Simulation study of district heating control based on load forecasting

At present, the development of district heating system (DHS) in China is mainly reflected in the scale and structure, there are still great disadvantages in system management and control. The problem of imbalance between the heat supply and the user’s demand is serious. In this paper, a DHS in Kaifeng of China was taken as the research object, and the control model of secondary return temperature of a typical thermal power station which based on the step response is established. Based on the high-precision heating load prediction model of thermal power station, the primary side flow as the control variable, secondary return temperature as the controlled variable, and the generalized predictive control (GPC) algorithm as the control method, the secondary return temperature of the target system is accurately controlled; at the same time, particle swarm optimization (PSO) is used to determine parameters adaptively for parameter tuning in GPC; and the control strategy is simulated. Compared with the traditional Proportion integral differential (PID) control algorithm, The root-mean-square error and mean absolute percentage error of the simulation results of the control strategy and the set value are reduced by 22.24% and 22.33%, respectively, has the advantages of smaller overshoot and faster response, which can achieve the effective control of secondary return temperature and on-demand heating better.