Scenario-based stochastic framework for harmonic power markets using plug-in electric vehicles

Abstract

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.