Advancing urban electric vehicle charging stations: AI-driven day-ahead optimization of pricing and Nudge strategies utilizing multi-agent deep reinforcement learning
{"title":"Advancing urban electric vehicle charging stations: AI-driven day-ahead optimization of pricing and Nudge strategies utilizing multi-agent deep reinforcement learning","authors":"","doi":"10.1016/j.etran.2024.100352","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":15.0000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116824000420","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
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.
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.