计及电动汽车混合充电系统接入的综合能源系统鲁棒优化调度

doi:10.19595/j.cnki.1000-6753.tces.241696

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Abstract With the access of electric vehicles to the integrated energy system (IES), especially the vigorous development of wireless charging technology for electric vehicles in recent years, the spatiotemporal coupling between IES and wireless charging electric vehicles (WCEV) has become increasingly strong. This feature makes it challenging to optimize the scheduling of IES and WCEV and achieve a win-win situation between the two. Therefore, introducing the wireless charging system into the integrated energy system and forming a hybrid charging system with traditional wired charging electric vehicles is essential for improving the overall economic benefits of the system.
This paper takes the joint operation system composed of an integrated energy system and a hybrid charging system as the research object. The objective function considering the uncertainty of both is established. Considering the conflict of interests between superiors and subordinates, a master-slave game model is established to connect the wireless charging technology of electric vehicles to the joint operation system. A two-stage robust optimization method under multiple uncertainty scenarios of EV and IES is proposed, and a column and constraint generation algorithm ensures the optimization model of the joint operation system converges fast when solving complex multi-layer problems. As a result, the proposed scheduling method can maximize the operating benefits of the integrated energy system and minimize the operating cost of the hybrid charging system in the worst scenario.
This paper establishes the objective function of the upper-level integrated energy system to maximize its benefits and the objective function of the lower-level hybrid charging system to minimize the hybrid system's operating cost. The simulation results show that the proposed method can converge in the third time, and the convergence accuracy is within 0.06%.
Therefore, on the integrated energy system side, this paper adopts the integrated energy system uncertainty strategy proposed by two-level robustness. After robust optimization, it can effectively solve the uncertainty of wind and solar output and thermal power load, maximizing the system operation benefit. On the hybrid charging system side, this paper adopts the hybrid charging system uncertainty strategy proposed by distributed robustness. After robust optimization, the absolute value deviation between the empirical distribution and the worst distribution of wireless charging electric vehicles and wired charging electric vehicles is kept in the interval [0, 0.01], effectively reducing the operating cost of the hybrid charging system.

Key words： Integrated energy system hybrid charging system wireless charging master-slave game robust optimization

Received: 27 September 2024

PACS:	TM724
	TM73

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	Zhang Xian
	Ding Kehao
	Zhao Liyuan
	Yang Qingxin

Cite this article:

Zhang Xian,Ding Kehao,Zhao Liyuan等. Robust Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Hybrid Charging System[J]. Transactions of China Electrotechnical Society, 2025, 40(14): 4446-4459.

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[1] 董雷, 杨子民, 乔骥, 等. 基于分层约束强化学习的综合能源多微网系统优化调度[J]. 电工技术学报, 2024, 39(5): 1436-1453.
Dong Lei, Yang Zimin, Qiao Ji, et al.Optimal scheduling of integrated energy multi-microgrid system based on hierarchical constraint reinforcement learning[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1436-1453.
[2] 贾士铎, 康小宁, 黑皓杰, 等. 基于V2G负荷反馈修正的电热氢综合能源系统多层协调优化调度[J]. 电力系统自动化, 2023, 47(15): 100-110.
Jia Shiduo, Kang Xiaoning, Hei Haojie, et al.Multi-layer coordinated optimal dispatch of electric- thermal-hydrogen integrated energy system based on V2G load feedback correction[J]. Automation of Electric Power Systems, 2023, 47(15): 100-110.
[3] Wei Wei, Xu Lin, Xu Jierui, et al.Day-ahead optimal scheduling method of park-level integrated energy system considering V2G technology[C]//2021 11TH International Conference on Power and Energy Systems, Shanghai, China, 2021: 827-833.
[4] 王灿, 张羽, 田福银, 等. 基于双向主从博弈的储能电站与综合能源系统经济运行策略[J]. 电工技术学报, 2023, 38(13): 3436-3446.
Wang Can, Zhang Yu, Tian Fuyin, et al.Economic operation of energy storage power stations and integrated energy systems based on bidirectional master-slave game[J]. Transactions of China Electro- technical Society, 2023, 38(13): 3436-3446.
[5] 袁坤龙, 张少康, 常冉, 等. 阶梯式碳交易机制下计及电-气-热综合能源系统需求响应优化运行[J]. 电气技术, 2024, 25(1): 8-16.
Yuan Kunlong, Zhang Shaokang, Chang Ran, et al.Optimal operation of demand response of electricity- gas-heat integrated energy system under the stepped carbon trading mechanism[J]. Electrical Engineering, 2024, 25(1): 8-16.
[6] 王义, 靳梓康, 王要强, 等. 考虑电动汽车共享储能特性的园区综合能源系统低碳运行[J]. 电力系统自动化, 2024, 48(5): 21-29.
Wang Yi, Jin Zikang, Wang Yaoqiang, et al.Low-carbon operation of park integrated energy system considering shared energy storage character- istics of electric vehicles[J]. Automation of Electric Power Systems, 2024, 48(5): 21-29.
[7] 王海洋, 李珂, 张承慧, 等. 基于主从博弈的社区综合能源系统分布式协同优化运行策略[J]. 中国电机工程学报, 2020, 40(17): 5435-5444.
Wang Haiyang, Li Ke, Zhang Chenghui, et al.Distributed coordinative optimal operation of com- munity integrated energy system based on stackelberg game[J]. Proceedings of the CSEE, 2020, 40(17): 5435-5444.
[8] Xie Haixiang, Gao Shan.Optimal scheduling of electric vehicles charging and discharging strategy based on real-time price considering multi-party interest[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2023, 18(7): 1111-1119.
[9] Dong Yingchao, Zhang Hongli, Ma Ping, et al.A hybrid robust-interval optimization approach for integrated energy systems planning under uncer- tainties[J]. Energy, 2023, 274: 127267.
[10] Cao Yan, Huang Liang, Li Yiqing, et al.Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique[J]. International Journal of Electrical Power and Energy Systems, 2020, 117: 105628.
[11] Li Guanguan, Li Qiqiang, Yang Xue, et al.General nash bargaining based direct P2P energy trading among prosumers under multiple uncertainties[J]. International Journal of Electrical Power and Energy Systems, 2022, 143: 108403.
[12] 侯慧, 刘鹏, 黄亮, 等. 考虑不确定性的电-热-氢综合能源系统规划[J]. 电工技术学报, 2021, 36(增刊1): 133-144.
Hou Hui, Liu Peng, Huang Liang, et al.Planning of electricity-heat-hydrogen integrated energy system considering uncertainties[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 133-144.
[13] 吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485.
Wu Mengxue, Fang Fang.Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.
[14] Yan Qin, Zhang Bei, Kezunovic M.Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 2096-2106.
[15] Wang Yifei, Wang Xiuli, Shao Chengcheng, et al.Distributed energy trading for an integrated energy system and electric vehicle charging stations: a nash bargaining game approach[J]. Renewable Energy, 2020, 155: 513-530.
[16] Zeng Bo, Dong Houqi, Sioshansi R, et al.Bilevel robust optimization of electric vehicle charging stations with distributed energy resources[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5836-5847.
[17] Zhong Junjie, Li Yong, Cao Yijia, et al.Robust coordinated optimization with adaptive uncertainty set for a multi-energy microgrid[J]. IEEE Transa- ctions on Sustainable Energy, 2023, 14(1): 111-124.
[18] 杨庆新, 张献, 章鹏程. 电动车智慧无线电能传输云网[J]. 电工技术学报, 2023, 38(1): 1-12.
Yang Qingxin, Zhang Xian, Zhang Pengcheng.Intelligent wireless power transmission cloud network for electric vehicles[J]. Transactions of China Elec- trotechnical Society, 2023, 38(1): 1-12.
[19] 薛明, 杨庆新, 章鹏程, 等. 无线电能传输技术应用研究现状与关键问题[J]. 电工技术学报, 2021, 36(8): 1547-1568.
Xue Ming, Yang Qingxin, Zhang Pengcheng, et al.Application status and key issues of wireless power transmission technology[J]. Transactions of China Electrotechnical Society, 2021, 36(8): 1547-1568.
[20] 周玮, 蓝嘉豪, 麦瑞坤, 等. 无线充电电动汽车V2G 模式下光储直流微电网能量管理策略[J]. 电工技术学报, 2022, 37(1): 82-91.
Zhou Wei, Lan Jiahao, Mai Ruikun, et al.Research on power management strategy of DC microgrid with photovoltaic, energy storage and EV wireless power transfer in V2G mode[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 82-91.
[21] 朱继忠, 董瀚江, 李盛林, 等. 数据驱动的综合能源系统负荷预测综述[J]. 中国电机工程学报, 2021, 41(23): 7905-7923.
Zhu Jizhong, Dong Hanjiang, Li Shenglin, et al.Review of data-driven load forecasting for integrated energy system[J]. Proceedings of the CSEE, 2021, 41(23): 7905-7923.
[22] 杨挺, 赵黎媛, 刘亚闯, 等. 基于深度强化学习的综合能源系统动态经济调度[J]. 电力系统自动化, 2021, 45(5): 39-47.
Yang Ting, Zhao Liyuan, Liu Yachuang, et al.Dynamic economic dispatch for integrated energy system based on deep reinforcement learning[J]. Automation of Electric Power Systems, 2021, 45(5): 39-47.