Capacity and operation optimization of hybrid microgrid for economic zone using a novel meta-heuristic algorithm

Capacity and operation optimization of hybrid microgrid for economic zone using a novel meta-heuristic algorithm

In the urgent pursuit of sustainable development and the mitigation of global warming, a crucial transition to renewable energy models is imperative. To promote reliable and renewable energy for sustainable economic goals, this study introduces the Mixing and Exploring Algorithm (MEXA), a novel optimization algorithm designed for a hybrid microgrid system, spanning three economic zones outlined by the Bangladeshi government. The microgrids integrate solar and wind energy with batteries, diesel generators, and electrolyzers. MEXA, inspired by Genetic Algorithms (GA) and Grey Wolf Optimizer (GWO), incorporates an innovative “deduplication” component to enhance its optimization capabilities. Through regional and seasonal analyses, the study assesses MEXA’s adaptability to varying renewable energy availability while optimizing operational conditions of the diesel generator. MEXA aims for cost-effective installation and operation, optimal renewable energy utilization, minimal power disruptions, efficient energy management, and significant hydrogen production. A comparative analysis with established algorithms - GA, GWO, and Particle Swarm Optimization (PSO) - highlights MEXA’s superiority with an average Multi-objective Function (MOF) value outperformance: 0.46% over GA, 13.59% over GWO, and 40.08% over PSO. Moreover, MEXA demonstrates superior stability, exhibiting a standard deviation improvement of 29.32% over GA, 97.66% over GWO, and 95.16% over PSO. This multifaceted approach stands as a promising solution for bolstering economic growth, minimizing environmental impact, and enhancing energy sustainability in the face of contemporary global challenges.