Integrating Distributed Model Predictive Control into Multi-time Scale Optimization of Energy Systems

The adoption of model predictive control with rolling optimization and feedback correction is one of the key techniques to realize multi-timescale optimal scheduling of integrated energy systems. In view of the complexity of model predictive control for overall online optimization, this paper proposes a multi-timescale optimal scheduling method based on distributed model predictive control for integrated energy systems. To further reduce the power fluctuation caused by the prediction error in the real-time phase and the difficulty in solving the real-time optimization model, the overall optimization problem is decomposed by an optimization scheduling strategy based on distributed model predictive control. The overall optimization problem is decomposed by an optimization scheduling strategy based on the predictive control of a distributed model. By coordinating and controlling each subsystem, the whole system can be optimized online to meet its dynamic adjustment requirements. The simulation results show that the method can improve the control performance of the system and at the same time increase the economy of the system operation.