Multi-Objective Optimization and Energy Systems Modeling for Carbon Neutrality: Artificial Intelligence-Based Approaches

Year 2025, Volume: 8 Issue: 2, 113 - 121, 25.12.2025

Ahmet Elbir

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

References

• Alabi, T. M., Agbajor, F. D., Yang, Z., Lu, L., & Ogungbile, A. J. (2023). Strategic potential of multi-energy system towards carbon neutrality: A forward-looking overview. Energy and Built Environment, 4(6), 689-708.
• Aziz, S., Ahmed, I., Khan, K., & Khalid, M. (2024). Emerging trends and approaches for designing net-zero low-carbon integrated energy networks: A review of current practices. Arabian Journal for Science and Engineering, 49(5), 6163-6185.
• Behzadi, A., & Sadrizadeh, S. (2023). A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach. Energy Conversion and Management, 277, 116590.
• Chen, C., Hu, Y., Karuppiah, M., & Kumar, P. M. (2021). Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments, 47, 101358.
• Dong, H., & Zhang, L. (2023). Transition towards carbon neutrality: Forecasting Hong Kong's buildings carbon footprint by 2050 using a machine learning approach. Sustainable Production and Consumption, 35, 633-642.
• Fadaee, M., & Radzi, M. A. M. (2012). Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review. Renewable and sustainable energy reviews, 16(5), 3364-3369.
• Fonseca, J. D., Commenge, J. M., Camargo, M., Falk, L., & Gil, I. D. (2021). Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach. Applied Energy, 290, 116746.
• Ghenai, C., Albawab, M., & Bettayeb, M. (2020). Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renewable Energy, 146, 580-597.
• Jing, R., Zhu, X., Zhu, Z., Wang, W., Meng, C., Shah, N., ... & Zhao, Y. (2018). A multi-objective optimization and multi-criteria evaluation integrated framework for distributed energy system optimal planning. Energy Conversion and Management, 166, 445-462.
• Kim, I., Kim, B., & Sidorov, D. (2022). Machine learning for energy systems optimization. Energies, 15(11), 4116.
• Mah, D. N. Y., Wu, Y. Y., Ip, J. C. M., & Hills, P. R. (2013). The role of the state in sustainable energy transitions: A case study of large smart grid demonstration projects in Japan. Energy Policy, 63, 726-737.
• Manikandan, S., Kaviya, R. S., Shreeharan, D. H., Subbaiya, R., Vickram, S., Karmegam, N., ... & Govarthanan, M. Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals–A review. Sustainable Development.