Smart Electric Vehicle Charging Algorithm to Reduce the Impact on Power Grids: A Reinforcement Learning Based Methodology

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Federico Rossi;Cesar Diaz-Londono;Yang Li;Changfu Zou;Giambattista Gruosso
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引用次数: 0

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.
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
电动汽车(ev)的日益普及给电网管理带来了重大挑战,特别是在保持网络稳定性和优化能源成本方面。现有的基于模型预测控制(MPC)的电动汽车充放电调度方法往往难以平衡计算效率和实时可操作性。这一差距凸显了对更先进的方法的需求,这些方法可以有效地减轻电动汽车活动对电网的影响,同时又不会过度简化系统动力学。在这里,我们提出了一种新的调度方法,使用预训练的强化学习(RL)框架来解决这一挑战。该方法结合实际电网仿真监测关键电气点和变量,同时排除了实际电网动态的影响,简化了分析。该方法提出了调度问题,以最小化成本,最大化辅助服务交付的回报,并减轻指定网格节点的网络过载。在基准电网上对该方法进行了验证,并模拟了现实的充电站使用场景。结果表明,该方法具有较强的鲁棒性,能够有效地解决电动汽车智能调度问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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