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OPTIMISING FLOW TEMPERATURES The utility itself controls and manages the flow temperature from the production side. The challenge here is knowing the actual heat demand and operating conditions to avoid or reduce safety margins and run production as close to the limit as possible while still meeting their customers’ comfort level. Traditionally, flow temperatures have been based on theoretic models or a limited number of carefully selected critical points. But with the rollout of remotely read smart meters, which is now required with the revised EED directive, comes the opportunity for utilities to both base and validate decisions on a much higher number of measurements.
delivery quality, but the tool showed that they were no longer necessary. As a result, this utility was able to lower the return temperature in the entire network by almost 2 degrees C resulting in significant savings. Another crucial element is the end users’ heat installations. Often, these have been installed incorrectly, are faulty in some way, or conditions may simply have changed leaving them no longer optimised for a particular building or purpose. In single- family houses, fixing the problem will be the home owner’s responsibility, where as a professional, e.g. a plumber, will be called for apartment blocks, commercial buildings etc. The challenge this poses for most utilities is that while their responsibility ends on the primary side of the installation, it is
very much affected by what happens on the secondary side – but neither home owners nor plumbers have the overall performance of the district heating system as their primary focus, and this is reflected in their behaviour, i.e. their approach to optimisation. MOTIVATION FOR CHANGE Behavioural changes can be triggered through different stimuli. If the incentive is strong enough, a person will change a certain behaviour even if it is hard to do. Many initiatives have been made in recent years to give end users access to their own consumption data, expecting that this insight would prompt them to e.g. improve their energy behaviour or heat installation. But in practice, this has proven difficult as most people seem to not have enough interest in energy consumption.
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As an example, the analytics platform Heat Intelligence enables utilities to see what goes on underground in their supply area. Combining smart meter data with a GIS model of their pipe network, the tool calculates how heat travels through the distribution network and creates a digital twin of exactly that. This transparency allows utilities to see the consequences of their temperature optimisation. There is a huge potential in data-driven algorithms for optimising flow temperatures. Some of our Danish customers succeeded in lowering their flow temperature by 6-8 degrees C based on their efforts working with data from their meter reading solution. OPTIMISING RETURN TEMPERATURES Lowering return temperatures is less straightforward because they not only depend on what happens in the distribution network but are also closely linked to what goes on inside the buildings. Smart meter data and analytics help utilities identify conditions – both inside and outside – that can be optimised. Using Heat Intelligence, one of our customers was able to remove several bypasses in their distribution network. They had originally been placed in certain areas to ensure a satisfactory
For example, motivational tariffs either rewarding or punishing end users based on e.g. return temperatures are often inefficient, because the size of a potential penalty or reward is too small compared to their total heat bill and the hassle of uncovering the actual problem. For a plumber called out to fix a building’s heat installation, the main barrier will often be limited knowledge of the root cause of the problem hindering them from making the link to the best long-term solution for the utility. The result will therefore often be a quick fix that solves the issue at hand but may have a negative effect on the overall heat system. In a joint project with one of our customers, we are exploring the idea that making heat installation optimisation easier to do will be more effective – which matches the Fogg behaviour model on how to successfully trigger behavioural changes. In other words, rather than increasing the reward for making a certain change, we want to improve the easiness and convenience of making it.
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