Synergistic Frameworks in Renewable Energy and Industrial Systems: Integrating AI, Optimization, and Socioeconomic Dynamics for Sustainable Supply Chains

Abstract

With the growing demand for sustainable energy across the globe, the shift from fossil fuel based systems to renewable energy technologies has gained momentum, necessitating the need for intelligent and resilient supply chains. This review examines synergy approaches in which artificial intelligence (AI), optimization methods and socio-economic aspects are combined to improve the performance of renewable energy or industrial systems. The study emphasizes that the integration of various energy sources and energy storage technologies in HRES can enhance energy efficiency, reliability, and sustainability. Advanced optimization techniques are explained, and the need for using them to achieve a balance of the technical, economic and environmental goals, as well as to reduce the operating costs and to increase the system performance, is emphasized. Predictive maintenance, energy forecasting, resource allocation, and intelligent grid management are explored, showcasing AI's promise of enhancing operational efficiency and aiding in data-driven decision-making. The study also highlights the role socioeconomic issues such as community involvement, job creation, engaging stakeholders and benefiting the parties in a fair manner play in the sustainability of renewable energy projects. Additionally, model transparency and sustainability are discussed. Future directions highlight the importance of supportive policy frameworks, financial incentives, circular economy approaches, and interdisciplinary collaboration. The use of AI, optimization methods, and socio-economic factors offers a comprehensive approach to creating energy systems that are sustainable and resilient. These synergistic approaches can help solve global energy problems and can also contribute to environmental protection, economic development and social development.