Optimization of Dynamic Dispatch for Multiarea Integrated Energy System Based on Hierarchical Learning Method

The integrated energy system (IES) with various energy demands and distributed energy resources has been a significant approach to improve the efficiency of energy utilization. Considering the uncertainties of renewable energy sources and loads, the energy dispatch optimization for multiarea IESs is studied in this paper. Different from the most current studies, not only the electrical power and heat distribution in each area is optimized, but also the coordination power dispatch between areas. A hierarchical learning method is proposed inhere to improve the operation performance of the multiarea IESs. The proposed method with data-driven way is a model-free method which has no requirement for the accurate mathematical model. With the hierarchical structure, the electrical power dispatched between areas is optimized in upper layer, together with the dispatching optimization in each area at lower layer, to decrease the operation cost for the system and power demand from the power grid. Finite horizon discrete dynamic process model is adopted to simulate the data for learning. The simulation results show the effectiveness of the optimization policy can achieve an economic and stable operation for the multiarea IES.