Projects per year
The ability of UK housing with heat-pump-based heating systems to respond to requests for immediate changes to load was assessed using a bottom-up stock modelling approach. Detailed building simulation models of the most common types of UK housing were developed and their ability to respond to signals to drop or pick up load tested under two different operating strategies: on-demand heating and off-peak heating with supporting thermal storage. Both the thermal storage and heat pump capacity were sized prior to undertaking the responsive load simulations. The performance of each building was simulated over a calendar year, with the response to load variation signals constrained by thermal comfort requirements and hot water needs, which took priority. Without thermal storage and following a typical on-demand heating pattern, approximately 20% of heating systems could respond to a drop load or pick up load signal. Switching to an off-peak heating pattern with sized thermal storage resulted, firstly, in the entire operation of the heat pump could be shifted to off peak periods. Secondly, the overall ability to respond to a drop load request was almost unchanged, but typically over 80% of systems could respond to a pick up load signal. The aggregate response figures mask significant seasonal and intraday variations in response, with the ability to respond being limited during periods of low heating and hot water demand. The addition of thermal storage reduced this variability.
|Number of pages||10|
|Publication status||Published - 30 Nov 2018|
|Event||uSIM - Urban Energy Simulation: Scaling-up building simulation for urban and community energy analysis - University of Strathclyde, Glasgow, United Kingdom|
Duration: 30 Nov 2018 → 30 Nov 2018
Conference number: 1
|Conference||uSIM - Urban Energy Simulation|
|Period||30/11/18 → 30/11/18|
- demand flexibility
- heat pumps
1/04/16 → 31/03/19
Script for calculating thermal energy storage sizing for energy flexibility over varying timescales.
OccDem - a program to generate statistically-based occupancy and occupant-driven electrical demand profiles.