A hybrid APSO–ANFIS optimization based load shifting technique for demand side management in smart grids

Abstract

Cost and performance are considered important parameters to obtain an optimized configuration for smart grids. In this paper, a new optimization approach, based on a hybrid adaptive particle swarm with an adaptive neurofuzzy inference system (ANFIS) algorithm, has been proposed. This approach allows optimizing demand side management (DSM) using the load shifting technique. The impact of the latter on consumer profile, electricity pricing mechanisms, and overall grid performance are illustrated. In this simulation, the focus lies on modeling DSM using a day-ahead load shifting approach as a minimization problem. Simulation experiments have been tested separately on three different demand zones, namely, residential, commercial, and industrial zones. A comparative study of solutions was performed, focusing on both reduced peak demand and operational costs. The obtained results demonstrate that the optimization presented in this article approach outperforms the other approaches by achieving greater savings in the residential and commercial sectors. The study proved a significant reduction in peak demand. In fact, values of 23.76%, 17.61% and 16.5% in peak demand reduction are achieved in the case of residential, commercial, and industrial sectors, respectively. Furthermore, operational cost reductions of 7.52%, 9.6%, and 16.5% are obtained for the three different cases.