Abstract
Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements.
Highlights
Long, realistic simulated time series of wind have many potential uses
Realistic simulated time series of wind have many potential uses. They are a crucial component in erosion modeling [1], hurricane modeling [2], ocean surface wind modeling for climate research [3], climate impact studies [4], ocean transport and sea state modeling [5], wind insurance risk [6], power production and wind turbine performance [7] and building energy simulations [8]
We introduce a model for synthetic generation of both wind speed and direction and assess how well its generated wind captures the statistical properties of observed wind at various spatial and temporal scales both at individual locations and across multiple locations
Summary
Realistic simulated time series of wind have many potential uses. They are a crucial component in erosion modeling [1], hurricane modeling [2], ocean surface wind modeling for climate research [3], climate impact studies [4], ocean transport and sea state modeling [5], wind insurance risk [6], power production and wind turbine performance [7] and building energy simulations [8]. Some argue that Markov chain models to generate synthetic wind speed series should not be used for time steps under one hour [33]. A second-order Markov chain model to simulate wind power at two sites simultaneously was developed [40]. We simulate both speed and direction with a Markov-switching vector autoregressive (MSVAR) model fit to the corresponding u and v components. This model was first introduced with respect to wind generation in [38], and here, the simulation algorithm is generalized to capture seasonality in addition to diurnal cycles, and a nonparametric transformation of the components to normality captures the skewness and kurtosis.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
