Optimization and Intelligent Control in Hybrid Renewable Energy Systems Incorporating Solar and Biomass

The globe faces an urgent need to close the energy demand-supply gap. Addressing this difficulty requires constructing a Hybrid Renewable Energy System (HRES), which has proven to be the most appropriate solution. HRES allows for integrating two or more renewable energy resources, successfully addressing the issue of intermittent availability of non-conventional energy resources. Optimization is critical for improving the HRES’s performance parameters during implementation. This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies. However, energy fluctuations present a problem with the power quality of HRES. To address this issue, the research paper introduces the Generalized Dynamic Progressive Neural Fuzzy Controller (GDPNFC), which regulates power flow within the proposed HRES. Furthermore, a unique approach called Enhanced Multi-Objective Monarch Butterfly Optimization (EMMBO) is used to optimize technical parameters. The simulation tool used in the research work is HOMER (Hybrid Optimization of Multiple Energy Resources)-PRO, and the system’s power quality is assessed using MATLAB 2016. The research paper concludes with comparing the performance of existing systems to the proposed system in terms of power loss and Total Harmonic Distortion (THD). It was established that the proposed technique involving EMMBO outperformed existing methods in technical optimization.