Optimal Sizing and Placement of Renewable Energy Systems in Smart Grids using Machine Learning Optimization

This study investigates the optimal sizing and placement of renewable energy systems within a smart grid framework using advanced optimization techniques. Through the application of genetic algorithms, we identify the most effective configuration to enhance energy generation while minimizing system costs and environmental impact. Our optimization model converged after 100 iterations, revealing optimal sizing parameters of 500 kW for solar panels and 300 kW for wind turbines, projected to generate approximately 900 MWh/year and 600 MWh/year, respectively. Moreover, our optimized placement strategy entails installing solar panels on commercial building rooftops and wind turbines on the urban outskirts. To validate our approach, energy generation was forecasted for the next 12 months using the optimized parameters, showing close alignment with historical patterns. These findings highlight the efficacy of our optimization model in capturing system dynamics and accurately predicting future energy production. Overall, our study underscores the importance of integrating advanced optimization methodologies with renewable energy planning to foster sustainable energy infrastructures in smart grid environments.