Adjusting heat demands using the operational data of district heating systems

Abstract

The main objective of this study is to investigate variations and peak values of actual heat demand profiles in a centralized district heating (DH) network. Operational heat data is utilized to discover that almost no weather correlation exists during warm months when supply temperatures exceed 60 °C; thermal inertia of buildings affects their behavior differently in terms of needed space heating; and thermal energy storage (TES) units can be important assets for improving the performance of DH networks. To draw such concluding remarks, two approaches namely scaling design heat demand and the weighted moving average (WMA) are used. Analysis of heat demand profiles reveals that the fourth generation DH (4GDH) shows promise in the Russian context. It is also observed that the discrepancy between the projected and the actual values is 7–14% higher than average, and the variation of temperature in the supply line is 1.5 times that in the return line. Through designing TESs or DH plants using more precise heat demand data, DH operator is able to reduce the energy price by lowering infrastructure costs. By implementing WMA strategies and incorporating advanced control techniques, it is also possible to save 40%–50% energy per year, which results in lower energy fees for the end-users.