Optimal allocation of PV units using metaheuristic optimization considering PEVs charging demand

The distribution system is seeing a dramatic shift as a result of the increasing use of distributed generators (DGs) and plug-in electric vehicles (PEVs), or plug-in hybrid electric cars. The research endeavors to optimize the allocation of photovoltaic (PV) based DGs within radial distribution systems (RDS) while accommodating the load demand stemming from PEVs. A weighted-sum based multiobjective (WMO) technique is employed in this study to optimize three fundamental technical metrics of the distribution network: achieving the best possible voltage stability index (VSI) while reducing real power loss and total voltage variation to a minimum. Initially, the study investigates the impact of both conventional and PEVs load demand, considering PEVs load demand on distribution system performance under three charging scenarios: a situation involving peak charging, scenario involving off-peak charging, and scene of random charging. Subsequently, PV units are strategically planned, taking into account the PEVs demand within the distribution system utilizing an innovative weighted multiobjective electric eel foraging optimization (WMOEEFO) algorithm, its effectuality is validated with weighted multiobjective differential evolutionary (WMODE) and weighted multiobjective grey wolf optimization (WMOGWO) algorithms on standard test system IEEE 33-bus.