A hybrid shuffled frog leaping algorithm and intelligent water drops optimization for efficiency maximization in smart microgrids considering EV energy storage state of health

Abstract

To reduce fossil fuel consumption, carbon dioxide emissions, and greenhouse gas emissions, countries all over the world have been gradually directing their attention toward the development and application of microgrids (MGs) that run on renewable energy sources. The MG concept has been gaining increased interest, particularly with respect to distribution systems. On the other hand MGs are equipped with new technologies such as plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles, which have become viable alternatives to traditional combustion-engine cars. In this paper the novel optimization method for efficiency maximization in smart MGs in the presence of demand response, was proposed. This method combines a hybrid shuffled frog leaping algorithm (SFLA) and intelligent water drop optimization. In this situation, the EV energy storage system (ESS) state of health (SOH) model was considered to adjust the ESS temperature set point. SFLA is a new member of intelligent algorithms and a new member in the family of memetic algorithms. For this purpose, simulation results were made in MATLAB software environment to demonstrate the effectiveness of the proposed methodology. In order to verify proposed algorithm, simulations were made along with some conventional optimization methods. The results show that the proposed optimization method, can effectively improve the performance of MG power flow, when it is compared with other methods.