Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_ENWL_036 | |
| Title | LV Predict II | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research(Energy Models) 20%; Other Power and Storage Technologies(Electricity transmission and distribution) 80%; |
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| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact Electricity North West Limited |
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| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 May 2024 | |
| End Date | 31 December 2025 | |
| Duration | ENA months | |
| Total Grant Value | £719,000 | |
| Industrial Sectors | Power | |
| Region | North West | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , Electricity North West Limited |
| Web Site | https://smarter.energynetworks.org/projects/NIA_ENWL_036 |
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| Objectives | This research project will build on the physical and statistical modelling framework developed in the first LV Predict NIA project, LV Predict researched physical models for determining the temperature within LV cables, the resulting stress cycles and physical damage the cables sustained and constructed statistical modelling methods to predict this damage using commonly held LV data (such as customer numbers and cable types). LV Predict II will refine and extend the probabilistic framework by improving the skill and reliability of the model, expanding the model to other network assets, and applying the model in decision making. This extended probabilistic framework is a necessary step to allow BAU tools to be developed in the future. Specifically the project will:i. Improving and validating the skill and reliability of the model ii. Integrating model outputs into decision making processesMeasurement Quality Statement:There are no plans to take measurements as part of this project, the methodology will use already available measurements and data.Data Quality Statement:Data generated and/or processed in the course of this project will be reviewed to provide assurance that outputs meet the required standards. Data will be managed in line with the RIIO ED2 Data Best Practice Guidance and Data Assurance Guidance.This project does not plan to undertake any processing of personal data. Phase 1Task 1.1:Improve the predictive skill and reliability of the current framework byUsing additional historic data to account for demand changes.Generating a larger set of physical model training data for use in training simulations.Including other sources of demand, such as non-domestic customer demand and electric heating.Demonstrating the use of monitoring data within the modelling framework, including quantification of the possible benefits of deploying this type of monitoring technology.Implementing new and innovative data sources (such as LV protection and monitoring devices) to test the validity and reliability of the model.Incorporating unstructured data such as fault reports.Task 1.2:Translate the complex modelling framework into a series of replicable steps that can be detailed within a report. This methodology will have defined inputs (such as cable type, estimated age, and number of households served), and a series of equations that can be used to establish the probability of failure. Sensitivity analysis will be undertaken to understand the prediction ability and define statistically significant thresholds for cable replacement. This will provide confidence that it is possible to use the modelling results in a replicable fashion, and the results are interpretable.Phase 2Task 2.1:Using the outputs of Phase 1 generate a methodology report which details:Step by step guide to the methodology.Clear definition of the inputs and outputs required by a user of the methodology.Engineering justification for the validity of the methodology.Details of any underlying assumptions, limitations, dependencies, and latent conditions to be aware of in the use of the methodology.The analysis that underpins the methodology as an Annex to the report.Task 2.2:Generate a Cost Benefit Analysis to assess the merit of implementing a proactive LV cable replacement regime in the future. This CBA will use the research undertaken in Phase 1 to quantify the costs and benefits of utilising a proactive maintenance approach compared to the counterfactual of the current reactive approach.Benefits of LV Predict IIThe current asset management approach to LV underground mains cables is reactive sectional replacement upon failure and a proactive replacement of specific types of cable where a safety issue is identified. Spend profiles for LV cables are predominantly based on historic replacement rates, and there is currently no method of assessing LV cable condition, and no consideration of the potentially increased rate of degradation over time related to changing loading. Having a robust method of identifying the diminishing health of LV cables over time supports a new condition-based approach to the LV cable management and the justification of a new investment driver. It is expected, that without intervention, the increase in demand on the LV system will increase the degradation of rates of cables, causing higher failure rates. The project will produce a CBA to quantify the financial benefits of this new approach. The objectives are:Produce a methodology for deriving LV cable condition from available data sources.Produce a supporting CBA for the methodology. | |
| Abstract | As we transition to net zero, the level and volatility of demands across the network will increase, with the greatest impact expected on the LV network particularly, underground cables. Without intervention, this increase in demand will increase the degradation rate of LV cables, causing higher failure rates.LV Predict researched physical models for determining the temperature within LV cables, the stress cycles and physical damage cables sustained and constructed statistical modelling methods to predict this damage using commonly held LV data. LV Predict II will build on this work and will refine and extend the probabilistic framework by improving the skill and reliability of the model, expanding the model to other network assets, and applying the model in decision making. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/04/25 | |
