Optimal day ahead energy consumption management in grid‐connected microgrids

Abstract

The day-ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on input-output characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day-ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load-shaping based demand-side management strategy is adopted to reduce the peak loads and enhance the DG's unit operational costs. The impact of demand-side management and price-driven demand response programs on convex and nonconvex energy management system (EMS) problems is investigated. Furthermore, the short-term scheduling horizon of 15-minutes resolution time is considered for both solar and wind power to maintain forecast accuracy. The state-of-art optimization algorithm of quantum particle swarm optimization is devised to solve the proposed problem in the presence of the nonconvex cost function of DGs. The technical performance indices for each demand response program is evaluated, and the best alternative demand response program is chosen by implementing analytical hierarchy process. The proposed algorithm efficiently solves the nonconvex EMS problem, and the simulation results yield a 12.11% reduction in operating cost without compromising customer satisfaction. Computational time, convergence characteristics, and solution effectiveness in contrast to recently reported metaheuristic algorithms are examined for the effectiveness of the suggested algorithm.