Energy Sharing Incentive Strategy of Prosumers Considering Conditional Value at Risk and Integrated Demand Response
Sun Yi1, Li Fei1,2, Hu Yajie1, Chen Minghao1, Zheng Shunlin1
1. School of Electrical and Electronic Engineering North China Electric Power University Beijing 102206 China; 2. State Grid Hebei Electric Power Co. Ltd Shijiazhuang 050000 China
Abstract：With the wide spread of distributed new energy source and the multiple types of user's energy demand, traditional energy consumers are transforming into prosumers with both power production and consumption capabilities. In order to study the scheme of energy sharing between prosumers in the context of integrated energy, an incentive model for energy sharing between prosumers is designed, which considers the uncertainty risk of distributed new energy output and user electricity consumption and the integrated demand response. The integrated energy service provider (IESP) is the bridge between the energy market and prosumers, and forms a community of interests with multiple prosumers to lead the energy sharing activities between them. In order to promote the energy sharing between prosumers, it must be proved that all participants can benefit from the implementation of the energy sharing scheme. Therefore, the cost model of each participant before and after energy sharing needs to be established separately. Firstly, the basic cost model of integrated energy service provider and prosumers is established, and the conditional value at risk is used to represent the uncertainty risk of prosumers' electricity production and consumption. Secondly, based on the agreed energy sharing trading mechanism, the energy sharing incentive subsidy is used as a variable to establish an energy sharing model, that is, the cost of the community of interests is minimized. Then, the adaptive allocation strategy for shared benefits is designed, and the equivalent value of shared energy by prosumers is used to express their market contribution, so as to calculate the incentive subsidy amount. And based on the Nash game model, it is proved that the proposed energy sharing incentive mechanism has a unique optimal solution, and all participants can benefit from it. In test 1, by controlling the existence of the two mechanisms of integrated demand response and energy sharing, it was found that integrated demand response could receive better economic benefits than energy sharing, and the combination of the two can further stimulate their respective advantages, reducing the overall cost by 21.63%. And the energy sharing mechanism can reduce the use of energy storage equipment by prosumers. In test 2, it is verified that the net cost savings of prosumers under the adaptive benefit allocation model are positively correlated with their market contribution, which ensures the fairness of market participants. The experiment on the change of risk weight shows that it is necessary to balance the overall economy and risk prevention. Finally, the qualitative analysis of energy trading price shows that the trading price between IESP and prosumers has little impact on the energy sharing strategy, while the energy price interacting with the energy market has obvious impact on the energy sharing strategy. When the power purchase price of IESP rises, the power sharing between prosumers will be more active. When the gas purchase price of IESP rises, the electricity and gas energy sharing between prosumers will be more active. When the price of electricity sold from the IESP increases, the overall sharing level will decline, and the prosumers will be more willing to transfer the surplus electricity to the external grid. Therefore, in the implementation of energy sharing scheme, the price policy of local energy market should be considered, and the integrated demand response can also be used together to fully mobilize the enthusiasm of prosumers.
孙毅, 李飞, 胡亚杰, 陈明昊, 郑顺林. 计及条件风险价值和综合需求响应的产消者能量共享激励策略[J]. 电工技术学报, 2023, 38(9): 2448-2463.
Sun Yi, Li Fei, Hu Yajie, Chen Minghao, Zheng Shunlin. Energy Sharing Incentive Strategy of Prosumers Considering Conditional Value at Risk and Integrated Demand Response. Transactions of China Electrotechnical Society, 2023, 38(9): 2448-2463.
[1] 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. [2] 姜云鹏, 任洲洋, 李秋燕, 等. 考虑多灵活性资源协调调度的配电网新能源消纳策略[J]. 电工技术学报, 2022, 37(7): 1820-1835. Jiang Yunpeng, Ren Zhouyang, Li Qiuyan, et al.An accommodation strategy for renewable energy in distribution network considering coordinated dispatching of multi-flexible resources[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1820-1835. [3] 任景, 薛晨, 马晓伟, 等. 源荷联动调峰辅助服务市场两阶段模型[J]. 电力系统自动化, 2021, 45(18): 94-102. Ren Jing, Xue Chen, Ma Xiaowei, et al.Two-stage model of peak regulation ancillary service market with source-load interaction[J]. Automation of Electric Power Systems, 2021, 45(18): 94-102. [4] 潘虹锦, 高红均, 刘畅, 等. 综合能源服务商-多用户利益共同体协调运营优化策略研究[J]. 中国电机工程学报, 2021, 41(9): 3195-3207. Pan Hongjin, Gao Hongjun, Liu Chang, et al.Research on coordinated operation optimization strategy of integrated energy service provider and multi-users interest community[J]. Proceedings of the CSEE, 2021, 41(9): 3195-3207. [5] 陈永龙, 朱金大, 杨冬梅, 等. 基于多方利益博弈的园区级综合能源系统经济优化运行技术研究[J]. 高电压技术, 2021, 47(1): 102-110. Chen Yonglong, Zhu Jinda, Yang Dongmei, et al.Research on economic optimization operation technology of park-level integrated energy system based on multi-party interest game[J]. High Voltage Engineering, 2021, 47(1): 102-110. [6] 刘起兴, 胡亚平, 许丹莉, 等. 计及综合能源服务商调节域的综合需求响应市场机制[J]. 电网技术, 2021, 45(5): 1932-1941. Liu Qixing, Hu Yaping, Xu Danli, et al.Integrated demand response market mechanism considering adjustable region of integrated energy service providers[J]. Power System Technology, 2021, 45(5): 1932-1941. [7] Sheikhi A, Bahrami S, Ranjbar A M.An autonomous demand response program for electricity and natural gas networks in smart energy hubs[J]. Energy, 2015, 89: 490-499. [8] 祁兵, 郑顺林, 孙毅, 等. 考虑需求侧动态及耦合特性的激励型综合需求响应优化建模[J]. 中国电机工程学报, 2022, 42(5): 1783-1798. Qi Bing, Zheng Shunlin, Sun Yi, et al.A model of incentive-based integrated demand response considering dynamic characteristics and multi-energy coupling effect of demand side[J]. Proceedings of the CSEE, 2022, 42(5): 1783-1798. [9] 孙毅, 胡亚杰, 郑顺林, 等. 考虑用户响应特性的综合需求响应优化激励策略[J]. 中国电机工程学报, 2022, 42(4): 1402-1412. Sun Yi, Hu Yajie, Zheng Shunlin, et al.Integrated demand response optimization incentive strategy considering users' response characteristics[J]. Proceedings of the CSEE, 2022, 42(4): 1402-1412. [10] 刘静琨, 张宁, 康重庆. 电力系统云储能研究框架与基础模型[J]. 中国电机工程学报, 2017, 37(12): 3361-3371, 3663. Liu Jingkun, Zhang Ning, Kang Chongqing.Research framework and basic models for cloud energy storage in power system[J]. Proceedings of the CSEE, 2017, 37(12): 3361-3371, 3663. [11] 康重庆, 刘静琨, 张宁. 未来电力系统储能的新形态: 云储能[J]. 电力系统自动化, 2017, 41(21): 2-8, 16. Kang Chongqing, Liu Jingkun, Zhang Ning.A new form of energy storage in future power system: cloud energy storage[J]. Automation of Electric Power Systems, 2017, 41(21): 2-8, 16. [12] Akter M N, Mahmud M A, Haque M E, et al.An optimal distributed energy management scheme for solving transactive energy sharing problems in residential microgrids[J]. Applied Energy, 2020, 270: 115133. [13] Liu Nian, Wang Jie, Wang Lingfeng.Hybrid energy sharing for multiple microgrids in an integrated heat-electricity energy system[J]. IEEE Transactions on Sustainable Energy, 2019, 10(3): 1139-1151. [14] Cui Shichang, Wang Yanwu, Li Chaojie, et al.Prosumer community: a risk aversion energy sharing model[J]. IEEE Transactions on Sustainable Energy, 2020, 11(2): 828-838. [15] Mediwaththe C P, Shaw M, Halgamuge S, et al.An incentive-compatible energy trading framework for neighborhood area networks with shared energy storage[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 467-476. [16] 唐家俊, 张智, 杨莉, 等. 考虑地域优势及合作成本的增量配售电公司联盟策略[J]. 电力系统自动化, 2021, 45(20): 84-92. Tang Jiajun, Zhang Zhi, Yang Li, et al.Alliance strategy for incremental distribution and retail companies considering geographical advantage and cooperation cost[J]. Automation of Electric Power Systems, 2021, 45(20): 84-92. [17] 麻秀范, 余思雨, 朱思嘉, 等. 基于多因素改进Shapley的虚拟电厂利润分配[J]. 电工技术学报, 2020, 35(增刊2): 585-595. Ma Xiufan, Yu Siyu, Zhu Sijia, et al.Profit allocation to virtual power plant members based on improved multifactor shapley value method[J]. Transactions of China Electrotechnical Society, 2020, 35(S2): 585-595. [18] Wang Hao, Huang Jianwei.Incentivizing energy trading for interconnected microgrids[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 2647-2657. [19] 金国彬, 潘狄, 陈庆, 等. 考虑源荷不确定性的直流配电网模糊随机日前优化调度[J]. 电工技术学报, 2021, 36(21): 4517-4528. Jin Guobin, Pan Di, Chen Qing, et al.Fuzzy random day-ahead optimal dispatch of DC distribution network considering the uncertainty of source-load[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4517-4528. [20] 马瑞, 金艳, 刘鸣春. 基于机会约束规划的主动配电网分布式风光双层优化配置[J]. 电工技术学报, 2016, 31(3): 145-154. Ma Rui, Jin Yan, Liu Mingchun.Bi-level optimal configuration of distributed wind and photovoltaic generations in active distribution network based on chance constrained programming[J]. Transactions of China Electrotechnical Society, 2016, 31(3): 145-154. [21] 夏鹏, 刘文颖, 张尧翔, 等. 考虑风电高阶不确定性的分布式鲁棒优化调度模型[J]. 电工技术学报, 2020, 35(1): 189-200. Xia Peng, Liu Wenying, Zhang Yaoxiang, et al.A distributionally robust optimization scheduling model considering higher-order uncertainty of wind power[J]. Transactions of China Electrotechnical Society, 2020, 35(1): 189-200. [22] 刘怀东, 冯志强, 王锦桥, 等. 计及条件风险价值的综合能源系统经济调度[J]. 电网技术, 2018, 42(5): 1385-1392. Liu Huaidong, Feng Zhiqiang, Wang Jinqiao, et al.Economic dispatch of integrated energy systems considering conditional value-at-risk[J]. Power System Technology, 2018, 42(5): 1385-1392. [23] 帅轩越, 王秀丽, 吴雄, 等. 计及条件风险价值下基于合作博弈的多微网协同优化调度[J]. 电网技术, 2022, 46(1): 130-137. Shuai Xuanyue, Wang Xiuli, Wu Xiong, et al.Cooperative optimal scheduling of multi-microgrids based on cooperative game considering conditional value at risk[J]. Power System Technology, 2022, 46(1): 130-137. [24] 车泉辉, 娄素华, 吴耀武, 等. 计及条件风险价值的含储热光热电站与风电电力系统经济调度[J]. 电工技术学报, 2019, 34(10): 2047-2055. Che Quanhui, Lou Suhua, Wu Yaowu, et al.Economic dispatching for power system of concentrated solar power plant with thermal energy storage and wind power considering conditional value-at-risk[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 2047-2055. [25] 张虹, 葛得初, 侯宁, 等. 基于WCVaR模型的分布式发电系统供需互动能量管理研究[J]. 中国电机工程学报, 2019, 39(15): 4468-4477. Zhang Hong, Ge Dechu, Hou Ning, et al.Research on the interactive energy management of supply and demand in a distributed generation system based on the WCVaR model[J]. Proceedings of the CSEE, 2019, 39(15): 4468-4477. [26] 文云峰, 瞿小斌, 肖友强, 等. 耦合能量枢纽多区域电—气互联能源系统分布式协同优化调度[J]. 电力系统自动化, 2019, 43(9): 22-30. Wen Yunfeng, Qu Xiaobin, Xiao Youqiang, et al.Distributed coordinated optimal dispatch of multi-regional electricity-gas integrated energy systems with energy hubs[J]. Automation of Electric Power Systems, 2019, 43(9): 22-30. [27] Nguyen T A, Crow M L.Stochastic optimization of renewable-based microgrid operation incorporating battery operating cost[J]. IEEE Transactions on Power Systems, 2016, 31(3): 2289-2296. [28] Li Pan, Wang Hao, Zhang Baosen.A distributed online pricing strategy for demand response programs[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 350-360. [29] 孙宇军, 王岩, 王蓓蓓, 等. 考虑需求响应不确定性的多时间尺度源荷互动决策方法[J]. 电力系统自动化, 2018, 42(2): 106-113, 159. Sun Yujun, Wang Yan, Wang Beibei, et al.Multi-time scale decision method for source-load interaction considering demand response uncertainty[J]. Automation of Electric Power Systems, 2018, 42(2): 106-113, 159. [30] Rockafellar R T, Uryasev S.Conditional value-at-risk for general loss distributions[J]. Journal of Banking & Finance, 2002, 26(7): 1443-1471. [31] 王昀, 谢海鹏, 孙啸天, 等. 计及激励型综合需求响应的电-热综合能源系统日前经济调度[J]. 电工技术学报, 2021, 36(9): 1926-1934. Wang Yun, Xie Haipeng, Sun Xiaotian, et al.Day-ahead economic dispatch for electricity-heating integrated energy system considering incentive integrated demand response[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1926-1934. [32] Wang Jianxiao, Zhong Haiwang, Qin Junjie, et al.Incentive mechanism for sharing distributed energy resources[J]. Journal of Modern Power Systems and Clean Energy, 2019, 7(4): 837-850. [33] 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.
2023, Vol. 38 
