This paper implements and validates a real-time Energy Management System (EMS) for DC microgrid operation using a power hardware-in-the-loop (PHIL) test bench. The test bench replicates renewable sources, a general load, and a Hybrid Energy Storage System (ESS) comprising a battery, an emulated fuel cell, an emulated electrolyzer, and a virtual hydrogen tank. Constructed with dSPACE and Simulink, the PHIL test bench facilitates the control and monitoring of DC sources, DC loads, and controllable DC/DC converters. The EMS technique combines an Optimal Dispatching (OD) algorithm with Model Predictive Control (MPC). While OD effectively reduces the total cost of grid utilization, it falls short in responding to actual disruptions and uncertainties. To address these issues, MPC is integrated with OD. This combination of OD with MPC offers two main advantages: it ensures a reduction in grid utilization and effectively manages perturbations and sudden interruptions.

International audience

This paper adopts two optimization strategies, the Genetic algorithm and the Particle Swarm optimization algorithm to achieve an optimal sizing of a Grid-connected hybrid system. These strategies will be used to minimize the total cost of the architecture as well as evaluating its profitability while respecting the constraints of all the system components. However, to validate the strategies’ results a comparison with HOMER software will be made. Furthermore, the impact of the site location on the profitability and the sizing has also been studied in order to show the robustness of the proposed approaches.