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NETWORK / 25 / JUNE 2016 Get to The first Network Asset Performance conference is on 21 September in Birmingham. Get more out of your assets, mitigate risk and drive innovation. events.networks. online/asset A Read more about the future of decarbonised heat in the UK on page 28 where we review the outcomes of Imperial College's recent report as well as calls to introduce regulation in the sector. Delivering Pinpoint Although Guru Systems was responsible for overall delivery and the IT development aspects of Pinpoint, a range of other partners were involved. These included: FairHeat Carbon Alternatives Open Data Institute BPE Solictors Indelible Data The organisations that allowed access to their networks for the Pinpoint trial were: Octavia Housing Network Group The Guinness Partnership in the table far le. However, they are all caused by a common factor – lack of data. Too many UK heat network operators do not have the right information at the right point in the decision-making process to optimise their chances of creating heat networks with sustainably high performance. As project Pinpoint progressed, it was this missing data element that it sought to remedy. Guru developed a web- based platform to capture, visualise, analyse and share heat network performance data. Using smart heat meters, this system can monitor a variety of performance types and points on a network, going far beyond the standard plant room monitoring that is the norm – and which gives a limited picture of whole system performance. The system, for example, measures flow temperatures, return temperatures and flow rates at every end point in the network. Importantly, however, the platform Guru developed for Pinpoint was not a standalone piece of monitoring technology, but one that was bolstered with a large set of experience-based data on heat network performance from the company's own systems, other metering systems, building management systems and M-Bus data loggers. Aer validation, all this data was fed into a performance management database and used to generate suggested interventions, with associated costs, to improve network performance. The result of interventions can be seen immediately thanks to real- time data capture and, as the performance management database gathers more information, interventions should become more nuanced and more accurate. "The platform will become more intelligent the more data you show to it," says Cole. Even with its current data sets, however, Pinpoint has shown that it can prompt some transformative interventions to improve heat network performance (see charts below). It was tested cross three relatively diverse heat networks and in each case improved efficiencies and enabled savings. Again, detailed information about these interventions and the underlying problems that made them necessary are publicly available under the terms of the project's funding, but some key areas were heat interface unit behaviours, the UK validation regimes for heat interface units and the tendency for high- efficiency losses to be clustered in terminal network runs. There were also some revelatory findings about the load capacity of UK heat networks and an overwhelming tendency for networks to be significantly oversized for the loads they are required to handle. This latter finding is in part the result of the fact that the Heat Network Code of Practice – the only existing best practice guidance for owners and developers of UK heat networks – uses a Danish heat demand curve as a baseline for estimating capacity required. The Pinpoint study found that UK demand does not follow this pattern and many networks are therefore oversized by 25-35%. In turn, this suggests that many are spending 25-35% too much on the capital costs of installing a heat network in which efficiency will inevitably be capped at a low level. This finding creates a clear case for updates to be made to the Heat Network Code of Practice, according to Cole. Losses dominated by terminal branches Breakdown of network losses per flat by component (kWh pa) Plant, laterals and risers 71% 15% 14% 2,675 1,905 400 370 Terminal branch Flow Return } Major reduction in losses Breakdown of network losses per flat by component (kWh pa) Plant, laterals and risers -79% -21% -65% Reduction 1,820 -68% Terminal branch Flow Return } Losses 850 Bypasses valves 585 = Additional insulation 85 = FTemp 240 130 315 405 910 = Reduce temps • Replace valve • DHW/SH settings • Keepwarm } Losses dominated by terminal branches Major reduction in losses