Prasun Bhattacharjee
Dr. Somenath Bhattacharya
Abstract
Even though India currently possesses the fourth-biggest installed wind power generation competence in the world, it needs to evolve faster to satiate the mounting energy demand of its developing financial system while curbing the subsequent greenhouse gas discharge for accomplishing its Paris Agreement pledges. Offshore wind power generation opportunities can play a pivotal role to achieve the remarkable goal set by the Government of India of establishing 140 GW wind power generation capability by 2030 at the same time evade land procurement disputes. The current paper focuses on the cost optimization of offshore wind power generation in the Gulf of Khambhat of India using artificial intelligence-assisted approaches. Genetic Algorithm and Binary Particle Swarm Optimization technique have been employed concurrently for five layouts with similar area of 1 km2 but different aspect ratios. The experimentation outcomes verify the dependency of cost of energy with the layout aspect ratio and the better suitability of the Genetic Algorithm technique in minimizing the offshore wind power generation cost for all layout configurations compared with the Binary Particle Swarm Optimization
Keywords- Offshore Wind Power, Cost Optimization, Artificial Intelligence, Genetic Algorithm, Binary Particle Swarm Optimization, Gulf of Khambhat, Layout Aspect Ratio