{"title":"减少对电网影响的智能电动车充电算法:一种基于强化学习的方法","authors":"Federico Rossi;Cesar Diaz-Londono;Yang Li;Changfu Zou;Giambattista Gruosso","doi":"10.1109/OJVT.2025.3559237","DOIUrl":null,"url":null,"abstract":"The increasing penetration of electric vehicles (EVs) presents a significant challenge for power grid management, particularly in maintaining network stability and optimizing energy costs. Existing model predictive control (MPC)-based approaches for EV charging and discharging scheduling often struggle to balance computational efficiency with real-time operationability. This gap highlights the need for more advanced methods that can effectively mitigate the impact of EV activities on power grids without oversimplifying system dynamics. Here, we propose a novel scheduling methodology using a pre-trained Reinforcement Learning (RL) framework to address this challenge. The method integrates real grid simulations to monitor critical electrical points and variables while simplifying analysis by excluding the influence of real grid dynamics. The proposed approach formulates the scheduling problem to minimize costs, maximize rewards from ancillary service delivery, and mitigate network overloads at specified grid nodes. The methodology is validated on a benchmark electric grid, where realistic charging station utilization scenarios are simulated. The results demonstrate the method's robustness and ability to efficiently cope with the EV smart scheduling problem.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1072-1084"},"PeriodicalIF":5.3000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960355","citationCount":"0","resultStr":"{\"title\":\"Smart Electric Vehicle Charging Algorithm to Reduce the Impact on Power Grids: A Reinforcement Learning Based Methodology\",\"authors\":\"Federico Rossi;Cesar Diaz-Londono;Yang Li;Changfu Zou;Giambattista Gruosso\",\"doi\":\"10.1109/OJVT.2025.3559237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing penetration of electric vehicles (EVs) presents a significant challenge for power grid management, particularly in maintaining network stability and optimizing energy costs. Existing model predictive control (MPC)-based approaches for EV charging and discharging scheduling often struggle to balance computational efficiency with real-time operationability. This gap highlights the need for more advanced methods that can effectively mitigate the impact of EV activities on power grids without oversimplifying system dynamics. Here, we propose a novel scheduling methodology using a pre-trained Reinforcement Learning (RL) framework to address this challenge. The method integrates real grid simulations to monitor critical electrical points and variables while simplifying analysis by excluding the influence of real grid dynamics. The proposed approach formulates the scheduling problem to minimize costs, maximize rewards from ancillary service delivery, and mitigate network overloads at specified grid nodes. The methodology is validated on a benchmark electric grid, where realistic charging station utilization scenarios are simulated. The results demonstrate the method's robustness and ability to efficiently cope with the EV smart scheduling problem.\",\"PeriodicalId\":34270,\"journal\":{\"name\":\"IEEE Open Journal of Vehicular Technology\",\"volume\":\"6 \",\"pages\":\"1072-1084\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960355\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Vehicular Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10960355/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Vehicular Technology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10960355/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Smart Electric Vehicle Charging Algorithm to Reduce the Impact on Power Grids: A Reinforcement Learning Based Methodology
The increasing penetration of electric vehicles (EVs) presents a significant challenge for power grid management, particularly in maintaining network stability and optimizing energy costs. Existing model predictive control (MPC)-based approaches for EV charging and discharging scheduling often struggle to balance computational efficiency with real-time operationability. This gap highlights the need for more advanced methods that can effectively mitigate the impact of EV activities on power grids without oversimplifying system dynamics. Here, we propose a novel scheduling methodology using a pre-trained Reinforcement Learning (RL) framework to address this challenge. The method integrates real grid simulations to monitor critical electrical points and variables while simplifying analysis by excluding the influence of real grid dynamics. The proposed approach formulates the scheduling problem to minimize costs, maximize rewards from ancillary service delivery, and mitigate network overloads at specified grid nodes. The methodology is validated on a benchmark electric grid, where realistic charging station utilization scenarios are simulated. The results demonstrate the method's robustness and ability to efficiently cope with the EV smart scheduling problem.