Demand response with pricing schemes and consumers mode constraints for energy balancing in smart grids

Demand response (DR) is currently gaining widespread attention because it persuades consumers to participate actively in the electricity market, intending to balance power demand with available generation. The DR shaves peak energy use by shifting or curtailing demand or driving energy conservation concerning price variation or incentive payments to meet growing energy demand with the available generation, and provides new opportunities for power usage scheduling. To avail this opportunity, this work proposes DR energy management scheme (DREMS) based on multi-objective wind-driven optimization (MOWDO) algorithm for the power scheduling problem of the smart home, which has smart appliances: controllable (power and time) and critical appliances. The first class of appliances are controllable with adjustable power and time. On the other hand, critical appliances do not tolerate adjustment in time or power. The first class contributes to utility bill payment and peak-to-average demand ratio (PADR) minimization, and the second class contributes to comfort enhancement. The aim is to reduce utility bills, PADR, and discomfort, and achieve the desired trade-off between payment and PADR, and payment and discomfort. The power usage scheduling problem is formulated as an optimization problem with integer programming for four modes of operation. The DREMS based on the MOWDO algorithm resolves the optimization problem, producing an optimal operation schedule for four modes of operation. To assess its effectiveness, the MOWDO algorithm is compared with the existing MOPSO algorithm using various performance metrics. Simulation results demonstrate that the MOWDO returned an optimized operation schedule that successfully achieves the desired trade-off between payment and PADR, as well as payment and discomfort.