A study on smoothing effect on output fluctuation of distributed wind power generation
Central Res. Inst. of Electr. Power Ind., Yokosuka, Japan
DOI: 10.1109/TDC.2002.1177602 Conference: Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, Volume: 2
Wind power generation in Japan has shown a marked increase in recent years. This increase has aroused growing concerns about the adverse effects of wind power generation on power system control, operation, and planning, because wind power output cannot be controlled and fluctuates much more than that of the conventional power plants. However, if wind power generators am distributed over a wide area, a "smoothing effect" an output fluctuation of distributed wind turbines could be expected because of the stochastic nature of wind. This paper examines the smoothing effect of output fluctuation of dispersed wind turbines. The remits reveal that the output of a single wind turbine fluctuates almost its rated power in ten minutes and little smoothing effect is expected in a wind farm scale (i.e., within sum kilometers) for a period of 10 minutes. On the other hand, a smoothing effect can be expected for a wider area in 100 minutes.
Available from: Luciano De Tommasi
- "The scaling factor √ N applied to the dynamic part of the output is justified by the assumption of incoherent fluctuations. In fact, when fluctuations are incoherent, the ratio between the fluctuation amplitude of a wind farm and of a single turbine is √ N ,. Since wind coherence is directly reflected on the coherence of wind turbine outputs. It is however worthwhile to note that Davenport's coherence expression (10) shows that only sufficiently high frequency components are truly incoherent and therefore damped with the factor √ N. Coherence of low frequency components might be taken into account by weighting them with a factor closer to the one occurring in steady state . "
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ABSTRACT: An important aspect related to wind energy integration into the electrical power system is the fluctuation of the generated power due to the stochastic variations of the wind speed across the area where wind turbines are installed. Simulation models are useful tools to evaluate the impact of the wind power on the power system stability and on the power quality. Aggregate models reduce the simulation time required by detailed dynamic models of multiturbine systems.In this paper, a new behavioral model representing the aggregate contribution of several variable-speed-pitchcontrolled wind turbines is introduced. It is particularly suitable for the simulation of short term power fluctuations due to wind turbulence, where steady-state models are not applicable.The model relies on the output rescaling of a single turbine dynamic model. The single turbine output is divided into its steady state and dynamic components, which are then multiplied by different scaling factors. The smoothing effect due to wind incoherence at different locations inside a wind farm is taken into account by filtering the steady state power curve by means of Gaussian filter as well as applying a proper damping on the dynamic part.The model has been developed to be one of the building-blocks of a model of a large electrical system, therefore a significant reduction of simulation time has been pursued. Comparison against a full model obtained by repeating a detailed single turbine model, shows that a proper trade-off between accuracy and computational speed has been achieved.
Procedia Computer Science 05/2010; 1(1):269-278. DOI:10.1016/j.procs.2010.04.030
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ABSTRACT: Recently there has been a marked increase in wind power generation. From a power system point of view, because a wind turbine is an intermittent generator with large output fluctuation, any increase in the number of wind turbines gives rise to concerns about the adverse effects of wind turbines on power quality. The smoothing effects of wind turbine output fluctuation are of great importance in assessing the impacts of a large number of wind turbines. This article examines smoothing effects at a wind farm. First it presents a summary of wind measurements taken at two locations with six masts over a period of 1 year on both flat and complex terrain. Then the spatial coherence of wind speed is analysed, paying special attention to its dependence on the distance between observation points, wind direction, wind velocity and fluctuation frequency. Approximation equations for coherence of frequency and distance are obtained by applying Davenport's expression to the observed data. Second, coherence between turbine output at a wind farm is investigated; the results indicate that coherence for wind speed and turbine output shows a considerable resemblance. The article also examines smoothing effects at a wind farm using power spectral density through a theoretical approach. The study proves that smoothing effects can be approximated with a lowpass filter and that the effects at a wind farm should not be taken into account for periods of more than 10 min in case of assessing them on the safe side. Copyright © 2004 John Wiley & Sons, Ltd.
Wind Energy 04/2004; 7(2):61 - 74. DOI:10.1002/we.109 · 3.07 Impact Factor
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ABSTRACT: The objective of this paper is to introduce the concept of wind farms linked by SMES systems. In this work, the SMES system is applied to a wind farm that is interconnected with a grid through a back-to-back DC link for the variable speed operation of the wind turbines. This system enables the output power leveling of the wind farm depending on the power demand and can reduce the capacity of the converter system by selecting an optimal discharge/charge rate of the SMES. By using the stored energy of the SMES, this system can also compensate the inertia of the blades so that the wind turbine speed can be rapidly controlled depending on the wind condition. This paper describes the design condition of the SMES for the output power leveling of the wind farm and discusses the SMES configuration for a 100-MW class wind farm.
IEEE Transactions on Applied Superconductivity 07/2005; 15(2-15):1951 - 1954. DOI:10.1109/TASC.2005.849343 · 1.24 Impact Factor
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