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Optimal control study of home energy management with cooperative dispatch of electric vehicles and energy storage devices

机译:电动汽车与储能装置协同调度的家庭能源管理优化控制研究

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In order to solve the problem that the charging and discharging characteristics of electric vehicles (EVs) are not fully utilized by household users with distributed photovoltaic (DPV), which leads to low photovoltaic utilization efficiency and poor household electricity cost and economy, this paper proposes an optimal control strategy for household energy management based on cooperative scheduling of EVs and energy storage devices. In this paper, we firstly classify the household electric devices based on the load characteristics, and then formulate the control strategies of electric vehicles and energy storage devices based on the relationship between the total load power and the PV output, and propose the objectives of household side, environment and grid side based on this, and solve the multi-objective problem by using the improved gray wolf optimization (GWO) algorithm based on Tent mapping chaos optimization. The effectiveness of the model and algorithm is verified by simulating and analyzing a household customer under real-time electricity price. The results show that the system operating conditions can be improved to achieve the expected goals of minimizing household electricity cost and maximizing PV consumption under the conditions of energy management optimal control. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
机译:针对分布式光伏(DPV)用户未充分利用电动汽车(EV)的充放电特性,导致光伏利用效率低、家庭用电成本和经济性差的问题,提出了一种基于电动汽车与储能装置协同调度的家庭能源管理最优控制策略。本文首先根据负载特性对家用电器件进行分类,然后根据总负荷功率与光伏输出的关系制定电动汽车和储能设备的控制策略,并在此基础上提出户侧、环境侧和电网侧的目标,并采用基于Tent映射混沌优化的改进灰狼优化(GWO)算法求解多目标问题。通过对某家庭客户在实时电价下的模拟分析,验证了模型和算法的有效性。结果表明,在能源管理最优控制条件下,系统运行条件可以得到改善,以达到家庭用电成本最小化、光伏消耗最大化的预期目标。(c) 2023 作者。由以下开发商制作:Elsevier Ltd.这是 CCBY-NC-ND 许可 (http://creativecommons.org/licenses/by-nc-nd/4.0/) 下的开放获取文章。

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