhou: Zhejiang University, 2022.
[10]	李学平, 王健民, 卢志刚, 等. 基于非合作博弈与收益共享契约的能源服务商定价策略[J]. 电力自动化设备, 2022, 42(3): 1-8.
	LI Xueping, WANG Jianmin, LU Zhigang, et al. Pricing strategy of energy service provider based on non-cooperative game and revenue sharing contract[J]. Electric Power Automation Equipment, 2022, 42(3): 1-8.
[11]	陈玥. 多能源多主体系统运营趋优机制设计与均衡分析理论[D]. 北京: 清华大学, 2020.
	CHEN Yue. Optimum-approaching operation of multi-energy multi-agent systems: mechanism design and equilibrium analytics[D]. Beijing: Tsinghua University, 2020.
[12]	PAUDEL A, CHAUDHARI K, LONG C, et al. Peer-to-peer energy trading in a prosumer-based community microgrid: a game-theoretic model[J]. IEEE Transactions on Industrial Electronics, 2019, 66(8): 6087-6097. doi: 10.1109/TIE.2018.2874578 URL
[13]	梅生伟, 刘锋, 魏韡. 工程博弈论基础及电力系统应用[M]. 北京: 科学出版社, 2016.
[14]	刘念, 赵璟, 王杰, 等. 基于合作博弈论的光伏微电网群交易模型[J]. 电工技术学报, 2018, 33(8): 1903-1910.
	LIU Nian, ZHAO Jing, WANG Jie, et al. A trading model of PV microgrid cluster based on cooperative game theory[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1903-1910.
[15]	LUO C L, ZHOU X Y, LEV B. Core, shapley value, nucleolus and Nash bargaining solution: a survey of recent developments and applications in operations management[J]. Omega, 2022, 110: 102638. doi: 10.1016/j.omega.2022.102638 URL
[16]	冯昌森, 沈佳静, 赵崇娟, 等. 基于合作博弈的智慧能源社区协同运行策略[J]. 电力自动化设备, 2021, 41(4): 85-93.
	FENG Changsen, SHEN Jiajing, ZHAO Chongjuan, et al. Cooperative game-based coordinated operation strategy of smart energy community[J]. Electric Power Automation Equipment, 2021, 41(4): 85-93.
[17]	HAN L Y, MORSTYN T, MCCULLOCH M. Incentivizing prosumer coalitions with energy management using cooperative game theory[J]. IEEE Transactions on Power Systems, 2019, 34(1): 303-313. doi: 10.1109/TPWRS.2018.2858540 URL
[18]	WANG H, HUANG J W. Incentivizing energy trading for interconnected microgrids[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 2647-2657. doi: 10.1109/TSG.2016.2614988 URL
[19]	CUI S C, WANG Y W, LIU X K, et al. Economic storage sharing framework: asymmetric bargaining-based energy cooperation[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7489-7500. doi: 10.1109/TII.2021.3053296 URL
[20]	胡健, 于娣, 张晓杰. 电力P2P交易中计及社会福利的产消者合作联盟[J/OL]. 中国电机工程学报. (2023-02-09)[2023-08-07]. https://doi.org/10.13334/j.0258-8013.pcsee.222319.
	HU Jian, YU Di, ZHANG Xiaojie. Prosumer alliance in P2P transaction of electricity considering social welfare[J/OL]. Proceedings of the CSEE. (2023-02-09)[2023-08-07]. https://doi.org/10.13334/j.0258-8013.pcsee.222319.
[21]	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.
[22]	LI J Y, ZHANG C R, XU Z, et al. Distributed transactive energy trading framework in distribution networks[J]. IEEE Transactions on Power Systems, 2018, 33(6): 7215-7227. doi: 10.1109/TPWRS.59 URL
[23]	万灿, 贾妍博, 李彪, 等. 城镇能源互联网能源交易模式和用户响应研究现状与展望[J]. 电力系统自动化, 2019, 43(14): 29-40.
	WAN Can, JIA Yanbo, LI Biao, et al. Research status and prospect of energy trading mode and user demand response in urban energy internet[J]. Automation of Electric Power Systems, 2019, 43(14): 29-40.
[24]	徐博强, 赵建立, 张沛超, 等. 基于负荷准线和纳什谈判的高比例新能源消纳方法[J]. 电力系统自动化, 2023, 47(15): 36-45.
	XU Boqiang, ZHAO Jianli, ZHANG Peichao, et al. High-proportion renewable energy accommodation method based on customer directrix load and Nash bargaining[J]. Automation of Electric Power Systems, 2023, 47(15): 36-45.
[25]	帅轩越, 王秀丽, 吴雄, 等. 计及电热需求响应的共享储能容量配置与动态租赁模型[J]. 电力系统自动化, 2021, 45(19): 24-32.
	SHUAI Xuanyue, WANG Xiuli, WU Xiong, et al. Shared energy storage capacity allocation and dynamic lease model considering electricity-heat demand response[J]. Automation of Electric Power Systems, 2021, 45(19): 24-32.
[26]	谢元皓, 林声宏, 朱建全. 基于广义纳什议价的多微电网配电系统多主体协同能量管理策略[J/OL]. 电力自动化设备. (2023-07-17)[2023-08-07]. https://doi.org/10.16081/j.epae.202307015.
	XIE Yuanhao, LIN Shenghong, ZHU Jianquan. Multi-stakeholder collaborative energy management strategy for multi-microgrid distribution system based on generalized Nash bargaining[J/OL]. Electric Power Automation Equipment. (2023-07-17)[2023-08-07]. https://doi.org/10.16081/j.epae.202307015.
[27]	KIM H, LEE J, BAHRAMI S, et al. Direct energy trading of microgrids in distribution energy market[J]. IEEE Transactions on Power Systems, 2020, 35(1): 639-651. doi: 10.1109/TPWRS.59 URL
[28]	刘轶涵, 徐青山, 杨永标, 等. 计及配电网潮流约束下基于广义纳什议价理论的工业用户共享储能配置[J]. 电网技术, 2023, 47(2): 571-585.
	LIU Yihan, XU Qingshan, YANG Yongbiao, et al. Energy storage allocation shared by industrial users based on generalized Nash bargaining theory considering power flow constraints of distribution network[J]. Power System Technology, 2023, 47(2): 571-585.
[29]	ZHONG X Q, ZHONG W F, LIU Y, et al. Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations[J]. Energy, 2022, 246: 123428. doi: 10.1016/j.energy.2022.123428 URL
[30]	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.
[31]	葛少云, 程雪颖, 刘洪, 等. 园区多微网P2P电-碳耦合交易市场设计[J]. 高电压技术, 2023, 49(4): 1341-1349.
	GE Shaoyun, CHENG Xueying, LIU Hong, et al. Market design of P2P electricity carbon coupling transaction among multi-microgrids in a zone[J]. High Voltage Engineering, 2023, 49(4): 1341-1349.
[32]	吴锦领, 楼平, 管敏渊, 等. 基于非对称纳什谈判的多微网电能共享运行优化策略[J]. 电网技术, 2022, 46(7): 2711-2723.
	WU Jinling, LOU Ping, GUAN Minyuan, et al. Operation optimization strategy of multi-microgrids energy sharing based on asymmetric Nash bargaining[J]. Power System Technology, 2022, 46(7): 2711-2723.
[33]	朱星旭. 电力系统运行的分布式在线优化算法研究[D]. 济南: 山东大学, 2020.
	ZHU Xingxu. Research on distributed online optimization algorithm for power system operation[D]. Jinan: Shandong University, 2020.
[34]	吴晨雨. 电热综合能源系统的建模及优化运行[D]. 南京: 东南大学, 2019.
	WU Chenyu. Modeling and optimal operation of electrothermal comprehensive energy system[D]. Nanjing: Southeast University, 2019.
[35]	崔明勇, 宣名阳, 卢志刚, 等. 基于合作博弈的多综合能源服务商运行优化策略[J]. 中国电机工程学报, 2022, 42(10): 3548-3564.
	CUI Mingyong, XUAN Mingyang, LU Zhigang, et al. Operation optimization strategy of multi integrated energy service companies based on cooperative game theory[J]. Proceedings of the CSEE, 2022, 42(10): 3548-3564.
[36]	马云聪, 武传涛, 林湘宁, 等. 一种基于半分布式结构化拓扑的云储能点对点交易策略研究[J]. 中国电机工程学报, 2022, 42(21): 7731-7746.
	MA Yuncong, WU Chuantao, LIN Xiangning, et al. Research on peer-to-peer transaction strategy of cloud energy storage based on semi-distributed structured topology[J]. Proceedings of the CSEE, 2022, 42(21): 7731-7746.
[37]	Gurobi optimizer reference manual 2020 [EB/OL]. [2023-08-07]. .http://www.gurobi.com
[38]	LI G G, LI Q Q, YANG X, et al. General Nash bargaining based direct P2P energy trading among prosumers under multiple uncertainties[J]. International Journal of Electrical Power & Energy Systems, 2022, 143: 108403. doi: 10.1016/j.ijepes.2022.108403 URL