Abstract:Global technological progress makes distributed energy resources(DERs) develop rapidly. Virtual power plant (VPP) can effectively integrate all types of DERs to participate in the power market as a whole, but how to realize the reasonable profit allocation among multiple interest members in VPP remains to be studied. This paper used the conditional value at risk (CVaR) to measure the risk of profit fluctuation caused by the uncertainty of VPP members, and constructed a VPP market competition model that takes into account various DERs to maximize VPP returns under the controllable risk conditions. On this basis, considering risk level, profit contribution and profit growth rate, a VPP profit distribution model suitable for multi-members is proposed based on the improved multifactor Shapley value method. The simulation results show that the model can effectively improve the overall return of VPP and its internal members, and can guarantee the fairness and rationality of profit allocation under various risk levels, encourage DERs to join VPP and improve alliance stability.
麻秀范, 余思雨, 朱思嘉, 王戈. 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(zk2): 585-595.
Ma Xiufan, Yu Siyu, Zhu Sijia, Wang Ge. Profit Allocation to Virtual Power Plant Members Based on Improved Multifactor Shapley Value Method. Transactions of China Electrotechnical Society, 2020, 35(zk2): 585-595.
[1] 国际能源署(IEA)《2018年世界能源展望》[R]. 2018. [2] 赵波, 汪湘晋, 张雪松, 等. 考虑需求侧响应及不确定性的微电网双层优化配置方法[J]. 电工技术学报, 2018, 33(14): 3284-3295. Zhao Bo, Wang Xiangjin, Zhang Xuesong, et al.Two-layer method of microgrid optimal sizing considering demand-side response and uncertainties[J]. Transac-tions of China Electrotechnical Society, 2018, 33(14): 3284-3295. [3] Nosratabadi S M, Hooshmand R A, Gholipour E.A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems[J]. Renewable and Sustainable Energy Reviews, 2017, 67: 341-363. [4] 赵冬梅, 殷加玞. 考虑源荷双侧不确定性的模糊随机机会约束优先目标规划调度模型[J]. 电工技术学报, 2018, 33(5): 1076-1085. Zhao Dongmei, Yin Jiafu.Fuzzy random chance constrained preemptive goal programming scheduling model considering source-side and load-side uncertainty[J]. Transactions of China Electrotechnical Society, 2018, 33(5): 1076-1085. [5] 范松丽, 艾芊, 贺兴. 基于机会约束规划的虚拟电厂调度风险分析[J]. 中国电机工程学报, 2015, 35(16): 4025-4034. Fan Songli, Ai Qian, He Xing.Risk analysis on dispatch of virtual power plant based on chance constrained programming[J]. Proceedings of the CSEE, 2015, 35(16): 4025-4034. [6] 徐辉, 焦扬, 蒲雷, 等. 计及不确定性和需求响应的风光燃储集成虚拟电厂随机调度优化模型[J]. 电网技术, 2017, 41(11): 3590-3597. Xu Hui, Jiao Yang, Pu Lei, et al.Stochastic scheduling optimization model for virtual power plant of integrated wind-photovoltaic-energy storage system considering uncertainty and demand response[J]. Power System Technology, 2017, 41(11): 3590-3597. [7] Dabbagh S R, Sheikh-El-Eslami M K. Risk assessment of virtual power plants offering in energy and reserve markets[J]. IEEE Transactions on Power Systems, 2015, 31(5): 3572-3582. [8] 卢强, 陈来军, 梅生伟. 博弈论在电力系统中典型应用及若干展望[J]. 中国电机工程学报, 2014, 34(29): 5009-5017. Lu Qiang, Chen Laijun, Mei Shengwei.Typical applications and prospects of game theory in power system[J]. Proceedings of the CSEE, 2014, 34(29): 5009-5017. [9] 董文略, 王群, 杨莉. 含风光水的虚拟电厂与配电公司协调调度模型[J]. 电力系统自动化, 2015, 39(9): 75-81, 207. Dong Wenlüe, Wang Qun, Yang Li.A coordinated dispatching model for a distribution utility and virtual power plants with wind/photovoltaic/hydro generators[J]. Automation of Electric Power Systems, 2015, 39(9): 75-81, 207. [10] Tan Zhongfu, Li Huanhuan, Ju Liwei, et al.Joint scheduling optimization of virtual power plants and equitable profit distribution using Shapely value theory[J]. Mathematical Problems in Engineering, 2018(7): 1-13. [11] 王晛, 张华君, 张少华. 风电和电动汽车组成虚拟电厂参与电力市场的博弈模型[J]. 电力系统自动化, 2019, 43(3): 155-164. Wang Xian, Zhang Huajun, Zhang Shaohua.Game model of electric power market involving virtual power plants composed of wind power and electric vehicles[J]. Automation of Electric Power Systems, 2019, 43(3): 155-164. [12] Cheng Yan, Fan Songli, Ni Jianmo, et al.An innovative profit allocation to distributed energy resources integrated into virtual power plant[C]// International Conference on Renewable Power Generation, Beijing, China, 2015, DOI: 10.1049/cp. 2015.0547 . [13] 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. [14] Wang Qianfan, Guan Yongpei, Wang Jianhui.A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output[J]. IEEE Transactions on Power Systems, 2011, 27(1): 206-215. [15] 韦鹏飞, 徐永海, 王金浩, 等. 基于拉丁超立方采样的节点敏感设备暂降免疫水平评估[J]. 电工技术学报, 2018, 33(15): 3415-3425. Wei Pengfei, Xu Yonghai, Wang Jinhao, et al.Sag immunity level evaluation of sensitive equipment at node based on Latin hypercube sampling[J]. Transactions of China Electrotechnical Society, 2018, 33(15): 3415-3425. [16] Carpinelli G, Caramia P, Varilone P.Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems[J]. Renewable Energy, 2015, 76: 283-295. [17] 邵振国, 黄伟达. 考虑出力不确定性的分布式电源谐波传播计算[J]. 电工技术学报, 2019, 34(增刊2): 674-683. Shao Zhenguo, Huang Weida.A calculation method of harmonic propagation consideringthe uncertainty of distributed generation[J]. Transactions of China Electrotechnical Society, 2019, 34(S2): 674-683. [18] Wang J, Shahidehpour M, Li Z.Security-constrained unit commitment with volatile wind power generation[J]. IEEE Transactions on Power Systems, 2008, 23(3): 1319-1327. [19] 刘文霞, 凌云頔, 赵天阳. 低碳经济下基于合作博弈的风电容量规划方法[J]. 电力系统自动化, 2015, 39(19): 68-74. Liu Wenxia, Ling Yundi, Zhao Tianyang.Cooperative game based capacity planning model for wind power in low-carbon economy[J]. Automation of Electric Power Systems, 2015, 39(19): 68-74. [20] Contreras J, Klusch M, Krawczyk J B.Numerical solutions to Nash-cournot equilibria in coupled constraint electricity markets[J]. IEEE Transactions on Power Systems, 2004, 19(1): 195-206. [21] 李梅. 基于决策者偏好视角的直觉模糊多属性决策方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2016. [22] 吴旭. 基于N-k故障的电力系统运行风险及脆弱性评估[D]. 北京: 华北电力大学, 2013. [23] PJM. Data Miner 2, <http:// dataminer2.pjm.com>. [24] National Renewable Energy Laboratory (NREL). https://data.nrel.gov. [25] IBM ILOG CPLEX 12.6, 2013, CPLEX 12.6, 2013, .6, 2013, CPLEX 12.6, 2013, http://www.cplex.com.