With respect to growing the nonlinear loads, it is appealing to employ Plug-in electric vehicles (PEVs) as harmonic compensators to distributedly mitigate harmonic pollution. Hence, this study is intended to propose a comprehensive stochastic optimization framework for PEVs participation considering their uncertainties to accurately settling harmonic power market by distribution system operators. The optimization framework consists of the total expected distortion payment function (DTPF) of the accepted harmonic compensators, such as PEVs and active power line conditioners (APLCs) as an objective function while fulfilling the constraints concerned with the grid, vehicle, and market price. The DTPF comprises availability, loss, and loss of opportunity cost terms based on the harmonic capability curve of the harmonic compensators. Furthermore, the Monte-Carlo simulation based on the contingency concept is utilized to generate scenarios for covering the uncertainties of the PEVs, and then the scenarios solve by adaptive particular tunable fuzzy particle swarm method. This study demonstrates the efficiency of the proposed framework and the outperformance of the PEVs compared with APLCs investigated on a 14-node microgrid.

关于增加非线性负载，采用插电式电动汽车（PEV）作为谐波补偿器以分散减轻谐波污染是有吸引力的。因此，本研究旨在为PEV的参与提出一个综合的随机优化框架，考虑其不确定性，以准确解决配电系统运营商的谐波电源市场。该优化框架由公认的谐波补偿器（例如PEV和有源电力线调节器（APLC））的总预期失真补偿函数（DTPF）作为目标函数，同时满足与电网，车辆和市场价格有关的约束。DTPF包括基于谐波补偿器的谐波能力曲线的可用性，损失和机会成本损失项。此外，利用基于权变概念的蒙特卡罗模拟方法，生成了覆盖电动汽车不确定性的场景，然后通过自适应的特殊可调模糊粒子群算法进行求解。这项研究表明，与在14节点微电网上研究的APLC相比，拟议框架的效率和PEV的出色表现。