Hybrid Energy System Planning: Optimizing Solar and Wind Power for Maximum Efficiency and Cost Reduction

This project optimizes a hybrid renewable energy system with photovoltaic (PV) panels and wind turbines, aiming to minimize costs and maximize energy output. Advanced optimization techniques, including Simulated Annealing (SA), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Artificial Bee Colony (ABC), are used to find near-optimal solutions within practical constraints. The decision variables include the number of PV panels and wind turbines, while the objective functions minimize costs and maximize energy output. Each algorithm offers unique advantages, with SA balancing global and local searches, GA emphasizing population diversity, GWO leveraging cooperative behavior, and ABC enhancing exploration and exploitation. Case studies reveal trade-offs between solar and wind configurations, demonstrating the potential of hybrid systems for cost-effective, energy-efficient solutions.