一种控制与优化电动汽车群协调充电过程的算法研究

Roham Torabi, C. Sabino, Á. Gomes
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引用次数: 3

摘要

在不久的将来,越来越多的电动汽车(ev)将需要具有足够算法的控制系统来管理其充电过程,并避免在加强低压配电网基础设施方面进行大量投资。电动汽车作为内燃机的替代品,将给电力系统、电网运营商、电力供应商和消费者带来新的挑战。电动汽车日益普及的影响将体现在配电网层面,因为基础设施容量的缺乏可能会阻碍越来越多的电动汽车同时充电。通过同一配电变压器充电的所有电动汽车之间没有局部协调的情况可能会导致新的峰值需求,可能会阻碍所有车辆的充电。因此,越来越多的电动汽车将需要一个控制系统来管理它们的充电过程。本研究提出开发一种分散式充电控制系统,以协调方式控制和优化电动汽车数量的充电过程。因此,为更多的电动汽车充电是可行的,而无需投资增加电网基础设施的容量。为了实现这一目标,该算法考虑了用户的偏好,例如他们的下一次使用时间和期望的充电状态,同时考虑了每个用户的电价方案(即不同的价格结构/价值和合同功率)。将为配电网运营商设置优化目标,使同时充电的电动汽车数量最大化,为消费者设置优化目标,使与充电成本的最小偏差最小化。各种实体都对这种管理感兴趣。例如,配电网运营商感兴趣的是在不大规模加固电网的情况下管理充电以纳入最大数量的电动汽车,而消费者感兴趣的是最小化充电成本和提高供电可靠性。预计将开发出一种灵活的算法,可用于具有不同电网特性和消费者负荷概况的场景。将不同的收费模式和不同类型的电价整合起来,是一种更现实的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of an Algorithm to Control and Optimize the Coordinated Charging Process of a Group of Electric Vehicles
The increasing number of electric vehicles (EVs) in the near future will require control systems endowed with adequate algorithms able to manage their recharging process and avoiding massive investment in reinforcement of low voltage distribution network infrastructure. The deployment of EVs as an alternative to internal combustion engines will pose new challenges for electric power systems, grid operators, electricity suppliers and consumers. The impacts of the increasing penetration of EVs will be felt at the level of the distribution grid, as the lack of infrastructure capacity may hinder the increasing number of EVs from being simultaneously charged. Scenarios in which there is no local coordination between all the EVs to be charged through the same distribution power transformer may lead to new peak demands, possibly impeding the charging of all the vehicles. Therefore, the increasing number of EVs will require a control system to manage their recharging process. This study proposes the development of a decentralized charging control system, which is able to control and optimize the charging process of number of EVs in a coordinate way. Thus charging a greater number of EVs will be feasible without needing to invest in increasing the capacity of the grid infrastructure. To accomplish this, the algorithm considers the users preferences, such as their next time of use and desired state of charge while taking every users tariff scheme into consideration (i.e. different price structures/values and contracted power). The optimization objectives will be set for both the distribution grid operator, maximizing the number of EVs being charged simultaneously and for the consumers, minimizing the deviation from the minimum cost of the charge. Various entities are interested in such management. For instance, the distribution grid operator is interested in managing the charging to incorporate the maximum number of EVs without massively reinforcing the grid, whereas the consumers are interested in minimizing the cost of charging and increasing the reliability of supply. It is expected to develop a flexible algorithm, which may be used in scenarios with different electric grid characteristics and consumer load profiles. The integration of different charging patterns and various types of electricity tariffs leads to a more realistic approach.
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