Energy management strategy for a hybrid micro‑grid system using renewable energy

Abstract

This paper introduces an energy management strategy for a hybrid renewable micro-grid system. The efficient operation of a hybrid renewable micro-grid system requires an advanced energy management strategy able to coordinate the complex interactions between different energy sources and loads. This strategy must consider some factors such as weather fluctuations and demand variations. Its significance lies in achieving the overarching objectives of these systems, including optimizing renewable energy utilization, reducing greenhouse gas emissions, promoting energy independence, and ensuring grid resilience. The intermittent nature of renewable sources necessitates a predictive approach that anticipates the energy availability and adjusts the system operation. The aim of this study was to develop an energy management system for a hybrid renewable micro-grid system to optimize the deployment of renewable energy resources and increase their integration in the power system. Therefore, the main objective of this work was to develop an energy management strategy that controls the flow of energy between the hybrid micro-grid system and the load connected directly as well as the load connected to the utility grid using MATLAB/Simulink software. The second objective was to control the charging and discharging of the battery. The results show that the developed algorithm was able to control the energy flow between the hybrid micro-grid system and the utility grid and also to ensure a proper relation between the charging /discharging rate of the battery based on their operating conditions. In this application, the battery was charged at higher power. It was seen that a higher charging power enables to fully recharge the battery in a shorter amount of time than usual. The results have shown that it is possible to maximize the charging time by using a greater power and this algorithm ensures the state of charge (SOC) of battery to remain in the admissible limits (between 20 and 100%).