Abstract
Demand Response (DR) mechanism can be implemented in power system at diverse points. In this paper, particle swarm optimization and genetic algorithm is integrated with Electric Vehicle (EV) charging mechanism for demand response (events and non-events). The EV's may act as load to the grid (during charging operation) and can support peak loads (during discharging operation). The rebates will be provided to the users based on the reduction of electricity consumption specifically at demand response (DR) events. Users can achieve the better DR expenses by decreasing the consumption during DR events and boosting the consumption in non-event epochs. To understand users' spurs based on DR (events and non-events), the proposed metaheuristic algorithm (PSO and GA) manages the energy consumption and optimizes the best cost by maximizing the reward for EV users throughout different DR event schedules. The simulation results relate the performance of revenues, cost and rebates without impacting EV's charging demand.
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