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A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Author

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  • Riccardo De Blasis

    (Department of Finance, Management and Technology, LUM University, 70010 Casamassima, Italy)

  • Giovanni Batista Masala

    (Department of Economics and Business Sciences, University of Cagliari, 09124 Cagliari, Italy)

  • Filippo Petroni

    (Department of Management, Università Politecnica delle Marche, 60121 Ancona, Italy)

Abstract

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:388-:d:478903
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    References listed on IDEAS

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