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The development and application of a temporal MARKAL energy system model using flexible time slicing

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  • Kannan, Ramachandran

Abstract

Detailed temporal consideration has been a major challenge for energy systems models with typical time horizons of years and decades. This presents particular issues in investigating electricity generation, capacity and storage, whilst retaining broader trade-offs sectors, technology pathways and timing of investments. This paper reports on a methodology for temporal disaggregation in the widely applied energy service driven, technology rich, cost optimizing, linear programming MARKAL energy system model. A flexible time slicing feature is developed to enhance representation of diurnal and seasonal electricity demand curves through disaggregation of resource availability and energy service demands. In a first application of a temporal UK MARKAL model, a range of runs investigate the role of electricity storage at supply and demand sides. The results display considerably enhanced insights, notably on the role and preference of demand-side electricity storage over supply-side storage. On average, the system chooses about 7-10% of electricity demand as storage. On the supply side, hydrogen-based electricity storage is greatly preferred but stored-hydrogen is used in the transport sector rather than for power system balancing mechanism.

Suggested Citation

  • Kannan, Ramachandran, 2011. "The development and application of a temporal MARKAL energy system model using flexible time slicing," Applied Energy, Elsevier, vol. 88(6), pages 2261-2272, June.
  • Handle: RePEc:eee:appene:v:88:y:2011:i:6:p:2261-2272
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    Cited by:

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    6. Timmerman, Jonas & Vandevelde, Lieven & Van Eetvelde, Greet, 2014. "Towards low carbon business park energy systems: Classification of techno-economic energy models," Energy, Elsevier, vol. 75(C), pages 68-80.
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    11. Chung, Mo & Park, Hwa-Choon, 2012. "Building energy demand patterns for department stores in Korea," Applied Energy, Elsevier, vol. 90(1), pages 241-249.
    12. García-Gusano, Diego & Espegren, Kari & Lind, Arne & Kirkengen, Martin, 2016. "The role of the discount rates in energy systems optimisation models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 56-72.
    13. Qadrdan, Meysam & Chaudry, Modassar & Jenkins, Nick & Baruah, Pranab & Eyre, Nick, 2015. "Impact of transition to a low carbon power system on the GB gas network," Applied Energy, Elsevier, vol. 151(C), pages 1-12.
    14. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    15. Xiaoyang Sun & Baosheng Zhang & Xu Tang & Benjamin C. McLellan & Mikael Höök, 2016. "Sustainable Energy Transitions in China: Renewable Options and Impacts on the Electricity System," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-20, November.
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    19. Nicolas Weidmann & Ramachandran Kannan & Hal Turton, 2012. "Swiss Climate Change and Nuclear Policy: A Comparative Analysis Using an Energy System Approach and a Sectoral Electricity Model," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 148(II), pages 275-316, June.

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