ABSTRACT
Energy scheduling of community microgrids (CMG) by maximizing the available resources and minimizing the operating cost is considered as a very critical and challenging optimization problem. Most of the reported algorithms (heuristic and non-heuristic) did not consider the presence of complex equality and inequality constraints of the CMG. Additionally, the degradation cost of energy storage systems (ESS) is rarely incorporated in the optimization model. In response, this chapter formulates and presents a multi-objective optimization model for CMG energy scheduling, considering the degradation cost of the ESS. Then, it employs a recently developed but very efficient and popular meta-heuristic optimization technique, namely the grey wolf optimization (GWO) technique, to solve the modeled problem. The GWO algorithm works in the principle of imitating the hunting behavior of grey wolves in nature. It is a large-scale search method and its model structure is different from other meta-heuristic techniques. The proposed solution method can produce quality solutions with a competitive computational effort. Moreover, this chapter compares the results of the proposed approach with other popular meta-heuristic techniques, namely the genetic algorithm and reported algorithms in the literature. Presented results validate the effectiveness and competitiveness of the employed GWO algorithm compared to other techniques in CMG energy scheduling.
