Optimized Voltage Feed-Forward Control for Photovoltaic-Battery DC Microgrid Using Enhanced Grey Wolf Algorithm

The integration of Photovoltaic (PV) systems with battery storage in microgrids presents significant challenges in maintaining voltage stability and system efficiency due to the fluctuating nature of solar energy generation and variable load demands. Traditional control strategies, such as feedback control, often fail to respond quickly enough to these fluctuations, leading to voltage instability and reduced system performance. Therefore, this paper presents an optimized voltage feed-forward control for photovoltaic-battery DC microgrid using Enhanced Grey Wolf Algorithm (EGWO) to optimize the system's performance, stability, and energy efficiency. This work uses an Arduino microcontroller, DC-DC converter, battery, IoT device, and optocoupler. The power from a solar panel is effectively converted to the level needed for battery charging using a DC-DC converter. A DC microgrid powered by a PV system and supported by battery energy storage requires precise control to balance energy generation, storage, and consumption. EGWO is integrated with voltage feed-forward control to enhance the performance of a photovoltaic-battery DC microgrid. This work is implemented by using MATLAB, and the BLYNK web app is used to display the results. This work contributes to the advancement of smart grid technologies by providing an optimized control solution that ensures reliable operation, energy efficiency, and reduced dependency on non-renewable energy sources in DC microgrid systems.