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Projects: Projects for Investigator
Reference Number	NIA_UKPN0070
Title	Envision
Status	Completed
Energy Categories	Other Cross-Cutting Technologies or Research(Energy system analysis) 80%; Other Power and Storage Technologies(Electricity transmission and distribution) 20%;
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 (Other Systems Analysis) 100%
Principal Investigator	Project Contact No email address given UK Power Networks
Award Type	Network Innovation Allowance
Funding Source	Ofgem
Start Date	01 December 2020
End Date	01 September 2022
Duration	ENA months
Total Grant Value	£1,971,000
Industrial Sectors	Power
Region	London
Programme	Network Innovation Allowance
Investigators	Principal Investigator	Project Contact , UK Power Networks (100.000%)
	Industrial Collaborator	Project Contact , UK Power Networks (0.000%)
Web Site	https://smarter.energynetworks.org/projects/NIA_UKPN0070
Objectives	It is proposed to bring in information from third parties and process data, which we have, to generate information about our network. A number of different elements need to be developed to be able to have software based visibility of the network. Work Package 1: Software based network visibility Trial Magic MDI (Maximum Demand Indicator) at scale in partnership with CKI Delta. Magic MDI is a machine learning solution to model maximum demand on secondary substations demonstrated in an earlier business funded proof of concept (PoC). This proved the model on ground mounted secondary substations and provided maximum monthly demand. The Magic MDI PoC was based on manual handling of data inputs and a very limited number of substations. As part of this work package, we will extend the trials to a larger part of our network and automate all data handling with the outcome of demonstrating a data-first approach in achieving blanket network visibility. Additionally we will define the requirements and best practice for BAU integration long term.We will extend the capabilities of the model to include pole-mounted transformers and model the full daily profiles of secondary substations. It is our ambition to increase granularity of the model down to LV feeder ways and include other data such as smart meters.Work Package 2: Third party visibility of connected Distributed Energy Resources (DER) We will demonstrate it is commercially and technically possible to procure useful external data sets and that processes can be developed to internally manage this data to drive value. We will focus on data products and their value to internal users, and plan to start with EV charge data products.These useful deliverables include: visibility of the process and documentation of the contract between us, the data aggregator and the technology vendors ( data suppliers); data value; data processing and cleansing; stakeholder engagement processes; and a commercial business case for the ongoing existence of a DER Gateway. We will progress and build a gateway portal that collects data from a limited number of suppliers and presents actionable information to stakeholders. This portal will provide the technical proof and knowledge needed to develop a full solution in the future.Work Package 3: Value of data under re-deployment device delivery strategies or on one feederAssess the value of data from redeployed devices. UK Power Networks deploy Weezaps, Alvins and Bidoyngs and re-deploy each device to multiple locations. We will demonstrate how data from these devices can be used to increase network visibility. The planned approach will be to review sample data from devices and assess the strengths and weaknesses of the data.The overall target of the project is to identify the cheapest unit cost for visibility.  Deliverables per work package.Work Package 1: Software based network visibility In this work package a model to estimate secondary substation demand will be developed based on machine learnig techniques and unique databases. This model will be trialled in the UK Power Networks network and the accuracy of estimation will be defined. As part of the model development and accuracy improvement activities, a methodology for the prioritisation of hardware monitoring devices will be produced. Finally a user interface will be developed for easier use of the estimated values.Work Package 2: Third party visibility of connected DER In this work package we shall identify the data offerings in the market and the value for a DNO or DSO. Then we will undertake a technical trial of developing a portal that collects these data from data suppliers and presents the results in a digestable way to the network operator. Lastly we will identify the possible commercial possibilities for such a portal to exist.Work Package 3: Value of data under re-deployment device delivery strategies or on one feederIn this work package we will identify the availability and quality of data from existing redeployable devices. We will define the best way to take advantage of them for visibility purposes. The objectives of the project are to have:1) a single system with LV network data on customer energy consumption, energy generation, and DER information (volumes and types)2) a model for the LV network consumption per secondary substation3) a user interface to visualise and use the above data
Abstract	The objectives of the project are to have:1) a single system with LV network data on customer energy consumption, energy generation, and DER information (volumes and types)2) a model for the LV network consumption per secondary substation3) a user interface to visualise and use the above data
Publications	(none)
Final Report	(none)
Added to Database	02/11/22