Scheduling planning for virtual power plants based on an improved cost allocation method

doi:10.3969/j.issn.2097-0706.2025.01.002

Abstract

Abstract:

The high proportion of electric vehicles （EVs） and distributed power sources would affect the power balance of the power system. Virtual power plants（VPPs） provide a new method to enhance the utilization rate of renewable energy and balance the load. Existing studies rarely address the cooperation between VPPs and EV charging stations（CSs） managed by different stakeholders. A cooperative operation framework is proposed for a multi-stakeholder VPP-EV charging station system. The conflict of interests of different stakeholders was resolved by the $τ$ value cost allocation method，while a feedback mechanism was established to collect stakeholders' opinions，continuously optimizing and improving the cost allocation scheme. A hierarchical reinforcement learning（HRL） algorithm was applied to the proposed model，to effectively overcome the challenges related to large state-action spaces and reward coefficient by decomposing the complex problem into multiple sub-problems and implementing control and optimization at different levels. Numerical cases were presented to demonstrate the effectiveness of the proposed method.

Key words: virtual power plant, electric vehicle charging station, multi-stakeholder, feedback mechanism, $τ$ value cost allocation method, hierarchical reinforcement learning algorithm, renewable energy

CLC Number:

TK01

SU Rui, WANG Xilong, JIANG Yan, SONG Chenhui. Scheduling planning for virtual power plants based on an improved cost allocation method[J]. Integrated Intelligent Energy, 2025, 47(1): 10-17.

Figures/Tables 7

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Table 1

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References 26

[1]	曾梦隆, 韦钢, 朱兰, 等. 交直流配电网中电动汽车充换储一体站规划[J]. 电力系统自动化, 2021, 45(18): 52-60.
	ZENG Menglong, WEI Gang, ZHU Lan, et al. Planning of electric vehicle charging-swapping-storage integrated station in AC/DC distribution network[J]. Automation of Electric Power Systems, 2021, 45(18): 52-60.
[2]	赵昊天, 王彬, 潘昭光, 等. 支撑云-群-端协同调度的多能园区虚拟电厂: 研发与应用[J]. 电力系统自动化, 2021, 45(5): 111-121.
	ZHAO Haotian, WANG Bin, PAN Zhaoguang, et al. Research and application of park-level multi-energy virtual power plants supporting cloud-cluster-end multi-level synergetic dispatch[J]. Automation of Electric Power Systems, 2021, 45(5): 111-121.
[3]	杨立滨, 曹阳, 魏韡, 等. 计及风电不确定性和弃风率约束的风电场储能容量配置方法[J]. 电力系统自动化, 2020, 44(16): 45-52.
	YANG Libin, CAO Yang, WEI Wei, et al. Configuration method of energy storage for wind farms considering wind power uncertainty and wind curtailment constraint[J]. Automation of Electric Power Systems, 2020, 44(16): 45-52.
[4]	田立亭, 程林, 郭剑波, 等. 虚拟电厂对分布式能源的管理和互动机制研究综述[J]. 电网技术, 2020, 44(6): 2097-2108.
	TIAN Liting, CHENG Lin, GUO Jianbo, et al. A review on the study of management and interaction mechanism for distributed energy in virtual power plants[J]. Power System Technology, 2020, 44(6): 2097-2108.
[5]	李明扬, 董哲. 含分布式新能源和需求响应负荷的虚拟电厂定价机制及优化调度[J]. 综合智慧能源, 2024, 46(10): 12-17. doi: 10.3969/j.issn.2097-0706.2024.10.002
	LI Mingyang, DONG Zhe. Pricing mechanism and optimal scheduling of virtual power plants containing distributed renewable energy and demand response loads[J]. Integrated Intelligent Energy, 2024, 46(10): 12-17. doi: 10.3969/j.issn.2097-0706.2024.10.002
[6]	JU L W, LI H H, ZHAO J W, et al. Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response[J]. Energy Conversion and Management, 2016, 128: 160-177.
[7]	周亦洲, 孙国强, 黄文进, 等. 计及电动汽车和需求响应的多类电力市场下虚拟电厂竞标模型[J]. 电网技术, 2017, 41(6): 1759-1767.
	ZHOU Yizhou, SUN Guoqiang, HUANG Wenjin, et al. Strategic bidding model for virtual power plant in different electricity markets considering electric vehicles and demand response[J]. Power System Technology, 2017, 41(6): 1759-1767.
[8]	张凯杰, 丁国锋, 闻铭, 等. 虚拟电厂的优化调度技术与市场机制设计综述[J]. 综合智慧能源, 2022, 44(2): 60-72. doi: 10.3969/j.issn.2097-0706.2022.02.009
	ZHANG Kaijie, DING Guofeng, WEN Ming, et al. Review of optimal dispatching technology and market mechanism design for virtual power plants[J]. Integrated Intelligent Energy, 2022, 44(2): 60-72. doi: 10.3969/j.issn.2097-0706.2022.02.009
[9]	FAN S, LIU J, WU Q, et al. Optimal coordination of virtual power plant with photovoltaics and electric vehicles: a temporally coupled distributed online algorithm[J]. Applied Energy, 2020, 277: 115583.
[10]	张卫国, 宋杰, 郭明星, 等. 考虑电动汽车充电需求的虚拟电厂负荷均衡管理策略[J]. 电力系统自动化, 2022, 46(9):118-126.
	ZHANG Weiguo, SONG Jie, GUO Mingxing, et al. Load balancing management strategy for virtual power plants considering charging demand of electric vehicles[J]. Automation of Electric Power Systems, 2022, 46(9): 118-126.
[11]	SHARMA S, ABHYANKAR A R. Loss allocation for weakly meshed distribution system using analytical formulation of shapley value[J]. IEEE Transactions on Power Systems, 2017, 32(2): 1369-1377.
[12]	LI Y, LIU W J, SHAHIDEHPOUR M, et al. Optimal operation strategy for integrated natural gas generating unit and power-to-gas conversion facilities[J]. IEEE Transactions on Sustainable Energy, 2018, 9(4): 1870-1879.
[13]	MEI J, CHEN C, WANG J H, et al. Coalitional game theory based local power exchange algorithm for networked microgrids[J]. Applied Energy, 2019, 239: 133-141.
[14]	WANG H, JIA Y W, SHI M G, et al. A mutually beneficial operation framework for virtual power plants and electric vehicle charging stations[J]. IEEE Transactions on Smart Grid, 2023, 14(6): 4634-4648.
[15]	刘建行, 刘方. 基于深度强化学习的梯级水蓄风光互补系统优化调度策略研究[J]. 广东电力, 2024, 37(5): 10-22.
	LIU Jianhang, LIU Fang. Research on optimized dispatching strategy of cascade hydropower-pumping-storage-wind-photovoltaic multi-energy complementary system based on deep reinforcement learning[J]. Guangdong Electric Power, 2024, 37(5): 10-22.
[16]	ZHANG N, YAN J, HU C G, et al. Price-matching-based regional energy market with hierarchical reinforcement learning algorithm[J]. IEEE Transactions on Industrial Informatics, 2024, 20(9): 11103-11114.
[17]	林彦旭, 高辉. 基于SSA-VMD-BiLSTM模型的充电站负荷预测方法[J]. 广东电力, 2024, 37(6): 53-61.
	LIN Yanxu, GAO Hui. Load prediction method of charging station based on SSA-VMD-BiLSTM model[J]. Guangdong Electric Power, 2024, 37(6): 53-61.
[18]	WANG H, SHI M G, XIE P, et al. Electric vehicle charging scheduling strategy for supporting load flattening under uncertain electric vehicle departures[J]. Journal of Modern Power Systems and Clean Energy, 2023, 11(5):1634-1645.
[19]	WANG H, MEMBER G S, JIA Y, et al. A hybrid incentive program for managing electric vehicle charging flexibility[J]. IEEE Transactions on Smart Grid, 2023, 14(1): 476-488.
[20]	CLEGG S, MANCARELLA P. Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems[J]. IEEE Transactions on Sustainable Energy, 2015, 7(2): 718-731.
[21]	LI Y S, GAO D W, GAO W, et al. A distributed double-Newton descent algorithm for cooperative energy management of multiple energy bodies in energy Internet[J]. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5993-6003.
[22]	ALSKAIF J, SEKULOSK M, VAN L G, et al. Blockchain-based fully peer-to-peer energy trading strategies for residential energy systems[J]. IEEE Transactions on Industrial Informatics, 2022, 18(1):231-241.
[23]	李明扬, 窦梦园. 基于强化学习的含电动汽车虚拟电厂优化调度[J]. 综合智慧能源, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
	LI Mingyang, DOU Mengyuan. Optimal scheduling of virtual power plants integrating electric vehicles based on reinforcement learning[J]. Integrated Intelligent Energy, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
[24]	LIU N, YU X H, WANG C, et al. Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers[J]. IEEE Transactions on Power Systems, 2017, 32(5): 3569-3583.
[25]	JIANG X Y, SUN C, CAO L L, et al. Peer-to-peer energy trading with energy path conflict management in energy local area network[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2269-2278.
[26]	ZHANG N, SUN Q, YANG L, et al. Event-triggered distributed hybrid control scheme for the integrated energy system[J]. IEEE Transactions on Industrial Informatics, 2021, 18(2): 835-846.

算法	求解时间最小值/s	求解时间最大值/s	求解时间平均值/s	目标函数最大值方差/s²
PPO	1.276	16.561	15.227	0.041
PSO	1.641	15.652	14.984	0.035
DDPG	9.634	11.638	10.191	0.015
HRL	9.721	11.701	10.227	0.012