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Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing

In: Applied Operations Research and Financial Modelling in Energy

Author

Listed:
  • Sezer Çabuk

    (Middle East Technical University)

  • Özenç Murat Mert

    (Middle East Technical University)

  • A. Sevtap Selcuk-Kestel

    (Middle East Technical University)

  • Erkan Kalaycı

    (Middle East Technical University)

Abstract

Hydro inflow forecasting is crucial for effective hydro optimization, virtual power plant pricing, volume risk management, and weather derivatives pricing in the electricity markets. Predicting hydro inflow allows the decision-makers to economically use water for optimal periods, quantify volume risk and determine effective portfolio management strategies. This study aims pricing a hydroelectricity power plant as a Virtual Power Plant based on Turkish energy markets. For pricing of such a non-standard option, inflow and price scenarios and optimization model with constraints are performed. For the hydro inflow forecasting utilized for the optimization model, SARIMAX with precipitation as an exogenous variable is applied. In addition to the point forecasts, we generate various inflow scenarios based on the residuals as a stochastic process for defined VPP. Moreover, a hydro optimization problem where the objective function maximizes the expected value of generation by tracing generated inflow and price scenarios is made. Price scenarios are simulated using the hourly behavior of historical Day-Ahead Market. The optimization outputs are evaluated according to different prices and inflow levels. For a defined VPP, Volume at Risk measure is defined to measure the risky volume for the valuation of VPP.

Suggested Citation

  • Sezer Çabuk & Özenç Murat Mert & A. Sevtap Selcuk-Kestel & Erkan Kalaycı, 2021. "Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing," Springer Books, in: André B. Dorsman & Kazim Baris Atici & Aydin Ulucan & Mehmet Baha Karan (ed.), Applied Operations Research and Financial Modelling in Energy, pages 125-152, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-84981-8_7
    DOI: 10.1007/978-3-030-84981-8_7
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