Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand

Abstract

Due to China's power supply structure, the conventional power units are responsible for the deep load shaving regulation to meet the high penetration challenge of renewable energy. To improve the capability of the peaking load shaving and the power regulation quality, battery energy storage systems (BESS) can be used to cooperate power units to satisfy the multi-objective regulation needs. This paper proposes a novel unified control scheme to smooth the power output of the power plant and meet the strict power load demands distributed from the automatic generation centre (AGC). The proposed coordination control strategy consists of unit load demand scheduler, multi-objective reference governor, fuzzy logic based model predictive control (FMPC) for the boiler-turbine unit, and one-step model predictive control for battery energy storage system. Based on the control scheme, we can achieve: 1) The operation of the boiler-turbine unit is more energy-saving and reliable while the service life of the valves is extended; 2) With the participation of battery energy storage system, the power output of the boiler-turbine unit is smooth and the tracking performances of the unified generation unit, including tracking accuracy and transient behaviour, are improved; 3) Various constraints can be explicitly coped with under the framework of model predictive control.