Multi-agent modeling for energy storage charging station scheduling strategies in the electricity market: A cooperative learning approach

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Xintao Zheng, Changjiang Ju, Genke Yang, Jian Chu
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引用次数: 0

Abstract

With integration of an energy storage system (ESS), an energy storage charging station serves as pivotal intermediaries between the smart grid and electric vehicles (EVs). This station utilizes the ESS to enhance grid stability and facilitate energy management. Participation in electricity market transactions offers revenue opportunities for charging stations, but it also introduces operational challenges, due to fluctuating electricity market prices and diverse energy demands and supplies. In this paper, we study the operation strategy optimization problem for the charging station, addressing economic and service challenges influenced by market volatility and energy diversity. The optimization objective considers not only maximizing economic benefits from the electricity market and EV services but also minimizing penalties associated with EV service quality. We propose a model that accounts for the dynamics of the electricity market, uncertainties from EV demands, and disturbances from green power generation, optimizing the power scheduling of the ESS and multiple charging piles (CPs) to determine transaction power in the market. The cooperative scheduling strategies for the ESS and CPs are learned using the proposed heterogeneous Multi-agent Deep Deterministic Policy Gradient method. This approach features distributed agents learning to determine decision variables for both the ESS and CPs, while a joint critic network assesses the station’s overall objectives to guide their cooperative learning. The proposed method was tested against three state-of-the-art benchmark methods, which showed our method achieves better results.
电力市场中储能充电站调度策略的多智能体建模:一种合作学习方法
通过集成储能系统(ESS),储能充电站成为智能电网和电动汽车之间的关键中介。该站利用ESS提高电网稳定性,方便能源管理。参与电力市场交易为充电站提供了收入机会,但由于电力市场价格波动以及能源需求和供应多样化,充电站也带来了运营挑战。本文研究充电站运行策略优化问题,以解决市场波动和能源多样性影响下的经济和服务挑战。优化目标既考虑电力市场和电动汽车服务的经济效益最大化,又考虑与电动汽车服务质量相关的处罚最小化。我们提出了一个考虑电力市场动态、电动汽车需求不确定性和绿色发电干扰的模型,优化ESS和多个充电桩(CPs)的电力调度,以确定市场交易功率。采用提出的异构多智能体深度确定性策略梯度方法学习了ESS和CPs的协同调度策略。这种方法的特点是分布式代理学习来确定ESS和CPs的决策变量,而联合评论网络评估站点的总体目标来指导他们的合作学习。通过对三种最先进的基准方法的测试,表明该方法取得了较好的效果。
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
Journal of energy storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
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