A quantitative review of wind farm control with the objective of wind farm power maximization

Kheirabadi, Ali C.; Nagamune, Ryozo

doi:10.1016/j.jweia.2019.06.015

Cited by 114 publications

(81 citation statements)

References 79 publications

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“…This once again emphasizes the importance and added value of model correction in estimating the true power gain that is likely to be observed in (quasi-)dynamic operation. The resulting power gain likelihood presented here can be compared to reported gains in literature, where Kheirabadi and Nagamune (2019) have performed a comprehensive review of the reported power gains published across different model fidelities (wind tunnel and field tests). The spread in the reported values is very large, ranging from power loss of −7.9 % to power gains of 46 % for different wind farm layouts and specifications.…”

Section: Validationmentioning

confidence: 99%

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Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

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Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

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Section: Validationmentioning

confidence: 99%

“…The characteristics of the wake have been analysed in various studies, e.g. Knudsen et al (2015) for an overall review. Numerous wind tunnel experiments have shown the potential of redirecting the wake, e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

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“…Large uncertainties, associated with both active wake control strategies (i.e. derating and yaw-based wake deflection) and, not least, among various simulation approaches as well as measurements, were also reported in (Kheirabadi and Nagamune, 2019). Important conclusions from this study is further, that 1) full-scale tests provide the most conservative evaluations of the potential of active wake control; and consistently 2) "added layers of realism in terms of simulated wind conditions tend 20 to deteriorate the performance of wind farm controllers".…”

Section: Introductionmentioning

confidence: 90%

Integrated wind farm layout and control optimization

Pedersen¹,

Larsen²

2020

Preprint

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Abstract. Design of an optimal wind farm topology and wind farm control scheduling depends on the chosen metric. The objective of this paper is to investigate the influence of optimal wind farm control on the optimal wind farm layout in terms of power production. A successful fulfilment of this goal requires: 1) an accurate and fast flow model; 2) selection of the minimum set of design parameters that rules the problem; and 3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is a priori limited to wind turbine derating. A high-speed linearized CFD RANS solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disk method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space spanned by these (2N + Nd Ns N) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed in two subsets, which in turn define a nested set of optimization problems to achieve the fastest possible optimization procedure. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control in the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

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“…Another peculiar constraint, which supplements constraint (9), is that a maximal number of wind turbines of a definite type may be limited also. Besides, a minimal number of wind turbines of that type may exists, which must be necessarily bought being imposed by, e. g., a contract binding the wind farm projector and a supplier of wind turbines.…”

Section: K Nmentioning

confidence: 99%

“…Firstly, the ranks of energy criterion and cost criterion are presumed to be equal. This is not always true, especially for terrains where the wind power technology is just at its beginning and minimization of costs is more important than maximization of energy [9]. Secondly, uncertainty of the parameters of wind statistics (i. e., a wind speed distribution) cannot be completely removed.…”

Section: Introductionmentioning

confidence: 99%

Iterative Power Maximization by One-Half Cost Dichotomy for Optimizing Wind Farm Deployment

Romanuke

2019

KPI SN

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Background. In deploying a wind farm, it is important to hold a balance of energy generated by wind turbines and costs spent on buying and installing them. However, ranking the energy and costs is commonly uncertain. Besides, the existing methods of optimal wind farm deployment are pretty slow, whereas even optimal numbers of wind turbines of certain types along with their costs and energy produced by them may need to be frequently recalculated. Objective. The goal is to develop a practically rapid method of maximizing the produced energy by minimizing the costs. The method should get rid of ranking the energy and costs. Besides, it should speed up the process of finding optimal numbers of wind turbines. The optimality here is to be interpreted in wide sense implying also fitting wind statistics, controlling the costs and production, and adjusting to energy markets. Methods. Once an expected power for every wind turbine type is calculated, a power maximization problem is formulated in the form of an integer linear programming problem, where optimal numbers of wind turbines of certain types are to be found. This problem involves a span of acceptable annual energy formed by a maximum and a minimum of the annual desired energy. Additionally, costs are constrained. The minimal and maximal numbers of wind turbines of a definite type are constrained also. First, the power maximization problem is solved by an arbitrary large constraining costs. Then the constraining costs are decreased until the solution is nonempty. If the solution is empty, the costs are increased. Every next step of either the decrement or increment is twice smaller than the previous one. This process is continued until the change in the costs becomes sufficiently insignificant. Results. The optimization process is rapidly executed requiring only a few iterations to achieve an optimal solution. In particular, solving optimization problems with five known wind turbine types takes up to one tenth of a second, so a bunch of such problems is solved within a second or so. In general, the optimization requires no less than 3 iterations. After the first iteration, the constraining costs drop too low and the problem has no solution. However, the empty solution at the third iteration is not excluded, and a nonempty solution can appear after a few empty solutions. Nevertheless, an apparent economical impact after applying the wind farm deployment optimization is expectedly strong. There is an example with saving almost 18.4 million euros, which is 28.2 % of the initial (non-optimized) costs. Such gains, however, are expectedly decreasing as the relative difference between a maximum and a minimum of the annual desired energy is shortened. Conclusions. The presented approach is a method of successive optimization. It allows to avoid solving the twocriterion problem for simultaneous energy maximization and cost minimization for deploying wind farms. The computational core of this method is that the expected power output is maximized via solving an integer linear ...

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A quantitative review of wind farm control with the objective of wind farm power maximization

Cited by 114 publications

References 79 publications

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Integrated wind farm layout and control optimization

Iterative Power Maximization by One-Half Cost Dichotomy for Optimizing Wind Farm Deployment

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