Novel Strategy for Optimal Active and Reactive Power Flows Management in a Small-Scale Microgrid System Under Forecasting Uncertainties

This paper proposes a Multi-Stage Active and Reactive Energy Management System (MS-AREMS) for a Micro-grid System (MGS). The proposed approach emphasises two stages for effective power management even in the presence of forecast uncertainties. The offline stage calculate optimal active and reactive power setpoints for each energy source a day ahead, leveraging energy consumption and renewable power generation forecasts. The online stage employs Extremum-Seeking Control (ESC) to adjust the power setpoints in real time thus reducing forecast uncertainties. The objectives are to minimize energy costs, carbon emissions and power source variations while providing services to the main electricity grids such as reducing peak loads and improving the power factor. The proposed system is validated using real-world data collected from a laboratory located in Rabat, Morocco. Performance comparisons reveal a 60% reduction in the objective function when evaluated against the rule-based approach. In comparison to an offline energy management strategy, the proposed method achieves a 5.8% reduction in the objective function.