Multi-time Scale Optimization Scheduling Of Integrated Energy System Based On Distributed Model Predictive Control

The integrated energy system has the characteristics of mutual transformation and synergistic operation of multiple energy sources,which can effectively improve the efficiency of comprehensive energy utilization and solve energy and environmental problems.To reduce the impact of system prediction errors on actual operation,the prediction accuracy can be improved by dividing the time scale step by step,and the output of each part of the system can be adjusted online according to the model prediction control method.However,the centralized model prediction control method has a high model order and is not easy to expand,so it is not suitable for the optimal scheduling of integrated energy systems with multiple distributed units.The distributed model predictive control method divides the system into several closely related subsystems and replaces the overall system optimization with the cooperative optimization of each subsystem,which effectively reduces the model order and the online solution difficulty.In this paper,we focus on the multi-timescale optimal operation and scheduling of integrated energy systems under distributed model predictive control.Firstly,a multi-timescale optimal scheduling method of integrated energy system based on distributed model predictive control is proposed to realize flexible scheduling of integrated energy system through the coordination of each subsystem.A day-ahead and intra-day rolling optimization model is established with the objectives of optimal system daily operation economy,minimum system daily operation cost and unit start-up and shutdown penalty cost.The overall optimization problem is decomposed in the real-time stage by using the optimal scheduling strategy based on distributed model predictive control,and each subsystem performs state estimation and optimizes its own performance index based on the input sequence of other subsystems in the previous moment.Through the coordinated control of each subsystem,the online optimization of the whole system is realized to meet its dynamic adjustment requirements.Simulation results show that this method can improve the system control performance and improve the economy of system operation at the same time.Secondly,considering the different nature of various energy transmission,the study of hybrid time-scale optimal operation of integrated energy system is carried out by dividing different control sub-layers.Given a fixed dispatch period for each energy sub-layer,and based on the distributed model predictive control method,the optimization of each sub-layer is realized.The results show that the distributed model prediction method has the advantages of smoothing the power regulation of each coupling element in the system and improving the system control performance in the optimization of the integrated energy system taking into account the energy transmission characteristics.