Energy flexible optimal operation of centralized hot water system in university dormitories based on model predictive control

Energy flexibility is an important way to realize the real-time balance of regional energy supply and demand, and it can also reduce the operating cost of the system combined with peak–valley electricity price. The operation of central hot water system in university dormitories can provide abundant energy flexibility due to the heat storage characteristics of water tank and pipe network. In order to quantify the potential of the flexibility of the central hot water system in university dormitories to optimize the operation of the system, an optimization model of the central hot water system based on model predictive control was proposed in this study. The proposed model consists of the water consumption prediction model based on the circulation neural network, the heat source model based on the linear model, and the hot water system temperature prediction model based on the RC (resistance and the capacitance) model. Putting emphasis on the reduction of operating cost as the objective function, genetic algorithm is used to find the optimal operation strategy. The results obtained from simulation showed that using energy flexibility to transfer hot water load from peak electricity price period to valley electricity price period could save 30% and 9.4% operating costs in summer and winter, respectively, and increase 64.4% power consumption during valley electricity price period in typical winter months, while bringing more suitable hot water temperature and comfort.