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Reference Number NIA_NPG_012
Title Improving Demand Forecasting
Status Completed
Energy Categories OTHER POWER and STORAGE TECHNOLOGIES(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
Northern Powergrid
Award Type Network Innovation Allowance
Funding Source ENA Smarter Networks
Start Date 01 October 2016
End Date 01 October 2017
Duration 12 months
Total Grant Value £120,000
Industrial Sectors Power
Region Yorkshire & Humberside
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , Northern Powergrid (100.000%)
Web Site http://www.smarternetworks.org/project/NIA_NPG_012
Objectives The objectives of this project are: 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). 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. 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. To create a high resolution, short-time step early warning system for EV deployment triggers. To increase the resolution of the Element Energy load forecasting model to secondary substation level. 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: 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. 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.
Abstract At present, a range of different planning tools and datasets are used to generate future low carbon technology (LCT) load scenarios for various distribution networks planning and operations functions including: connections forecasting, system planning, design planning and units forecasting. With ongoing changes to the policy landscape, economy, technology costs and customer behaviour, these planning tools require regular updates to ensure they remain accurate. Similarly, there are learnings and large datasets from various network innovation projects that need to be integrated into these tools in a coherent and appropriately aligned manner across the various planning systems. For example, there are now many large datasets available on the consumption behaviour of various customer types (e. g. from numerous smart meter and customer monitoring trial datasets) as well as the performance and implications for distribution networks of various low carbon technologies (e. g. electric vehicles, heat pumps, distributed generation, etc.) and customer interventions (e. g. time-of-use tariffs, direct control and other demand-side response arrangements). With the increasing complexity and amount of data required to capture these changes and learnings, an innovative new approach to the generation of LCT uptake scenarios is required. The new approach must be able to ensure that the latest data and innovation learnings are easily, coherently and consistently populated across the various DNO planning and operations systems in which they are required. A related problem is that there is currently no way to estimate the demand side response (DSR) potential available from the specific mix of customers at each distribution network asset to obtain an overview of the DSR capacity (from both domestic and commercial customers) under a variety of scenarios. As such, it is not possible to assess the costs and benefits of various DSR intervention options in relation to reinforcement deferral outcomes at individual network assets. To be able to make informed decisions on DSR priorities, and to effectively utilise the findings of various DSR trials to date, it is necessary to develop a tool that is able to map domestic and commercial DSR potential at a substation level for a variety of scenarios and to relate this to the capacity and forecast loading levels at each of these substations. Such a tool would also need to be able to take account of DSR opportunities created by current and forecast levels of LCT uptake in line with the LCT load scenario system described above. Finally, there is an increasing need for LCT uptake and load forecasting that is resolved to the individual secondary substation level and that is able to take into account the consumption and technology adoption behaviours that are unique to the mix of specific customers connected to each secondary substation. It is, therefore, necessary to expand Northern Powergrid’s load forecasting and LCT uptake systems to be able to resolve future loads and technology impacts at this higher level of asset resolution for a variety of future scenarios and to relate this to the relevant substation capacity and DSR opportunities available. This project is designed to address the prediction and modelling challenges described through the development of an improved computational modelling and forecasting process across the following five work packages: Work Package 1: New scenarios for improved planning consistency and accuracy Work Package 2: Integrating LCNF and other UK trial learnings Work Package 3: Demand Side Response potential at substation level Work Package 4: Additional analysis of electric vehicle deployment and charging requirements Work Package 5: Secondary substation peak load growth dataNote : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 31/08/18