OPTIMIZATION AND RESEARCH OF OPERATING MODES OF WIND-SOLAR POWER PLANTS UNDER DIFFERENT LOAD CONFIGURATIONS

Abstract

This study focuses on the design, simulation, and optimization of a hybrid wind-solar power system integrated with a Battery Management System (BMS), with the primary objective of enhancing the efficiency and sustainability of renewable energy sources. Employing MATLAB Simulink, the project models the interactions between photovoltaic (PV) panels, wind turbines, and battery storage to achieve maximal energy capture and effective storage management. The key objectives include implementing Maximum Power Point Tracking (MPPT) and passive cell balancing techniques within the BMS to optimize energy conversion and battery longevity. The methodology involved developing detailed system models and incorporating a Perturb & Observe (P&O) algorithm for MPPT, which dynamically adjusts power conversion parameters to suit changing environmental conditions. Additionally, a Machine Learning algorithm was integrated to predict energy generation, providing a sophisticated tool for enhanced energy management. Experimental results demonstrated the system’s ability to adaptively optimize operations, significantly improving the energy efficiency and operational stability of the hybrid system. The MPPT controller effectively maintained optimal power levels, while the BMS ensured uniform charge distribution among batteries, thereby prolonging their lifespan and performance. This project establishes a foundational framework for future research in hybrid renewable energy systems, suggesting that further exploration into adaptive control strategies and the integration of additional renewable sources could enhance system reliability and efficiency.