• Modelling buildings based on actual heat response and basic weather information Predicting how buildings will behave under different circumstances will be a key tool for planning production, using the buildings for thermal storage or evaluating the need for renovating buildings. Modelling can be used for energy labelling buildings and making suggestions for improvement based on thermal profiles. Knowing that a building consistently performs poorly when cold winds blow from the west, or that it would be profitable to replace the windows so they can absorb more solar energy would allow utilities to take the necessary action to improve their energy performance. • Leakage detection Some energy meters are able to measure both forward flow and return flow. This allows utilities to identify buildings where treated water from the distribution network is lost in buildings or substations. Monitoring and reducing the leakage level saves costs for adding and heating new treated water to the system. It can also detect installations where water intrusion into the district heating system is causing problems with the quality of the treated water. • Shaping peak demand to use the existing infrastructure in a more efficient way To stay competitive and to run an optimal production it is often desirable to reduce demand peaks. Intelligent meters can be used for power limiting a heat installation forcing the end user to shape their demand peak. This can create the right circumstances for connecting more and more buildings to the existing district heating system, which is a very important topic in the new EU future where there will be a push for more district heating. The answer is not always simply to renew all pipes in the ground in order to improve capacity as this can increase the costs and make district heating less attractive. • Introduction of billing schemes that support a more energy efficient heat supply Such schemes could perhaps be based on the end users’ degree of flexibility rather than their energy consumption allowing them to use more heat when surplus heat is available in the network and to rely on thermal storage during peak hours. Frequent meter data also delivers a range of other benefits. These include continuous surveillance of the meters so that errors resulting from the installation or general errors such as broken temperature sensors can be quickly detected and rectified. In addition, frequent data allows for the possibility of introducing new services to the end users. For example, the utility can offer online energy management services or even consider taking responsibility for operating the end user’s heat installation in the most energy efficient way.
Fundamentally, you cannot optimise what you do not measure. If utilities cannot measure it, they are unable to control and manage their energy demand, which is critical when they need to optimise their production and run the distribution network closer to the limits. In short, meter data provides the necessary transparency to do so. The more frequent data a utility has, the more precise its basis for optimisation, and thus, the more value it can create.
eButler is Kamstrup’s online solution for visualising meter data. The solution stands out by being a practical tool that can produce tangible results for end users.
Some of the most relevant application areas in terms of improving energy efficiency are:
• Identification of faulty or misadjusted substations Frequent data can provide the information needed to easily identify opportunities for improvement. Utilities can then proactively contact the end users in question to help them optimise their heat installation. Swedish research estimates that as much as 75% of all substations can be improved in terms of efficiency and that the problems can be identified using hourly values from smart meters. 1 • Monitoring temperature levels in the distribution network Temperatures in the distribution network must be lowered in order to improve energy efficiency and to create the right circumstances for the integration of e.g. solar and heat pumps in the network. Frequent data provides online updated information about the actual temperatures in the network, which is the basis for determining the lowest acceptable level of the forward temperature to still provide a satisfactory service to the end users. This is especially important in low temperature district heating networks where it is often a matter of adjusting a few degrees depending on the season. As for the return temperature, the end users who are causing a return temperature that is too high can quickly be identified, approached and guided. • Identification of heat and water loss in the distribution network By combining frequent data from the end user with information from strategic locations in the overall distribution network, utilities will be able to identify the difference between the energy fed into the individual network zones compared to the heat that is actually consumed in the buildings. This allows them to continuously monitor the heat loss and quickly spot negative or positive trends.
1/ Gadd, H. (2014). To analyse measurements is to know! http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=4811901&fileOId=4811970
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