dc.creator | Halloran, Claire E. | es |
dc.creator | Lizana, Jesús | es |
dc.creator | Fele, Filiberto | es |
dc.creator | McCulloch, Malcolm | es |
dc.date.accessioned | 2023-11-27T11:16:59Z | |
dc.date.available | 2023-11-27T11:16:59Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | Halloran, C.E., Lizana, J., Fele, F. y McCulloch, M. (2023). Data-based, high spatiotemporal resolution heat pump demand for power system planning. Applied Energy, 355 (122331). https://doi.org/10.1016/j.apenergy.2023.122331. | |
dc.identifier.issn | 0306-2619 | es |
dc.identifier.issn | 1872-9118 | es |
dc.identifier.uri | https://hdl.handle.net/11441/151646 | |
dc.description.abstract | Decarbonizing the residential building sector by replacing gas boilers with electric heat pumps will dramatically
increase electricity demand. Existing models of future heat pump demand either use daily heating demand
profiles that do not capture heat pump use or do not represent sub-national heating demand variation. This
work presents a novel method to generate high spatiotemporal resolution residential heat pump demand
profiles based on heat pump field trial data. These spatially varied demand profiles are integrated into
a generation, storage, and transmission expansion planning model to assess the impact of spatiotemporal
variations in heat pump demand. This method is demonstrated and validated using the British power system in
the United Kingdom (UK), and the results are compared with those obtained using spatially uniform demand
profiles. The results show that while spatially uniform heating demand can be used to estimate peak and total
annual heating demand and grid-wide systems cost, high spatiotemporal resolution heating demand data is
crucial for spatial power system planning. Using spatially uniform heating demand profiles leads to 15.1 GW
of misplaced generation and storage capacity for a 90% carbon emission reduction from 2019. For a 99%
reduction in carbon emissions, the misallocated capacity increases to 16.9-23.9 GW. Meeting spatially varied
heating load with the system planned for uniform national heating demand leads to 5% higher operational
costs for a 90% carbon emission reduction. These results suggest that high spatiotemporal resolution heating
demand data is especially important for planning bulk power systems with high shares of renewable generation. | es |
dc.format | application/pdf | es |
dc.format.extent | 14 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Applied Energy, 355 (122331). | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Heat electrification | es |
dc.subject | Capacity expansion planning | es |
dc.subject | Battery storage | es |
dc.title | Data-based, high spatiotemporal resolution heat pump demand for power system planning | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | RYC2021-033960-I | es |
dc.relation.projectID | VII-PPITUS 2023/00000487 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0306261923016951 | es |
dc.identifier.doi | 10.1016/j.apenergy.2023.122331 | es |
dc.journaltitle | Applied Energy | es |
dc.publication.volumen | 355 | es |
dc.publication.issue | 122331 | es |
dc.contributor.funder | MCIN/AEI/ 10.13039/501100011033, Spain and European Union NextGenerationEU/PRTR RYC2021-033960-I | es |
dc.contributor.funder | University of Seville’s ‘’VII Plan Propio de Investigación y Transferencia”, Spain grant 2023/00000487 | es |