Feb 2017
Electricity Distribution
Network Constraint Early Warning Systems (NCEWS)
Feb 2017
May 2019
SP Energy Networks, SP Distribution and SP Manweb
Jim Whyte (Senior Project Engineer)
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Network Innovation Allowance
This is phase 1 of a multi-phase investigation requirement into the benefit of SM data in LV network Design and Planning. Full SM coverage is realistically expected to take between 3-5years (2017-20 or beyond if deployment delayed). It has been started by the industry that operational benefit will not be achieved from SM’s until 60% penetration. Within the early years of SM Penetration (2017-18) it is hoped to utilise pockets of high level SM penetration to carry out fundamental research and try to derive early benefit from SM data.

The Project will focus on the provision of Data Science research learnings but will require improved Data Analytical capability from our existing LV connectivity understanding,

WP1 – LV Connectivity Improvement

  • Fixed timescale SME Consultant support

WP2 – Full Time Data Science KTP Researcher (2 years)

  • Voltage Limit Excursion: Data Science research on Minimum Data and SM monitoring points required for LV network Voltage excursion monitoring Systems

  • Power Constraint: Use of Power profile Limits and After Diversity Maximum Demand’s (ADMD’s) for provision of initial ‘aggregated’ thermal levels intelligence

WP3 – Dissemination

  • Dissemination will be mainly achieved through demonstration of web based GIS ‘Visualization’ systems to key internal Business experts and external industry Stakeholders.

  • Final report on the learnings found and documentation of next steps in the use SM Monitoring Data in Planning will be provided.

 There are a number of objectives within this project,


  • Improve Data Analytical preparedness of current LV connectivity models which are embedded with GIS linear Asset Management systems for future high levels of SM data penetration
  • Increase visibility and understanding of Customer and LCT relationship to ‘aggregated’ LV circuit Component level using SM data and other related customer connectivity intelligence


  • Integrate initial volumes of SM ‘Profile Limit’ data to research minimising Network Monitoring requirement to provide early and ongoing warning of Network Constraint
  • Provide Big Data/Data Science research Knowledge Transfer capability to SPEN
  • Transfer expert LV Network Constraint Management into SMART systems utilising Data Analytics and Big Data
  • Provide next step requirements for BAU use of Early Network Constraint monitoring systems in the management of increasingly Dynamic Smart Grids: Within full SM and increasing LCT penetration scenarios


  • Proof of Concept (POC) SM Data Visualisation systems to demonstrate Business value from Constraint early warning systems in Network Planning Management (Connections and Reinforcement Management)
Within the identified risks from the lack of the ability to control the Supplier led penetration of Smart Meters and consumer lead penetration of LCT technologies the Success of this project will be measured from,


  • Improvement in Data Analytical translation of Key LV Network Topologies,
    • Successful breakdown of LV circuits into network components

    • Understanding of Customer and LCT device connectivity to LV network components:

  • Greatest chance of success as it is independent of identified risks and working with a well-established LV connectivity model in SPEN


  • Data Science research clarification of minimum SM Network Monitoring points required to give Network Constraint Early Warning systems: Success may be limited to key network topologies and LCT penetration scenarios.

  • Knowledge transfer of Big Data/Data Science expertise into SPEN and expert network management expertise into SMART systems

  • Next steps understanding of the Potential and Scale of Data Analytical requirements from full SM penetration


  • Delivery to internal expert business users a POC GIS Visualisation system for access to SM business intelligence on Network Constraint understanding