Wind farm output smoothing through co-ordinated control and short-term wind speed prediction

Clemow, Philip; Green, Tim; Hernandez-Aramburo, C.A.

doi:10.1109/pes.2010.5590112

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…This objective is similar to that employed by DeRijcke et al ; however, they did not estimate the power derivative using the difference between subsequent time steps but instead use a specified time interval. A similar measure of controlling for power fluctuations between successive time steps was introduced by Clemow et al , and in their formulation, they took the absolute value of the power fluctuations and normalized by the average farm power. In the second formulation for minimizing farm power variability, the term J 3 was added to J 1 , which is defined as

J_{3} = \frac{w_{2}}{T_{h}} \int_{t}^{t + T_{h}} {(P_{italicavg} - \sum_{i = 1}^{N_{T}} P_{i})}^{2} italicdt

where P avg is calculated from

P_{avg} = \frac{1}{T_{h}} \int_{t - T_{h}}^{t} ((), \sum_{i = 1}^{N_{T}} P_{i}) italicdt

…”

Section: Effect Of Power Variability Minimization In the Objective Fumentioning

confidence: 99%

The effect of timescales on wind farm power variability with nonlinear model predictive control

Saunders

Marshall

Hines³

2017

Wind Energy

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Model predictive control techniques enable operators to balance multiple objectives in large wind farms, but the controller design depends on modeling effects that propagate at different timescales. This paper uses nonlinear model predictive control to investigate how wind farm power variability can be reduced both by varying ratios of three timescales impacting the system control and by inclusion of a power variability minimization measure in the controller objective function. Tests were conducted to assess how different timescale ratios affect the average farm power and power variability. Power variability measures are shown to be sensitive to the ratio of the incident wind period and the turbine time delay, particularly for cases with dominant incident wind frequencies. The average farm power increases in a series of steps as the controller time horizon increases, which corresponds to time horizon values required for wakes disturbances to propagate to downstream turbines. A second set of tests was conducted in which various measures of power variability were incorporated into the controller objective function and shown to yield significant reductions in farm power variability without significant reductions in farm power output. The controller was found to utilize two different approaches for achieving power variability reduction depending on the formulation of the controller objective function. These results have important implications for the design and operation of wind power plants, including the importance of considering the frequency components of wind during turbine siting and the potential to reduce power variability through the use of farm‐level coordinated control. Copyright © 2017 John Wiley & Sons, Ltd.

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J_{3} = \frac{w_{2}}{T_{h}} \int_{t}^{t + T_{h}} {(P_{italicavg} - \sum_{i = 1}^{N_{T}} P_{i})}^{2} italicdt

where P avg is calculated from

P_{avg} = \frac{1}{T_{h}} \int_{t - T_{h}}^{t} ((), \sum_{i = 1}^{N_{T}} P_{i}) italicdt

…”

Section: Effect Of Power Variability Minimization In the Objective Fumentioning

confidence: 99%

The effect of timescales on wind farm power variability with nonlinear model predictive control

Saunders

Marshall

Hines³

2017

Wind Energy

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“…At each moment, the difference between the original power waveform and the smoothed power waveform is compensated by the ESS. A large number of coordinated control approaches have been presented so far [19][20][21]. The majority of these approaches use the first-order low-pass filter (FLF) to track the waveform of the windfarm output power (WFOP) [22].…”

Section: Wwwietdlorgmentioning

confidence: 99%

“…P Out data are saved in the 20-min window (defined as W 20 P Out ) for the smoothing calculations in the next sections. Once the new data are received, the oldest existing data in the window are replaced by the new data.…”

Section: Fluctuation Mitigation Constraints (Fmc)mentioning

confidence: 99%

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Mitigation of windfarm power fluctuation by adaptive linear neuron‐based power tracking method with flexible learning rate

Jannati¹,

Hosseinian²,

Vahidi³

et al. 2014

IET Renewable Power Generation

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Most wind turbine generators installed in large windfarms are of variable speed types operating at the maximum power point tracking mode to generate the maximum amount of power. Owing to this fact and regarding the random nature of the windspeed, the output power of the windfarm fluctuates. Fluctuating power is a serious problem for high capacity power plants and should be smoothed. As an effective factor on the required battery energy storage system (BESS) capacity value, tracking is the most important part performed by a coordinated control system in the power smoothing process. An ADAptive LInear NEuron (ADALINE)-based power tracking method with a flexible learning rate is proposed in this study. Furthermore, a particle swarm optimisation-based calculation of the learning rate is presented for optimising the proposed tracking method which reduces the required BESS capacity and the investment cost. Moreover, a charging/discharging algorithm for the BESS units is proposed which decreases the number of required BESS units and increases their useful life by reducing the switching activity as well. To evaluate the performance of the proposed coordinated control approach, the real output data of a 99 MW windfarm are tested. The simulation results verify the effectiveness of the proposed approach.

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“…In the past, first attempts to smooth power have focussed on single turbine control , without including interaction between individual turbines. More recently, some studies have looked into the coordination within a wind farm , mainly focussing on the control architectures but testing them for simple artificially generated wind variations. In the current study, we focus on power smoothing in large wind farms with fluctuating velocity fields that are obtained from time‐resolved turbulent‐flow simulations, i.e., based on large‐eddy simulations (LES) .…”

Section: Introductionmentioning

confidence: 99%

Power smoothing in large wind farms using optimal control of rotating kinetic energy reserves

2014

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In large wind farms, self-induced turbulence levels significantly increase the variability of generated power in a range of time scales from a few seconds to several minutes. In the current study, we investigate the potential for reducing this type of variability by dynamically controlling the rotating kinetic energy reserves that are present in the farm's wind turbines. To this end, we reduce the burden of frequency regulation on remaining conventional units when they are displaced in favor of wind turbines. We focus on the development of a theoretical benchmark framework in which we explore the trade-off between high energy extraction and low variability using optimal coordinated control of multiple turbines subject to a turbulent wind field. This wind field is obtained from a large-eddy simulation of a fully developed wind farm boundary layer. The controls that are optimized are the electric torque and the pitch angles of the individual turbines as function of time so that turbines are accelerated or decelerated to optimally extract or store energy in the turbines' rotating inertia. Results are presented in terms of Pareto fronts (i.e., curves with optimal trade-offs), and we find that power variations can be significantly reduced with limited loss of extracted energy. For a one-turbine case, such an optimal control leads to large potential reductions of variability but mainly for time scales below 10 s if we limit power losses to a few percent. Variability over longer time scales (10-100 s) is reduced considerably more for coordinated control. For instance, restricting the energy-loss incurred with smoothing to 1%, and looking at time scales of 50 s, we manage to reduce variability with a factor of 6 for a coordinated case with 24 turbines, compared with a factor of 1.4 for an uncoordinated case.

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Wind farm output smoothing through co-ordinated control and short-term wind speed prediction

Cited by 5 publications

References 9 publications

The effect of timescales on wind farm power variability with nonlinear model predictive control

The effect of timescales on wind farm power variability with nonlinear model predictive control

Mitigation of windfarm power fluctuation by adaptive linear neuron‐based power tracking method with flexible learning rate

Power smoothing in large wind farms using optimal control of rotating kinetic energy reserves

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