Economics at your fingertips  

A high-resolution spatio-temporal energy demand simulation to explore the potential of heating demand side management with large-scale heat pump diffusion

Sven Eggimann, Jim W. Hall and Nick Eyre

Applied Energy, 2019, vol. 236, issue C, 997-1010

Abstract: Localisation of energy technologies and policies is increasing the need for high-resolution spatial and temporal energy demand simulation modelling, which goes beyond annual and national scale. Increasing the temporal resolution is crucial for demand side management modelling or for the simulation of load profile changes due to the installation of new technologies such as heat pumps. Increasing the spatial resolution enables regional energy planning and capturing the spatial dynamics of drivers of energy demand. Yet regional and local energy grids are interconnected with national and continental networks, so to capture multi-scale effects, high resolution is required everywhere. A high-resolution bottom-up engineering energy demand simulation model is introduced, which projects energy demands both for a high spatial and high temporal scale and enables spatial explicit simulation of model parameters. The model is applied for exploring implications of the electrification of heat by a large-scale uptake of heat pumps for water and space heating in the United Kingdom and to simulate heat pump related demand side management opportunities. We simulate a change in peak electricity heating load of −0.4 t– 21.5 GW for 50% heat pump uptake for space heating demands across different scenarios resulting in an increase of total peak electricity demand of 3.3–31.2 GW (6.3–59.8%). The simulation results show considerable regional differences in change of electricity load factors (−17.2–8.4%) and peak electricity demands (−9.9–206.1%). The potential to reduce national electricity peak load with managed heat pump load profiles for heating is simulated to be 0.2–5.8 GW (0.4–11.1%). These results exemplify the importance of discussing heat-pump induced change in peak electricity demands within a scenario context. Including different drivers in energy demands and their variability considerably affects the scale of anticipated electricity peak demand.

Keywords: Energy transition; Technology diffusion; Load profile; Peak load; Spatial mapping; Infrastructure system (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-03-16
Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:997-1010