To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the character...To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.展开更多
基金supported by the Scientific Development Pro-gram of Shandong Province(Grant No.2012GGB01071)the Doctoral Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XNBS1225)the School Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XN110108)
文摘To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.
