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

Author

Listed:
  • 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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    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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