Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting

Abstract

Abstract Demand Side Management (DSM) is one of the ways to create interaction between the MicroGrids (MGs) and increase consumer participation in management schemes. Different algorithms and strategies have been used to execute consumption management programs which often cover a limited number of loads in several specific types. In this paper, first, the load shift method, as an optimization problem to reduce system demand peak and subscriber’s bills for various loads in smart MGs, is solved by Hybrid Particle Swarm Optimization algorithm with Sinusoidal and Cosine Acceleration Coefficient (H-PSO-SCAC). Then the study is aimed at measuring the effect of the proposed program on the generation and presence of MGs in the market for improving the level of social welfare. The results are performed on a Smart Grid (SG) consisting of three residential, commercial and industrial MGs which include different types of controllable loads. The results show that the highest percentages of peak load reduction after the implementation of the DSM program by (H-PSO-SCAC) algorithm for the three MGs are 23%, 19% and 19%, respectively. Also, the highest percentages of reduction in subscriber’s bill for the three MGs are 16.8%, 19.2% and 20.5%, respectively. The proposed algorithm has performed much better in reducing bills and peak loads than most other methods such as Logarithmic Function (LF), Multi Agent (MA), Evolutionary Algorithm (EA), and Symbiotic Organisms Search (SOS). The findings show that the proposed program can reduce peak load, reduce subscriber’s bills, save production costs, help balance the supply and demand, and improve the level of social welfare from the perspective of the distribution system operator.