Power Management and Control of a Photovoltaic System with Hybrid Battery-Supercapacitor Energy Storage Based on Heuristics Methods

Grid-scale electrical energy storage (EES) systems are enabling technologies to enhance the flexibility and reliability of electricity grids with high penetration of intermittent renewable energy sources such as solar and wind. They allow excess of generation to be stored for later use and can respond quickly to power fluctuations. Unfortunately, there is no single type of EES technology that can effectively fulfill all the desired requirements. Hybrid EES (HEES), combining two or more EES, are an emerging, viable solution. While batteries have a higher energy density, supercapacitors (SC) have a higher power density and are characterized by a fast discharge rate. The combination of both technologies results in a HESS solution which can address the challenges associated with the large-scale deployment of distributed renewable energy sources and enhance the grid reliability. The goal is to design a power management strategy to enhance the performance of the HEES. This paper proposes various robust design methods for the control of the power electronics converters and enhance the performance of the power management of the HEES. These robust design strategies are based on pole placement, linear matrix inequalities (LMI), particle swarm optimization (PSO) and genetic algorithm (GA). The performance of these control schemes is compared in terms of the transient response time and robustness. The results obtained demonstrate the effectiveness of the power management strategy (PMS) for the photovoltaic (PV) system with HEES and the enhanced robustness of the controllers using GA and PSO-based tuning techniques.