Abstract
Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i) the modeling of a nonlinear and non-stationary stochastic process; (ii) the practical implementation of the model we proposed; (iii) the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts.
Highlights
Climate change calls for the reduction of greenhouse gas emissions and a growing development of renewable energy sources
The out-of-sample predictive power of our MS-AR-Generalized AutoRegressive Conditional Heteroscedastic (GARCH) model is evaluated based on its performance on one-step ahead forecasts
State-of-the-art models include the class of Markov-Switching Autoregressive (MSAR) models as initially applied to wind power time series in [15]
Summary
Climate change calls for the reduction of greenhouse gas emissions and a growing development of renewable energy sources. Benefiting from favorable governmental policies and large wind resources, countries in the north-west of Europe are rapidly increasing their wind power capacities. Onshore installations have prevailed, but offshore wind energy is growing significantly. In Denmark, the latest figures stated that wind power accounted for about 22% of the domestic electricity supply and, out of 3802 MW wind power capacity, 868 MW were installed offshore [1]. The current trend is towards the development of large-scale offshore projects capable of generating several hundreds of MW each. Sitting wind farms out at sea has substantial advantages of (i) more space available; (ii) a decrease of the frequency and duration of low wind speeds and (iii) an increased persistence for high wind speeds
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