Multidimensional Firefly Algorithm for Solving Day-Ahead Scheduling Optimization in Microgrid

In this paper, an improved metaheuristic optimization algorithm based on the firefly algorithm, called multidimensional firefly algorithm (MDFA), is presented for solving day-ahead scheduling optimization in a microgrid. The proposed algorithm takes the output of power generations among a quantity of distributed energy resources during 24 h together rather than a single hour as a firefly separately. The proposed algorithm is combined with strategy of solving equality constraint replacing the use of the penalty-function technique. It is also enhanced by using a novel method in parameters self-adaption instead of applying fixed values, resulting in avoiding tuning frequently the algorithm parameters during the process of optimization. The MDFA is utilized for optimization of energy production cost in a microgrid. The superiority of the MDFA is demonstrated by using the classic test power system proved in the previous literature. The solutions obtained by MDFA are compared with the results found by five famous optimization algorithms. The high performance of MDFA is established by the quality with the minimum total cost, the reliability of gained solutions, the speed of convergence, and the ability to satisfy various constraints.