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Pan-European CVaR-Constrained Stochastic Unit Commitment in Day-Ahead and Intraday Electricity Markets

Author

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  • Moritz Nobis

    (Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW), RWTH Aachen University, 52056 Aachen, Germany
    Current address: Schinkelstraße 6, 52062 Aachen, Germany.)

  • Carlo Schmitt

    (Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW), RWTH Aachen University, 52056 Aachen, Germany)

  • Ralf Schemm

    (Energy Technology, Aachen University of Applied Sciences, 52428 Jülich, Germany)

  • Armin Schnettler

    (Siemens AG, 80333 Munich, Germany)

Abstract

The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.

Suggested Citation

  • Moritz Nobis & Carlo Schmitt & Ralf Schemm & Armin Schnettler, 2020. "Pan-European CVaR-Constrained Stochastic Unit Commitment in Day-Ahead and Intraday Electricity Markets," Energies, MDPI, vol. 13(9), pages 1-35, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2339-:d:355332
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    References listed on IDEAS

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    1. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    2. Jan Abrell & Friedrich Kunz, 2015. "Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market," Networks and Spatial Economics, Springer, vol. 15(1), pages 117-147, March.
    3. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    4. Morales, Juan M. & Zugno, Marco & Pineda, Salvador & Pinson, Pierre, 2014. "Electricity market clearing with improved scheduling of stochastic production," European Journal of Operational Research, Elsevier, vol. 235(3), pages 765-774.
    5. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, September.
    6. Fürsch, Michaela & Malischek, Raimund & Lindenberger, Dietmar, 2012. "Der Merit-Order-Effekt der erneuerbaren Energien - Analyse der kurzen und langen Frist," EWI Working Papers 2012-14, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    7. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    8. Christopher Kath, 2019. "Modeling Intraday Markets under the New Advances of the Cross-Border Intraday Project (XBID): Evidence from the German Intraday Market," Energies, MDPI, vol. 12(22), pages 1-35, November.
    9. Müller, C. & Hoffrichter, A. & Wyrwoll, L. & Schmitt, C. & Trageser, M. & Kulms, T. & Beulertz, D. & Metzger, M. & Duckheim, M. & Huber, M. & Küppers, M. & Most, D. & Paulus, S. & Heger, H.J. & Schnet, 2019. "Modeling framework for planning and operation of multi-modal energy systems in the case of Germany," Applied Energy, Elsevier, vol. 250(C), pages 1132-1146.
    10. Matthias Nowak & Werner Römisch, 2000. "Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty," Annals of Operations Research, Springer, vol. 100(1), pages 251-272, December.
    11. Wang, Peng & Zareipour, Hamidreza & Rosehart, William D., 2011. "Characteristics of the prices of operating reserves and regulation services in competitive electricity markets," Energy Policy, Elsevier, vol. 39(6), pages 3210-3221, June.
    12. Alison L. Gibbs & Francis Edward Su, 2002. "On Choosing and Bounding Probability Metrics," International Statistical Review, International Statistical Institute, vol. 70(3), pages 419-435, December.
    13. John Bower & Derek W. Bunn, 2000. "Model-Based Comparisons of Pool and Bilateral Markets for Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 1-29.
    14. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    1. Despoina I. Makrygiorgou & Nikos Andriopoulos & Ioannis Georgantas & Christos Dikaiakos & George P. Papaioannou, 2020. "Cross-Border Electricity Trading in Southeast Europe Towards an Internal European Market," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. Yuya Tanigawa & Narayanan Krishnan & Eitaro Oomine & Atushi Yona & Hiroshi Takahashi & Tomonobu Senjyu, 2023. "Clustering Method for Load Demand to Shorten the Time of Annual Simulation," Energies, MDPI, vol. 16(5), pages 1-22, February.

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