Probabilistic optimal management of active and reactive power in distribution networks using electric vehicles with harmonic compensation capability

Abstract

Electrical vehicles (EVs) are among the fastest-growing electrical loads that change both temporally and spatially at distribution networks. Moreover, the existence of uncertain parameters, such as EVs as well as domestic loads in power networks, poses serious operational challenges for them. Accordingly, stochastic studies of system performance are a must. Against this background, this paper aims to present a stochastic multi-objective method for the problem of simultaneous active and reactive power management as well as harmonic compensation in distribution networks in the presence of EVs and non-linear devices (NLDs). This method minimizes costs associated with power generation and losses. Besides, it improves the total harmonic distortion of voltage (THDv) at network buses subject to network and EV constraints. In the proposed method, to strike a balance between exploration and exploitation abilities, a hybrid technique named the “PSO-GA optimization algorithm” was used to take advantage of both the genetic algorithm (GA) and the particle swarm optimization (PSO) method. Accordingly, the effectiveness of the proposed method was examined on a standard IEEE 33-bus distribution network populated with EVs equipped with on-board bidirectional chargers. The results obtained showed that the proposed model improved network power quality indices as well as economic and technical issues of EVs in parking lots.