IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v128y2019icp66-74.html
   My bibliography  Save this article

High performance computing for energy system optimization models: Enhancing the energy policy tool kit

Author

Listed:
  • Sharma, Tarun
  • Glynn, James
  • Panos, Evangelos
  • Deane, Paul
  • Gargiulo, Maurizio
  • Rogan, Fionn
  • Gallachóir, Brian Ó

Abstract

Energy system optimization models (ESOMs) form a critical component of a suite of modelling tools used by policy makers to understand (i) evolving complexity in energy systems arising from intersectoral coupling and other considerations at different spatial and temporal resolutions and (ii) uncertainty and sensitivity to assumptions and model parameters which entails analysis of a multitude of scenarios. Such enquiries are partly restricted by increasing computational times which can range from hours to days. To appease this restriction, we report our attempts at formalizing the performance testing of running ESOMs on a High Performance Computing (HPC) facility. The goal is to provide an assessment of the potential of a HPC environment to minimize solution time. Reporting on the outcomes, we present the scaling performance of the Irish TIMES, ETSAP-TIAM and JRC EU TIMES models by demonstrating solution time improvement on scaling across components of a HPC facility. Such facilities permit parallel runs of model instances. We identify and characterize the benefits and trade-offs of forking as a strategy in solution time reduction. Such capability permits policy makers and modellers to pose and derive insights to increasingly relevant questions on inter-sectoral coupling and risks that energy systems face due to uncertainty.

Suggested Citation

  • Sharma, Tarun & Glynn, James & Panos, Evangelos & Deane, Paul & Gargiulo, Maurizio & Rogan, Fionn & Gallachóir, Brian Ó, 2019. "High performance computing for energy system optimization models: Enhancing the energy policy tool kit," Energy Policy, Elsevier, vol. 128(C), pages 66-74.
    • Handle: RePEc:eee:enepol:v:128:y:2019:i:c:p:66-74
    DOI: 10.1016/j.enpol.2018.12.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421518308607
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2018.12.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2014. "Sensitivity analysis of an energy-economy model of the residential building sector," Working Papers hal-01016399, HAL.
    2. Deane, J.P. & Chiodi, Alessandro & Gargiulo, Maurizio & Ó Gallachóir, Brian P., 2012. "Soft-linking of a power systems model to an energy systems model," Energy, Elsevier, vol. 42(1), pages 303-312.
    3. Welsch, Manuel & Deane, Paul & Howells, Mark & Ó Gallachóir, Brian & Rogan, Fionn & Bazilian, Morgan & Rogner, Hans-Holger, 2014. "Incorporating flexibility requirements into long-term energy system models – A case study on high levels of renewable electricity penetration in Ireland," Applied Energy, Elsevier, vol. 135(C), pages 600-615.
    4. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2015. "Global sensitivity analysis of an energy-economy model of the residential building sector," Policy Papers 2015.01, FAERE - French Association of Environmental and Resource Economists.
    5. Richard Loulou & Maryse Labriet, 2008. "ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure," Computational Management Science, Springer, vol. 5(1), pages 7-40, February.
    6. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    7. 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.
    8. Chaudry, Modassar & Wu, Jianzhong & Jenkins, Nick, 2013. "A sequential Monte Carlo model of the combined GB gas and electricity network," Energy Policy, Elsevier, vol. 62(C), pages 473-483.
    9. Koltsaklis, Nikolaos E. & Georgiadis, Michael C., 2015. "A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints," Applied Energy, Elsevier, vol. 158(C), pages 310-331.
    10. Céline Guivarch & Robert Lempert & Evelina Trutnevyte, 2017. "Scenario techniques for energy and environmental research: An overview of recent developments to broaden the capacity to deal with complexity and uncertainty," Post-Print halshs-01579281, HAL.
    11. Richard Loulou, 2008. "ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation," Computational Management Science, Springer, vol. 5(1), pages 41-66, February.
    12. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.
    13. Heuberger, Clara F. & Rubin, Edward S. & Staffell, Iain & Shah, Nilay & Mac Dowell, Niall, 2017. "Power capacity expansion planning considering endogenous technology cost learning," Applied Energy, Elsevier, vol. 204(C), pages 831-845.
    14. Bale, Catherine S.E. & Varga, Liz & Foxon, Timothy J., 2015. "Energy and complexity: New ways forward," Applied Energy, Elsevier, vol. 138(C), pages 150-159.
    15. Hughes, Nick & Strachan, Neil, 2010. "Methodological review of UK and international low carbon scenarios," Energy Policy, Elsevier, vol. 38(10), pages 6056-6065, October.
    16. Tattini, Jacopo & Gargiulo, Maurizio & Karlsson, Kenneth, 2018. "Reaching carbon neutral transport sector in Denmark – Evidence from the incorporation of modal shift into the TIMES energy system modeling framework," Energy Policy, Elsevier, vol. 113(C), pages 571-583.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hofbauer, Leonhard & McDowall, Will & Pye, Steve, 2022. "Challenges and opportunities for energy system modelling to foster multi-level governance of energy transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Hampton, Harrison & Foley, Aoife, 2022. "A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design," Energy, Elsevier, vol. 260(C).
    3. Brinkerink, Maarten & Gallachóir, Brian Ó & Deane, Paul, 2019. "A comprehensive review on the benefits and challenges of global power grids and intercontinental interconnectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 274-287.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Blanco, Herib & Gómez Vilchez, Jonatan J. & Nijs, Wouter & Thiel, Christian & Faaij, André, 2019. "Soft-linking of a behavioral model for transport with energy system cost optimization applied to hydrogen in EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    4. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    5. Gjorgiev, Blazhe & Garrison, Jared B. & Han, Xuejiao & Landis, Florian & van Nieuwkoop, Renger & Raycheva, Elena & Schwarz, Marius & Yan, Xuqian & Demiray, Turhan & Hug, Gabriela & Sansavini, Giovanni, 2022. "Nexus-e: A platform of interfaced high-resolution models for energy-economic assessments of future electricity systems," Applied Energy, Elsevier, vol. 307(C).
    6. Crow, Daniel J.G. & Giarola, Sara & Hawkes, Adam D., 2018. "A dynamic model of global natural gas supply," Applied Energy, Elsevier, vol. 218(C), pages 452-469.
    7. Pedinotti-Castelle, Marianne & Pineau, Pierre-Olivier & Vaillancourt, Kathleen & Amor, Ben, 2022. "Freight transport modal shifts in a TIMES energy model: Impacts of endogenous and exogenous modeling choice," Applied Energy, Elsevier, vol. 324(C).
    8. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
    9. Berntsen, Philip B. & Trutnevyte, Evelina, 2017. "Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives," Energy, Elsevier, vol. 126(C), pages 886-898.
    10. Daniel Scamman & Baltazar Solano-Rodríguez & Steve Pye & Lai Fong Chiu & Andrew Z. P. Smith & Tiziano Gallo Cassarino & Mark Barrett & Robert Lowe, 2020. "Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective," Energies, MDPI, vol. 13(8), pages 1-28, April.
    11. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2023. "Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning," Applied Energy, Elsevier, vol. 340(C).
    12. Barazza, Elsa & Strachan, Neil, 2020. "The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition," Energy Policy, Elsevier, vol. 138(C).
    13. David Huckebrink & Valentin Bertsch, 2021. "Integrating Behavioural Aspects in Energy System Modelling—A Review," Energies, MDPI, vol. 14(15), pages 1-26, July.
    14. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    15. Niina Helistö & Juha Kiviluoma & Hannele Holttinen & Jose Daniel Lara & Bri‐Mathias Hodge, 2019. "Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    16. Xexakis, Georgios & Hansmann, Ralph & Volken, Sandra P. & Trutnevyte, Evelina, 2020. "Models on the wrong track: Model-based electricity supply scenarios in Switzerland are not aligned with the perspectives of energy experts and the public," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    17. Sacha Hodencq & Mathieu Brugeron & Jaume Fitó & Lou Morriet & Benoit Delinchant & Frédéric Wurtz, 2021. "OMEGAlpes, an Open-Source Optimisation Model Generation Tool to Support Energy Stakeholders at District Scale," Energies, MDPI, vol. 14(18), pages 1-30, September.
    18. Welsch, M. & Howells, M. & Bazilian, M. & DeCarolis, J.F. & Hermann, S. & Rogner, H.H., 2012. "Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code," Energy, Elsevier, vol. 46(1), pages 337-350.
    19. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    20. Mauleón, Ignacio, 2019. "Optimizing individual renewable energies roadmaps: Criteria, methods, and end targets," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:128:y:2019:i:c:p:66-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.