IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v269y2020ics0306261920304505.html
   My bibliography  Save this article

Sensitivity analysis of time aggregation techniques applied to capacity expansion energy system models

Author

Listed:
  • Buchholz, Stefanie
  • Gamst, Mette
  • Pisinger, David

Abstract

Time aggregation techniques have shown great potential for efficiently solving comprehensive energy system models. Numerous studies document significant solution time reductions while maintaining high solution qualities. However, most of these studies lack providing guidelines for which aggregation techniques to use in a given setup, and why. A recent interest of comparing different aggregation techniques has arisen, and this paper contributes to that trend. We present a sensitivity analysis methodology that studies how different problem changes, influence the aggregation technique performances. The performance is measured as the ability of the aggregated problem to fully or partially replicate the solution achieved by the non-aggregated problem. The applicability of the methodology is illustrated through a case study considering three types of problem changes namely changes in the wind availability, changes in the aggregated problem size, and changes in the energy system design. By applying the suggested methodology to ten different aggregation techniques, key properties which seem to ensure promising performance are identified.

Suggested Citation

  • Buchholz, Stefanie & Gamst, Mette & Pisinger, David, 2020. "Sensitivity analysis of time aggregation techniques applied to capacity expansion energy system models," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920304505
    DOI: 10.1016/j.apenergy.2020.114938
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920304505
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114938?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. 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.
    2. Sadeghi, Hadi & Rashidinejad, Masoud & Abdollahi, Amir, 2017. "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1369-1394.
    3. Ludig, Sylvie & Haller, Markus & Schmid, Eva & Bauer, Nico, 2011. "Fluctuating renewables in a long-term climate change mitigation strategy," Energy, Elsevier, vol. 36(11), pages 6674-6685.
    4. Scott, Ian J. & Carvalho, Pedro M.S. & Botterud, Audun & Silva, Carlos A., 2019. "Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar, 2015. "Analyzing major challenges of wind and solar variability in power systems," Renewable Energy, Elsevier, vol. 81(C), pages 1-10.
    6. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    7. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    8. Pina, André & Silva, Carlos & Ferrão, Paulo, 2012. "The impact of demand side management strategies in the penetration of renewable electricity," Energy, Elsevier, vol. 41(1), pages 128-137.
    9. Abdin, Islam F. & Zio, Enrico, 2018. "An integrated framework for operational flexibility assessment in multi-period power system planning with renewable energy production," Applied Energy, Elsevier, vol. 222(C), pages 898-914.
    10. 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.
    11. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    12. Haller, Markus & Ludig, Sylvie & Bauer, Nico, 2012. "Decarbonization scenarios for the EU and MENA power system: Considering spatial distribution and short term dynamics of renewable generation," Energy Policy, Elsevier, vol. 47(C), pages 282-290.
    13. Oree, Vishwamitra & Sayed Hassen, Sayed Z. & Fleming, Peter J., 2017. "Generation expansion planning optimisation with renewable energy integration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 790-803.
    14. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2017. "Generation expansion planning with high share of renewables of variable output," Applied Energy, Elsevier, vol. 190(C), pages 1275-1288.
    15. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    16. Stefanie Buchholz & Mette Gamst & David Pisinger, 2019. "Rejoinder on: A comparative study of time aggregation techniques in relation to power capacity-expansion modeling," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 421-425, October.
    17. 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.
    18. 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.
    19. De Jonghe, Cedric & Delarue, Erik & Belmans, Ronnie & D'haeseleer, William, 2011. "Determining optimal electricity technology mix with high level of wind power penetration," Applied Energy, Elsevier, vol. 88(6), pages 2231-2238, June.
    20. Oriol Raventós & Julian Bartels, 2020. "Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models," Energies, MDPI, vol. 13(4), pages 1-18, February.
    21. Doukas, Haris & Mannsbart, Wilhelm & Patlitzianas, Konstantinos D. & Psarras, John & Ragwitz, Mario & Schlomann, Barbara, 2007. "A methodology for validating the renewable energy data in EU," Renewable Energy, Elsevier, vol. 32(12), pages 1981-1998.
    22. Pina, André & Silva, Carlos A. & Ferrão, Paulo, 2013. "High-resolution modeling framework for planning electricity systems with high penetration of renewables," Applied Energy, Elsevier, vol. 112(C), pages 215-223.
    23. Xie, Shiwei & Hu, Zhijian & Wang, Jueying, 2020. "Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks," Applied Energy, Elsevier, vol. 261(C).
    24. Li, Y.P. & Huang, G.H. & Chen, X., 2011. "Planning regional energy system in association with greenhouse gas mitigation under uncertainty," Applied Energy, Elsevier, vol. 88(3), pages 599-611, March.
    25. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
    26. Karl-Kiên Cao & Kai von Krbek & Manuel Wetzel & Felix Cebulla & Sebastian Schreck, 2019. "Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models," Energies, MDPI, vol. 12(24), pages 1-51, December.
    27. Xie, Shiwei & Hu, Zhijian & Wang, Jueying, 2019. "Scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system," Applied Energy, Elsevier, vol. 255(C).
    28. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    29. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Kopanos, Georgios M. & Pistikopoulos, Efstratios N. & Georgiadis, Michael C., 2014. "A spatial multi-period long-term energy planning model: A case study of the Greek power system," Applied Energy, Elsevier, vol. 115(C), pages 456-482.
    30. Stefanie Buchholz & Mette Gamst & David Pisinger, 2019. "A comparative study of time aggregation techniques in relation to power capacity expansion modeling," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 353-405, October.
    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. Xia, Tian & Huang, Wujing & Lu, Xi & Zhang, Ning & Kang, Chongqing, 2020. "Planning district multiple energy systems considering year-round operation," Energy, Elsevier, vol. 213(C).
    2. Klemm, Christian & Wiese, Frauke & Vennemann, Peter, 2023. "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution," Applied Energy, Elsevier, vol. 334(C).
    3. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2022. "A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems," Energy, Elsevier, vol. 258(C).
    4. Gonzato, Sebastian & Bruninx, Kenneth & Delarue, Erik, 2021. "Long term storage in generation expansion planning models with a reduced temporal scope," Applied Energy, Elsevier, vol. 298(C).
    5. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).

    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. 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.
    2. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    3. Gonzato, Sebastian & Bruninx, Kenneth & Delarue, Erik, 2021. "Long term storage in generation expansion planning models with a reduced temporal scope," Applied Energy, Elsevier, vol. 298(C).
    4. Collins, Seán & Deane, John Paul & Poncelet, Kris & Panos, Evangelos & Pietzcker, Robert C. & Delarue, Erik & Ó Gallachóir, Brian Pádraig, 2017. "Integrating short term variations of the power system into integrated energy system models: A methodological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 839-856.
    5. Stefanie Buchholz & Mette Gamst & David Pisinger, 2020. "Finding a Portfolio of Near-Optimal Aggregated Solutions to Capacity Expansion Energy System Models," SN Operations Research Forum, Springer, vol. 1(1), pages 1-40, March.
    6. Oree, Vishwamitra & Sayed Hassen, Sayed Z. & Fleming, Peter J., 2019. "A multi-objective framework for long-term generation expansion planning with variable renewables," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Poncelet, Kris & Delarue, Erik & D’haeseleer, William, 2020. "Unit commitment constraints in long-term planning models: Relevance, pitfalls and the role of assumptions on flexibility," Applied Energy, Elsevier, vol. 258(C).
    8. Scott, Ian J. & Carvalho, Pedro M.S. & Botterud, Audun & Silva, Carlos A., 2019. "Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Vrionis, Constantinos & Tsalavoutis, Vasilios & Tolis, Athanasios, 2020. "A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach," Applied Energy, Elsevier, vol. 259(C).
    10. 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.
    11. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2024. "Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems," Energy, Elsevier, vol. 290(C).
    12. Thomas Heggarty & Jean-Yves Bourmaud & Robin Girard & Georges Kariniotakis, 2024. "Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems," Post-Print hal-04383397, HAL.
    13. Göke, Leonard & Kendziorski, Mario, 2022. "Adequacy of time-series reduction for renewable energy systems," Energy, Elsevier, vol. 238(PA).
    14. 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.
    15. Prina, Matteo Giacomo & Nastasi, Benedetto & Groppi, Daniele & Misconel, Steffi & Garcia, Davide Astiaso & Sparber, Wolfram, 2022. "Comparison methods of energy system frameworks, models and scenario results," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    16. Mertens, Tim & Poncelet, Kris & Duerinck, Jan & Delarue, Erik, 2020. "Representing cross-border trade of electricity in long-term energy-system optimization models with a limited geographical scope," Applied Energy, Elsevier, vol. 261(C).
    17. Kittel, Martin & Hobbie, Hannes & Dierstein, Constantin, 2022. "Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms," Energy, Elsevier, vol. 247(C).
    18. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    19. Li, Can & Conejo, Antonio J. & Liu, Peng & Omell, Benjamin P. & Siirola, John D. & Grossmann, Ignacio E., 2022. "Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1071-1082.
    20. Kittel, Martin & Hobbie, Hannes & Dierstein, Constantin, 2022. "Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 247, pages 1-15.

    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:appene:v:269:y:2020:i:c:s0306261920304505. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.