Improving Wind Energy'S Economic Efficiency In Digha, West Bengal, India: A Metaheuristic Optimization Method Employing Genetic Algorithms And Particle Swarm Optimization Along The Bay Of Bengal Coast

The expanding requirement for sustainable energy has led to enhanced interest in optimizing the economic functioning of wind farms, particularly in coastal regions with plentiful wind resources. This study emphases on the yearly profit optimization of a projected wind farm located in Digha, West Bengal, India—an area along the Bay of Bengal identified for its firm wind flow patterns. Two renowned metaheuristic algorithms—Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)—were engaged to boost the annual profit by optimizing key design and effective parameters of the wind farm, involving turbine placement, hub height, and rotor diameter. Wind data specific to the Digha region were used in the simulation to ensure realistic modelling of energy output and cost considerations. The relative scrutiny discloses that the Genetic Algorithm unswervingly produces higher annual profit values than PSO under identical input conditions. GA's superior performance is attributed to its robust exploration capabilities and adaptability to complex, multi-dimensional search spaces typical of wind farm optimization problems. The study construes that GA is a more competent and reliable tool for profit-driven wind energy optimization in coastal Indian settings. These findings offer valued insights for policymakers, engineers, and renewable energy developers pursuing to capitalize on pecuniary returns from wind energy reserves in the Bay of Bengal region.