Oct 2016
Electricity Distribution
Improving Demand Forecasting
Oct 2016
Oct 2017
Northern Powergrid
Melissa MacLennan, tel. 01977 605927
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Network Innovation Allowance
The scope of this project includes enhancements to the desktop load forecasting model that Element Energy previously developed for Northern Powergrid, along with a suite of new desktop modelling tools, to provide more accurate technology uptake and load forecasting for GSP, primary and secondary substations across the Northern Powergrid licence areas.

While the scope of this project will include the provision of consistent low carbon technology uptake forecasts to Northern Powergrid’s broader business planning and operations systems, changes to the fundamental approach and functionality of these other systems are out of scope.

The objectives of this project are:

  1. To develop a LCT uptake forecasting tool that is easily updated (using automation algorithms where possible) with the latest uptake drivers (e.g. technology costs, policy incentives, consumer perceptions, hassle factors and other social and economic drivers). 
  2. To integrate the latest innovation learnings from various LCNF and other UK technology and customer monitoring trials into the LCT uptake forecasting tool as well as the Element Energy load forecasting model.
  3. To develop a tool for mapping DSR potential to, tested against each substation in the Northern Powergrid, network based on the unique mix of domestic, commercial and industrial customers connected to each substation.
  4. To create a high resolution, short-time step early warning system for EV deployment triggers.
  5. To increase the resolution of the Element Energy load forecasting model to secondary substation level.
  6. To share the learning developed through this project with other DNOs to allow integration of the new approaches into their own models.
The project will be considered successful if the aforementioned objectives are realised.

In addition to meeting the objectives listed above, the tools and models developed in this project will be assessed against the following success criteria:

  1. They are able to efficiently transfer required outputs and datasets between each other and the broader business planning systems to which they are providing forecast data.

  2. The new outputs produced are able to contribute significantly to new planning insights around forecast loads and reinforcement deferral options.

Where the tools are intended to be regularly updated, that this can be accomplished in a time-efficient and robust manner.