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Reference Number NIA_SPEN0009
Title Data Intelligence for Network Operations (DINO) Phase 1.
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) 100%
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 September 2015
End Date 01 July 2017
Duration 22 months
Total Grant Value £850,000
Industrial Sectors Power
Region Scotland
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , SP Energy Networks (100.000%)
Web Site http://www.smarternetworks.org/project/NIA_SPEN0009
Objectives Each discrete Work Package has Objectives: WP 1: Discovery To produce a fully scoped problem definition and desired outcomes WP 2: DINO PoC Build To create a "near to operational" proof of concept test bed that can demonstrate how these problems could be addressed WP 3: DINO Evaluation To assess the efficacy of the data management and analysis methods employed. WP 4: BaU Adoption To identify the necessary business changes required to facilitate the adoption of DINO principles as BaU and justify efficacy of doing so. WP 5: Further Enhancements Identification and exploration of further enhancements with different business use cases that can deliver additional DNO / customer benefits and confirm best practice to move into BaU can be replicated across all DNO business areas. WP 6: Dissemination- Dissemination of project activities, scope, deliverables to UK DNOs, customers and vendors at several points within the project in order to stimulate the uptake and development Each discrete Work Package has a Success Criteria: WP 1: Discovery The delivery of detailed problem statements for the chosen use cases. WP 2: DINO PoC Build Delivery of a near operational PoC to demonstrate problem solutions and best practice WP 3: DINO Evaluation Successful iteration of use cases. Determination if an optimal means of managing an operationally efficient solution to the problems is evident WP 4: BaU Adoption The production of proposals for how BaU adoption could be facilitated. WP 5: Further Enhancements The publication of a set of proposed next steps and the likely benefits they will deliver. WP 6: Dissemination The sharing of experience with UK DNOs and vendors, to allow optimum support for smart grid.
Abstract This project seeks to research the two levels of "large volume data management" problems which Distribution Network Operators (DNOs) will experience more and more as they move towards a "Smart Grid":1. The issue of too much data DNO Network Management Centres (NMCs) are presently inundated with data from the network, be it analogues, alarms or events. As emerging smart technology becomes more prevalent on our networks this issue will only be exasperated, be it through the integration of smart meter data, Dynamic Rating / Active Network Management (ANM) schemes or additional sensors to monitor the impact of low carbon technology. It is estimated that this increase of data could be factors of 1,000 times greater than presently received, particularly if smart meter information is considered. Hence, we need to turn large volumes of data into useful information suitable for supporting operational decisions. This is a big problem area and in order to focus we have taken a use case led approach. This approach allows us to take a narrow route through a large problem. Hence, in this initial Phase of the DINO project we will look at the use case of handling alarms from Network Controllable Points (NCP), which represents a real "too much data" problem experienced today. This will give us a basic understanding of concepts that we would then look to apply to other areas of monitoring e. g. smart metering, in follow on projects / Phases. Addressing this business issue would provide immediate benefit and hence, would demonstrate how to facilitate the acceptance of new data practices. 2. The issue of data exchange/discovery Passing data between multiple systems and ensuring that only one current version of truth exists is an ongoing issue for all DNOs. Without solving this it is hard to understand the full context (network, asset, communications) that information relates to. As part of the process of identifying the solution for the business use cases identified in (1) we will also investigate the potential future data infrastructure required for DNOs as they build out their smart grid infrastructure. Within the context of the business use cases outlined in (1) we would like to understand the benefits of: Modelling the solution in the Smart Grid Architecture Model (SGAM) framework The potential use of the Common Information Model (CIM) or other service oriented standards to help with data discovery and exchange How systems and standards will work to handle the use cases emerging from multiple data streams e. g. smart meters, substation monitors, ANM etc. An indication of how we could move to an improved system in a step-wise fashion, given that many legacy systems will remain in-situ Although base technology exists to address these problems, the best methodology to do so is unproven. This project is research based as it evaluates different ways of managing, analysing and visualising data. 1. Chose data related use cases in key business areas (NCP alarms) Identify key business contacts to engage in problem identification. Identify any existing IFI/LCNF activity that may have attempted to address these problems and cross reference. Prioritise identified issues based on business impact and perceived ability to resolve. Use information as a basis to identify a partner with the expertise to assist in this area. 2. Analyse existing processes and innovate on how they are best solved Initiate workshops withchosen partner to expand issues faced in NCP use case area, and brainstorm the ideal end goal solution. End goal solutions must be innovative/novel rather than "fix the existing system" as this would be very much BAU. Prioritise developed use cases. Identify what data methodology and infrastructure is needed to solve the problem; assessment of existing IT/OT estate; location and structure of information; best approach to enabling access to the data. This includes understanding the applicability of SGAM, CIM, IEC61850, new network models, service bus technology etc. in achievement of business goals3. Data Science Determine novel data science/analytics/visualisation techniques to understand which methods are best suited to business needs of DNO Leverage other grid based analytics research Understand what data is available, its reliability, what data we actually need to achieve the business goal we have set4. Test the theory Based on the previous steps build a test lab Proof of Concept to run business use cases. Create a functioning representation of the chosen business issues in as close to real world situations as possible. 5. Measure business improvements Iterate through use cases to refine ideas, refine the system and produce a view of "best practice" approach Evaluate new solution with business users to assess improvements and scope any additional functionality ideas. Develop mechanisms for quantifying improvement, including value to customer. Plan for transition of business positive/viable solutions into business as usual. 6. Plan for next steps Incrementally, we will add more use cases to see how system can extend and be re-used by other business areas. In this phase we also evaluate the continued use of legacy systems versus any new systems we have trialled.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 09/08/18