Abstract

In this paper a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics: this is achieved by creating so called Observation Points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model, but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computational lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to SOWFA simulations in three- and nine-turbine cases under static and changing environmental conditions.The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model based dynamic wind farm control.

Highlights

  • In recent years, the topic of wind farm control has gained traction as renewable energies become more and more relevant for 25 the current and future energy mix

  • The distribution and characteristics of the Observation Points (OPs) are adapted to account for the new parametric model, and to take complex flow conditions into account

  • We present a computational lightweight wind field model to allow for a simulation environment in which heterogeneous 15 flow conditions are possible

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Summary

Introduction

The topic of wind farm control has gained traction as renewable energies become more and more relevant for 25 the current and future energy mix. A first approach was presented by Jensen (1983) which motivated years later the development of more refined steady-state models, such as the Zone FLORIS model (Gebraad et al, 2014) With these 30 low computational cost and easy-to-implement wake descriptions it is possible to develop a model-based control algorithm. The authors extend the momentum conservation equations to incorporate time-varying free stream wind velocity effects They couple the model to a 65 dynamic description of floating platforms, restricted by mooring lines. In this paper we aim to overcome these issues and bring the FLORIDyn approach into a form where it can incorporate 80 heterogeneous and changing flow conditions, wind shear and added turbulence levels To achieve these changes, we rework the framework to use a Gaussian FLORIS model (Bastankhah and Porté-Agel, 2016). It is built to 100 provide the heterogeneous field conditions to evaluate the developed FLORIDyn model

The Gaussian FLORIS model
The Zone FLORIDyn model
Changes to the FLORIDyn approach
Distribution of the Observation Points
Wind speed at the rotor plane
Travel speed
Including directional dependency and Observation Point propagation
Calculation of CT and CP
Wind field model
Three turbine case
Comparison of the wind farm start up and steady state
Comparison during a yaw angle change
Nine turbine case
Performance
Conclusions and recommendations
Unfiltered difference between yawed and baseline case
Averaged velocity in the nine turbine case
Full Text
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