Plug-in hybrid electric vehicles aggregation and real-time active power control simulation analysis in distribution systems

This paper presents a control strategy for plug-in hybrid electrical vehicles (PHEVs) demand in power distribution systems to control the peak load due to PHEVs charging. The stochastic nature for both instants of start charging time and the initial state of charge (SOC) are fully exploited to model the intermittent demand nature in future smart grids. The appropriate control methodology adopted in this work is the decentralized control. Each power conditioning unit (PCU) interface of PHEV charger extracts control variable from the grid and the PHEV internal state, then considers a proper smart action. The developed control method uses the real-time electricity tariff rate, maximum time permit of the vehicle plugged in, and the initial state of each vehicle's battery as control variables. To achieve such control, a fuzzy logic controller (FLC) is designed to work with the PCU of each vehicle charger. The analysis of the developed control method is performed using accelerated quasi-static time-series (QSTS) power-flow method. The QSTS utilizes the Lagrange polynomial function as an accelerator for the forward/backward power-flow method. Extensive simulations of the unbalanced three-phase IEEE 123-node radial feeder are carried out with a combination of commercial, industrial, residential daily demands, and PHEVs penetration at different electricity rates. The results show the effectiveness of the developed control strategy in dynamic peak shaving at various scenarios as well as the superior performance of the developed accelerated quasi-static time-series power-flow method.