Net Zero decarbonisation of domestic buildings will happen through low carbon heating, smart tariffs and energy efficiency retrofits of building fabric. The representation of the interactivity between these measures is limited in forecasting tools. Future demand, and network reinforcement requirements, may currently be overestimated. It is also unknown if there are opportunities for DNOs to promote energy efficiency as an alternative to reinforcement.
DEFENDER will seek to address these issues through two workstreams. Workstream 1 will create an analysis tool capable of generating pre- and post-retrofit load profiles based on actual domestic data for use in network forecasting. Workstream 2 will carry out an economic analysis of opportunities for electricity networks to promote energy efficiency retrofit within current and future commercial and regulatory arrangements.
Benefits
Project DEFENDER will aim to develop a new tool to forecast the demand profile of various archetypes of domestic building pre- and post-retrofits for energy efficiency. As current demand growth forecasts only account for energy efficiency in a limited way, more accurate forecasting represents a significant opportunity to reduce future reinforcement costs through a less pessimistic view of demand growth. Furthermore, should the economic analysis identify opportunities to derive network benefits by increasing uptake of efficiency retrofits, this may allow customers to reduce their carbon footprint and save on the costs of the net zero retrofit of their homes. UK wide, this is estimated to require £330bn investment.
Learnings
Outcomes
The project has produced:
A tool which can produce profiles of baseload electrical demand and electrified and non-electrified heat demand for individual house archetypes or a set of buildings aggregated together to represent a network area.
Heat demand can be generated using real temperature data or representative day equivalent temperatures.
A categorisation method for assigning UK building stock into a series of base archetypes based on static factors, with a series of sub-archetypes representing stages of energy efficiency retrofit.
A methodology for representing the transition between stages of energy efficiency retrofit for domestic buildings which integrates with representation of low carbon heating uptake within Distribution Future Energy Scenario modelling.
A suite of tools for assessing the value of energy efficiency investment to managing network constraints alongside flexibility procurement and reinforcement.
A potential approach for DNOs proportionally investing in energy efficiency retrofit which could be trialled in a follow-on project.
A set of reports of learning on the impact of energy efficiency on electrified heating demand and the potential value of DNO investment in energy efficiency.
Learning from DEFENDER has allowed NGED to scope out the role of the DNO in the retrofit of energy efficiency measures and strategically approach Standard Licence Condition 31E to promote “the uptake of measures to improve Energy Efficiency, where such services cost-effectively alleviate the need to upgrade or replace electricity capacity” in RIIO-ED2. It has highlighted the large cost gaps between investment and the value of energy efficiency to the running of the distribution network, and that relative highest value is concentrated in a small percentage of network areas. This has driven a strategy to engage with other players in the retrofit industry and work to embed the limited DNO role in a larger stack of funding and initiatives to collectively deliver on the national retrofit challenge. For example, as with how the PIONEER SIF project investigated this in conjunction with local authorities.
Lessons Learnt
The learning from the DEFENDER project has been captured and shared in a variety of ways:
1) An open-source tool with integrated help guides for generating heat demand profiles for network planning and at the household level
2) As-built design documents for the household and network planning levels of the profiling tool modules
3) A methodology document on the approach to creating house archetypes and integration energy efficiency transitions into DFES scenarios
4) A CBA report on the per-household benefit of energy efficiency
5) A network investigation report on analysis of the impact of the newly developed profiles on growth forecasting of three 11kV feeders
6) A technical design document on the EE assessment tools developed to appraise investment in energy efficiency
7) An analysis and insights report on the outcomes of using the EE assessment tools to assess the value of EEE investment to DNOs
8) The final project dissemination webinar and materials
DEFENDER’s stated end use cases are the integration of pre- and post-retrofit heat demand modelling into existing forecasting processes and identifying where and how a DNO such as NGED could invest in energy efficiency. Next steps in each of these use cases are considered below, as well as discussion of other potential use cases for the Glow Simulator tool.
Integration into forecasting processes
The energy efficiency scenario methodology and Glow Simulator tool have been produced in Workstream 1 with an eye each upon the present and the future of Distribution Future Energy Scenario modelling. Currently, NGED does not produce network forecasts more granular than to the Electricity Supply Area (i.e. primary substation HV busbar) level. As such, replication of the kind of investigation carried out in work package 1.4 will first necessitate the development of an HV forecasting process. NGED has set developing this capability as an objective in ED2. In the meantime, the toolset can be used to generate scenarios at ESA level.
BAU implementation would also require developing the underpinning building stock models to allow for generation of base scenarios for energy efficiency modelling. There are various ways to achieve this:
Develop the proposed GIS automation process. This would use GIS shapefiles and EPC lookup tables to assign archetypes to a network area to generate the current base scenario. A high-level description of how this process would work can be found in the D1.4-2 Scenario Methodology report.
Generate base scenarios by manually assigning building stock to feeders using GIS maps and the EPC database. This would be a time-consuming process in the initial development, but could be made quicker in the uptake by developing a lookup process to only update buildings that have changed in the EPC database in between DFES model runs.
Develop a rough estimation model using only commonly occurring sub-archetypes. This would be a quick and easy way to develop a ballpark assessment of energy efficiency related demand impact.
As an interim measure, the outputs of the tool could be integrated into the next DFES round using a single diversified model. This would be created by generating a scenario based on the relative proportions of archetypes across a licence area, then dividing each half hourly value by the number of archetypes used to generate it. This single profile would directly replace any usage of the Elexon domestic customer model in demand estimation.
DEFENDER has already determined a methodology for tying energy efficiency scenarios into the adoption of electrified heating technologies in DFES modelling. However, the low, medium and high retrofit scenarios were developed and tested for the Customer Transformation scenario. Further work is required to determine whether low, medium or high scenarios (or an additional scenario determined to be more applicable) should be used in other DFES scenarios.
Investing in energy efficiency
The findings of Workstream 2 on the value of energy efficiency to DNOs require further exploration before any scheme can be considered for BAU. As such, NGED has launched the PIONEER (Proportional Investment of Networks in Energy Efficiency Retrofits) Strategic Innovation Fund project. PIONEER will tackle the remaining gaps in the role of the DNO in funding EE measures, including:
Validation of the modelled outcomes of EE measures through pre and post metering of installed EE measures in different house types.
Understanding of the homeowner/landlord customer journeys of heat pump procurement and key touch points in influencing decision making on EE.
Understanding of the funding landscape for EE measures and commercial models for EE retrofit providers.
The extent to which EE will be deployed in the absence of DNO intervention.
Setting this in the context of the learnings from DEFENDER that the value to the DNO of energy efficiency is relatively highest in areas with high costs per megawatt for increasing network capacity (largely rural spaces), and that this value runs only the hundred of pounds per household.
Additional use cases for the Glow Simulator
The DEFENDER project has generated significant interest outside of its stated remit focusing on demand forecasting and constraint management. Below is a list of additional use cases which could be explored, either by networks through BAU or innovation funded activity or by non-network organisations through their own activities:
Integrating the sub-archetype profiles into customer facing connections tool (e.g. NGED’s Connect Lite tool). This would allow customers to be given information on their projected demand characteristics when installing low carbon heating measures to inform them of the potential benefits to them of energy efficiency retrofit.
Integrating the outputs of the Glow Simulator into modelling tools used by HV and LV planners, to give planners more granular information on the loads on individual feeders or even phases.
Developing data-driven advice tools for customers seeking information on managing their electricity demand and decarbonise their own homes.
Developing a heat pump sizing tool, which uses the calculated heat demand model to allow installers, local authorities, housing associations or other to more accurately choose the most efficient heat pump size to meet demand for the housing stock under consideration.
A potential opportunity has arisen which would exploit the cross-modality of the heat demand model to simulate network demand for modelling novel heating approaches, as well as training models for trading optimisation. This is in partnership with a gas distribution network and is currently in the middle of funding application. More may be announced within the coming months.
Additional use cases for the Glow Simulator
The DEFENDER project has generated significant interest outside of its stated remit focusing on demand forecasting and constraint management. Below is a list of additional use cases which could be explored, either by networks through BAU or innovation funded activity or by non-network organisations through their own activities:
Integrating the sub-archetype profiles into customer facing connections tool (e.g. NGED’s Connect Lite tool). This would allow customers to be given information on their projected demand characteristics when installing low carbon heating measures to inform them of the potential benefits to them of energy efficiency retrofit.
Integrating the outputs of the Glow Simulator into modelling tools used by HV and LV planners, to give planners more granular information on the loads on individual feeders or even phases.
Developing data-driven advice tools for customers seeking information on managing their electricity demand and decarbonise their own homes.
Developing a heat pump sizing tool, which uses the calculated heat demand model to allow installers, local authorities, housing associations or other to more accurately choose the most efficient heat pump size to meet demand for the housing stock under consideration.
A potential opportunity has arisen which would exploit the cross-modality of the heat demand model to simulate network demand for modelling novel heating approaches, as well as training models for trading optimisation. This is in partnership with a gas distribution network and is currently in the middle of funding application. More may be announced within the coming months.
