IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9517-d1004289.html
   My bibliography  Save this article

Advanced Spatial and Technological Aggregation Scheme for Energy System Models

Author

Listed:
  • Shruthi Patil

    (Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
    These authors contributed equally to this work.)

  • Leander Kotzur

    (Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
    These authors contributed equally to this work.)

  • Detlef Stolten

    (Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
    Chair for Fuel Cells, RWTH Aachen University, c/o Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany)

Abstract

Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper considers the simultaneous spatial and technological aggregation of energy system models. To that end, a novel two-step aggregation scheme is introduced. First, model regions are spatially aggregated to obtain a reduced region set. The aggregation is based on model parameters such as VRES time series, capacities, etc. In addition, spatial contiguity of regions is considered. Next, technological aggregation is performed on each VRES, in each region, based on their time series. The aggregations’ impact on accuracy and complexity of a cost-optimal, European energy system model is analyzed. The model is aggregated to obtain different combinations of numbers of regions and VRES types. Results are benchmarked against an initial resolution of 96 regions, with 68 VRES types in each. System cost deviates significantly when lower numbers of regions and/or VRES types are considered. As spatial and technological resolutions increase, the cost fluctuates initially and stabilizes eventually, approaching the benchmark. Optimal combination is determined based on an acceptable cost deviation of <5% and the point of stabilization. A total of 33 regions with 38 VRES types in each is deemed optimal. Here, the cost is underestimated by 4.42 % , but the run time is reduced by 92.95 % .

Suggested Citation

  • Shruthi Patil & Leander Kotzur & Detlef Stolten, 2022. "Advanced Spatial and Technological Aggregation Scheme for Energy System Models," Energies, MDPI, vol. 15(24), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9517-:d:1004289
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9517/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9517/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Samsatli, Sheila & Samsatli, Nouri J., 2018. "A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies," Applied Energy, Elsevier, vol. 220(C), pages 893-920.
    4. Karl-Kiên Cao & Johannes Metzdorf & Sinan Birbalta, 2018. "Incorporating Power Transmission Bottlenecks into Aggregated Energy System Models," Sustainability, MDPI, vol. 10(6), pages 1-32, June.
    5. Fischer, Manfred M., 1980. "Regional taxonomy : A comparison of some hierarchic and non-hierarchic strategies," Regional Science and Urban Economics, Elsevier, vol. 10(4), pages 503-537, November.
    6. 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.
    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. Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron, 2019. "Are complex energy system models more accurate? An intra-model comparison of power system optimization models," Applied Energy, Elsevier, vol. 255(C).
    9. Tony H. Grubesic & Ran Wei & Alan T. Murray, 2014. "Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(6), pages 1134-1156, November.
    10. S. W. Hess & J. B. Weaver & H. J. Siegfeldt & J. N. Whelan & P. A. Zitlau, 1965. "Nonpartisan Political Redistricting by Computer," Operations Research, INFORMS, vol. 13(6), pages 998-1006, December.
    11. Frysztacki, Martha Maria & Hörsch, Jonas & Hagenmeyer, Veit & Brown, Tom, 2021. "The strong effect of network resolution on electricity system models with high shares of wind and solar," Applied Energy, Elsevier, vol. 291(C).
    12. Scaramuzzino, Chiara & Garegnani, Giulia & Zambelli, Pietro, 2019. "Integrated approach for the identification of spatial patterns related to renewable energy potential in European territories," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 1-13.
    13. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    14. 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.
    15. David Severin Ryberg & Martin Robinius & Detlef Stolten, 2018. "Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe," Energies, MDPI, vol. 11(5), pages 1-19, May.
    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. David Franzmann & Heidi Heinrichs & Felix Lippkau & Thushara Addanki & Christoph Winkler & Patrick Buchenberg & Thomas Hamacher & Markus Blesl & Jochen Lin{ss}en & Detlef Stolten, 2023. "Green Hydrogen Cost-Potentials for Global Trade," Papers 2303.00314, arXiv.org, revised May 2023.

    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. 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).
    2. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
    3. 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).
    4. 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).
    5. Frysztacki, Martha Maria & Hagenmeyer, Veit & Brown, Tom, 2023. "Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?," Energy, Elsevier, vol. 281(C).
    6. Valeriya Azarova & Mathias Mier, 2021. "Unraveling the Black Box of Power Market Models," ifo Working Paper Series 357, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    7. Neumann, Fabian & Hagenmeyer, Veit & Brown, Tom, 2022. "Assessments of linear power flow and transmission loss approximations in coordinated capacity expansion problems," Applied Energy, Elsevier, vol. 314(C).
    8. 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.
    9. 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).
    10. Phillips, K. & Moncada, J.A. & Ergun, H. & Delarue, E., 2023. "Spatial representation of renewable technologies in generation expansion planning models," Applied Energy, Elsevier, vol. 342(C).
    11. 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).
    12. 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).
    13. Rui Fragoso & Conceição Rego & Vladimir Bushenkov, 2016. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 179-198, December.
    14. Raventós, Oriol & Dengiz, Thomas & Medjroubi, Wided & Unaichi, Chinonso & Bruckmeier, Andreas & Finck, Rafael, 2022. "Comparison of different methods of spatial disaggregation of electricity generation and consumption time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    15. Ben Beck & Meghan Winters & Trisalyn Nelson & Chris Pettit & Simone Z Leao & Meead Saberi & Jason Thompson & Sachith Seneviratne & Kerry Nice & Mark Stevenson, 2023. "Developing urban biking typologies: Quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics," Environment and Planning B, , vol. 50(1), pages 7-23, January.
    16. Nolting, Lars & Praktiknjo, Aaron, 2022. "The complexity dilemma – Insights from security of electricity supply assessments," Energy, Elsevier, vol. 241(C).
    17. Martin Kueppers & Christian Perau & Marco Franken & Hans Joerg Heger & Matthias Huber & Michael Metzger & Stefan Niessen, 2020. "Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization," Energies, MDPI, vol. 13(16), pages 1-15, August.
    18. Quarton, Christopher J. & Samsatli, Sheila, 2020. "The value of hydrogen and carbon capture, storage and utilisation in decarbonising energy: Insights from integrated value chain optimisation," Applied Energy, Elsevier, vol. 257(C).
    19. Juan Carlos Duque & Raúl Ramos & Jordi Suriñach, 2007. "Supervised Regionalization Methods: A Survey," International Regional Science Review, , vol. 30(3), pages 195-220, July.
    20. 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).

    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:15:y:2022:i:24:p:9517-:d:1004289. 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.