CASE STUDY: CONNECTING BUILDINGS TO LOW-TEMPERATURE HEAT NETWORKS

Recent data highlights varying performance levels between the newer and older buildings. In the newer building, two rooms exhibited issues, but these were tied to pre-existing conditions. While the heating system may require some refurbishment, the findings suggest a minimal need for emitter replacements. On the other hand, the older building experienced performance problems in multiple rooms, many of which also had pre-existing issues. This indicates a need for significant emitter replacements in the older structure—with or without district heating (DH).
In conclusion, DH may well be a way forward. Data showing minimal upgrades needed can reassure building operators, making DH a more attractive and feasible option.

By Simran Chaggar, Fairheat

Published in Hot Cool, edition no. 7/2024 | ISSN 0904 9681 |

1. Introduction and context

The UK government has identified low-temperature heat networks (LTHN) as one of the most cost-effective routes to decarbonising heat. It’s estimated that the sector will need to grow from 3% to 20% by 2050 . Does this explosion in DH networks also force building owners to change their internal heating systems? If so, we not only have the cost for DH networks, but many have to refurbish their heating system. Luckily, this study indicates that many buildings can operate with low-temperature DH systems, but many have pre-conditions that cause lower comfort than required.

Lower operating flow and return temperatures are beneficial for District Heat Networks (DHNs) in general and especially for increasing the seasonal coefficient of performance for heat pumps, thereby increasing the heat network generation efficiency and reducing customer tariffs.

2. Challenges with connecting existing buildings to new heat networks

The main challenge in meeting these goals is that existing commercial buildings in the UK are designed to operate with flow temperatures of 82°C and return temperatures of 71°C. Heat network operators will likely have very strict performance requirements to maximize the network efficiency.
These performance requirements are likely to be the following:

1. Larger temperature differential between flow and return
2. Lower operating temperatures resulting in reduced average emitter (radiator) temperature

Figure 2 below outlines that if radiators of the same size and type were operating at temperatures in line, this would reduce the average emitter temperature of more than 20°C.

Figure 2: Comparison of the average emitter temperature experienced across a radiator circuit for a typical existing commercial building against the case of a low-temperature ready building.

The average emitter temperature is calculated using an average of the flow and return temperatures. This parameter is one of the factors that influences the output of an emitter.
Reducing the average emitter temperature reduces the radiator’s output, risking insufficient heating, which can result in emitter replacements.

It is important to note that heat emitters are historically designed with contingencies, and as such, they may be oversized, which could reduce the requirement for replacement across the building.

3. Assessing the level of building level upgrades

One way to assess suitability is to use a building heat loss model. This model compares the building heat losses against the installed emitter capacity at lower temperatures.
The main challenges are as follows:

1. Availability of as-built building fabric information, difficult due to building age or change of ownership
2. Industry figures based on building regulations at the time can inflate heat losses and result in more upgrades than required.

This highlights the need for empirical data to be used over theoretical calculations when making these decisions.

The three main impacts of emitter replacements are increased cost, increased disruption, and increased decanting requirements. Together, these three impacts can make building operators more reluctant to connect their buildings to a heat network.

4. Temperature lowering testing

4.1. Introduction

• What is the aim: In-field test to replicate the same average emitter temperature within a building by lowering operating temperatures to understand whether existing emitters are oversized
• Implementation period: October to April over the heating period
• Success indicators: Ambient room temperature is measured against a selected comfort set-point

4.2. Case study

The research lowered operating temperatures within an old and newer building to test whether the connection to a DHN would be feasible with the existing emitters.

Table 1: Case study parameters for the older and newer buildings which were subjected to temperature lowering

The district heating network proposes to operate with a temperature regime of 75/50°C (flow and return, respectively) with an average operating temperature of 62.5°C.
To replicate this, both buildings had their boiler set-points lowered to 65°C and were compared against a required comfort set-point of 19-20°C.

4.3. Results

4.3.1. Newer building

Figure 4 shows the ambient temperature in different rooms across the building, as represented by the lines on the test. The data focuses on the coldest week in January when temperatures reach below 0°C.

Industry guidance within the UK recommends that -3°C be used as the design condition for emitter sizing. As such, external temperatures within this region would ensure the building performance is stressed and in line with industry guidance.

Figure 4 suggests that most rooms are performing above the comfort set point. However, some rooms continuously appear to be struggling during occupied hours of the day.

Figure 4: Results of the newer building during the coldest week of the test (January).

The data in Figure 5 facilitates comparing performance before and after the test to understand if the test caused any worsening.

The pre-test data indicates that there appear to be some pre-existing issues where the rooms did not meet the set point, even at higher external ambient temperatures. That suggests that the test did not impact performance.

Figure 5: Results from the newer building where only poor-performing rooms have been presented, as well as baseline data showing ambient performance before the test commencing

4.3.2. Older building

Figure 6 outlines the data for the newer building over the coldest week in January and highlights the poor-performing rooms only. In general, these rooms struggle to consistently meet the set point of 20°C across the test during occupied hours.

Figure 6: Results of the older building during the coldest week of the test (January). This graph only presents poor-performing rooms to make the data set more manageable.

Figure 7 identifies that many of the rooms struggle to meet the comfort set point, indicating pre-existing issues in many of the rooms.

Figure 7: Newer building data during coldest week in January with baseline data to compare performance against

Figure 8 singles out one room in particular. The pre-test data and data during the test compares the same external ambient temperature of 3-4°C. The corresponding ambient temperature data suggests a slight decrease in performance indicating that there may have been some worsening of performance in the room as a result of the test.

Figure 8: Profile of one poor-performing room within the newer building where the data suggests a worsening of performance as a result of the test.

5. Summary and conclusions

In summary, based on the available data, we can see that the newer building’s performance showed two rooms struggling. However, these two rooms were shown to have pre-existing issues. There is a suggestion that the heating system needs some refurbishment, regardless of the heat supply.

This suggests that little to no emitter replacements are required in this building. However, the older building had multiple rooms with performance issues. Some had pre-existing issues, which suggests a high level of emitter replacements required in this building.

The next step of this research is to gather further data from a wider variety of building typologies, ages, and uses. Furthermore, it would be useful to understand if empirical data can be extracted from the process to draw conclusions outside of peak conditions. This would be particularly suitable for buildings like hospitals where there may be a risk of lowering temperatures.