Harnessing Coastal Winds: Metaheuristic Optimization of Onshore Wind Power Costs in Digha, West Bengal
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Keywords:
Cost Optimization, Genetic Algorithm, Metaheuristics, Onshore Wind Energy, Particle Swarm OptimizationAbstract
The economic viability of onshore wind energy is critical for sustainable power generation,
particularly in coastal regions with abundant wind resources. This study presents a metaheuristic-based
optimization framework for minimizing the levelized cost of electricity (LCOE) for onshore wind power
generation in Digha, West Bengal, leveraging Genetic Algorithm (GA) and Particle Swarm Optimization
(PSO). A comprehensive comparative analysis reveals that GA achieves a lower generation cost of USD
0.0031 per kWh, outperforming PSO, which yields USD 0.0043 per kWh. The enhanced performance of
GA is attributed to its superior global search efficiency and adaptability in complex optimization
landscapes, leading to more effective wind turbine placement and power dispatch strategies. These findings
underscore the efficacy of evolutionary algorithms in optimizing wind energy systems, offering valuable
insights for policymakers and energy planners aiming to enhance the cost-effectiveness of renewable
energy deployment in coastal regions.
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References
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