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Reference Number NIA_SPEN0016
Title Network Constraint Early Warning Systems (NCEWS)
Status Completed
Energy Categories OTHER POWER and STORAGE TECHNOLOGIES(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
SP Energy Networks
Award Type Network Innovation Allowance
Funding Source ENA Smarter Networks
Start Date 01 February 2017
End Date 01 May 2019
Duration 27 months
Total Grant Value £352,800
Industrial Sectors Power
Region Scotland
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , SP Energy Networks (99.999%)
  Other Investigator Project Contact , ScottishPower Manweb plc (0.001%)
Web Site http://www.smarternetworks.org/project/NIA_SPEN0016
Objectives There are a number of objectives within this project,WP1Improve 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 intelligenceWP2Integrate 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 scenariosWP3Proof 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,WP1 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 SPENWP2Data 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 penetrationWP3Delivery to internal expert business users a POC GIS Visualisation system for access to SM business intelligence on Network Constraint understanding
Abstract The ability for Smart Meters (SM) to provide cost effective LV network monitoring data has the potential to help solve the problems that will be created from future UK domestic ‘Prosumer’ Low carbon energy use. Increasing volumes of domestic customer’s investment in Low Carbon Technology (LCT) will drive Distribution Network Operators (DNOs) to establish dynamic ‘System Operation’ LV ‘Smart Grid’ network management systems rather than higher cost traditional reinforcement. A central pillar of this requirement is detailed visibility and analysis of customer dynamic energy use and the effect this has on overall LV network voltage and thermal constraint. The mandated SM roll-out and access by the DNO to voltage and energy use data is an ideal opportunity to help derive this visibility but will require research to prove its overall value and use. When working within this potential ‘Big Data’ flood of information from SMs the problem is to understand what level of monitoring is required when and where. This will be dependent on network construction and topology, individual customer energy use and growing network energy access requirement from increasing penetration of LCT. It is hoped to derive recognition algorithms that can identify constraint or drift towards constraint through analysis of the SM data and improved understanding of background GIS network models. To help minimise the overall SM data requirement it will explore generating automatic classification of network constraint levels, Low risk of constraint background monitoring - The majority of the network Increased risk action trigger requirements - Increasing requirement as LCT penetration increases Analysis by exception ‘connection’ systems - Traditional network extension activity. To solve the stated problems focus of this Phase 1 SM research project will be on the adaptation of existing customer connectivity systems and analysis of the integration requirements for SM monitoring data, Explore the Improvement of existing LV connectivity systems for step change in Data Analytical requirement from application of SM data Data Science led research on initial use of Alerts & Measurement Profile Limits to provide minimum SM monitoring requirements for sufficient gateway Early Warning Network Constraint Visibility Build understanding of the scale of future requirements when utilising the full penetration of Dynamic SM Network profile data within Smart Grid Systems This project will utilise the Innovate UK Knowledge Transfer Partnership (KTP) programme with Heriot Watt University to jointly fund a Data Science/Data Analytical researcher to carry out the main research requirement. This approach is intended to, Minimise costs for this initial investigation Maximise the research learnings potential through integration with a research institution Transfer new Data Analytical and Data Science knowledge and skills into the business. Transfer existing Network Management skills in SMART systems This Data Science researcher will need proven Data Analytical support to improve the capability of existing LV connectivity systems, Provide adaptive Python based algorithms for creation of ordered connectivity LV circuits and breakdown into relevant circuit Components e. g. Changes in Circuit size, Teed circuits etc. Detail connectivity relationship between the individual consumer, consumer LCT requirement and Circuit Component sectionsNote : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 14/09/18