P21
METER DATA + FACTS = SAVINGS In close cooperation with a number of our customers, Kamstrup has created an analytical tool based on a model connecting information from the meters with facts about the utility’s pipe network (Figure 1). Its calculations are therefore highly specific and relevant as the basis for infrastructure optimisation – both in terms of reducing losses and prioritising investments in network maintenance and capacity. There are threemain things making this tool unique and disruptive in its approach to smart meter data analytics. First up is its very high accuracy. Because the model is based on thousands upon thousands of measurements from smart meters, it provides a very high statistical significance. While the individual heat meter in a given household may not on its own provide a valid view of the development in the network, the collective meter base delivers exactly that – every single day. This adds the seasonal and historical perspective that will reveal developments and trends over time, which is lacking with airborne thermography. Secondly, because the tool analyses the measurements from all endpoints in the distribution network based on specific knowledge about its pipes, utilities are able to calculate temperature and flow anywhere in their network. This includes nodes where there is no heat meter installed. Using the model to aggregate meter data makes it possible to effectively create virtual meters or probes to get an overview of e.g. temperatures and heat loads for specific branch sections of the network. Finally, the tool does not require district heating utilities to invest in any additional field equipment – in or above the ground. Instead, it’s all about usingwhat is already out there. In today’s world, where the technological development of any business takes place at a breath-defying speed, it doesn’t get anymore disruptive than that. ADVANCED MAPPING OF HEAT LOSS Network losses are perhaps the biggest cost driver for district heating utilities so the potential in eliminating them is enormous. Combining temperature and flow measurements from energy meters with information about the length and size of the pipes allows them to calculate temperatures in all parts of the network and accurately map their heat loss: how much, where and – perhaps most importantly – why. If a building’s forward temperature is lower than expected according to the model (i.e. the red circles on Figure 2), this can indicate abnormal heat loss, poor performing pipe insulation, a defect service pipe or incorrect meter installation, all of which result in lost revenue for the utility. If it is higher than expected (i.e. the blue circles on Figure 2), it can denote a leakage or perhaps an unknown or misadjusted bypass creating circulation that keeps the network temperature up. Bypasses are often placed by the end of a road, and as a result, the circulation they generate, much like a network leakage, is not measured by the heat meter installed inside the house.
Whatever the reason, knowing the actual state of the network is the prerequisite for being able to act in a timely manner and reap the benefits as opposed to finding out tomorrow exactly what you should have done today. For a utility, lowering their return temperature by just one degree would generate immediate savings worth thousands of Euros. NETWORK LOAD MONITORING As more and more buildings are connected to the district heating network, utilities must constantly consider their capacity. Because planning, building and expanding infrastructure is economically heavy, logistically comprehensive and time-consuming, there is great value in maximising utilisation of the current capacity. This enables utilities to prioritise and postpone infrastructure investments. Also, knowing their exact capacity helps them minimise the risk of oversizing new pipes, which will otherwise mean higher costs and more heat loss. By linking energy meter flow measurements in a specific area with detailed pipe characteristics, utilities get a precise picture of the load throughout the network. This allows them to target the money they invest in network maintenance by e.g. estimating pipe lifetimes based on their load and stress level instead of just their age. It also provides important knowledge for planning future expansion of their supply area. This is crucial as the blueprint for a so-called average household becomes increasingly blurry. Low-energy houses, renewable energy sources and increased awareness of energy behaviour all contribute to the need for utilities to calculate their own dedicated coincidence factor rather than applying one that was defined at a time when the district heating landscape looked very different. With the calculation model, utilities can use the smart meter data from their network to do exactly that. With an increasing number and complexity of the decisions that utility professionals must make every day, data-based decision support is key to an optimised distribution network that ensures utilities full return on their investments. After all, facts are easier to convert into Euros than gut feelings. ONLY THE BEGINNING There is undoubtedly a lot of money to be found in the ground using smart meter data analytics – and the great news is that we are only at the beginning of this treasure hunt. Imagine the savings that can be achieved with detailed impact analysis of e.g. a smaller pipe dimension, better pipe insulation, adding a bypass or targeting and improving the 5 % of end users that have the highest return temperatures. There are many more levels of data-driven optimisation to uncover with this tool and its ability to simulate all kinds of future scenarios, such as new pipe types and network configurations. On the path to 4th generation district heating, that is not a bonus but a necessity.
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