Optimal scheduling of electric vehicles charging and discharging in a smart parking-lot

Seyed Ashkan Nejati, B. Chong, Mahyar Alinejad, Shahriar Abbasi
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引用次数: 2

Abstract

With large penetration and uncontrolled charging and discharging of Electric Vehicles (EVs) due to rapid increasing demand for clean energy, the upstream grid may face many technical issues such as energy security and reliability risk. Therefore, it is essential to carry out smart scheduling for charging and discharging of EVs. In this paper, a residential parking-lot scheme using 200 EVs as an example is developed to schedule the charging and discharging of EVs which is based on the initial and final value of the State Of Charge (SOC) of all EVs requested by the owner one day in advance, and the information the expected arrival and departure time to the parking-lot. However, errors most probably exist in this scheme where owners’ requests cannot be met because of random behaviours of the other EV owners which will require the imposition of penalty. An optimisation problem is formulated to maximise the Smart Parking-Lot (SPL) profit considering random behaviours EV owners and the penalty imposed. In this paper, the aim is achieved by defining some random behaviours and penalty flexibility. The related optimising problem is solved by using the Particle Swarm Optimisation (PSO) algorithm. The effectiveness of the proposed method is verified through four different scenarios; in the first scenario, some random behaviours of EV owners are considered. In the last three scenarios, some flexibilities in penalising of the EV owners have been considered. The simulation results of all scenarios are compared and used to demonstrate the features of the proposed scheduling method.
智能停车场电动汽车充放电优化调度
随着对清洁能源需求的快速增长,电动汽车的大规模普及和充放电不受控制,上游电网可能面临能源安全和可靠性风险等诸多技术问题。因此,对电动汽车的充放电进行智能调度是十分必要的。本文以200辆电动汽车为例,开发了一种住宅停车场方案,该方案根据车主提前一天要求的所有电动汽车的SOC初始值和最终值,以及预计到达和离开停车场的时间信息,来调度电动汽车的充放电。然而,该方案很可能存在错误,即由于其他电动汽车车主的随机行为而无法满足车主的要求,从而需要施加罚款。考虑电动汽车车主的随机行为和罚款,提出了一个优化问题,以使智能停车场(SPL)利润最大化。本文通过定义一些随机行为和惩罚灵活性来实现这一目标。利用粒子群优化算法解决了相关的优化问题。验证了该方法的有效性通过四个不同的场景;在第一种情况下,考虑电动汽车车主的一些随机行为。在过去的三个场景,一些惩罚的灵活性EV业主考虑。通过对各场景的仿真结果进行比较,验证了所提调度方法的特点。
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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