An improved marine predators algorithm for the optimal design of hybrid renewable energy systems

Abstract

Microgrid technologies are exciting energy sources that are economically feasible for current and future applications in light of increased energy demand and the depletion of traditional sources. This article focuses on the latest metaheuristic algorithm, the marine predators algorithm (MPA), in the field of energy. To investigate a method for reducing the system’s investment costs in Minia, Egypt, the combination of reinforcement learning (RL) with MPA is used to build a new method, Deep-MPA, where RL principles are applied to adjust and enhance the lack of MPA in global searching. The exploration/exploitation ratio is regulated by varying the step size, which affects the efficiency of the MPA. Instead of updating the value of the parameter for all agents in the same manner, RL principles are used to update it on the basis of the current individual state. Additionally, to resolve the common challenge of using RL to determine the appropriate global search parameters for MPA, Deep-MPA is used to design a hybrid renewable energy microgrid system, which includes photovoltaic panels, a wind turbine system, a diesel generator, and battery storage systems. These are some of the criteria and constraints that this system may require to ensure its stability, robustness, performance, and load satisfaction. The proposed Deep-MPA is verified by contrasting the results with different algorithms in the CEC’2017 test. Moreover, Wilcoxon’s test validates the statistical significance of the Deep-MPA. The energy cost is reduced by 6% of total consumption.