基于MIMO模糊逻辑控制器的热-混合储能系统近似最优能量管理

Compared to one-type of energy storage device, hybrid energy storage systems (HESSs) offer benefits for Auto generation control (AGC) command tracking and can reduce investment in energy storage. Traditional control method, although effective in meeting the matching of AGC commands at a specific moment, often lacks coordination across multiple time intervals, resulting in frequent and irregular charging/ discharging which reduces the overall lifetime. To address this, this paper presents an approximate optimal operation strategy for Thermal-HESS system, aiming to enhance the AGC performance of the generating unit and improve the energy management capability of the HESSs. Firstly, an auto-adjust Markov Chain prediction method is proposed to forecast the power demand of the AGC command tracking to determine power demand's tendency. Secondly, a stochastic model predictive control (SMPC)-based optimal model, which considers the current step and cost-to-go function, is proposed. However, the SMPC based model is multiple-step optimal operational problem, which will increase the computational burden of the controller. Therefore, this paper further designs a Multiple-Input-Multiple-output (MIMO) fuzzy logic controller to approximate the optimal alternative to the cost-to-go function of SMPC model, meeting the computational and application requirements more effectively. Finally, numerical case studies are conducted to demonstrate the effectiveness of the proposed method in AGC command tracking and HESSs energy management.