Distributed smart charging of electric vehicles for balancing wind energy

Kevin Mets, F. Turck, Chris Develder
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引用次数: 34

Abstract

To meet worldwide goals of reducing CO2 footprint, electricity production increasingly is stemming from so-called renewable sources. To cater for their volatile behavior, so-called demand response algorithms are required. In this paper, we focus particularly on how charging electrical vehicles (EV) can be coordinated to maximize green energy consumption. We present a distributed algorithm that minimizes imbalance costs, and the disutility experienced by consumers. Our approach is very much practical, as it respects privacy, while still obtaining near-optimal solutions, by limiting the information exchanged: i.e. consumers do not share their preferences, deadlines, etc. Coordination is achieved through the exchange of virtual prices associated with energy consumption at certain times. We evaluate our approach in a case study comprising 100 electric vehicles over the course of 4 weeks, where renewable energy is supplied by a small scale wind turbine. Simulation results show that 68% of energy demand can be supplied by wind energy using our distributed algorithm, compared to 73% in a theoretical optimum scenario, and only 40% in an uncoordinated business-as-usual (BAU) scenario. Also, the increased usage of renewable energy sources, i.e. wind power, results in a 45% reduction of CO2 emissions, using our distributed algorithm.
平衡风能的电动汽车分布式智能充电
为了实现减少二氧化碳足迹的全球目标,越来越多的电力生产来自所谓的可再生能源。为了迎合他们的波动行为,需要所谓的需求响应算法。在本文中,我们特别关注如何协调充电电动汽车(EV),以最大限度地提高绿色能源消耗。我们提出了一种分布式算法,以最小化不平衡成本和消费者所经历的负效用。我们的方法非常实用,因为它尊重隐私,同时通过限制信息交换仍然获得接近最优的解决方案:即消费者不分享他们的偏好,截止日期等。协调是通过交换与特定时间的能源消耗相关的虚拟价格来实现的。我们在一个案例研究中评估了我们的方法,该案例研究包括100辆电动汽车,为期4周,其中可再生能源由小型风力涡轮机提供。模拟结果表明,使用我们的分布式算法,68%的能源需求可以由风能提供,而在理论最优方案中为73%,在不协调的常规业务(BAU)方案中仅为40%。此外,使用我们的分布式算法,可再生能源(如风能)的使用增加,导致二氧化碳排放量减少45%。
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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