IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i20p4006-d278843.html
   My bibliography  Save this article

Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

Author

Listed:
  • Peter Lopion

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

  • Peter Markewitz

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

  • Detlef Stolten

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany
    Chair of fuel cells, RWTH Aachen University, c/o Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

  • Martin Robinius

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

Abstract

Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

Suggested Citation

  • Peter Lopion & Peter Markewitz & Detlef Stolten & Martin Robinius, 2019. "Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects," Energies, MDPI, vol. 12(20), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:4006-:d:278843
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/20/4006/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/20/4006/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tattini, Jacopo & Ramea, Kalai & Gargiulo, Maurizio & Yang, Christopher & Mulholland, Eamonn & Yeh, Sonia & Karlsson, Kenneth, 2018. "Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model," Applied Energy, Elsevier, vol. 212(C), pages 265-282.
    2. David L. McCollum & Wenji Zhou & Christoph Bertram & Harmen-Sytze Boer & Valentina Bosetti & Sebastian Busch & Jacques Després & Laurent Drouet & Johannes Emmerling & Marianne Fay & Oliver Fricko & Sh, 2018. "Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals," Nature Energy, Nature, vol. 3(7), pages 589-599, July.
    3. Welder, Lara & Ryberg, D.Severin & Kotzur, Leander & Grube, Thomas & Robinius, Martin & Stolten, Detlef, 2018. "Spatio-temporal optimization of a future energy system for power-to-hydrogen applications in Germany," Energy, Elsevier, vol. 158(C), pages 1130-1149.
    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. David L. McCollum & Wenji Zhou & Christoph Bertram & Harmen-Sytze Boer & Valentina Bosetti & Sebastian Busch & Jacques Després & Laurent Drouet & Johannes Emmerling & Marianne Fay & Oliver Fricko & Sh, 2018. "Author Correction: Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals," Nature Energy, Nature, vol. 3(8), pages 699-699, August.
    6. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    7. Martin Drechsler & Jonas Egerer & Martin Lange & Frank Masurowski & Jürgen Meyerhoff & Malte Oehlmann, 2017. "Efficient and equitable spatial allocation of renewable power plants at the country scale," Nature Energy, Nature, vol. 2(9), pages 1-9, September.
    8. Bosetti, Valentina & Marangoni, Giacomo & Borgonovo, Emanuele & Diaz Anadon, Laura & Barron, Robert & McJeon, Haewon C. & Politis, Savvas & Friley, Paul, 2015. "Sensitivity to energy technology costs: A multi-model comparison analysis," Energy Policy, Elsevier, vol. 80(C), pages 244-263.
    9. Lopion, Peter & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "A review of current challenges and trends in energy systems modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 156-166.
    10. O. Schmidt & A. Hawkes & A. Gambhir & I. Staffell, 2017. "The future cost of electrical energy storage based on experience rates," Nature Energy, Nature, vol. 2(8), pages 1-8, August.
    11. David L. McCollum & Jessica Jewell & Volker Krey & Morgan Bazilian & Marianne Fay & Keywan Riahi, 2016. "Quantifying uncertainties influencing the long-term impacts of oil prices on energy markets and carbon emissions," Nature Energy, Nature, vol. 1(7), pages 1-8, July.
    12. Seljom, Pernille & Tomasgard, Asgeir, 2015. "Short-term uncertainty in long-term energy system models — A case study of wind power in Denmark," Energy Economics, Elsevier, vol. 49(C), pages 157-167.
    13. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    14. Laura Díaz Anadón & Erin Baker & Valentina Bosetti, 2017. "Integrating uncertainty into public energy research and development decisions," Nature Energy, Nature, vol. 2(5), pages 1-14, May.
    15. 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.
    16. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
    17. Ma, Tieju & Nakamori, Yoshiteru, 2009. "Modeling technological change in energy systems – From optimization to agent-based modeling," Energy, Elsevier, vol. 34(7), pages 873-879.
    18. Felix Creutzig & Peter Agoston & Jan Christoph Goldschmidt & Gunnar Luderer & Gregory Nemet & Robert C. Pietzcker, 2017. "The underestimated potential of solar energy to mitigate climate change," Nature Energy, Nature, vol. 2(9), pages 1-9, September.
    19. 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.
    20. Raimund M. Kovacevic & Georg Ch. Pflug & Maria Teresa Vespucci (ed.), 2013. "Handbook of Risk Management in Energy Production and Trading," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9035-7, September.
    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. Pilpola, Sannamari & Lund, Peter D., 2020. "Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways," Energy, Elsevier, vol. 201(C).
    2. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    3. Leonard Goke & Jens Weibezahn & Christian von Hirschhausen, 2021. "A collective blueprint, not a crystal ball: How expectations and participation shape long-term energy scenarios," Papers 2112.04821, arXiv.org, revised Dec 2022.
    4. Lohr, C. & Schlemminger, M. & Peterssen, F. & Bensmann, A. & Niepelt, R. & Brendel, R. & Hanke-Rauschenbach, R., 2022. "Spatial concentration of renewables in energy system optimization models," Renewable Energy, Elsevier, vol. 198(C), pages 144-154.

    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. Charlotte Senkpiel & Audrey Dobbins & Christina Kockel & Jan Steinbach & Ulrich Fahl & Farina Wille & Joachim Globisch & Sandra Wassermann & Bert Droste-Franke & Wolfgang Hauser & Claudia Hofer & Lars, 2020. "Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches," Energies, MDPI, vol. 13(18), pages 1-30, September.
    2. Fodstad, Marte & Crespo del Granado, Pedro & Hellemo, Lars & Knudsen, Brage Rugstad & Pisciella, Paolo & Silvast, Antti & Bordin, Chiara & Schmidt, Sarah & Straus, Julian, 2022. "Next frontiers in energy system modelling: A review on challenges and the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Pablo E. Carvajal & Asami Miketa & Nadeem Goussous & Pauline Fulcheri, 2022. "Best Practice in Government Use and Development of Long-Term Energy Transition Scenarios," Energies, MDPI, vol. 15(6), pages 1-21, March.
    4. Martínez-Gordón, R. & Morales-España, G. & Sijm, J. & Faaij, A.P.C., 2021. "A review of the role of spatial resolution in energy systems modelling: Lessons learned and applicability to the North Sea region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    5. 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).
    6. Fattahi, A. & Sijm, J. & Faaij, A., 2020. "A systemic approach to analyze integrated energy system modeling tools: A review of national models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Gils, Hans Christian & Gardian, Hedda & Kittel, Martin & Schill, Wolf-Peter & Zerrahn, Alexander & Murmann, Alexander & Launer, Jann & Fehler, Alexander & Gaumnitz, Felix & van Ouwerkerk, Jonas & Bußa, 2022. "Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Nybø, Astrid, 2020. "Transitioning remote Arctic settlements to renewable energy systems – A modelling study of Longyearbyen, Svalbard," Applied Energy, Elsevier, vol. 258(C).
    9. Pizarro-Alonso, Amalia & Ravn, Hans & Münster, Marie, 2019. "Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Koppelaar, Rembrandt H.E.M. & Keirstead, James & Shah, Nilay & Woods, Jeremy, 2016. "A review of policy analysis purpose and capabilities of electricity system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1531-1544.
    11. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    12. Raffaele Salvucci & Stefan Petrović & Kenneth Karlsson & Markus Wråke & Tanu Priya Uteng & Olexandr Balyk, 2019. "Energy Scenario Analysis for the Nordic Transport Sector: A Critical Review," Energies, MDPI, vol. 12(12), pages 1-19, June.
    13. Siala, Kais & Mier, Mathias & Schmidt, Lukas & Torralba-Díaz, Laura & Sheykhha, Siamak & Savvidis, Georgios, 2022. "Which model features matter? An experimental approach to evaluate power market modeling choices," Energy, Elsevier, vol. 245(C).
    14. Gregor Semieniuk & Emanuele Campiglio & Jean‐Francois Mercure & Ulrich Volz & Neil R. Edwards, 2021. "Low‐carbon transition risks for finance," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 12(1), January.
    15. Mai, Trieu & Bistline, John & Sun, Yinong & Cole, Wesley & Marcy, Cara & Namovicz, Chris & Young, David, 2018. "The role of input assumptions and model structures in projections of variable renewable energy: A multi-model perspective of the U.S. electricity system," Energy Economics, Elsevier, vol. 76(C), pages 313-324.
    16. Guo, Rui & Meunier, Simon & Protopapadaki, Christina & Saelens, Dirk, 2023. "A review of European low-voltage distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    17. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    18. Lore Abart-Heriszt & Susanna Erker & Gernot Stoeglehner, 2019. "The Energy Mosaic Austria—A Nationwide Energy and Greenhouse Gas Inventory on Municipal Level as Action Field of Integrated Spatial and Energy Planning," Energies, MDPI, vol. 12(16), pages 1-22, August.
    19. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    20. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.

    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:gam:jeners:v:12:y:2019:i:20:p:4006-:d:278843. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.