通过多个停车场的电动车充电调度实现微电网收益最大化
Microgrid Revenue Maximization by Charging Scheduling of EVs in Multiple Parking Stations
AI总结：
本文研究了多停车场环境下微电网中电动车充电调度问题，提出了一种近优算法以在满足局部和全局峰值约束下最大化收益，通过谷填策略降低全局峰值，仿真结果表明算法性能优于理论下限。
摘要：
如今，全球电动汽车（EV）的使用量迅速增长。尽管电动汽车在环境和经济方面有明显优势，但其高需求充电任务对现有电力电网基础设施构成巨大挑战。在微电网中，作为传统电网的小规模版本，电动车充电调度更具挑战性。这是因为，微电网所有者作为大型电力客户，希望削减其全局峰值需求，即多个停车场的聚合需求，以降低总电力成本。虽然在单站场景中，电动车充电调度问题已广泛研究，但涉及多个站点且受全局峰值约束的微电网层面问题尚未解决。本文旨在提出一种在由单一公用事业提供商管理的微电网中，具有多个充电站的近优电动车充电调度机制。目标是在满足局部和全局峰值约束的同时最大化总收益。然而，该问题本质上是一个NP难的混合整数线性问题，难以解决，需要设计近似算法。我们设计了一种对偶调度算法，该算法在多项式时间内运行，并达到有界近似比。此外，所提出的全局调度算法应用了谷填策略以进一步降低全局峰值。仿真结果表明，所提算法的性能为最优值的98%，远优于通过我们的近似分析获得的理论下限。我们的算法将现有替代算法获得的峰值需求降低了16%，同时实现了更好的资源利用率。
英文摘要：
Nowadays, there has been a rapid growth in global usage of the electronic vehicles (EV). Despite apparent environmental and economic advantages of EVs, their high demand charging jobs pose an immense challenge to the existing electricity grid infrastructure. In microgrids, as the small-scale version of traditional power grid, however, the EV charging scheduling is more challenging. This is because, the microgrid owner, as a large electricity customer, is interested in shaving its global peak demand, i.e., the aggregated demand over multiple parking stations, to reduce total electricity cost. While the EV charging scheduling problem in single station scenario has been studied extensively in the previous research, the microgrid-level problem with multiple stations subject to a global peak constraint is not tackled. This paper aims to propose a near-optimal EV charging scheduling mechanism in a microgrid governed by a single utility provider with multiple charging stations. The goal is to maximize the total revenue while respecting both local and global peak constraints. The underlying problem, however, is a NP-hard mixed integer linear problem which is difficult to tackle and calls for approximation algorithm design. We design a primaldual scheduling algorithm which runs in polynomial time and achieves bounded approximation ratio. Moreover, the proposed global scheduling algorithm applies a valley-filling strategy to further reduce the global peak. Simulation results show that the performance of the proposed algorithm is 98% of the optimum, which is much better than the theoretical bound obtained by our approximation analysis. Our algorithm reduces the peak demand obtained by the existing alternative algorithm by 16% and simultaneously achieves better resource utilization.
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