Abstract
Random charging of plug-in electric vehicles (PEVs) particularly during the peak load hours could impairment the performance of future smart grids. This paper presents genetic algorithms (GAs) for optimal scheduling of LTC and switched shunt capacitors (SSCs) to improve the performance of smart grid with PEV charging at consumer premises in residential feeders and PEV charging stations (PEV-CSs) in distribution networks. The forecasted daily load curves associated with PEV-CSs and residential feeders populated with PEVs are first generated and then incorporated in the GA-based optimal LTC and SSC scheduling solution. Simulation results without and with optimal scheduling are presented for a 449 node smart grid system with 5 PEV-CSs considering random and coordinated charging of 264 PEVs in 22 low voltage residential networks.
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