Experimental analysis of a new adaptive energy management algorithm for real-time active and reactive power control for microgrid under forecasts uncertainties

This paper proposes a Multi-Stage Active and Reactive Energy Management System (MS-AREMS) and power flow control strategy for a MicroGrid System (MGS) integrating renewable power sources and Energy Storage System (ESS). The first stage employs Particle Swarm Optimization (PSO) to calculate optimal active and reactive power setpoints for each energy source a day ahead, leveraging energy consumption and renewable power generation forecasts. To address forecast uncertainties, a real-time optimization stage based on Extremum-Seeking (ES) adjusts the power setpoints dynamically, ensuring optimal power planning. The MS-AREMS objectives are to minimize energy costs, carbon emissions, power fluctuations, and demand peaks while improving the grid power factor, thereby minimizing power losses. These optimized power references are transmitted to a control stage, which uses a proportional-integral artificial neural network (ANN-PI) controller to regulate each energy source’s active and reactive powers. The proposed EMS is validated using real-world data collected from a laboratory located in Rabat, Morocco. Compared to existing solutions, it reduces the total cost function by 33%, including an 18.3% reduction in peak demand and an 15.39% decrease in carbon emissions. Additionally, it ensures an optimal power management that efficiently handles forecast uncertainties and a precise power tracking with errors below 3% for most sources while maintaining voltage stability.