Article Open Access

Algorithms and Modeling for Optimizing Sustainable Energy Systems

Mohammed Abdul Jaleel Maktoof, Alhamza Abdulsatar Shaker, Hamdi Abdullah Nayef, Nada Adnan Taher, Thamer Kadum Yousif Al Hilfi, Siti Sarah Maidin

Abstract


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.


Keywords


Hybrid Renewable Energy System, Solar-Wind Optimisation, Neural Network Forecasting, Particle Swarm Optimisation, Intelligent Energy Management

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DOI: https://doi.org/10.52088/ijesty.v5i1.1457

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Copyright (c) 2025 Mohammed Abdul Jaleel Maktoof, Alhamza Abdulsatar Shaker, Hamdi Abdullah Nayef, Nada Adnan Taher, Thamer Kadum Yousif Al Hilfi, Siti Sarah Maidin

International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674