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With the development of society and economy, energy shortage is increasingly serious. How to improve the utilization rate of energy is main problem at present. The integrated energy system, is to comprehensively plan, coordinately control, intelligently dispatch and multiply interact with multiple energy flows such as cold, heat, power and gas, which has broken the existing models of individual plan, design and operation of each energy supply system, and becomes a new model of future energy utilization. In this paper, a multiple time-scale cooperative optimization is proposed based on model predictive control, which is for the control optimization of energy storage, cold storage and heat storage to fulfill the minimization of total operation cost of integrated energy system based on the charge mode of maximum demand. The implementation of cost optimization has 3 steps which are month-in-advance off-line planning, day-ahead dispatch and short-term online MPC control. This optimization strategy not only reduces the power imbalance of control system caused by the error of model prediction of renewable energy and load, but also ensures the overall optimization efficiency of system. A park in Beijing is taken as an example to analyze and verify the effectiveness of the proposed model and algorithm.

Multiple time-scale optimization for the dispatch of integrated energy system based on model predictive control