Return Temperature Optimization

Return temperature optimization reduces pump energy consumption, distribution losses and generation costs in existing district heating networks — while unlocking additional capacity reserves within existing pipe diameters. The excess consumption method systematically identifies the customers with the greatest optimization potential using standard heat meter data, with 90% of deficiencies typically found on the secondary side of customer installations. Combined with on-site inspections and incentive tariffs, improving the efficiency of condensing boilers (flue gas condensation below 55 °C), heat pumps and solar thermal systems becomes a permanent component of network operations.

Motivation

Large Temperature Spread

A network with high spread transports the same amount of heat with significantly less volume flow. Since pump power scales cubically with volume flow, pump energy consumption decreases disproportionately. At the same time, heat losses in the pipelines decrease because the mean water temperature in the network drops.

Low Return Temperature

A low return temperature is not merely the consequence of high spread, but has independent significance for the generation side:

• Heat pumps achieve a higher coefficient of performance when the return temperature as source temperature is kept low.
• Solar thermal systems achieve higher yields because the temperature level of the return feed drops.
• Condensing boilers can only utilize the latent heat in the flue gas when the return temperature is below the dew point of the flue gases (approx. 55 °C for natural gas).

Capacity Reserve in Existing Networks

A well-balanced network with optimized return temperature can transfer more capacity at given nominal pipe diameters — an important reserve when additional consumers are to be connected or existing customers expanded.

Excess Consumption Method

The excess consumption method allows the assessment, using simple heat meter data, of which customers are placing excessive load on the network. The starting point is the fundamental equation of heat transfer:

$Q = m \cdot c_p \cdot \Delta T = V \cdot \rho \cdot c_p \cdot \Delta T$

For a given heat quantity $Q$, the temperature spread $\Delta T$ is inversely proportional to the circulated volume $V$. A customer who achieves less spread than specified therefore consumes more water than necessary — they generate an excess consumption.

Defining Reference Values

First, a reference temperature spread $\Delta T_{\text{REF}}$ is defined. This may come from the technical connection agreement (TAV) or be derived as a realistic target value from the network parameters. This yields a reference volume for each customer $i$:

$V_{i,\text{REF}} = \frac{Q_{i,\text{WZ}}}{\rho \cdot c_p \cdot \Delta T_{\text{REF}}}$

Here, $Q_{i,\text{WZ}}$ is the heat quantity measured by the heat meter of customer $i$ during the observation period.

Calculating Excess Consumption

The excess consumption is the difference between the actually measured volume $V_{i,\text{WZ}}$ and the reference volume:

$V_{i,\text{MV}} = V_{i,\text{WZ}} - V_{i,\text{REF}}$

A positive value means the customer has consumed more water than would have been necessary at the reference spread. For the evaluation, only the heat meter data is required — heat quantity $Q$ in kWh and water volume $V$ in m$^3$ — which are stored in every modern heat meter. Customers are then ranked by their excess consumption. The optimization focus is directed at the worst-performing customers with the greatest excess consumption.

Influence on the Return Temperature

Excess consumption alone does not indicate how strongly an individual customer affects the overall return temperature of the network. For this purpose, the weighted excess consumption $\Delta T_{i,\text{RL}}$ is calculated, which takes into account the observation period and the mean temperature spread of the respective customer. The parameter $\Delta T_{i,\text{RL}}$ describes by how many Kelvin the return temperature at the feed-in point would drop if customer $i$ were fully optimized to the reference spread.

This weighted approach allows differentiated prioritization: A large consumer with a slightly elevated return temperature can have a greater impact on the overall network than a small consumer with a severely elevated return temperature. The ranking by $\Delta T_{i,\text{RL}}$ is therefore crucial for an efficient optimization strategy.

Three-Step Approach

1. Data Collection and Evaluation

The basis is the heat meter data of all customers in the network:

• Heat quantity $Q$ in kWh
• Water volume $V$ in m$^3$
• Observation period: at least one quarter of the heating season, ideally a full year

For each customer, the full-load hours, excess consumption and influence on the return temperature are calculated in a spreadsheet. A ranking is then created that places the worst-performing customers at the top. This evaluation alone often yields surprising insights into where the greatest optimization potentials lie within the network.

2. Assessment

The worst-performing customers in the ranking are examined during an on-site inspection with a specialist. The objective is to analyze the current situation and derive concrete optimization proposals. Typical causes of elevated return temperatures include:

• Defective or sticking control valves
• Incorrect controller settings (e.g. excessively high secondary temperatures)
• Unsuitable hydraulic integration (missing or incorrectly sized heat exchangers)
• Inadequate domestic hot water preparation (circulation losses, Legionella prevention cycling)

A comprehensive IEA study shows that 90 % of deficiencies are on the secondary side — of which approximately 60 % relate to secondary-side heat transfer (heating) and 30 % to domestic hot water preparation.

3. Implementation and Verification

The implementation of identified measures differs depending on the area of responsibility:

• Primary side (transfer station, control valves, heat exchangers): The network operator can generally implement these measures independently, as the transfer station is within their ownership or area of responsibility.
• Secondary side (customer’s heating system): Here a conflict of interest arises — the network operator benefits from the optimization, but the costs fall on the customer. This conflict must be resolved through contractual arrangements or financial incentives.

Verification is carried out by repeating the evaluation after implementation of the measures. The comparison of excess consumption values before and after optimization quantifies the progress achieved.

Incentive Tariffs for Low Return Temperatures

The conflict of interest described above can be mitigated through alternative tariff models. Instead of billing heat exclusively by energy quantity (kWh), the circulated volume or the return temperature can additionally be factored into the pricing.

Such incentive tariffs reward customers with efficient system integration through lower heat prices, while customers with elevated return temperatures pay a surcharge. For network operations, this model is ideally economically neutral or positive, because the saved pump energy and investment costs compensate for the foregone additional revenues. At the same time, a permanent incentive for customer-side optimization is created — independent of on-site inspections.

Conclusion

The analysis of excess consumption is a simple, data-driven method for identifying and prioritizing return temperature optimization potential in existing heating networks. It requires no additional measurement technology but utilizes the heat meter data that is available in any case. The combination with on-site inspections and incentive tariffs makes return temperature optimization a continuous process — not a one-off action, but a permanent component of network operations. Simulation tools such as VICUS Districts support the assessment of how changed return temperatures affect the overall network and help derive the optimal strategy.

Further reading: Network Temperatures covers the fundamentals of temperature selection in thermal networks, Hydraulic Balancing describes the measures for ensuring uniform flow through all consumers, and Transfer Stations explains the technical design of the interface between network and customer installation.

References and Standards

• AGFW FW 309 — Energy Evaluation of District Heating and Cooling
• Frederiksen, S.; Werner, S. (2013): District Heating and Cooling. Studentlitteratur, Lund.