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Short-term hydropower optimization driven by innovative time-adapting econometric model

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  • Avesani, Diego
  • Zanfei, Ariele
  • Di Marco, Nicola
  • Galletti, Andrea
  • Ravazzolo, Francesco
  • Righetti, Maurizio
  • Majone, Bruno

Abstract

The ongoing transformation of the electricity market has reshaped the hydropower production paradigm for storage reservoir systems, with a shift from strategies oriented towards maximizing regional energy production to strategies aimed at the revenue maximization of individual systems. Indeed, hydropower producers bid their energy production scheduling 1 day in advance, attempting to align the operational plan with hours where the expected electricity prices are higher. As a result, the accuracy of 1-day ahead prices forecasts has started to play a key role in the short-term optimization of storage reservoir systems. This paper aims to contribute to the topic by presenting a comparative assessment of revenues provided by short-term optimizations driven by two econometric models. Both models are autoregressive time-adapting hourly forecasting models, which exploit the information provided by past values of electricity prices, with one model, referred to as Autoarimax, additionally considering exogenous variables related to electricity demand and production. The benefit of using the innovative Autoarimax model is exemplified in two selected hydropower systems with different storage capacities. The enhanced accuracy of electricity prices forecasting is not constant across the year due to the large uncertainties characterizing the electricity market. Our results also show that the adoption of Autoarimax leads to larger revenues with respect to the use of a standard model, increases that depend strongly on the hydropower system characteristics. Our results may be beneficial for hydropower companies to enhance the expected revenues from storage hydropower systems, especially those characterized by large storage capacity.

Suggested Citation

  • Avesani, Diego & Zanfei, Ariele & Di Marco, Nicola & Galletti, Andrea & Ravazzolo, Francesco & Righetti, Maurizio & Majone, Bruno, 2022. "Short-term hydropower optimization driven by innovative time-adapting econometric model," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921017244
    DOI: 10.1016/j.apenergy.2021.118510
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    3. Heidarpanah, Mohammadreza & Hooshyaripor, Farhad & Fazeli, Meysam, 2023. "Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market," Energy, Elsevier, vol. 263(PE).

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