Demand Forecasting Encapsulating Domestic Efficiency Retrofits (DEFENDER)

Objective(s)

Develop an understanding of the electricity demand profile of UK domestic building stock pre- and post-retrofits to building fabric. Produce a methodology for integrating pre- and post-retrofit domestic demand profiles into network forecasting. Assess the potential savings on network reinforcement and flexibility from accounting for energy efficiency in demand forecasting. Perform an economic assessment of the potential benefits to networks from increased penetration of domestic retrofit interventions.

Problem(s)

Net Zero decarbonisation of domestic building stock is expected to be achieved through a combination of: low carbon heating (primarily through electrified heat pumps); smart tariffs and demand management incentive schemes; and energy efficiency retrofits of building fabric. Understanding the net effect of these measures in combination is crucial for accurate electricity network forecasting, but the representation of this interactivity in forecasting best practice is minimal. In particular, the influence of building fabric on heat pump performance, as modulated by energy efficiency retrofits, is captured in limited fashion. As such, future domestic electricity demand, and therefore network reinforcement requirements, may currently be overestimated. Furthermore, it is currently unknown where there is a business case for DNOs promote adoption of energy efficiency measures as a cost-effective option to mediate or obviate reinforcement or flexibility costs.

Method(s)

The DEFENDER project will develop, through two workstreams, the capability of electricity networks to accurately assess the impact of energy efficiency retrofits on current and future demand, and understanding of the business case for retrofit investment as an alternative to reinforcement.

Workstream 0 – Specification This workstream will carry out workshops with all project partners and National Grid engineers to produce the specification for the Workstream 1 demand profiling tool and the Workstream 2 investment appraisal tool.

Workstream 1 – Development of Pre- and Post-Retrofit Profiling Tool This workstream will create an analysis tool capable of generating domestic ADMDs and load profiles based on actual heat data for alarge number of property types for use in network forecasting and planning. This will be achieved through integrating a number of inputs:

• A smart meter database of 15,000 homes’ gas and electricity consumption data owned by Hildebrand, a project partner.
• Heat Transfer Coefficients (HTC) from the SMETERS project, which used BEIS validated algorithms to calculate building thermal performance from smart meter data.
• A Building Decarbonisation Options Appraisal Tool developed by Carbon Trust, a project partner, which can determine the impact of retrofit measures on the building’s heat loss factor (U value).
• The Energy Performance of Buildings Register, which contains EPCs for homes in England and Wales, which include heating and fabric data amongst other useful information.
• Local weather data.

The workstream will consist of the following work packages:

WP1.1. Tool development part one: Pre- and post-retrofit demand ADMD and half-hourly profiles.

This work package will develop the capability for simulating historical and future power demands, taking into account different energy efficiency measures. A model will be trained on the smart meter dataset to be capable of deriving before and after building fabric retrofit heat demand and quantify this as historical and future ADMD and load profiles, including transference of gas demand to electricity demand.

WP1.2. Tool development part two: Network planning outputs

This work package will develop the capability for using machine learning techniques on the profiles developed in WP1 to cluster houses into housing archetypes and calculate mean annual load profiles and ADMDs for each archetype with and without energy efficiency measures.

WP1.3. Testing of profiling tool

This work package will develop, agree and implement a system integration and user acceptance (UAT) process for the profiling tool between project partners. Following successful approval of the tool after testing, it will develop the necessary handover materials.

WP1.4. Applying the profiling tool to DFES forecasting

This work package will define a methodology will be defined for applying the outputs of the profiling tool to Distribution Future Energy Scenario (DFES) modelling. The utility of the new profiles will then be analysed with a network case study. The outcomes of these studies will be compared and contrasted with National Grid’s existing forecasts using its current profiles and alternative approaches as identified by a literature review.

WP1.5. Cost-benefit analysis of energy efficiency improvement

This work package will carry out a cost-benefit analysis of the value to network operators of investment in the energy efficiency of homes. This will be done in two ways: firstly by conducting a CBA of investment in energy efficiency per house archetype and the case study area in WP1.4, and secondly by transforming the outputs of the profiling tool into inputs to the retrofit investment tool developed in Workstream 2.

Workstream 2 – Appraising Investment in Energy Efficiency

This workstream will carry out an economic analysis of the business case and opportunities for National Grid to promote energy efficiency retrofit. It will support this analysis with the creation of a tool for constraint management optioneering capable of assessing the value of energy efficiency retrofit, while accounting for the uncertainty in investment outcomes. This tool will be developed for a limited area of National Grid Electricity Distribution’s network, with a view to ensuring its building blocks are reusable as part of potential further economic analysis around energy efficiency.

WP2.1. Development of investment appraisal tool

This work package will develop the economic assessment methodology and investment appraisal tool in R or Python in agile fashion, based on the specification document, with regular feedback to National Grid and updates to the design document.

WP2.2. Analysis and insights

This work package will carry out an economic analysis using the investment appraisal tool to consider the conditions where the value from energy efficiency retrofits is most certain for electricity networks. It will evaluate the outputs of the investment appraisal tool and assess what the outcomes mean at various scales (e.g. strategic business plan level vs day-to-day operational) and will analyse the remaining knowledge gaps. It will capture the existing regulatory and commercial context for electricity networks on energy efficiency, assessing the possibilities within existing strictures and the opportunities that may develop in the future.

• Documents and Links

  NIA Project Registration and PEA Document 

  Annual Progress Report 2022 

  Modelling Demand

  As Built Guide - House Level Functions 

  As Built Guide - Network Planning Functions 

  Forecast Scenario Methodology Report

  Case Study Report

  Cost-Benefit Analysis

  Technical Design Document 

  Analysis and Insights Report

  Dissemination webinar Slides

  Closedown Report 

• Scope

  Absent network monitoring on the HV and LV networks to provide measured load data, network loading is typically forecast using a profile class-based allocation method. Load growth due to LCT connections such as heat pumps is calculated from technology baselines and added to these profile classes. Traditional network connection costs are met by both the user and the DNO and its customers. As such, when connection assessments identify reinforcement needs, the costs to both increases. It is likely that current methods are overestimating these requirements by overstating the existing and future demand on these networks by not incorporating demand shifts due to energy efficiency retrofits.

  Incorporating real pre- and post-retrofit energy demand data, into network modelling will provide a more realistic picture of the current and future demand of domestic buildings. It is expected that these profiles will lower the overall forecast demand, both in maximum demand and in representative daily profiles. This would allow for significant cost saving in new connection and general reinforcement expenditure by reducing the need for both. In the long term, these savings can be passed along to customers in DUOS charge reductions. Savings may also be passed along to connection applicants and customers by reducing or eliminating reinforcement charges.

  Currently, within the RIIO-ED2 Business Plan it is projected in National Grid Electricity Distribution’s Best View that there will be an approximately increase in peak demand of more than 2GW, resulting in a primary and secondary reinforcement spend in ED2 of £635m, at a cost of approximately £318k per MW. Around 600k heat pumps are expected to be installed within the same period, increasing electrical demand from heating by approximately 1.8GW under current modelling estimates. A 6% reduction in this demand due to energy efficiency retrofit, as per a 2020 Committee on Climate Change study on residential heat decarbonisation trajectories, could result in an estimated £38m in reinforcement savings in ED2 alone.

• Success Criteria
  • A profiling tool will be delivered which is capable of generating archetype demand profiles for domestic buildings pre- and post-retrofit, including transference from gas to electric heating.
  • An investment appraisal tool will be delivered which is capable of supporting analysis of the business case for National Grid investing or promoting energy efficiency as constraint management option.
  • The economic assessment will identify what, if any, are the most certain potential benefits to networks from energy efficiency.
  • The economic assessment will identify what, if any, are the opportunities to pursue these benefits within the existing regulatory and commercial landscape.
  • The profiling tool will be reusable and can be re-run with updated data.
  • The outputs of these tools can be integrated into distribution network forecasting and planning.
  • The methodology for the tools will be replicable across all distribution networks.
• Potential for New Learning

  Parties are expected to learn from this project the impact of energy efficiency retrofit on current and future domestic demand, how the findings may be implemented in network modelling and the overall effect on forecasting planning this would have. Furthermore, the project will develop learning of the network benefits to customers from increased uptake of energy efficiency and how networks may best access the benefits.

  Learning from this project will be published within the DEFENDER final reports. These reports will contain a summary of the learning and descriptions of the approach used to create the tools developed by the project. The learning will also be presented at National Grid Innovation Showcase events. This knowledge and information will allow DNOs to develop a similar approach to domestic building modelling and more accurately identify their own reinforcement needs due to changes in energy demand.

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