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.

Highlights

  • The production of energy from renewable sources has been undergoing a significant increase in recent years

  • The energy produced by a wind turbine, and more generally by a wind power plant, is subject to considerable fluctuations over time related to the characteristics of wind and the location of the plant [3]

  • The income depends on energy production and electricity prices

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Summary

Introduction

The production of energy from renewable sources has been undergoing a significant increase in recent years. Despite that, when dealing with the income generated from selling the energy produced from a wind power plant, one has to consider two main sources of uncertainty: wind speed and energy prices. Both variables have a random nature that should be considered when building a model [2]. The random trend of wind concerns both its speed and its direction [4,5]. Wind speed and direction are log range autocorrelated

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