Local demand management of charging stations using vehicle-to-vehicle service: A welfare maximization-based soft actor-critic model

IF 15 1区 工程技术 Q1 ENERGY & FUELS Etransportation Pub Date : 2023-10-01 DOI:10.1016/j.etran.2023.100280
Akhtar Hussain , Van-Hai Bui , Petr Musilek
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引用次数: 1

Abstract

Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

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基于车对车服务的充电站本地需求管理:基于福利最大化的软行为者批评模型
交通电气化有可能减少交通部门的碳排放。然而,电动汽车(ev)的日益普及可能会使配电系统过载。这在拥有多个电动汽车充电器和拥有许多电动汽车的充电站的地方变得突出。因此,本研究提出了一个基于福利最大化的软行为者评价(SAC)模型,以减轻配电系统中由于电动汽车的高渗透率而导致的变压器过载。分两步对各充电站的需求进行局部管理,避免高峰负荷时段网络过载。首先,建立了基于福利最大化的优化模型,通过车对车(V2V)服务实现电动汽车车主福利最大化。在这一步中,考虑了电动汽车车主对不同参数(能量水平、电池退化和车队运营商提供的激励)的敏感性。然后,通过将福利值(从福利最大化模型中获得)纳入奖励函数,训练基于深度强化学习的方法(软行为者批评家)。总电力需求(在变压器层面)和变压器容量也包含在奖励函数中。代理(车队运营商)通过与环境的交互学习电动汽车本地需求管理的最优定价策略。每辆电动汽车通过决定在这段时间内他们愿意充电/放电的电量(V2V)来响应动作(价格)。离线训练,训练后的模型可用于不同类型充电站的实时需求管理。仿真结果表明,该方法可以在不违反变压器容量限制的情况下,通过V2V对不同充电站的需求进行有效管理。
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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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