Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) and plug-in (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation are, thus, helpful the reduction of greenhouse effect pollution. However, they bring up some technical problems that should be resolved. Due ever-increasing demand these PHEVs, simultaneous connection large PEVs PHEVs grid can cause overloading, which results disturbance overall power system stability quality a blackout. Such situations avoided by adequately manipulating available from vehicle demand. State charge (SoC) is leading performance parameter optimized using computational techniques efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), its five variants were applied allocate connected intelligently. For this, maximization average SoC considered fitness function, each PHEV once day so maximum number cars charged daily. To statistically compare six algorithms, one-way ANOVA was used. Simulation statistical obtained maximizing highly non-linear objective function show with Variant 5 achieved improvements terms time best value. The APSO-5 solution has considerable percentage increase compared other APSO four datasets considered. Moreover, after 30 trials, gives highest possible value among all algorithms.