Multi-Objective Optimization of a Hybrid Renewable Energy System Using Electromagnetic Frequency Regulator for Applications at the UFRN Campus in Macau/RN, Brazil: A Genetic Algorithm Approach

This paper presents a multi-objective optimization approach for the design of a hybrid renewable energy generation system (HRES) composed of photovoltaic (PV) modules, wind turbine (WT), battery bank (BB), electromagnetic frequency regulator (EFR), and backup diesel generator. Hourly series of solar irradiance and wind speed were collected for the UFRN campus in Macau/RN/Brazil. Detailed modeling of each component (including temperature effects on PV, corrected power curve of WT, and battery SoC dynamics) and calculation of discounted cash flow for 20 years were developed. A Genetic Algorithm (GA) was customized with real representation, uniform crossover, and adaptive Gaussian mutation, including elitism. The optimal configuration, PV≈35kWp, WT=30kW, ESS≈200kWh, EFR=30kW, achieved net present value (NPV) of R$ 1.86 M in 2015 values ​​(R$ 3.11 M in 2025) and discounted payback in approximately 9 years. Sensitivity analysis to tariffs, costs, and climate scenarios confirmed technical and economic robustness. Comparison with the 2015 reference project showed gains of up to 17% in NPV. The integration of EFR and the method employed increased the capture and quality of wind energy, reducing conversion and maintenance costs.