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Stochastic optimization of trading strategies in sequential electricity markets

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  • Kraft, Emil
  • Russo, Marianna
  • Keles, Dogan
  • Bertsch, Valentin

Abstract

Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day-ahead and intraday trading decisions jointly for a portfolio with controllable and volatile renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other.

Suggested Citation

  • Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2021. "Stochastic optimization of trading strategies in sequential electricity markets," Working Paper Series in Production and Energy 58, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    • Handle: RePEc:zbw:kitiip:58
    DOI: 10.5445/IR/1000134346
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    Cited by:

    1. Finhold, E. & Gärtner, C. & Grindel, R. & Heller, T. & Leithäuser, N. & Röger, E. & Schirra, F., 2023. "Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: A rolling horizon case study," Applied Energy, Elsevier, vol. 352(C).
    2. Dirk Lauinger & Franc{c}ois Vuille & Daniel Kuhn, 2023. "Frequency Regulation with Storage: On Losses and Profits," Papers 2306.02987, arXiv.org, revised Mar 2024.
    3. Miguel Angel Muñoz & Pierre Pinson & Jalal Kazempour, 2023. "Online decision making for trading wind energy," Computational Management Science, Springer, vol. 20(1), pages 1-31, December.
    4. Fraunholz, Christoph & Miskiw, Kim K. & Kraft, Emil & Fichtner, Wolf & Weber, Christoph, 2021. "On the role of risk aversion and market design in capacity expansion planning," Working Paper Series in Production and Energy 62, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    5. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    6. Klyve, Øyvind Sommer & Klæboe, Gro & Nygård, Magnus Moe & Marstein, Erik Stensrud, 2023. "Limiting imbalance settlement costs from variable renewable energy sources in the Nordics: Internal balancing vs. balancing market participation," Applied Energy, Elsevier, vol. 350(C).

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    Keywords

    OR in energy; Electricity markets; Multi-stage stochastic programming; Uncertaintymodeling; Risk modeling;
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