Predicting the impact of smart metering

by Mark Thompson

In many countries around the world, smart metering technologies are now either being deployed or on the brink of deployment – but in many cases, there is still a lot of uncertainty about the effects that their introduction will have for network operators, suppliers and customers.

We all know that smart meters will enable remote operations and generate enormous amounts of data – and that they should deliver a wide range of benefits if the necessary improvements to business processes, information access and IT systems can be made. Energy companies know that they will face real challenges, but they have lacked the tools to technically assess the impact of smart metering on downstream systems and processes.

Meter data simulators have the potential to change this situation completely. A simulator works by creating a logical model of the low-voltage distribution network – from the grid down to the substations, phases, and smart meters that have been connected. The simulated model can then be used to generate “dummy” meter data and mimic the live operations of the meter to simulate a range of scenarios.

In our work on simulation so far, we are focusing on modelling different types of power outage. These outages are a major pain-point in the energy sector: network operators are penalised for failing to maintain a secure supply, while energy suppliers will face a flurry of customer complaints and information requests until the fault is fixed.

Working with one of our Distribution Network Operator clients, we are developing a solution that will be able to simulate a wide range of outage scenarios and understand how meter data can help the client to respond more quickly and effectively.

For example, we can model a situation where an electrical fault causes one or more of the feeders at a substation to go offline. The customers whose meters are connected to the affected feeders lose power; but before they do so, we simulate the meters sending a “last gasp” message to the central database, which informs the network operator that they have gone off supply. The simulator also updates its internal database to reflect the de-energised state of the meter.

By aggregating these messages and identifying patterns, the network operator can identify which customers are affected, and can start to diagnose the problem even before it sends an engineer out to the site.

Such simulations can help a network operator evaluate the effects of remedial action in order to establish the best working practices before the meters themselves are actually deployed. An energy retailer could also use the simulator in a similar scenario to help plan its customer care strategy during a power outage.

Outages and restorations have been the focus of our initial work with the simulator, but we are also keen to start harnessing its potential in other areas, to assess the benefits for all parties involved in the smart metering rollout.

For example, if suppliers are allowed to collect meter data on a daily or even half-hourly basis, they should be able to provide real-time insight into consumption – but what would be the best way to let customers interact with this data? What effect will the introduction of new services such as interval billing have on existing IT systems? What new processes and safeguards would need to be put in place if we wanted to use the data to identify and support vulnerable customers? What new and valuable insights can be gained from all the technical meter data that suppliers will now receive?

By generating realistic simulated data on a large scale and feeding it into participants’ test systems, we can help them get a much more accurate idea of the future requirements and technical impact of this new technology. This will enable them to prepare effectively and hit the ground running as soon as their smart meter rollout is complete.

Even after the rollout, a simulator could continue to help with impact assessments for the development of new products and meter technologies as new generations of meters become available.

In fact, it’s difficult to imagine a smart metering business scenario where simulation wouldn’t be useful. At the moment, we are still working with our clients to identify additional use cases, and the list is growing rapidly. We may not even have begun to explore its full potential yet, but over the next six to twelve months, we expect meter data simulation to become a hot topic in the industry.


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