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Electricity Distribution
Project Details
Documents
Ideas for future projects?
Oct 2019
Electricity Distribution
Virtual Monitoring Data (VM-Data)
Reference:
NIA_WPD_045
Status:
Live
Start Date:
Oct 2019
End Date:
Oct 2020
Funding Licencee(s):
Western Power Distribution
Contact:
Ricky Duke
Click here to send a question to the contact.
Funding Mechanism
Network Innovation Allowance
Research Area:
ED - Transition to low carbon future
Core Technology(ies):
Comms & IT and Measurement
Estimated Expenditure:
£2,748,755.00
Introduction:
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 WPD’s customers, including those with EV / LCT that can be fed into a Virtual Monitoring tool for the LV networks.
Objectives:
The project will fulfil two key objectives:
Validation
and enhancement of the model developed in last year’s 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.
Expected Benefits:
Providing virtual LV network monitoring and improving the accuracy of our records for LCT installations will result in greater confidence around the actual capacity of the network. This will have a knock-on impact on customers seeking increased capacity or new connections. A more accurate assessment of existing capacity will allow for a more accurate assessment of potential reinforcement activities ensuring fairness between the customers benefitting from the capacity and the general customer base. An understanding of the connected PV capacity is useful to help control engineers determine the degree to which the network loads are offset by embedded generation.
Furthermore, better understanding of LV network loading and capacity will lead to improved network reliability for existing customers and faster customer connection times for new customers.