无未来信息的电动汽车在线协调充电决策算法
Online Coordinated Charging Decision Algorithm for Electric Vehicles without Future Information
摘要：
插电式电动汽车（PEV）与电网的大规模集成刺激了对高效充电协调机制的需求。可以示出，最优充电调度平滑了随时间的能量消耗，以使总能量成本最小化。然而，在实践中，很难完美地平滑能量消耗，因为在需要确定现有PEV的充电速率的时刻，未来PEV充电需求是未知的。在本文中，我们提出了一个在线协调充电决策（ORCHARD）算法，它最大限度地减少能源成本，而不知道未来的信息。通过严格的证明，我们表明，果园是严格可行的，在这个意义上说，它保证满足所有的收费需求之前，到期时间。与此同时，它达到了2.39的最佳竞争比。为了进一步降低算法的计算复杂性，我们提出了一种新的降低复杂性的算法，以取代标准的凸优化技术中使用的ORCHARD。通过大量的模拟，我们表明，ORCHARD和离线的最佳解决方案，利用完整的未来信息，平均性能差距小至14%。通过设置适当的加速因子，可以将平均性能差距进一步减小到6%以下。
英文摘要：
The large-scale integration of plug-in electric vehicles (PEVs) to the power grid spurs the need for efficient charging coordination mechanisms. It can be shown that the optimal charging schedule smooths out the energy consumption over time so as to minimize the total energy cost. In practice, however, it is hard to smooth out the energy consumption perfectly, because the future PEV charging demand is unknown at the moment when the charging rate of an existing PEV needs to be determined. In this paper, we propose an Online cooRdinated CHARging Decision (ORCHARD) algorithm, which minimizes the energy cost without knowing the future information. Through rigorous proof, we show that ORCHARD is strictly feasible in the sense that it guarantees to fulfill all charging demands before due time. Meanwhile, it achieves the best known competitive ratio of 2.39. To further reduce the computational complexity of the algorithm, we propose a novel reduced-complexity algorithm to replace the standard convex optimization techniques used in ORCHARD. Through extensive simulations, we show that the average performance gap between ORCHARD and the offline optimal solution, which utilizes the complete future information, is as small as 14%. By setting a proper speeding factor, the average performance gap can be further reduced to less than 6%.
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。
