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Energy system modeling with aggregated time series: A profiling approach

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  • 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
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    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.
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