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Adequacy of time-series reduction for renewable energy systems

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  • Göke, Leonard
  • Kendziorski, Mario

Abstract

To reduce computational complexity, macro-energy system models commonly implement reduced time-series data. For renewable energy systems dependent on seasonal storage and characterized by intermittent renewables, like wind and solar, adequacy of time-series reduction is in question. Using a capacity expansion model, we evaluate different methods for creating and implementing reduced time-series regarding loss of load and system costs. Results show that adequacy greatly depends on the length of the reduced time-series and how it is implemented into the model. Implementation as a chronological sequence with re-scaled time-steps prevents loss of load best but imposes a positive bias on seasonal storage resulting in an overestimation of system costs. Compared to chronological sequences, grouped periods require more time so solve for the same number of time-steps, because the approach requires additional variables and constraints. Overall, results suggest further efforts to improve time-series reduction and other methods for reducing computational complexity.

Suggested Citation

  • Göke, Leonard & Kendziorski, Mario, 2022. "Adequacy of time-series reduction for renewable energy systems," Energy, Elsevier, vol. 238(PA).
  • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019496
    DOI: 10.1016/j.energy.2021.121701
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    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. Renaldi, Renaldi & Friedrich, Daniel, 2017. "Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage," Energy, Elsevier, vol. 133(C), pages 784-795.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Karlo Hainsch & Leonard Göke & Claudia Kemfert & Pao-Yu Oei & Christian von Hirschhausen, 2020. "European Green Deal: Using Ambitious Climate Targets and Renewable Energy to Climb out of the Economic Crisis," DIW Weekly Report, DIW Berlin, German Institute for Economic Research, vol. 10(28/29), pages 303-310.
    9. Teichgraeber, Holger & Lindenmeyer, Constantin P. & Baumgärtner, Nils & Kotzur, Leander & Stolten, Detlef & Robinius, Martin & Bardow, André & Brandt, Adam R., 2020. "Extreme events in time series aggregation: A case study for optimal residential energy supply systems," Applied Energy, Elsevier, vol. 275(C).
    10. Hilbers, Adriaan P. & Brayshaw, David J. & Gandy, Axel, 2019. "Importance subsampling: improving power system planning under climate-based uncertainty," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Clemens Gerbaulet & Casimir Lorenz, 2017. "dynELMOD: A Dynamic Investment and Dispatch Model for the Future European Electricity Market," Data Documentation 88, DIW Berlin, German Institute for Economic Research.
    12. Reichenberg, Lina & Siddiqui, Afzal S. & Wogrin, Sonja, 2018. "Policy implications of downscaling the time dimension in power system planning models to represent variability in renewable output," Energy, Elsevier, vol. 159(C), pages 870-877.
    13. Deane, J.P. & Drayton, G. & Ó Gallachóir, B.P., 2014. "The impact of sub-hourly modelling in power systems with significant levels of renewable generation," Applied Energy, Elsevier, vol. 113(C), pages 152-158.
    14. Leahy, Eimear & Tol, Richard S.J., 2011. "An estimate of the value of lost load for Ireland," Energy Policy, Elsevier, vol. 39(3), pages 1514-1520, March.
    15. Kannan, Ramachandran, 2011. "The development and application of a temporal MARKAL energy system model using flexible time slicing," Applied Energy, Elsevier, vol. 88(6), pages 2261-2272, June.
    16. 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).
    17. Christian Growitsch & Raimund Malischek & Sebastian Nick & Heike Wetzel, 2015. "The Costs of Power Interruptions in Germany: A Regional and Sectoral Analysis," German Economic Review, Verein für Socialpolitik, vol. 16(3), pages 307-323, August.
    18. Mallapragada, Dharik S. & Papageorgiou, Dimitri J. & Venkatesh, Aranya & Lara, Cristiana L. & Grossmann, Ignacio E., 2018. "Impact of model resolution on scenario outcomes for electricity sector system expansion," Energy, Elsevier, vol. 163(C), pages 1231-1244.
    19. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    20. Zerrahn, Alexander & Schill, Wolf-Peter, 2017. "Long-run power storage requirements for high shares of renewables: review and a new model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1518-1534.
    21. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    22. Schill, Wolf-Peter, 2020. "Electricity Storage and the Renewable Energy Transition," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 4(10), pages 2059-2064.
    23. 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.
    24. Pavičević, Matija & Mangipinto, Andrea & Nijs, Wouter & Lombardi, Francesco & Kavvadias, Konstantinos & Jiménez Navarro, Juan Pablo & Colombo, Emanuela & Quoilin, Sylvain, 2020. "The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models," Applied Energy, Elsevier, vol. 267(C).
    25. Friedrich Kunz & Mario Kendziorski & Wolf-Peter Schill & Jens Weibezahn & Jan Zepter & Christian von Hirschhausen & Philipp Hauser & Matthias Zech & Dominik Möst & Sina Heidari & Björn Felten & Christ, 2017. "Electricity, Heat and Gas Sector Data for Modelling the German System," Data Documentation 92, DIW Berlin, German Institute for Economic Research.
    26. 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.
    27. 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.
    28. 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).
    29. Poncelet, Kris & Delarue, Erik & Six, Daan & Duerinck, Jan & D’haeseleer, William, 2016. "Impact of the level of temporal and operational detail in energy-system planning models," Applied Energy, Elsevier, vol. 162(C), pages 631-643.
    30. Baumgärtner, Nils & Shu, David & Bahl, Björn & Hennen, Maike & Hollermann, Dinah Elena & Bardow, André, 2020. "DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints," Energy, Elsevier, vol. 198(C).
    31. 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.
    32. Praktiknjo, Aaron J. & Hähnel, Alexander & Erdmann, Georg, 2011. "Assessing energy supply security: Outage costs in private households," Energy Policy, Elsevier, vol. 39(12), pages 7825-7833.
    33. Christian Growitsch & Raimund Malischek & Sebastian Nick & Heike Wetzel, 2015. "The Costs of Power Interruptions in Germany: A Regional and Sectoral Analysis," German Economic Review, Verein für Socialpolitik, vol. 16(3), pages 307-323, August.
    34. 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.
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