4.5 Article

Optimal operation scheduling of a pump hydro storage system coupled with a wind farm

Journal

IET RENEWABLE POWER GENERATION
Volume 15, Issue 1, Pages 173-192

Publisher

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/rpg2.12014

Keywords

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Funding

  1. China Three Gorges Corporation under China Three Gorges Corporation - University of Lisbon
  2. National Natural Science Foundation of China [51876118]
  3. Shanghai Science International Cooperation Project [18160743800]

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The study develops an optimization model to maximize revenue for a renewable power unit consisting of a wind farm and a hydro-pump storage power plant. In the day-ahead market scenario, the proposed methodology leads to an average yearly increase of net revenue ranging between 5% and 20%. Additionally, the coupling of the wind farm and hydropower plant can substantially reduce imbalance costs.
The variability in non-dispatchable power generation makes essential the improvement of production management. This study focuses on the development of an optimisation model for a renewable power unit, composed of a wind farm and a hydro-pump storage power plant, to maximise its revenue. The combination of the two technologies allows the mitigation of risks associated with wind production and electricity price variability. The problem is formulated using linear programming and encompasses the selling of electricity in the Iberian day-ahead market and through a bilateral contract. The model is tested and scrutinised with sets of real historical generation and price data. In the day-ahead market scenario, the proposed methodology leads to an average yearly increase of net revenue ranging between 5% and 20%. In the bilateral contract scenario, the coupling of the wind farm and hydropower plant can reduce the imbalances costs substantially. Furthermore, the work identifies a positive correlation between reservoir dimension, turbine capacity and revenue value. The study also detects a revenue reduction tendency related to the decrease in the volatility of electricity prices.

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