Holistic Operational Signatures for an energy-efficient district heating substation in buildings

District heating (DH) is recognized as a sustainable energy infrastructure in cities and buildings. DH substation operation is essential to bridging the gap between plants and thermal clients in achieving the intended DH values and visions. Therefore, in this study, an operation analysis method was developed for building-side DH substations. Specifically, Holistic Operational Signature (HOS) was suggested with multiple signature elements (x-OS) and representations to consider comprehensive operational correlations. A HOS-based analytics is proposed to provide more insights and sound reasoning about the DH substation design, operation, control, and heating efficiency potentials, compared with the existing Energy Signature. In a case study for winter, the proposed HOS method was applied to a target DH substation serving actual residential buildings using the real operational datasets. The six HOSs (HOSs 1–6) were derived with their six elements to identify the representative heating consumption levels and patterns, partial heating load ratios, and operation and control efficiency. Excessive water flow rates were identified based on the partial heating load elements (less than 50% in most cases) of all signatures. Inefficient signatures (HOSs 1–4) that do not follow the conventional control principle according to outdoor air temperatures were captured, especially for high heating demand patterns, which accounted for 72.5% of the total in winter. According to the signatures, the demand-based control can decrease the heating supply temperature setpoint by about 1.4 °C and 0.6 °C, respectively for HOS2 and the winter. It is recommended that the valve operation should be improved to lower the hunting levels (within ±1.5 °C) of heating water temperature control, especially in the afternoon with low heating demands. • Holistic Operational Signatures (HOS) are proposed for district heating (DH) substations. • The proposed operation analytics was applied into a real DH substation with field measurements. • Inefficient signatures were captured in the target buildings, accounting for about 72.5%. • Heating supply temperature setpoints were expected to decrease by about 0.6 °C in the winter.