Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units
In the smart grid (SG) users in residential sector adopt various load scheduling methods to manage their consumption behavior with specific objectives. In this paper, we focus on the problem of load scheduling under utility and rooftop photovoltaic (PV) units. We adopt genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), and proposed genetic wind driven optimization (GWDO) algorithm to schedule the operation of interruptible appliances (IA) and non interruptible appliances (Non-IA) in order to reduce electricity cost and peak to average ratio (PAR). For energy pricing combined real time pricing (RTP) and inclined block rate (IBR) is used because in case of only RTP their is possibility of building peaks during off peak hours that may damage the entire power system. The proposed algorithm shift load from peak consumption hours to off peak hours and to hours with high generation from rooftop PV units. For practical consideration, we also take into consideration pricing scheme, rooftop PV units, and ESS in our system model, and analyze their impacts on electricity cost and PAR. Simulation results show that our proposed scheduling algorithm can affectively reflect and affect users consumption behavior and achieve the optimal electricity cost and PAR.
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