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Peak Heat

Funding mechanismNetwork Innovation Allowance (NIA)
DurationFeb 2021 - May 2022
Estimated expenditure£270K
Research areaTransition to low carbon future
  • August 2021

    The calibrations between the building physics models and the PLEXOS models for the individual house heat demand were completed for all of the property archet…


  • Look at the latest heat pump loads based on current strategies around heat pump operation (it should be noted that there has been significant development in controls and optimisation strategies for heat pumps in the last few years).
  • Investigate the impact of heat pumps based on specific typology areas, considering the effects of clustering on our network.
  • Investigate the trade-off between smart shifting of loads and cost to upgrade the network.
  • Access the impact of a peak winter (1 in 20) on the network due to both direct (e.g. poorer heat pump performance in cold conditions) and indirect (e.g. customer behaviour during these events) effects.
  • Examine the potential market and role for domestic thermal storage.


Domestic heat electrification could have a major impact on Low Voltage (LV) and Medium Voltage distribution network peak loads. Further knowledge is required to understand the resultant load profiles of these new electricity loads and technology shifts (e.g. from Economy Seven storage to Heat Pumps), the impact they may have on networks, and the opportunities they present for flexibility. Furthermore, these loads could be either further compounded or dampened by domestic thermal storage. Off-gas grid areas and new builds are of particular interest since they are likely to experience transition to electric heating early on.


Following the project kick off the project will be comprised of the following work packages (WP):

  1. WP1: Archetype creation - defining the relevant archetypes of interest to establish the physical demand characteristics taking into account housing physical characteristics and customer factors (e.g. occupancy patterns).
  2. WP2: Heat market landscaping – characterising the range of technologies (e.g. maturity, cost, size etc.) potentially available and mechanisms which could be deployed to help deliver low carbon electric heating, including domestic thermal storage. Technologies covered will include ground source heat pumps, air source heat pumps, hot water tanks and phase change material heat stores.
  3. WP3: Customer modelling - exploring the range of impacts on load profiles from heating technologies, storage, and flexibility at a single customer level. Scenarios will include modelling the average winter load and the impact of ‘1 in 20’ peak winter condition for each customer archetype. This WP will use building physics modelling to calculate the heat demand of the different archetypes (graph of input power demand on a half hourly basis).
  4. WP4: Area typology modelling - representative mixes of house archetypes will be modelled for a sample of four representative distribution (LV) network community typologies at the primary substation level. Analysis of all the LV feeders associated with all of the distribution substations connected to a specific primary will be used to create a number of feeder archetypes (e.g. 10 feeder archetypes) for the purposes of modelling the system. We will provide the necessary distribution grid data (including number of customers connected to each LV feeder). This will help assess the impact that heat electrification will have on typical local distribution networks (average winter day, average winter peak and in a ‘1 in 20’ peak winter scenario). This WP will aggregate the demand profiles at the household level to the LV feeder level. This will be based on diversity assumptions as well as the nature of the distribution network (i.e. number of customers per feeder).
  5. WP5: CBA, Analysis and recommendations - drawing together all the findings from the research. This will include conducting a high-level cost benefit analysis (CBA) to identify the potential lowest cost options. This will principally entail comparing the long run marginal cost of upgrading the LV network versus the cost of installing different heat flexibility and thermal storage technologies as a way to reduce peak demand (and therefore the required cost to upgrade the LV network). This will be combined with the outputs of the modelling and market study to form a comprehensive set of evidence which we can use to inform its approach to heat electrification, especially as it relates to the implementation of thermal storage.