Optimizing Microgrid Scheduling with Renewable Energy Integration: A Comparative Study of Bio-Inspired Algorithms

Due to the increasing demand for reliable and sustainable fuel sources, a considerable amount of people is shifting towards microgrids, which are small scale programmable energy systems. The optimization problem concerning renewable energy's maximum possible performance is more complex and requires advanced techniques. In this paper, the challenge of microgrid scheduling is analyzed with the aid of bioinspired optimization algorithms, specifically the Manta Ray Foraging Optimisation (MRFO). The program seeks to minimize operating costs, reduce emissions, and increase fuel assurance by optimizing the scheduling of decentralized pro-microgrid participants such as combined heat and power (CHP) plants and other renewable energy sources. In order to test the developed method for achieving the optimal microgrid scheduling and determining if it meets the objectives, the research tests conventional Particle Swarm Optimization (PSO) in comparison with Markov Random Forest Optimization (MRFO). Several algorithms are tested and the analysis is conducted with the Global best value serving as a fountain. From the outcomes, it is clear that bio-inspired algorithms particularly MRFO are able to control microgrids and incorporate renewable energy sources while satisfying complex constraints. This paper provides new important insights into sustainable energy systems, proposing a method for economically and environmentally friendly operation of microgrids.