Historical-Data-Based Energy Management in a Microgrid With a Hybrid Energy Storage System

In a microgrid, due to potential reverse power profiles between the renewable energy source (RES) and the loads, energy storage devices are employed to achieve high self-consumption of RES and to minimize power surplus flowing back into the main grid. This paper proposes a variable charging/discharging threshold method to manage the energy storage system. In addition, an adaptive intelligence technique (AIT) is put forward to raise the power management efficiency. A battery-ultra-capacitor hybrid energy storage system (HESS) with merits of high energy and power density is used to evaluate the proposed method with on-site-measured RES output data. Compared with the particle swarm optimization (PSO) algorithm based on the precise predicted data of the load and the RES, the results show that the proposed method can achieve better load smoothing and self-consumption of the RES without the requirement of precise load and RES forecasting.