An improved LKF based SOC estimation and a power management strategy to enhance the cycle life of BES in a microgrid

Abstract

Nowadays, utility grid experiences significant challenges due to increased adoption of renewable energy resources (RES). The intermittency of RES is ridden by using battery energy storage (BES). The system control often aims at maximizing self-consumption, neglecting its effect on effective life of BES. A power management strategy (PMS) is proposed in this work that helps to increase the extraction of maximum energy without compromising on operational robustness of BES for grid applications. Maximum depth of discharge (DOD) is limited to a certain mean state of charge (SOC) to improve the cycle life of BES. A lifecycle model is derived for BES to compute a compensating term for estimating the DOD limit after each cycle. An enhancement to existing Linear Kalman filtering (LKF) technique is also presented in this work for SOC estimation of BES. A significant reduction in root mean square error (RMSE) is achieved using improved LKF-based SOC estimation technique. A second-order generalized integrator with a pre-filter based frequency locked loop (SOGI-WPF-FLL) controls microgrid under abnormal utility grid and load conditions. Resynchronization of microgrid with utility grid is demonstrated using SOGI-WPF-FLL filter without causing any maloperations. System is simulated under various operating conditions in MATLAB/Simulink environment and validated on a real-time OP5700-based test-bench.

Keywords

• Microgrid
• BES
• SOC estimation
• Kalman filtering
• PMS
• Grid-interfaced