Projects
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| Reference Number | NIA_ENWL_028 | |
| Title | LV Predict | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research (Energy system analysis) 30%; Other Power and Storage Technologies (Electricity transmission and distribution) 70%; |
|
| 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 50%; Systems Analysis related to energy R&D 50%; |
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| Principal Investigator |
Project Contact No email address given Electricity North West Limited |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 July 2021 | |
| End Date | 31 January 2023 | |
| Duration | ENA months | |
| Total Grant Value | £555,000 | |
| Industrial Sectors | Power | |
| Region | North West | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , Electricity North West Limited (100.000%) |
| Industrial Collaborator | Project Contact , Electricity North West Limited (0.000%) |
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| Web Site | https://smarter.energynetworks.org/projects/NIA_ENWL_028 |
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| Objectives | The project will develop a probabilistic framework which predicts the current state of the LV assets across a representative part of the network, most likely as a probability distribution of times to failure, or equivalently the probability of failure in a specific time interval. LV assets are those defined as operating at nominal voltages below 1kV and, for the purposes of this project, we will focus initially on the 400/230V cable network. The model aims to project the estimated degradation trajectory (likely manifesting as increasing failure probabilities) of the LV assets, based on a range of future operating scenarios. This will highlight the high-risk regions of the network that may require future investment to prepare for the increased demand on the network from the transition to Net Zero.The probabilistic framework will likely be based on Bayesian statistical methods, allowing a precise model to be built using a combination of data and expert opinion, accounting for the varying quality across data sources, to output a probabilistic solution that fully accounts for all identified sources of uncertainty. The framework will initially be constructed for one cable type and then expanded to cover other cable types and will consist of three internal models: a network demand model, a model of environmental conditions and a degradation model. The demand model will include a statistical characterisation of past and present demand patterns, as well as a representation of the way in which these would change if future developments were to follow a particular long-term and large-scale scenario. This will incorporate a wide range of possible data sources including different combinations of smart meters, annual consumption, novel LV monitors, and Maximum Demand Indicators. The environmental conditions model/ unit includes a probabilistic representation of the relevant environmental variables and conditions. This could include information on historical defects, ground conditions (e.g., soil chemistry, water table), road conditions and usage, weather, animal populations etc). The degradation model will be largely physics based, yet probabilistic in nature, representing the relationship between loading, environmental conditions, and rates of degradation.The data will be presented visually, showing the effect of historical cable usage, environment and other factors on the LV assets condition. These outputs will be presented in a Methods and Findings report.The project shall identify gaps in data and the works required to take the framework to full scale implementation. These findings shall be presented in a business case which shall include cost benefit analysis of all aspects of the frameworks functionality. The project will be split into three phases:Phase 1: Literature Review and Approach Finalisation Task 1.1: Literature Review: a review of current best practice, relevant innovation projects and methodologies. Task 1.2: Approach Finalisation: explore and decide what the user inputs and outputs of the development framework could be including;o Cable typeo Historic demand, including smart meter data where available;o LV asset location data, including cases where there are multiple cables in the same track;o Novel sensor data o Fault data and any available defect data.Phase 2: Framework Development,The framework will be designed in an Agile way, iteratively increasing the functionality of the model, whilst proving that each input source is appropriate, and that the prediction accuracy is improved. This approach will reduce risk and maximise cost effectiveness. First, a clear and complete mathematical specification of the entire problem will be written, allowing a skeleton version of the model to be built relatively quickly, and the complexity and performance of the model will develop over time. This will provide an unambiguous statement of all assumptions, physical constraints, damage functions, data transformations etc. New functionality is added to the framework and underlying models; for example, adding environment information to the degradation model. Elements of the algorithm will be implemented in code and tested before finalising, this allows for practicalities in the model implementation to feedback to the design of the algorithms.Phase 3: Business Case Development and Data VisualisationTask 3.1: Finalisation of Business Case: develop a full business case for further development and/or implementation of the modelTask 3.2: Produce Data Visualisation Outputs: produce the required data visualisation outputs to demonstrate the results Each phase will have the following objectives:Phase 1: Literature Review To understand best practice, innovations, and methodologies for modelling the degradation of LV assets. This will include review into generic methods to model the degradation of the polymers used in the manufacture of LV cable insulation, as well as more random events such as rodent damage and roadworks. Phase 2: Framework Development Develop a framework that is useful when testing a range of assumed model structures, and associated algorithms, to determine how accurately they can predict the current and future values of LV asset failure rates. To conduct as much validation of the models being trialed as is possible, to judge and communicate exactly what can and cannot be concluded. Identify gaps in both data and knowledge that could be populated at a later date. Phase 3: Business Case Development and Data Visualisation Understand the business case for implementation or further development of the framework. Investigate data visualisation methods to present and interpret the results of the model. | |
| Abstract | The Low Voltage (LV) distribution network (defined as 1kV and below) represents a significant proportion of network expenditure, yet until recently there has been relatively poor visibility of these assets. This project will identify and test novel methodologies that could contribute to enhanced asset management for LV network assets by introducing predictive methods based on models that can determine the probability of failure. The quantitative analysis within the project will focus on LV cables, but we anticipate there will be broader learning for other asset types. A business case for implementing further development of the framework shall also be prepared. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 19/10/22 | |
