使用改进的 MACD 算法,在考虑需求响应的情况下优化电动汽车调度计划

Mounir Bouzguenda, Muahmmad Hatatah, Sheharyar, Aamir Ali, Ghulam Abbas, Aamir Khan, Ezzeddine Touti, Amr Yousef, S. Mirsaeidi, Ahmed Alshahir
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摘要

电动汽车的日益普及给当前的电网带来了巨大的挑战,因为这些车辆数量的增加给配电网内的电力系统带来了额外的压力。针对电动汽车(EV)提出了一个拟议范例,随后将其划分为三个不同的调度区域,以评估其实用性。本研究围绕两个主要目标展开。第一个目标侧重于电动汽车所有者,其目的是最大限度地降低用电成本,同时还能因提供服务而获得补偿。第二个目标是利用聚合器为每个调度区制定不同的电价。此外,聚合器旨在将充电负荷需求从高峰时段转移到非高峰时段,并将充电需求分配给每个代理。本研究的作者建议利用充电和放电协调方法,特别是多代理充电和放电(MACD)算法,作为成功解决高峰时段充电需求问题的一种手段。所提算法的目标是在智能电网系统和电动汽车(EV)电池的背景下,利用车联网(V2G)技术有效处理高峰期增加的充电需求。重要的是,在不影响电动汽车(EV)性能或电动汽车车主所体验到的便利性的前提下实现这种减少。我们所考虑的算法可以降低各行业的电力充电成本。具体来说,家庭充电成本降低了 15%,企业大楼降低了 14.6%,工业园区电动汽车聚合商降低了 14.5%。
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An optimal dispatch schedule of EVs considering demand response using improved MACD algorithm
The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.
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