Bi-Directional Optimization of V2G Strategy Based on Multi-Objective Optimization: Balancing Grid Load and Reducing Electric Vehicle Charging Costs

Yilin Liu
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Abstract

With the rapid increase in the number of electric vehicles (EVs), vehicle-to-grid (V2G) technology plays a vital role in reducing the burden on the power system. This technology optimizes network load distribution through a two-way charging mechanism and effectively alleviates network load fluctuations. However, potential negative impacts on EV battery life should also be a cause for concern. Furthermore, the technology does not fundamentally change the charging behavior of electric vehicles. Against this background, this study proposes a multi-objective optimization strategy to adapt electricity price policy to network load fluctuations to control charging behavior. This strategy optimizes battery attenuation, charging costs, and network load fluctuations, aiming to alleviate network load fluctuations while completely solving user concerns about charging and battery maintenance costs. Simulation analysis has verified the effectiveness of this model in reducing grid load fluctuations and balancing user costs.
基于多目标优化的 V2G 策略双向优化:平衡电网负荷并降低电动汽车充电成本
随着电动汽车(EV)数量的快速增长,车联网(V2G)技术在减轻电力系统负担方面发挥着至关重要的作用。该技术通过双向充电机制优化网络负荷分配,有效缓解网络负荷波动。然而,对电动汽车电池寿命的潜在负面影响也应引起关注。此外,该技术并未从根本上改变电动汽车的充电行为。在此背景下,本研究提出了一种多目标优化策略,使电价政策适应网络负荷波动,从而控制充电行为。该策略优化了电池衰减、充电成本和网络负荷波动,旨在缓解网络负荷波动,同时彻底解决用户对充电和电池维护成本的担忧。仿真分析验证了该模型在减少电网负荷波动和平衡用户成本方面的有效性。
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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