电动汽车充电调度的混合整数-线性规划模型

Nicki Bodenschatz, M. Eider, A. Berl
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引用次数: 5

摘要

在过去的几年里,电动汽车的数量在稳步增长。向电动汽车的过渡面临着挑战,即如何将充电过程整合到电网中,同时又不会给电网带来过大的压力。为了防止这种情况,最近的研究解决了电动汽车充电的调度问题。尤其是电动汽车的充电问题更是研究的热点。已经有不同的解决方案来提高电网的稳定性,增加当地生产的可再生能源的摄入量,或者仅仅是为了降低成本。然而,这些求解方法都使用了不同的数学模型和不同的参数来表示充电调度问题。这导致了每个模型只适用于一个特殊用例的问题,其他用例可能需要其他参数来调度电动车队。为了解决这一问题,本文以混合整数线性规划的形式给出了通用电动车队成本最小化的详细数学模型。为了做到这一点,本文表明,不同的研究方法在其解决方案中使用不同的参数。然后,本文概述了电动车队的技术限制。在这些局限性的基础上,建立了大范围电动车队的混合整数-线性规划模型。此外,本文还提供了扩展模型的选项,以改善最优调度的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mixed-Integer-Linear-Programming Model for the Charging Scheduling of Electric Vehicle Fleets
The number of electric vehicles is steadily increasing of the past few years. This transition to electric vehicles bears the challenge, to integrate the charging processes into the grid without overstressing it. To prevent this, research has tackled lately the scheduling of electric vehicle charging. Especially the charging of electric vehicle fleets is in the focus of research. There are already different solution approaches to increase the grid stability, to increase the intake of locally produced renewable energy or simply to reduce the cost. However, all these solution approaches use different mathematical models with different parameters to represent the charging scheduling problem. This results in the problem that each model is applicable for a special use case only, other use cases might need other parameters for the scheduling of the electric vehicle fleet. To ease this problem, this paper provides a detailed mathematical model for the cost minimization of a general electric fleet in the form of a mixed-integer-linearprogram. In order to do this, the paper shows that different research approaches use different parameters in their solutions. Afterwards, the paper presents a general overview of technical limitations for the electric fleets. On foundation of these limitations a mixed-integer-linear-program model for a wide range of electric fleets is established. Also, the paper provides options to extend the model in order to improve the result of an optimal schedule.
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