A Solution to Demand Response with Soft-Computing Techniques

The demand response (DR) is one of the best possible solutions to reduce peak demand and energy consumption in the modern power system. The DR is also beneficial for energy management systems to balance, shift, and monitor the imperative equipment which depends on electricity price exposed by the utility company. This paper proposes a method to minimize the peak load through price weight given by the utility company. Three types of consumers have been considered for the study as residential, industrial, and commercial for the optimization model. The ‘per day electricity use’ for these consumers can be reduced by scheduling the elastic loads. Two soft-computing techniques, Symbiotic Organism Search (SOS) and Particle Swarm Optimization (PSO), have been designed to solve the scheduling problem. The outcome of SOS and PSO has been compared with the existing popular meta-heuristic technique, Genetic Algorithm (GA). The simulation results show that the recommended algorithm reduces cost while balancing the peak demand.