Feb 2013
Electricity Distribution
High Performance Computing Technologies for Smart Distributed Network Operation (HiPerDNO)
Live
Feb 2013
Unknown
UK Power Networks
UKPN Innovation Team
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Innovation Funding Incentive
None
Comms & IT
£5,100,000.00
The mass deployment of network equipment sensors and instrumentation, millions of smart meters, small scale embedded generation, and responsive load will generate vast amounts of data which potentially could be analysed in real time to identify trends or incipient faults. So called cloud and grid computing could enable scalable data mining, feature extraction, and near to real time state estimation. These and other High Performance Computing (HPC) tools and techniques have been recently developed to cost effectively solve large scale computational challenges in areas such as genomics, biomedicine, particle physics and other major scientific and engineering fields that require similarly scalable communications, computation and data analysis.

HiPerDNO is a European Commission funded FP7 ICT Energy STREP (Specific Targeted Research Projects) project which plans to develop solutions to address future electricity distribution networks.

The project will aim to address the following objectives:

  • Development and testing of novel high performance computing information and communications technology for active distribution networks

  • Development and testing of data mining features that extract relevant information

  • Development and testing of a high speed messaging layer

  • Calculation and utilisation of a typical measurement data set for large amounts of smart meter data in future low and medium voltage networks

  • Customer integration in active network operation

  • Development and testing of a real time distribution state estimator

  • Identification and analysis of new generation DMS functionalities

  • Development and testing of a new generation network service restoration algorithm

  • Development of novel state estimation algorithms for distribution Networks.