Advancing urban electric vehicle charging stations: AI-driven day-ahead optimization of pricing and Nudge strategies utilizing multi-agent deep reinforcement learning

IF 15 1区 工程技术 Q1 ENERGY & FUELS
Ziqi Zhang , Zhong Chen , Erdem Gümrükcü , Zhenya Ji , Ferdinanda Ponci , Antonello Monti
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引用次数: 0

Abstract

Public charging stations (CSs) serve for electric vehicles (EVs) to charge during urban travel. Optimizing the charging time, location distribution, and power of EVs can increase the revenue of charging system operators (CSOs) and provide flexible regulation resources for the power grid. However, the optimization scheduling of CSs involves the charging choices of various users, which are influenced by their autonomy and bounded rationality. To guide users and encourage their participation in the charging schedule, we introduce the Nudge method from behavioral economics. To achieve collaborative optimization of non-economic Nudges and economic incentive strategies applying to multiple charging stations in a complex nonlinear environment involving users, CSO, and the transportation network, we leverage multi-agent deep reinforcement learning (MADRL). We construct a simulation environment using historical and survey data tailored to real users. This environment facilitates the training of agent groups to enhance decision-making processes. Case studies in a metropolis demonstrate that the agent group aimed at revenue improvement yields significant improvements in the CSO's revenue compared to fixed service fees and pricing strategies without Nudges. Moreover, the agent group aimed at power curve tracking achieves a lower average relative error in aligning the total charging power with the desired curve of the power system. This paper integrates sociological methods into the optimization of physical systems by MADRL, providing a new approach for the scheduling of EV charging considering user behavior.

推进城市电动汽车充电站建设:利用多代理深度强化学习,实现人工智能驱动的定价和 "劝告 "策略的日前优化
公共充电站(CSs)为城市出行中的电动汽车(EV)提供充电服务。优化电动汽车的充电时间、地点分布和功率可以增加充电系统运营商(CSO)的收入,并为电网提供灵活的调节资源。然而,CSO 的优化调度涉及不同用户的充电选择,而这些选择又受到用户自主性和有限理性的影响。为了引导用户并鼓励他们参与充电调度,我们引入了行为经济学中的 Nudge 方法。为了在涉及用户、CSO 和交通网络的复杂非线性环境中实现适用于多个充电站的非经济 Nudges 和经济激励策略的协同优化,我们利用了多代理深度强化学习(MADRL)。我们利用为真实用户量身定制的历史数据和调查数据构建了一个模拟环境。该环境有助于对代理组进行培训,以增强决策过程。在一个大都市进行的案例研究表明,与固定服务费和无 "激励 "的定价策略相比,以提高收入为目标的代理组能显著提高 CSO 的收入。此外,以电力曲线跟踪为目标的代理组在使总充电功率与电力系统的理想曲线保持一致方面取得了较低的平均相对误差。本文通过 MADRL 将社会学方法融入物理系统优化,为考虑用户行为的电动汽车充电调度提供了一种新方法。
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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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