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Projects: Projects for Investigator
Reference Number	NIA_WPD_045
Title	Virtual Monitoring Data (VM-Data)
Status	Completed
Energy Categories	Other Cross-Cutting Technologies or Research(Energy Models) 30%; Other Cross-Cutting Technologies or Research(Energy system analysis) 40%; Other Power and Storage Technologies(Electricity transmission and distribution) 30%;
Research Types	Applied Research and Development 100%
Science and Technology Fields	PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%;
UKERC Cross Cutting Characterisation	Systems Analysis related to energy R&D (Energy modelling) 100%
Principal Investigator	Project Contact No email address given Western Power Distribution
Award Type	Network Innovation Allowance
Funding Source	Ofgem
Start Date	01 October 2019
End Date	01 October 2020
Duration	ENA months
Total Grant Value	£2,748,755
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_045
Objectives	The overall project method is to use IBMs cutting-edge Artificial Intelligence (AI) and cognitive analytics capability to further develop a model developed in the previous “LCT Detection” NIA project, which analyses changes in consumption patterns linked to EV/DER proliferation or other factors. The model will use MPAN-level consumption data from the Energy Market Data Hub (EMDH) plus detailed consumption data (half-hourly intervals or less). The detailed consumption data will also be used to create and refine a set of half hourly customer profiles, which will be used to extrapolate EMDH consumption data into virtual daily consumption profiles, which will be aggregated to achieve virtual feeder and transformer half hourly loading profiles. The project will establish a data platform and carry out an analytics programme based on data provided by us and ElectraLink. The project will be carried out in three phases: Discovery: A business analysis phase incorporating design thinking activities and setting out the work plan for subsequent phases. Execution: Five one month long “sprints” of data analytics activity covering two workstreams – Advanced LCT Detection and Profile Modelling. Consolidation: Bringing the two workstreams together to produce a final report and model which will enable us to predict load patterns on the LV network. The project will fulfil two key objectives: Validation and enhancement of the model developed in last years LCT Detection NIA project; and Development of a set of domestic half hourly consumption profiles which can be aggregated and used for virtual network monitoring at feeder level, as well as enabling enhanced network planning and demand prediction.
Abstract	The VM Data project will deliver a VM capability across our Low Voltage network. This will reduce need for physical monitoring and improve our knowledge of asset loading against time, thus avoiding the costs associated with physical monitoring and demonstrating RIIO-ED2 cost savings and transition to Distribution System Operator.Current lack of access to half-hourly data about household power flows on our network inhibits the understanding of LV network load flows, and of where electric vehicles and low carbon technologies are connected at LV level. With the acceleration of LCT take up, this could result in clustering on the network which then creates a need to install physical monitoring at substations to monitor the loading of the network. The VM Data project will investigate the feasibility of creating half-hourly load profiles for WPDs customers, including those with EV / LCT that can be fed into a Virtual Monitoring tool for the LV networks.
Publications	(none)
Final Report	(none)
Added to Database	02/11/22