IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v322y2022ics0306261922007589.html
   My bibliography  Save this article

Energy system modeling with aggregated time series: A profiling approach

Author

Listed:
  • Müller, Inga M.

Abstract

Energy system models with a high technical, spatial and temporal resolution are not always feasible and therefore require a complexity reduction through, for example, time series aggregation. Modeling results of aggregated time series are not always reliable but may show large deviations depending on the energy system configuration, model and selected data. By analyzing the interaction between time series and modeling results, this study contributes to an improved understanding of these deviations and identifies relevant time series characteristics such as extreme values or parameters describing the relation between time series. The proposed profiling embeds these parameters and adapts already aggregated time series to the characteristics of original time series within three iterative steps. Results of an analysis including three scenarios, time series from eleven years and eight aggregation variants show that profiling can reduce systematic overestimation or underestimation of installed capacities by an average of 86% to a maximum of 0.4 GW and decrease the standard deviation of the modeling results by 71%. Profiling can be used to better represent time series characteristics and minimize deviations of the modeling results, thus providing a complement to existing aggregation methods. Despite these substantial improvements in comparison to aggregation methods, analyses suggest that there is further potential to improve time series aggregation using a profiling approach. Potential directions for future research are discussed.

Suggested Citation

  • Müller, Inga M., 2022. "Energy system modeling with aggregated time series: A profiling approach," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922007589
    DOI: 10.1016/j.apenergy.2022.119426
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922007589
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119426?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. Teichgraeber, Holger & Küpper, Lucas Elias & Brandt, Adam R., 2021. "Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation," Applied Energy, Elsevier, vol. 304(C).
    2. 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.
    3. Schaber, Katrin & Steinke, Florian & Hamacher, Thomas, 2012. "Transmission grid extensions for the integration of variable renewable energies in Europe: Who benefits where?," Energy Policy, Elsevier, vol. 43(C), pages 123-135.
    4. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    5. Sonia Jerez & Isabelle Tobin & Robert Vautard & Juan Pedro Montávez & Jose María López-Romero & Françoise Thais & Blanka Bartok & Ole Bøssing Christensen & Augustin Colette & Michel Déqué & Grigory Ni, 2015. "The impact of climate change on photovoltaic power generation in Europe," Nature Communications, Nature, vol. 6(1), pages 1-8, December.
    6. Savvidis, Georgios & Siala, Kais & Weissbart, Christoph & Schmidt, Lukas & Borggrefe, Frieder & Kumar, Subhash & Pittel, Karen & Madlener, Reinhard & Hufendiek, Kai, 2019. "The gap between energy policy challenges and model capabilities," Energy Policy, Elsevier, vol. 125(C), pages 503-520.
    7. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    8. Frew, Bethany A. & Jacobson, Mark Z., 2016. "Temporal and spatial tradeoffs in power system modeling with assumptions about storage: An application of the POWER model," Energy, Elsevier, vol. 117(P1), pages 198-213.
    9. Grothe, Oliver & Schnieders, Julius, 2011. "Spatial Dependence in Wind and Optimal Wind Power Allocation: A Copula Based Analysis," EWI Working Papers 2011-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    10. Scarlat, Nicolae & Dallemand, Jean-François & Monforti-Ferrario, Fabio & Banja, Manjola & Motola, Vincenzo, 2015. "Renewable energy policy framework and bioenergy contribution in the European Union – An overview from National Renewable Energy Action Plans and Progress Reports," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 969-985.
    11. Grothe, Oliver & Schnieders, Julius, 2011. "Spatial dependence in wind and optimal wind power allocation: A copula-based analysis," Energy Policy, Elsevier, vol. 39(9), pages 4742-4754, September.
    12. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    13. 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).
    14. Thorsten Burandt & Konstantin Löffler & Karlo Hainsch, 2018. "GENeSYS-MOD v2.0 – Enhancing the Global Energy System Model: Model Improvements, Framework Changes, and European Data Set," Data Documentation 94, DIW Berlin, German Institute for Economic Research.
    15. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    16. Zappa, William & Junginger, Martin & van den Broek, Machteld, 2019. "Is a 100% renewable European power system feasible by 2050?," Applied Energy, Elsevier, vol. 233, pages 1027-1050.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
    3. Kuepper, Lucas Elias & Teichgraeber, Holger & Baumgärtner, Nils & Bardow, André & Brandt, Adam R., 2022. "Wind data introduce error in time-series reduction for capacity expansion modelling," Energy, Elsevier, vol. 256(C).
    4. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    5. 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.
    6. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Seljom, Pernille & Lind, Arne & Wagner, Fabian & Mesfun, Sennai, 2020. "Short-term solar and wind variability in long-term energy system models - A European case study," Energy, Elsevier, vol. 209(C).
    7. 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).
    8. Hilbers, Adriaan P. & Brayshaw, David J. & Gandy, Axel, 2023. "Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage," Applied Energy, Elsevier, vol. 334(C).
    9. 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).
    10. Yeganefar, Ali & Amin-Naseri, Mohammad Reza & Sheikh-El-Eslami, Mohammad Kazem, 2020. "Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources," Applied Energy, Elsevier, vol. 272(C).
    11. de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    12. 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.
    13. Reichenberg, Lina & Hedenus, Fredrik & Odenberger, Mikael & Johnsson, Filip, 2018. "The marginal system LCOE of variable renewables – Evaluating high penetration levels of wind and solar in Europe," Energy, Elsevier, vol. 152(C), pages 914-924.
    14. 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).
    15. 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.
    16. 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.
    17. 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.
    18. Chang, Miguel & Thellufsen, Jakob Zink & Zakeri, Behnam & Pickering, Bryn & Pfenninger, Stefan & Lund, Henrik & Østergaard, Poul Alberg, 2021. "Trends in tools and approaches for modelling the energy transition," Applied Energy, Elsevier, vol. 290(C).
    19. 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.
    20. Teichgraeber, Holger & Küpper, Lucas Elias & Brandt, Adam R., 2021. "Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation," Applied Energy, Elsevier, vol. 304(C).

    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:322:y:2022:i:c:s0306261922007589. 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.