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Impacts of heat decarbonisation on system adequacy considering increased meteorological sensitivity

Deakin, M., Bloomfield, H. ORCID: https://orcid.org/0000-0002-5616-1503, Greenwood, D., Sheehy, S., Walker, S. and Taylor, P. C. (2021) Impacts of heat decarbonisation on system adequacy considering increased meteorological sensitivity. Applied Energy, 298. 117261. ISSN 0306-2619 (In Press)

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Abstract/Summary

This paper explores the impacts of decarbonisation of heat on demand and subsequently on the generation capacity required to secure against system adequacy standards. Gas demand is explored as a proxy variable for modelling the electrification of heating demand in existing housing stock, with a focus on impacts on timescales of capacity markets (up to four years ahead). The work considers the systemic changes that electrification of heating could introduce, including biases that could be introduced if legacy modelling approaches continue to prevail. Covariates from gas and electrical regression models are combined to form a novel, time-collapsed system model, with demand-weather sensitivities determined using lasso-regularized linear regression. It is shown, using a GB case study with one million domestic heat pump installations per year, that the sensitivity of electrical system demand to temperature (and subsequently sensitivities to cold/warm winter seasons) could increase by 50% following four years of heat demand electrification. A central estimate of 1.75 kW additional peak demand per heat pump is estimated, with variability across three published heat demand profiles leading to a range of more than 14 GW in the most extreme cases. It is shown that the legacy approach of scaling historic demand, as compared to the explicit modelling of heat, could lead to over-procurement of 0.79 GW due to bias in estimates of additional capacity to secure. Failure to address this issue could lead to £100m overspend on capacity over ten years.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:98622
Publisher:Elsevier

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