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Performance estimation of a wind farm with a dependence structure between electricity price and wind speed

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  • Laura Casula
  • Guglielmo D'Amico
  • Giovanni Masala
  • Filippo Petroni

Abstract

This paper aimed to estimate the income generated by a wind turbine over a given time interval. The income depends on two main variables: the wind speed that determines the produced energy and electricity price. Both wind speed and electricity price evolve randomly in time and are correlated. To consider this dependency, we applied a vector autoregressive process (VAR) that links both variables. An application was performed using real data from a hypothetical wind turbine located in Sardinia (Italy). The income simulated by using the VAR model was closer to the empirical value compared with that obtained by simulating wind speed and electricity prices as independent variables. The results were also discussed in relation to the introduction of the SAPEI submarine cable, which produces a significant change in the income value.

Suggested Citation

  • Laura Casula & Guglielmo D'Amico & Giovanni Masala & Filippo Petroni, 2020. "Performance estimation of a wind farm with a dependence structure between electricity price and wind speed," The World Economy, Wiley Blackwell, vol. 43(10), pages 2803-2822, October.
    • Handle: RePEc:bla:worlde:v:43:y:2020:i:10:p:2803-2822
    DOI: 10.1111/twec.12962
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    References listed on IDEAS

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    Cited by:

    1. Laura Casula & Guglielmo D’Amico & Giovanni Masala & Filippo Petroni, 2020. "Performance estimation of photovoltaic energy production," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 267-285, December.
    2. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2021. "An Analysis of a Storage System for a Wind Farm with Ramp-Rate Limitation," Energies, MDPI, vol. 14(13), pages 1-25, July.
    3. Riccardo De Blasis & Giovanni Batista Masala & Filippo Petroni, 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm," Energies, MDPI, vol. 14(2), pages 1-16, January.
    4. Guglielmo D’Amico & Fulvio Gismondi & Filippo Petroni, 2020. "Insurance Contracts for Hedging Wind Power Uncertainty," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    5. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.

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