Abstract
The decarbonisation of heat in developed economies represents a significant challenge, with increased penetration of electrical heating technologies potentially leading to unprecedented increases in peak electricity demand. This work considers a method to evaluate the impact of rapid electrification of heat by utilising historic gas demand data. The work is intended to provide a data-driven complement to popular generative heat demand models, with a particular aim of informing regulators and actors in capacity markets as to how policy changes could impact on medium-Term system adequacy metrics (up to five years ahead). Results from a GB case study show that the representation of heat demand using scaled gas demand profiles increases the rate at which 1-in-20 system peaks grow by 60%, when compared to the use of scaled electricity demand profiles. Low end-use system efficiency, in terms of aggregate coefficient of performance and demand side response capabilities, are shown to potentially lead to a doubling of electrical demand-Temperature sensitivity following five years of heat demand growth.
Original language | English |
---|---|
Title of host publication | 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISBN (Electronic) | 9781728128221 |
DOIs | |
Publication status | Published - Aug 2020 |
Event | 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Liege, Belgium Duration: 18 Aug 2020 → 21 Aug 2020 |
Publication series
Name | 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings |
---|
Conference
Conference | 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 |
---|---|
Country/Territory | Belgium |
City | Liege |
Period | 18/08/20 → 21/08/20 |
Bibliographical note
Funding Information:This work was funded by the Engineering and Physical Sciences Research Council through grant no. EP/S00078X/1 (Supergen Energy Networks hub 2018). S. Sheehy is funded by an EPSRC studentship.
Publisher Copyright:
© 2020 IEEE.
Keywords
- demand modelling
- multi-vector systems
- power system reliability
- System adequacy