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SPEN partners CGI to develop a ‘master model’ as it targets digitalisation

Image: Andy Beecroft.

Image: Andy Beecroft.

ScottishPower Energy Networks (SPEN) has partnered with Canadian technology company CGI to build a so-called master model of its network.

The model forms part of the networks Smart Data Integration Fabric (SDIF) project, which is designed to assist the company as it transitions from a DNO to a DSO.

The multi-purpose and reusable digital master model will combine existing geospatial, connectivity, asset and telemetry data. It will incorporate CGI’s Integrated Network Model solution to help with challenges like the integration of multiple data sets.

The SDIF project is looking to improve open data access following on from advice set out by the Energy Data Task Force last year.

SPEN is hoping that building an accurate digital model will allow it to enable a broad transition to electric heating, transport distributed generation and storage technologies. The company said that such a transition will require greater monitoring of the network and a corresponding increase in data and data sharing that the platform can facilitate.

The SDIF platform form part of SPEN’s broader digitalisation strategy said Colin Taylor, Director of Processes and Technology at SP Energy Networks, helping to tackle decarbonisation and the transition to being a DSO.

“By creating a near real time digital model of our network, we can improve its reliability and flexibility and hence further improve the service we provide to our customers.”

SPEN launched its digitalisation strategy in December, highlighting a host of improvements the company had its eye on. These include applying digitalisation to aid control, automation, flexibility and demand side management.

Paul Buxton, vice president of Utilities Sector and North of England Region at CGI, added: “The SDIF solution will provide a strategic platform upon which new business capabilities can be created.

“The initial areas of benefit will be to improve the identification of fault locations to reduce restoration times and make better use of smart meter data to understand network status and target restoration activities, whilst managing outages more efficiently.”


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