Optimal energy management and scheduling of a microgrid with integrated electric vehicles and cost minimization

A combined electric vehicles (EVs) and controllable loads scheduling framework is presented in this paper for a microgrid aimed at minimizing the operating cost and emissions. The microgrid is equipped with renewable power generation by using wind turbines and solar photovoltaic panels. In this respect, EVs would be used for load profile flattening and controllable loads would be used to address the reserve requirements of the system mainly due to intermittent renewable power generation. The problem is formulated as a two-stage scheduling program to specify the expected operating cost and reserve. The first stage aims to minimize the total costs including the generation and reserve costs. The second stage seeks to minimize the redispatch costs due to volatile renewable power generation. The resulting optimization problem is then solved by using the modified manta ray foraging optimization algorithm known as "MMRFO". This algorithm is an efficacious one being capable of handling various types of optimization problems. The findings obtained from a 24-h analysis of an MG model demonstrate the superior performance of the MMRFO algorithm when compared to other established methodologies. The obtained results by applying the MMRFO method indicate high efficiency of this algorithm in comparison with some other well-known algorithms when tackling the combined EV and controllable loads scheduling problem in the presence of wind and solar power generation.