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Projects: Projects for Investigator
Reference Number	NIA_WPD_036
Title	LCT Detection
Status	Completed
Energy Categories	Other Cross-Cutting Technologies or Research(Energy system analysis) 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 (Computer Science and Informatics) 80%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 20%;
UKERC Cross Cutting Characterisation	Systems Analysis related to energy R&D (Other Systems Analysis) 100%
Principal Investigator	Project Contact No email address given Western Power Distribution
Award Type	Network Innovation Allowance
Funding Source	Ofgem
Start Date	01 October 2018
End Date	01 February 2019
Duration	ENA months
Total Grant Value	£346,020
Industrial Sectors	Power
Region	South West
Programme	Network Innovation Allowance
Investigators	Principal Investigator	Project Contact , Western Power Distribution (100.000%)
	Industrial Collaborator	Project Contact , Western Power Distribution (0.000%)
Web Site	https://smarter.energynetworks.org/projects/NIA_WPD_036
Objectives	The project will use IBMs cutting-edge AI and cognitive analytics capability to extract key information, related to EV/DER proliferation from the DTS dataset. The dataset has been constructed by ElectraLink using over six years of market interactions. This data contains 100s of millions of transactions, structured and unstructured market messages related to WPDs area, across over 100 market processes. The project is looking to improve modelling at LV in particular, though having more realistic distribution substation data/profiles will potentially benefit HV planning.ElectraLink will extract data sent across the DTS regarding consumption and export relating to WPDs network. This data will be analysed by IBMs cognitive analytics and where appropriate combined with third party datasets, to develop candidate locations for validation. Once validated, this improved an output that can be overlaid onto WPD substation information can be used to develop a reporting framework to enable WPD to forecast future requirements for network monitoring and potential sites for active network management. By using innovative data analytic techniques, this project tackles a key network and operational issue which forms a part of an overarching industry need – the increased requirement for data to support energy market operations. This project will take industry data from the Data Transfer Service (DTS) data set and apply leading-edge cognitive analytics to provide WPD improved visibility of EVs and DER to support forecasting of the proliferation of PV/EV across networks and other DER connections to support network planning including the options of active/flexible network management. The project will help define the requirements for the delivery of an enhanced dataset proof of concept model allowing us to leverage the analytics tools and techniques to support WPD to identify unregistered LCT, understand how best to validate suspected installations and to estimate the likely uptake of technologies in different areas for planning purposes. The project will overlay and analyse data on a number of representative network topologies across WPDs Electricity Service Areas (ESAs). It is likely that all the ESAs will be required in order to ensure a sufficient number of known locations to help train and validate the model. This is particularly true for heat pumps where there are relatively few records. The number of customers assessed for LCT identification will reflect the volumes required to generate a sufficiently large candidate set to validate the model and to identify regional differences in model effectiveness. Many of the project costs are not scale dependent. By using Electralinks DTS dataset, combining this with a range of other structured and unstructured data and then applying IBMs Cognitive analytics, the objective is to identify patterns in the data that indicate the presence of EV, PV or other LCTs that had not previously been identified. IBM will apply its Watson technology to perform advanced analytics on the ElectraLink, combined with other datasets. IBM will use a progressive and iterative methodology to detect patterns in the data that was not detected hitherto. By improving detection of LCT on the network, the project will also build the foundation for improving forecasting capabilities and, ultimately, garner an understanding the effectiveness and costs for the various options would allow for the validation process to be optimised.
Abstract	The energy market is complex and evolving – particularly with growing smart technologies and embedded, renewable generation. For DNOs, the increasing number of invisible changes (growth of Electric Vehicles (EV), photovoltaic (PV) and other Low Carbon Technologies (LCTs)) challenge existing network practices. At present, technology change is outpacing changes in modelling and forecasting of consumer uptake of smart, Distributed Energy Resources (DER) or Electric Vehicle (EV) technology; therefore, it is difficult to monitor or understand the change in requirements on the LV network under existing arrangements, without monitoring EV and DER impacts directly at source (or substation level). While smart meters will improve the visibility of network load and generation in the longer term, there is a need for a solution that can identify unregistered equipment. The problem this project addresses is how to improve WPDs ability to identify EVs, DERs and other LTCs connected to its network so that future operational and future investment decisions can be improved. It will also support some of the informational requirements needed in its transition to a DSO.
Publications	(none)
Final Report	(none)
Added to Database	09/11/22