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  

Asymmetric Consensus Learning-Based Multi-Type Electric Vehicle Collaborative Participation Demand Response Method

作　　者：潘超[1] 汤中卫 廖海君 周振宇[1] Pan Chao;Tang Zhongwei;Liao Haijun;Zhou Zhenyu(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing,102206,China)

机构地区：[1]华北电力大学电气与电子工程学院,北京102206

出　　处：《电工技术学报》2025年第7期2178-2190,共13页Transactions of China Electrotechnical Society

基　　金：国家电网有限公司总部科技项目(52094021N010(5400-202199534A-0-5-ZN));中国南方电网有限责任公司科技项目(1500002023030103JL00320)资助。

摘　　要：随着电动汽车(EV)的广泛应用和风电、光伏等可再生能源大规模接入电网,如何充分发挥EV需求响应潜力,解决电网功率波动、负荷稳定性差等问题具有重要意义。为此,该文提出一种基于非对称一致性学习的多类型EV协同参与需求响应方法。首先,将参与需求响应的EV分为灵活签约EV和固定签约EV,并提出多类型EV协同参与需求响应调度架构。其次,提出一种基于非对称一致性学习的多类型EV协同参与需求响应策略:灵活签约EV以最大化自身收益、里程保障以及负荷曲线方差加权差为目标,基于柔性强化学习进行自主需求响应决策并自主参与电网需求响应;基于灵活签约EV自主需求响应结果,固定签约EV以最小化聚合需求响应成本为目标,进行出力功率非对称一致性优化并聚合参与电网需求响应。所提非对称一致性学习算法能够高效处理高维度复杂非线性关系,具有较强的自主学习和泛化能力。最后,通过仿真算例验证所提多类型EV协同参与需求响应方法的有效性与合理性。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,pro

关 键 词：多类型电动汽车 柔性强化学习 非对称一致性 优化协同 需求响应 

分 类 号：TM73[电气工程—电力系统及自动化]