Multi-Objective Optimization and Energy Systems Modeling for Carbon Neutrality: Artificial Intelligence-Based Approaches
Abstract
This study addressed the artificial intelligence (AI)-based optimization of energy systems to achieve carbon neutrality goals. Methods developed to increase the efficiency of energy systems, reduce costs and minimize environmental impacts support both technical and economic sustainability. In the research, the integration of renewable energy sources and performance analysis of hybrid energy systems were carried out. In particular, the focus is on increasing energy and exergy efficiencies, reducing carbon emissions and optimizing electricity generation costs. According to the findings, optimized hybrid systems achieved 45.6% in energy efficiency and 38.2% in exergy efficiency, producing 78% less carbon emissions compared to conventional systems. In addition, the cost of electricity generation (LCOE) of these systems decreased by 24.2% to $0.072/kWh. These results demonstrate the effectiveness of AI-powered optimization and the importance of integrating renewable energy systems to achieve carbon neutrality goals. The study offers suggestions to reduce the carbon footprint and contribute to the sustainable transformation of the energy sector. In this context, future research areas such as the development of energy storage technologies, the deployment of smart grids, and the implementation of innovative energy management approaches are highlighted.
Keywords
Carbon Neutrality, Renewable Energy, Hybrid Energy Systems, Artificial Intelligence Based Optimization, Sustainable Energy Systems
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