Projects
Projects: Projects for Investigator |
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| Reference Number | ENA_10025656 | |
| Title | Predict4Resilience | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research(Energy Models) 50%; Other Power and Storage Technologies(Electricity transmission and distribution) 50%; |
|
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 10%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 10%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 30%; ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 50%; |
|
| UKERC Cross Cutting Characterisation | Systems Analysis related to energy R&D (Other Systems Analysis) 50%; Systems Analysis related to energy R&D (Energy modelling) 50%; |
|
| Principal Investigator |
Project Contact No email address given SPEN - SP Transmission Plc |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 March 2022 | |
| End Date | 01 May 2022 | |
| Duration | 2 months | |
| Total Grant Value | £133,368 | |
| Industrial Sectors | Power | |
| Region | Scotland | |
| Programme | ||
| Investigators | Principal Investigator | Project Contact , SPEN - SP Transmission Plc (100.000%) |
| Industrial Collaborator | Project Contact , SP Energy Networks (0.000%) Project Contact , National Grid Electricity Transmission (0.000%) |
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| Web Site | https://smarter.energynetworks.org/projects/ENA_10025656 |
|
| Objectives | ||
| Abstract | We will develop a Weather Fault tool which can: Forecast severe and extreme weather events. Improve the accuracy within the current 7-day forecasting window. Double the current forecasting window (to 14 days ahead). Predict specific network faults and risks.Our discovery phase will take our existing network data sets, coupled with weather data supplied by the Met Office and assess if this data is sufficient to support the project aims, or identify the gap to realise the required format and volume. As a result, the proposed project meets the scope for how novel uses of data and digital platforms can significantly improve network planning, modelling, and forecasting capabilities specifically by:1. Applying novel probabilistic techniques to our data sets2. Developing a digital platform within our control rooms to improve our forecasting capabilities3. Improving our network planning through proactive decision making based on data driven forecasts and impact analysis The main users of this innovation are control room engineers and asset managers. Our objectives are fully informed by their needs and they are our internal project sponsor. From our engagement to date, our users need a way to: 1. Reduce customer interruptions & minutes lost due to network asset extreme weather faults2. Improve accuracy, range, and specificity of fault prediction for network assets3. Communicate actionable outcomes from control roomThis project is led by SP Transmission and supported by SP Distribution and NGET. Both the transmission and distribution systems can be adversely affected by severe and extreme weather.Arup is selected as partner because their previous experience on the NIA project Forward Resilience Measures collaborated with NGET. Moreover, Arups own underlying energy resilience framework provides a holistic, practical and evidence- based approach to assess resilience, taking into consideration both the physical aspects and the less tangible aspects associated with human behavior in order to enable a common understanding of interdependencies and vulnerabilities, sudden shocks and chronic stresses.The MET office is a partner for their scientific knowledge and expertise, and their previous experience on NIA projects such as Advanced Weather Forecast for Dynamic Line Rating. The MET office is also the owner of the weather data. The Met Office is recognized as a world leader in Numerical Weather Prediction.The University of Glasgow, as the academic partner, have expertise in probabilistic forecasting, decision-making under uncertainty, and their extensive experience in energy forecasting. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 14/10/22 | |
