Modified manta ray foraging optimization algorithm based improved load frequency controller for interconnected microgrids

Abstract

Abstract The recent environmental crisis and global warming have compelled an energy transition, particularly in the power generation and transportation sectors. Increased energy competence and production will necessitate enormous efforts. Admittedly, power system inertia metrics have revealed reduced inertia attributes, which could lead to a catastrophic shutdown and security issues in future electrical power systems. The design of the load frequency controller is essential for improving frequency regulation and power system stability. Nonetheless, more research is needed into control development and design approaches that take into account renewable energy characteristics, complexities, connected electric vehicles (EVs), and uncertainties. Metaheuristic‐based design methods and fractional order control have recently demonstrated an improved response when compared to classical design methods and integer‐order controllers. This work presents an improved fractional order hybrid control system based on two modified versions of the Manta Ray Foraging Optimization (MRFO) Algorithm. The exploration and exploitation stages of the modified MRFO are enhanced by utilizing a chaotic map and a weighting factor. The transients and steady‐state performance of the studied two‐areas interconnected microgrids have been significantly improved by incorporating the advantages of both the suggested fractional order‐based control approach and the proposed modified MRFO algorithms. While assessing the feasibility of the developed controller and modified optimizers, uncertainty, renewable energy fluctuations, load transients, and electric vehicle attributes are all properly considered. The modified MRFO's performances are evaluated using 23 benchmark functions, and the results are compared to the original MRFO and recent well‐known optimization algorithms. Both statistical analysis and time‐domain results demonstrate the superiority of the proposed modified optimizers and the proposed load frequency controller.