一种面向聚合商的、利用支持IoT的移动设备的新型负荷分配策略
A novel load distribution strategy for aggregators using IoT-enabled mobile devices
AI总结：
针对电网聚合商因移动设备普及导致的负荷失衡问题，提出基于设备灵活属性的MINLP模型与低复杂度启发式算法，可有效降低效用损失，实用性优于传统求解器与调度方案。
摘要：
物联网（IoT）以及电动汽车（EV）等移动设备的快速普及，给电网带来了不可预测的负荷。在部分因地理位置、高峰时段等因素导致需求过高的电网聚合商（充电站）处，供需比失衡问题尤为严重。现有的需求响应解决方案仅依靠负荷的时间转移，未考虑设备的充电模式、移动能力等可支持地理迁移的属性，无法实现显著的优化效果。此外，部分聚合商的备用容量信息可助力将面临需求过剩的其他聚合商的负荷重新疏导，从而实现设备的迁移。本文将设备的这些灵活属性建模为混合整数非线性问题（MINLP），以最小化过剩负荷并提升所有设备的效用（收益）。我们提出了一种在线分布式低复杂度启发式算法，该算法根据需求和截止期限对设备进行优先级排序，以最小化效用的累计损失。我们在大量合成数据集上对所提启发式算法进行了测试，并在相同运行时长下与求解器/优化工具的解进行对比，证明了使用求解器的不实用性。我们还利用真实世界EV测试平台数据对所提方案及其他调度方案进行了测试，结果表明该方案生成可行调度具备实用性，且效用损失改善率至少达57.23%。
英文摘要：
The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stations) with excessive demand due to the geographic location, peak time, etc. Existing solutions on demand response cannot achieve significant improvements based only on time-shifting the loads without considering the device properties such as charging modes and movement capabilities to enable geographic migration. Additionally, the information on the spare capacity at a few aggregators can aid in re-channeling the load from other aggregators facing excess demand to allow migration of devices. In this paper, we model these flexible properties of the devices as a mixed-integer non-linear problem (MINLP) to minimize excess load and the improve the utility (benefit) across all devices. We propose an online distributed low-complexity heuristic that prioritizes devices based on demand and deadlines to minimize the cumulative loss in utility. The proposed heuristic is tested on an exhaustive set of synthetic data and compared with solutions from a solver/optimization tool for the same runtime to show the impracticality of using a solver. A real-world EV testbed data is also tested with our proposed solution and other scheduling solutions to show the practicality of generating a feasible schedule and a loss improvement of at least 57.23%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
