Machine Learning Strategies for Optimization of Renewable Energy Systems Integration

This research explores integrating renewable energy systems into existing infrastructure, using Genetic Algorithms (GA) and Deep Reinforcement Learning (DRL) to address objectives such as maximizing energy output, minimizing costs, and optimizing fault mitigation. GA, drawing inspiration from evolutionary principles, iteratively evolves candidate solutions, effectively navigating the solution space and identifying optimal tradeoffs. Performance metrics, such as renewable energy penetration as 95% and fault mitigation effectiveness as 80%, assess GA’s efficacy. Additionally, GA is evaluated based on Detection Time 2.25 s and fault isolation accuracy 92%. Meanwhile, DRL adapts system parameters through reinforcement learning to optimize energy production while minimizing costs, environmental impact, and improving fault mitigation. Metrics like peak load coverage (90%) and overall system efficiency (97%) further evaluate DRL’s performance. Results illustrate GA’s capability in offering diverse solutions along the Pareto front, allowing decision-makers a spectrum of tradeoff choices. Leveraging these advanced machine learning techniques contributes to the development of sustainable and efficient renewable energy systems integration strategies, emphasizing fault mitigation as an essential aspect of system optimization.