Gradient Descent-Based Optimization of Hybrid Renewable Energy Systems for Enhanced Stability and Cost Efficiency

This research optimizes a Hybrid Renewable Energy System (HRES) using Gradient Descent to achieve a stable, cost effective, and efficient energy supply. The proposed methodology integrates solar photovoltaic (PV) systems, wind turbines and battery storage to balance in energy contributions while maintaining voltage and frequency stability. Optimization is shown to significantly improve stability metrics such that voltage stability is 99% and frequency stability is 97%, thus meeting grid standards and maintaining reliable energy delivery under dynamic conditions. This optimized configuration increased solar energy contributions by 33%, reduced battery reliance by 25% and balanced the energy mix. Nevertheless, total energy generation (684 kWh) and efficiency (85.5%) were slightly reduced (639 kWh and 79.88%), respectively, illustrating the tradeoffs of improving system reliability. We conduct comparative analysis and show that Gradient Descent is a computationally efficient and stable optimization technique outperforming traditional heuristic methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in convergence speed and stability. Future research is needed in advanced predictive controls, adaptive learning algorithms, and other renewable sources to fill efficiency gaps and improve system scalability and sustainability.