Algorithms and Modeling for Optimizing Sustainable Energy Systems

The global transition toward sustainable energy necessitates intelligent, integrated solutions to overcome the intermittency of renewable sources. This paper presents and validates a comprehensive framework for optimising Hybrid Solar-Wind Energy (HSWE) systems by integrating advanced simulation, machine learning-based forecasting, and metaheuristic optimisation. Using meteorological and operational data from three distinct climate zones, we modelled and analysed a PV-wind-lithium-ion hybrid system. A neural network was employed for precise load forecasting, while Particle Swarm Optimisation (PSO) managed real-time resource allocation and storage dispatch. Comparative analysis reveals that the optimised hybrid system significantly outperforms standalone units, increasing energy production by up to 32%, improving overall energy efficiency to 92.3%, and reducing operational costs by over 36%. The simulation models demonstrated high fidelity, with predictions matching experimental field data with less than 1% error. Furthermore, the integration of predictive fault handling and intelligent load balancing enhanced system reliability, increasing the mean time between failures (MTBF) by over 70% and achieving 97.6% system availability. This research provides a validated, replicable framework for engineers and policymakers, demonstrating a practical pathway to developing efficient, economically viable, and resilient decentralised renewable energy infrastructure to meet global sustainability goals.