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Data-based, high spatiotemporal resolution heat pump demand for power system planning
Halloran, C.; Lizana, J.; Fele, F.; McCulloch, M. (2024). Data-based, high spatiotemporal resolution heat pump demand for power system planning. Appl. Energy 355: 122331. https://dx.doi.org/10.1016/j.apenergy.2023.122331
In: Applied Energy. Applied Science Publishers: London. ISSN 0306-2619; e-ISSN 1872-9118
Peer reviewed article  
Beschikbaar in  Auteurs 
Author keywords
    Heat electrification; Capacity expansion planning; Battery storage
Auteurs  Top 
  • Halloran, C.
  • Lizana, J.
  • Fele, F.
  • McCulloch, M.
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.
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