Fuzzy Multi-objective Optimization of Hybrid Renewable Energy Systems Using Genetic Algorithms
Keywords:
CO₂ emissions minimization, Energy system reliability, Fuzzy multi-objective Optimization, Genetic Algorithm (GA), Hybrid Renewable Energy Systems (HRES), Loss of Power Supply Probability (LPSP)Abstract
Combining solar panels, wind turbines, battery storage systems, and backup diesel generators into Hybrid Renewable Energy Systems (HRES) provides a reliable way to generate clean and sustainable electricity. However, designing the perfect HRES setup is a complicated process. Engineers must continuously manage conflicting objectives such as reducing initial and ongoing costs, ensuring a stable power supply, and lowering carbon emissions. Traditional mathematical methods often struggle to effectively address these competing priorities. To tackle this challenge, the study integrates a Fuzzy Multi-Objective Optimization (FMO) approach with Genetic Algorithms (GA) to determine the best HRES design. Using fuzzy logic introduces greater flexibility into the decision-making process. By defining membership functions for each objective, the system can better handle conflicting priorities in a way that is more natural than strict, conventional rules. Their model considers both financial aspects—such as capital and operating costs—and system reliability, which is measured using the Loss of Power Supply Probability (LPSP) index. Carbon emissions are treated as a key environmental factor. They explored different weighting scenarios to understand how emphasizing cost savings, grid stability, or environmental goals changes the final system configuration. The results show that adjusting these weightings significantly impacts the design of the energy system. For example, prioritizing cost leads to more use of diesel generators, reducing initial costs but increasing environmental impact; on the other hand, focusing on low emissions results in a stronger emphasis on solar and wind technologies. This research highlights how fuzzy-based multi-objective tools are effective in finding well-balanced, realistic configurations that make micro grids both dependable and environmentally friendly. These findings offer a useful guide for engineers working on the next generation of sustainable power systems. This research introduces a combined Fuzzy-Genetic Algorithm (Fuzzy-GA) approach to enhance component sizing for off-grid Hybrid Renewable Energy Systems (HRES). Utilizing fuzzy logic to address conflicting goals—cost, reliability, and emissions—enables an adaptive, human-focused design, creating a means to harmonize economic, technical, and environmental objectives.
References
P. Yambar, S. Sen, C. K. Shiva, and V. Basetti, “Fuzzy Logic-Based Energy Management for Renewable-Powered On-Road Wireless Charging Infrastructure,” 2026 International Conference on Electric Power and Renewable Energy (EPREC), pp. 1–6, Jan. 2026.
P. Whig, B. Bhatia, A. B. Bhatia, and P. Sharma, “Renewable Energy Optimization System Using Fuzzy Logic,” Algorithms for Intelligent Systems, pp. 177–198, 2023.
J. H. Holland, Adaptation in Natural and Artificial Systems. The MIT Press, 1992.
A. E. El-Iali, M. Doumiati, and M. Machmoum, “Fuzzy Logic Energy Management Optimization Through Genetic Algorithm for Plug-in Fuel Cell Electric Vehicles,” Energy, vol. 353, p. 141032, Apr. 2026.
S. Mehdi Ferhan and H. Agahi, “Multi-Objective Optimization of Hybrid Energy Systems,” Electrical Engineering Technical Journal, vol. 2, no. 2, pp. 1–16, Jun. 2025.
P. Das and T. Kumar, “Multi-Objective Non-Linear Programming Problem Based on Neutrosophic Optimization Technique and Its Application in Riser Design Problem,” 2015.
Y. M. Park, J. B. Park, and J. R. Won, “A Hybrid Genetic Algorithm/Dynamic Programming Approach to Optimal Long-Term Generation Expansion Planning,” International Journal of Electrical Power & Energy Systems, vol. 20, no. 4, pp. 295–303, May 1998.
A. A. Ashagire, K. H. Adjallah, and G. Bekele, “Optimal Sizing of Hybrid Energy Sources by Using Genetic Algorithm and Particle Swarm Optimization Algorithms Considering Life Cycle Cost,” 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp. 01–06, Oct. 2021.
H. Yang, W. Zhou, L. Lu, and Z. Fang, “Optimal Sizing Method for Stand-Alone Hybrid Solar–Wind System with LPSP Technology by Using Genetic Algorithm,” Solar Energy, vol. 82, no. 4, pp. 354–367, Apr. 2008.
Z. Mousavi and M. Saraj, “Multi Objective Geometric Programming with Interval Coefficients: A Parametric Approach,” Earthline Journal of Mathematical Sciences, pp. 395–407, Aug. 2019.
L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, Jun. 1965.
A. Bhattacharya, D. Chatterjee, and S. K. Goswami, “A Fuzzy Based Improved Power Sharing Methodology for Islanded Microgrid with Hybrid Sources,” Electric Power Systems Research, vol. 217, p. 109069, Dec. 2022.
B. Li, Y. Deng, Z. Li, J. Xu, and H. Wang, “Thermal-Economy Optimization for Single/Dual/Triple-Pressure HRSG of Gas-Steam Combined Cycle by Multi-Objective Genetic Algorithm,” Energy Conversion and Management, vol. 258, p. 115471, Apr. 2022.
A. M. Alharbi and A. A. Zaki Diab, “Enhancing off-Grid Hybrid Renewable Energy Systems Through Multi-Objective Optimization with Pareto Front Analysis,” Energy, vol. 351, p. 140728, May 2026.
P. Das, “Geometric Programming in Imprecise Domain with Application,” Zenodo, Oct. 2022.
R. K. Dolai and P. Das, “Analysing Rice Productivity in India Using a Fuzzy Logic Model Based on Soil Quality Parameters,” Spectrum of Engineering and Management Sciences, vol. 4, no. 1, pp. 15–28, Jan. 2026.
