基于非对称一致性学习的多类型电动汽车协同参与需求响应方法

doi:10.19595/j.cnki.1000-6753.tces.240578

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Abstract With the widespread adoption of electric vehicles (EVs) and the large-scale integration of renewable energy sources such as wind and solar power into the grid, fully leveraging the potential of EV demand response to address issues such as power fluctuations and poor load stability in the grid is of significant importance. Recently, various control methods for EVs participating in grid demand response have been proposed. However, these existing methods still face several challenges: First, the current methods insufficiently consider the coordination between the autonomous demand response of flexible-contract EVs and the aggregated demand response of fixed-contract EVs. Second, existing optimization methods for aggregated demand response strategies overlook the issue of information asymmetry resulting from differentiated characteristics among entities, leading to slow convergence in aggregate control and higher aggregate output costs. Third, existing optimization methods for autonomous demand response strategies utilize fixed discount rates to guide the learning of agents in flexible EVs but fail to achieve a dynamic balance between immediate rewards and long-term rewards, resulting in poor learning effectiveness. To address these challenges, this paper proposed a multi-type EV collaborative demand response method based on asymmetric consensus learning.
Firstly, EVs participating in demand response are divided into flexible-contract EVs and fixed-contract EVs, and a scheduling architecture for multi-type EV collaborative demand response is proposed. Within this framework, the collaboration between flexible-contract EVs and fixed-contract EVs in demand response is manifested in two aspects: 1) After flexible-contract EVs autonomously participate in demand response, fixed-contract EVs aggregate demand response based on their autonomous response shortfall, enabling both to jointly meet grid requirements; 2) Following the aggregation of demand response by fixed-contract EVs, the aggregated response results are fed back to flexible-contract EVs, prompting them to dynamically adjust their autonomous demand response strategies based on the feedback information.
Subsequently, a multi-type EV collaborative demand response strategy based on asymmetric consensus learning is proposed. Specifically, flexible-contract EVs aim to maximize the weighted difference of their income, mileage guarantee, and load curve variance of the power grid. They make autonomous demand response decisions and participate in grid demand response utilizing flexible reinforcement learning. Flexible-contract EVs can dynamically adjust discount rates based on autonomous demand response results, achieving a dynamic balance between immediate rewards and long-term rewards, effectively enhancing EV demand response profits, reducing mileage guarantee costs, and decreasing grid load volatility. Additionally, by fully considering differentiated information such as aggregator contracted capacity, fixed-contract EV quantity, and number of contracts, quantifying the confidence level of aggregator state information, and calculating asymmetric consensus communication weights among aggregators, the asymmetry optimization of fixed-contract EV aggregated demand response strategies is achieved. This enhances convergence speed in EV aggregation control and reduces aggregate output costs.The proposed asymmetric consensus learning algorithm is capable of efficiently handling high-dimensional complex nonlinear relationships, with strong autonomous learning and generalization capabilities.
Finally, the effectiveness and rationality of the proposed multi-type EV collaborative demand response method are verified through simulation examples. Simulation results demonstrate that the proposed method can increase autonomous demand response rewards for flexible-contract EVs by 34.42% and improve reward convergence speed by 27.96%. It also enhances fixed-contract EV aggregator incremental cost convergence speed by 36.36%, significantly improving peak shaving and load balancing in the grid while effectively ensuring user comfort. Future research will further explore the impact of real-time pricing incentives on optimizing multi-type EV collaborative demand response.

Key words： Multi-type electric vehicles flexible reinforcement learning asymmetric consensus optimization collaborative demand response

Received: 12 April 2024

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	Pan Chao
	Tang Zhongwei
	Liao Haijun
	Zhou Zhenyu

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Pan Chao,Tang Zhongwei,Liao Haijun等. Asymmetric Consensus Learning-Based Multi-Type Electric Vehicle Collaborative Participation Demand Response Method[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2178-2190.

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[1] 辛保安, 单葆国, 李琼慧, 等. “双碳”目标下“能源三要素”再思考[J]. 中国电机工程学报, 2022, 42(9): 3117-3126.
Xin Baoan, Shan Baoguo, Li Qionghui, et al.Rethinking of the “three elements of energy” toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE, 2022, 42(9): 3117-3126.
[2] 杨锡勇, 张仰飞, 林纲, 等. 考虑需求响应的源-荷-储多时间尺度协同优化调度策略[J]. 发电技术, 2023, 44(2): 253-260.
Yang Xiyong, Zhang Yangfei, Lin Gang, et al.Multi-time scale collaborative optimal scheduling strategy for source-load-storage considering demand response[J]. Power Generation Technology, 2023, 44(2): 253-260.
[3] 徐湘楚, 米增强, 詹泽伟, 等. 考虑多重不确定性的电动汽车聚合商参与能量-调频市场的鲁棒优化模型[J]. 电工技术学报, 2023, 38(3): 793-805.
Xu Xiangchu, Mi Zengqiang, Zhan Zewei, et al.A robust optimization model for electric vehicle aggregator participation in energy and frequency regulation markets considering multiple uncertainties[J]. Transa-ctions of China Electrotechnical Society, 2023, 38(3): 793-805.
[4] 鲁宗相, 李昊, 乔颖. 从灵活性平衡视角的高比例可再生能源电力系统形态演化分析[J]. 全球能源互联网, 2021, 4(1): 12-18.
Lu Zongxiang, Li Hao, Qiao Ying.Morphological evolution of high-proportion renewable energy power system from the perspective of flexibility balance[J]. Global Energy Internet, 2021, 4(1): 12-18.
[5] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报, 2023, 38(6): 1662-1677.
WuShan, BianXiaoyan, ZhangJingxian, et al. A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1662-1677.
[6] 周玮, 徐从明, 杨丹霞, 等. P2P能源共享下考虑意愿动态调整的电动汽车群需求响应策略研究[J]. 中国电机工程学报, 2023, 43(21): 8217-8230.
Zhou Wei, Xu Congming, Yang Danxia, et al.Research on demand response strategy of electric vehicles considering dynamic adjustment of willingness under P2P energy sharing[J]. Proceedings of the CSEE, 2023, 43(21): 8217-8230.
[7] Shi Xiaoying, Xu Yinliang, Guo Qinglai, et al.Optimal dispatch based on aggregated operation region of EV considering spatio-temporal distribution[J]. IEEE Transactions on Sustainable Energy, 2022, 13(2): 715-731.
[8] 黄小庆, 李隆意, 徐鹏鑫, 等. 多主体博弈共赢的电动汽车充电桩共享方法[J]. 电工技术学报, 2023, 38(11): 2945-2961.
Huang Xiaoqing, Li Longyi, XuPengxin, et al. Electric vehicle charging pile sharing method based on multi-subject game and win-win[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 2945-2961.
[9] Gao Xiang, Chan K W, Xia Shiwei, et al.A multiagent competitive bidding strategy in a pool-based electricity market with price-maker participants of WPPs and EV aggregators[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7256-7268.
[10] 王雨晴, 王文诗, 徐心竹, 等. 面向低碳交通的含新能源汽车共享站电-氢微能源网区间-随机混合规划方法[J]. 电工技术学报, 2023, 38(23): 6373-6390.
Wang Yuqing, Wang Wenshi, Xu Xinzhu, et al.Hybrid interval/stochastic planning method for new energy vehicle sharing station-based electro-hydrogen micro-energy system for low-carbon transportation[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6373-6390.
[11] 胡俊杰, 马文帅, 薛禹胜, 等. 基于CPSSE框架的电动汽车聚合商备用容量量化[J]. 电力系统自动化, 2022, 46(18): 46-54.
Hu Junjie, Ma Wenshuai, Xue Yusheng, et al.Quantification of reserve capacity provided by electric vehicle aggregator based on framework of cyber-physical-social system in energy[J]. Automation of Electric Power Systems, 2022, 46(18): 46-54.
[12] Kapoor A, Patel V S, Sharma A, et al.Centralized and decentralized pricing strategies for optimal scheduling of electric vehicles[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2234-2244.
[13] 徐弘升, 陆继翔, 杨志宏, 等. 基于深度强化学习的激励型需求响应决策优化模型[J]. 电力系统自动化, 2021, 45(14): 97-103.
Xu Hongsheng, Lu Jixiang, Yang Zhihong, et al.Incentive demand response decision optimization model based on deep reinforcement learning[J]. Automation of Electric Power Systems, 2021, 45(14): 97-103.
[14] 赵小瑾, 张开宇, 冯冬涵, 等. 基于强化学习的电动汽车集群实时优化调度策略[J]. 智慧电力, 2022, 50(1): 53-59, 81.
Zhao Xiaojin, Zhang Kaiyu, Feng Donghan, et al.Reinforcement learning-based real-time optimal scheduling strategy for electric vehicle clusters[J]. Smart Power, 2022, 50(1): 53-59, 81.
[15] 王建中, 焦振华, 叶伟强, 等. 考虑隐私保护的含电动汽车电网频率安全调度[J]. 电力系统及其自动化学报, 2023, 35(8): 143-151.
Wang Jianzhong, Jiao Zhenhua, Ye Weiqiang, et al.Frequency security scheduling of power grid with electric vehicles considering privacy protection[J]. Proceedings of the CSU-EPSA, 2023, 35(8): 143-151.
[16] 刘春涛, 宋运忠. 基于负荷均衡加载的电力系统分布式经济调度策略[J]. 电力系统保护与控制, 2022, 50(20): 139-148.
Liu Chuntao, Song Yunzhong.Distributed economic dispatch strategy of a power system based on load balancing loading[J]. Power System Protection and Control, 2022, 50(20): 139-148.
[17] 戴武昌, 刘艾冬, 申鑫, 等. 基于MADDPG算法的家用电动汽车集群充放电行为在线优化[J]. 东北电力大学学报, 2021, 41(5): 80-89.
Dai Wuchang, Liu Aidong, Shen Xin, et al.Online optimization of charging and discharging behaviors of domestic electric vehicle clusters based on MADDPG algorithm[J]. Journal of Northeast Electric Power University, 2021, 41(5): 80-89.
[18] Li Donghe, Yang Qingyu, Ma Linyue, et al.An electrical vehicle-assisted demand response management system: a reinforcement learning method[J]. Frontiers in Energy Research, 2023.
[19] Lu Jie, Tang C Y.Zero-gradient-sum algorithms for distributed convex optimization: the continuous-time case[J]. IEEE Transactions on Automatic Control, 2012, 57(9): 2348-2354.
[20] Zhang Ziang, Chow M Y.Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid[J]. IEEE Transactions on Power Systems, 2012, 27(4): 1761-1768.
[21] 章攀钊, 谢丽蓉, 马瑞真, 等. 考虑电动汽车集群可调度能力的多主体两阶段低碳优化运行策略[J]. 电网技术, 2022, 46(12): 4809-4825.
Zhang Panzhao, Xie Lirong, Ma Ruizhen, et al.Multi-player two-stage low carbon optimal operation stra-tegy considering electric vehicle cluster schedulable ability[J]. Power Grid Technology, 2022, 6(12): 809-4825.
[22] 何晨可, 朱继忠, 刘云, 等. 计及碳减排的电动汽车充换储一体站与主动配电网协调规划[J]. 电工技术学报, 2022, 37(1): 92-111.
He Chenke, Zhu Jizhong, Liu Yun, et al.Coordinated planning of electric vehicle charging-swapping- storage integrated station and active distribution network considering carbon reduction[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 92-111.
[23] 王育飞, 郑云平, 薛花, 等. 基于增强烟花算法的移动式储能削峰填谷优化调度[J]. 电力系统自动化, 2021, 45(5): 8-56.
Wang Yufei, Zheng Yunping, Xue Hua, et al.Optimal dispatch of mobile energy storage for peak load shifting based on enhanced firework algorithm[J]. Automation of Electric Power Systems, 2021, 45(5): 48-56.
[24] 李军, 梁嘉诚, 刘克天, 等. 计及用户响应度的电动汽车充放电优化调度策略[J]. 南方电网技术, 2023, 17(8): 123-132.
Li Jun, Liang Jiacheng, Liu Ketian, et al.Optimal scheduling strategy for electric vehicles charging and discharging considering user responsiveness[J]. Sou-thern Power System Technology, 2023, 17(8): 123-132.
[25] 李清涛, 卢钺, 刘洋, 等. 计及电动汽车的有源配电网新能源消纳两阶段调度策略[J]. 热力发电, 2022, 51(9): 54-62.
Li Qingtao, Lu Yue, Liu Yang, et al.Two-stage dispatch strategy for new energy consumption in active distribution network considering electric vehicles[J]. Thermal Power Generation, 2022, 51(9): 54-62.
[26] 李航, 李国杰, 汪可友. 基于深度强化学习的电动汽车实时调度策略[J]. 电力系统自动化, 2020, 44(22): 161-167.
Li Hang, Li Guojie, Wang Keyou.Real-time dispatch strategy for electric vehicles based on deep reinforce-ment learning[J]. Automation of Electric Power Systems, 2020, 44(22): 161-167.
[27] 王晓梅, 卢芳, 卢京祥, 等. 含分布式光伏和电动汽车的主动配电网电压一致性协同控制[J]. 电测与仪表, 2020, 57(11): 101-107, 134.
Wang Xiaomei, Lu Fang, Lu Jingxiang, et al.Consensus-based cooperative voltage control of distributed photovoltaic and electric vehicles in active distribution network[J]. Electrical Measurement & Instrumentation, 2020, 57(11): 101-107, 134.