Research On Load Simulation And Analysis Of Centralized Heating System In A University

Due to the large thermal inertia of the system,thermal inertia of the building,and the time-varying heat consumption pattern of the users,the actual heating process has different degrees of supply-demand mismatch and energy waste.In order to avoid the situation of oversupply on the heat source side,or on the user side,this paper establishes a heat load model around the building complex and different user heat inlets for a campus centralized heating system in Tianjin,and conducts simulation analysis and research.The main contents include:In order to solve the problem of mismatch between the heat supplied by the building complex and the demand heat load of users,the target demand room temperature of the building complex users is considered to match the demand side load.Starting from the basic heat transfer equation,the correlation between room temperature and integrated air temperature and load is considered,the model parameters are calibrated based on the historical data of the heat network to establish the building complex load model,and the 2019-2020 heating season and the 2020-2021 heating season are used as 2 cases for model calibration and validation,simulation and analysis.The results show that the relative deviations between the modeled and actual values of annual loads for case 1 and case 2 are 2.3 % and 0.01 %,respectively,when simulating the daily loads for the whole heating season.Combined with the relevant codes to set the target room temperature calculations,the annual loads can be reduced by 11.5 % and 32.2 % for the two calculations of case 1,and by 26.5 % and 38.4 % for the two calculations of case 2,respectively.The model gives the target and actual energy consumption per day for the 2021-2022 heating season.Compared with the same period last year,the temperature is slightly higher than last year,the number of repair reports is significantly reduced,and the room temperature meets the heat demand.The low zone runs for 43 days from 2021-2022,and the energy consumption is reduced by 17 % compared to the same period last year,with a corrected energy saving rate of10.16 % after considering the temperature factor.In order to avoid the situation of oversupply on the user side,combined with the regulation of the heat source side of the energy center,the time scale space scale as small as possible to achieve "heat on demand" and improve the efficient operation of the whole system,the study of the heat source side load model based on the end user control of the heat inlet to establish a load model,including the steady-state heat transfer equation based on The basic load model based on the steady state heat transfer equation and the dynamic revision model of thermal disturbance based on the simple model of machine language(two-way LSTM network)are studied,which can take into account the control of long-term trend and the accuracy of short-term prediction.The model simulation analysis shows that at the same energy consumption level,the reference model load operation can significantly improve the heating quality and ensure the thermal comfort of customers,such as the actual room temperature of 34# inlet and 14#inlet in 2020-2021 heating season fluctuates in the range of 4 °C～5 °C.If 34# inlet operates according to the target room temperature of 23 °C and 14# inlet operates according to the target room temperature of 22 °C,the annual load is basically equivalent.Combined with the actual operation of the system and user demand,appropriate reduction of target room temperature and adoption of time-sharing temperature zoning model can largely avoid excessive heating,significant energy saving and emission reduction,analysis of 34# and 14# inlets,considering time-sharing temperature,the load is significantly reduced,compared with the actual load,the target load is reduced by 10.6 % and 24.6 % respectively.A one-week regulation test was conducted on four thermal inlets of a separate building in the central heating system of an energy station of the university for the heating season 2020-2021,and the test results were analyzed.The results show that the heat inlet intelligent control method is suitable for scenarios where the temperature of the boiler water supply and the flow rate of the energy station controlling the inlet of the building do not fluctuate much,and the relative deviation of the actual daily load from the target load can be controlled within 15 % during the period,for example,from11.8 to 11.20.Compared with the conventional regulation method,the test regulation phase meets the indoor temperature demand while the load is significantly reduced,with a load reduction rate of 22.47 %～36.48 % after considering the temperature factor.