Optimized load frequency controller for microgrid with renewables and EVs based recent multi-objective mantis search algorithm

• The proposed controller improves frequency regulation in renewable based microgrid. • The new proposed method uses a multi-objective mantis search algorithm. • Electric vehicles are used as energy storage to support microgrid stability. • The system shows faster response and reduced tie-line power fluctuations. • The method outperforms traditional techniques in all examined scenarios. In recent years, the growing implementation of renewable energy sources (RESs) replaced the use of petroleum and coal, and the replacing of fuel-powered vehicles with electric vehicles (EVs) created various challenges in designing electrical microgrids (MGs) because of the intermittent nature of renewable energy and dependence on the environment. The load frequency controller (LFC) presents a critical factor for regulating an interconnected electrical MG system. Consequently, an optimal design of the controllers is crucial to ensure the stability, reliability, and superior performance of the electrical MG. The tie-line power must be eliminated by the controller, as well as the frequency oscillations of the interconnected MG. Consequently, that paper suggests an integral order. (IO) based on a proportional integral derivative (PID) controller for LFC in an interconnected electrical MG system. Furthermore, this article proposes a new practical implementation of the recently proposed multi-objective mantis search algorithm (MOMSA) for simultaneously calculating the optimal controller coefficients. The interconnected MG, considering the natural characteristics of RESs, EV behavior, and load changes, is used to validate the proposed controller and proposed optimization algorithm. The results of examined scenarios demonstrate that the suggested optimization algorithm and controller are highly effective in mitigating frequency oscillations and tie-line power fluctuations under the investigated scenarios. The proposed approach reduces these deviations by approximately 43 % to 51 %, 54 % to 67 %, and 73 % to 88 % compared to the particle swarm optimization (PSO), marine predator algorithm (MPA), and genetic algorithm (GA), respectively. Furthermore, the system stability is significantly improved, and a faster response during transient conditions is achieved when compared to previous algorithms.