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Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks

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  • Navarro-Espinosa, Alejandro
  • Mancarella, Pierluigi

Abstract

Electrification of heating by making use of the Electric Heat Pump (EHP) technology powered by increasing shares of electricity renewable sources is seen as a potential key approach to decarbonise the energy sector in many countries, and especially in the UK. However, the widespread use of EHPs in substitution of fuel boilers might cause significant issues in terms of electrical distribution network impact, particularly at the low voltage (LV) level. This has not been addressed properly in the studies carried out so far also due to lack of available data and suitable models. In this light, this paper introduces a novel and comprehensive probabilistic methodology based on Monte Carlo simulations and a relevant tool to assess the impact of EHPs on LV distribution networks. Real electricity and heat profiles are taken as a starting point of the studies. Both Air Source Heat Pump (ASHP) and Ground Source Heat Pump (GSHP) types are modeled as black boxes with performance and heat capacity characteristics changing with operating conditions according to manufacturers’ curves, addressing in particular the need for and impact of different types of Auxiliary Heating (AH) systems. A specific LV network analysis tool has been built that integrates the three-phase unbalanced power flow solution engine OpenDSS with the developed EHP models and is capable of properly addressing single-phase connections, adequately modeling the unbalanced nature of LV networks. Different metrics are used to quantify the impact of the considered technologies, with emphasis on thermal and voltage limits, according to current engineering standards. To cope with the many relevant uncertainties (EHP size, location in the network, operation pattern, reactive power consumption, network headroom, etc.), various case studies and sensitivity analyses have been carried out for representative suburban areas in the UK and for different scenarios in order to exemplify the developed methodology and illustrate the main drivers for impact and trends in the different cases. The tool can be adapted to perform studies for different situations and scenarios and can be used as decision making support by network operators, energy planners, policy makers, and so on, to better quantify the potential implications of large scale electrification of heating.

Suggested Citation

  • Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2014. "Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks," Applied Energy, Elsevier, vol. 127(C), pages 249-266.
    • Handle: RePEc:eee:appene:v:127:y:2014:i:c:p:249-266
    DOI: 10.1016/j.apenergy.2014.04.026
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    References listed on IDEAS

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    8. Fraga, Carolina & Hollmuller, Pierre & Schneider, Stefan & Lachal, Bernard, 2018. "Heat pump systems for multifamily buildings: Potential and constraints of several heat sources for diverse building demands," Applied Energy, Elsevier, vol. 225(C), pages 1033-1053.
    9. Protopapadaki, Christina & Saelens, Dirk, 2017. "Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties," Applied Energy, Elsevier, vol. 192(C), pages 268-281.
    10. Ma, Yiju & Azuatalam, Donald & Power, Thomas & Chapman, Archie C. & Verbič, Gregor, 2019. "A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks," Applied Energy, Elsevier, vol. 254(C).
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    13. Ahmed, Asim & Mancarella, Pierluigi, 2014. "Strategic techno-economic assessment of heat network options for distributed energy systems in the UK," Energy, Elsevier, vol. 75(C), pages 182-193.
    14. Raoul Bernards & Werner van Westering & Johan Morren & Han Slootweg, 2020. "Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models," Energies, MDPI, vol. 13(22), pages 1-21, November.
    15. Liu, Xuezhi & Wu, Jianzhong & Jenkins, Nick & Bagdanavicius, Audrius, 2016. "Combined analysis of electricity and heat networks," Applied Energy, Elsevier, vol. 162(C), pages 1238-1250.
    16. Javier Leiva & Rubén Carmona Pardo & José A. Aguado, 2019. "Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks," Energies, MDPI, vol. 12(7), pages 1-20, April.
    17. Johnson, R.C. & Royapoor, M. & Mayfield, M., 2021. "A multi-zone, fast solving, rapidly reconfigurable building and electrified heating system model for generation of control dependent heat pump power demand profiles," Applied Energy, Elsevier, vol. 304(C).
    18. Wang, Zhikun & Crawley, Jenny & Li, Francis G.N. & Lowe, Robert, 2020. "Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK," Energy, Elsevier, vol. 193(C).
    19. Abeysinghe, Sathsara & Wu, Jianzhong & Sooriyabandara, Mahesh & Abeysekera, Muditha & Xu, Tao & Wang, Chengshan, 2018. "Topological properties of medium voltage electricity distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 1101-1112.
    20. Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    21. Navarro-Espinosa, Alejandro & Thomas-Galán, Mauricio, 2023. "Firewood electrification in Chile: effects on household expenditure and energy poverty," Energy Policy, Elsevier, vol. 173(C).
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