Abstract
This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.
Highlights
To date, wind energy has become a promising alternative and renewable energy resource to handle the problem of energy crisis and environmental deterioration
Representation of the wind farm (WF) power curves should not be acquired as the summation of power curves produced by the individual wind turbine (WT)
In 2018, Zhao et al [20] developed a data-driven correction method for the refinement of a WF power curve under wind curtailment and the work in [20] presented the ability to be directly used in different cases without the need to tune any parameters, for WTs and for WFs
Summary
Wind energy has become a promising alternative and renewable energy resource to handle the problem of energy crisis and environmental deterioration. In 2018, Zhao et al [20] developed a data-driven correction method for the refinement of a WF power curve under wind curtailment and the work in [20] presented the ability to be directly used in different cases without the need to tune any parameters, for WTs and for WFs. On the basis of the extensive research on WT power curves, many previous works have established. If the WF is located on a hilly area where the positions of the WTs form a gradient, these models cannot be directly applied to calculate the total output power of the WF which results in an increase of the computation error In this case, if these simplified models are to be used a modification needs to be added beforehand.
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