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JEET, Vol. 13, No. 2, March 2018
Multi-Objective Optimal Predictive Energy Management Control of Grid-Connected Residential Wind-PV-FC-Battery Powered Charging Station for Plug-in Electric Vehicle
Adel Abdelaziz Abdelghany Elgammal
Area B - Electric Machinery and Power Electronics
Abstract Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi- Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.
Keyword Plug-in Electric Vehicles (PEVs),Predicative control,Wind-PV-FC battery charging station,Smart grid,Grid to Vehicle (G2V),Energy management,Multi-objective particle swarm optimization
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