Learning-Based Tube MPC for Multi-Area Interconnected Power Systems With Wind Power and HESS: A Set Identification Strategy

With the development of intelligent automation technology and advancement of modernization, the degree of interconnection between power systems is increasing. With the main purpose of involving hybrid energy storage systems (HESS) in optimizing system frequency, this work proposes a learning-based tube model predictive control (MPC) for the multi-area interconnected power systems with wind power and HESS. The suggested method has strong adaptability due to the introduction of a new robust constraint handled by a learning mechanism. By identifying the uncertainty set of coupling strength of online data in the learning stage, the optimal MPC problem is calculated in the adaptive stage, which effectively reduces the adverse effects of disturbances and noises in multi-area interconnected power systems. Moreover, an input to state stability criterion is provided to ensure the robust stability of the system with uncertain disturbances and noises. With simulations on a four-area interconnected power system with wind power and HESS, the effectiveness of proposed method is discussed on an improved IEEE 39-bus system. Note to Practitioners—To achieve a balance between load demand and power generation in multi-area interconnected power systems, various load frequency controls have been widely designed. The disturbances caused by high interconnectivity and the noise generated by electrical equipment pose a threat to the reliable operation of the power system. There is an urgent need to develop appropriate strategies to resist these interferences. So far, HESS have been widely applied in power systems with new energy to improve key system responses, such as power system frequency. This prompts defenders to design different types of external controllers to optimize the frequency deviation of the power system based on HESS. To tackle disturbances and noises which impose serious threat to system stability, we introduce a learning mechanism on the property of invariant sets and propose a learning-based tube MPC strategy that identifies disturbance invariant sets during the learning stage and calculates the optimal output during the adaptation stage. Moreover, simulations are presented to demonstrate that the proposed tube MPC strategy can provide satisfactory stability performance for interconnected power systems under disturbances and noises.