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A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets

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  • Katarzyna Maciejowska

Abstract

The changes in electricity markets expose RES producers and electricity traders to various risks, among which the price and the volume risk play a very important role. In this research, a portfolio building strategies are presented, which allow to dynamically choose a proportion of electricity traded in different electricity markets (day-ahead and intraday) and hence to optimize the behavior of an utility. Two types of approaches are considered: simple, assuming that the proportions are fixed, and data driven, which allows for thier fluctuation. In order to explore the market information, Structural Vector Autoregressive (SVAR) model is applied, which allows to estimate the relationship between variables of interest and to simulate their future distribution. The presented methods are evaluated with data coming from German electricity market. The results indicate that data driven trading strategies allow to increase the utility revenue and at the same time reduce the trading risk, measured by the predictability of the next day income and the revenue Value at Risk. It turns out that the approach based on Sharp Ratio provides the most robust results.

Suggested Citation

  • Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
    • Handle: RePEc:arx:papers:2205.00975
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    References listed on IDEAS

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    Cited by:

    1. Weronika Nitka & Rafał Weron, 2023. "Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 105-118.
    2. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    3. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    4. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.

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