Optimizing Renewable Energy Integration and Fossil Fuel Reduction in a Hybrid PV, Battery, Grid, and Diesel System: A Robust Energy Management Algorithm Approach

This research study introduces an advanced Energy Management Algorithm (EMA) that aims to improve the efficiency, sustainability, and robustness of a Hybrid PV, Battery, Grid, and Diesel energy system. The suggested Energy Management Algorithm (EMA) aims to tackle the crucial issue of efficiently and effectively balancing different energy resources in light of the rising integration of renewable energy sources and the escalating need for reliable power supply. The method incorporates considerations for the intermittent characteristics of solar photovoltaic (PV) generation, the energy storage capacity of batteries, the grid as a contingency power source, and the dependability of diesel generators in offgrid situations. Through the strategic scheduling of these components, the EMA effectively enhances energy efficiency, mitigates operational expenses, and diminishes the release of greenhouse gas emissions. The system demonstrates the capacity to adjust to varying environmental circumstances, load profiles, and grid availability, therefore guaranteeing an uninterrupted and sustainable energy provision. Moreover, it places a high priority on utilizing renewable sources in order to mitigate environmental consequences. By conducting comprehensive simulations, the suggested Energy Management Algorithm (EMA) exhibits notable advancements in system efficiency, economic feasibility, and environmental sustainability. This study makes a significant academic contribution to the field of energy management by proposing a helpful solution for hybrid energy systems. Furthermore, it advocates for the dependable and robust integration of renewable energy sources.