Efficient electricity consumption scheduling for residential load integrated with renewable energy resource in smart grid

Abstract

Satisfying consumer’s electricity demand at peak hours is an important problem in smart grid. From the perspective of consumers, the residential electricity consumption scheduling would aim to minimize the electricity cost and also wish to maintain their comfort. In contrast, the power utilities concentrate on to flatten the peak loads in the electricity demand. In this paper, both the viewpoints are taken into consideration while scheduling the residential appliances in smart grid. The Self Adaptive Mutated Particle Swarm Optimization Algorithm is proposed for solving the above problem. Simulations have been carried out and the results are compared with the Non-Dominated Sorting Genetic Algorithm II. From the results obtained, it is clearly proved that the proposed algorithm provides better schedules for the smart home, with minimized electricity cost and least Peak-to-Average Value whereas maximizing the user comfort. Moreover, the proposed algorithm shows its effectiveness with the increase in problem size.