电动汽车最优充电调度的复杂性研究

C. Rottondi, G. Verticale, G. Neglia
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引用次数: 4

摘要

电动汽车(ev)的大规模引入预计将显著增加电网的电力负荷,但也会促进可再生能源的开发:如果电动汽车车队的充电过程由负载聚合器等智能实体安排,电动汽车的电池可以通过在能源生产过剩时进行充电来降低可再生能源间歇性生产模式造成的能源生产高峰。为此目的,采用时变的能源价格,在能源生产过剩的情况下可以降低能源价格以激励能源消费(或在能源短缺的情况下提高能源价格以阻止能源利用)。本文研究了时变电价条件下以电池充电总成本最小为目标的电动汽车车队最优调度问题的复杂性。考虑的场景是车队所有者在调度范围开始时完全了解客户的旅行需求。我们证明了该问题具有多项式复杂度,给出了复杂度的下界和上界,并将其性能与不依赖于客户需求先验知识的基准方法进行了比较,以评估在基准方法的基础上,最优调度策略所需的额外复杂度是否值得获得的经济优势。数值结果表明,该优化调度策略可节省大量成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the complexity of optimal electric vehicles recharge scheduling
The massive introduction of Electric Vehicles (EVs) is expected to significantly increase the power load experienced by the electrical grid, but also to foster the exploitation of renewable energy sources: if the charge process of a fleet of EVs is scheduled by an intelligent entity such as a load aggregator, the EVs' batteries can contribute in flattening energy production peaks due to the intermittent production patterns of renewables by being recharged when energy production surpluses occur. To this aim, time varying energy prices are used, which can be diminished in case of excessive energy production to incentivize energy consumption (or increased in case of shortage to discourage energy utilization). In this paper we evaluate the complexity of the optimal scheduling problem for a fleet of EVs aimed at minimizing the overall cost of the battery recharge in presence of timevariable energy tariffs. The scenario under consideration is a fleet owner having full knowledge of the customers' traveling needs at the beginning of the scheduling horizon. We prove that the problem has polynomial complexity, provide complexity lower and upper bounds, and compare its performance to a benchmark approach which does not rely on prior knowledge of the customers' requests, in order to evaluate whether the additional complexity required by the optimal scheduling strategy w.r.t. the benchmark is worthy the achieved economic advantages. Numerical results show considerable cost savings obtained by the optimal scheduling strategy.
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