Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm

Calif, Rudy; Schmitt, François G

doi:10.5194/npg-21-379-2014

Cited by 57 publications

(31 citation statements)

References 55 publications

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“…This is due to the fact that the ESS can handle whole WP fluctuation as the capacity of ESS is large enough. And we also relates the average wind power since that value can affect level of the WP fluctuation as described in Equation (5). From these considerations of average wind speed and capacity of an ESS in the formulation of parameter k d , we can adapt this formulation to different size of WT or ESS using above Equation (8).…”

Section: Proposed Control Strategymentioning

confidence: 99%

“…As the value of the WP penetration increases, power systems have lower inertia and the power fluctuation from a wind turbine (WT) can reduce the power quality, sometimes leading to severe stability problems. Since WP is a stochastic process [3] highly dependent on turbulent fluctuations in wind speed [4,5], many studies have been carried out to integrate WP successfully without causing stability problems. To improve the stability when WP is integrated into a power system with high penetration, different methods for grid frequency regulation have been studied.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

2017

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This paper proposes a method for the coordinated control of a wind turbine and an energy storage system (ESS). Because wind power (WP) is highly dependent on wind speed, which is variable, severe stability problems can be caused in power systems, especially when the WP has a high penetration level. To solve this problem, many power generation corporations or grid operators have begun using ESSs. An ESS has very quick response and good performance for reducing the impact of WP fluctuation; however, its installation cost is high. Therefore, it is important to design the control algorithm by considering both the ESS capacity and WP fluctuation. Thus, we propose a control algorithm to mitigate the WP fluctuation by using the coordinated control between the wind turbine and the ESS by considering the ESS capacity and the WP fluctuation. Using de-loaded control, according to the WP fluctuation and ESS capacity, we can expand the ESS lifespan and improve grid reliability by avoiding the extreme value of state of charge (SoC) (i.e., 0 or 1 pu). The effectiveness of the proposed method was validated via MATLAB/Simulink by considering a small power system that includes both a wind turbine generator and conventional generators that react to system frequency deviation. We found that the proposed method has better performance in SoC management, thereby improving the frequency regulation by mitigating the impact of the WP fluctuation on the small power system.

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Section: Proposed Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

2017

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“…where k is an integer different from i, randomly chosen from the range [1,SN], and φ i,j is a random number from [−1, 1]. If the updated solution has better nectar than the old one, the employed bee will memorize the new solution and discard the old one.…”

Section: Original Abc Algorithmmentioning

confidence: 99%

“…With the increasing penetration level of wind power, challenges from the variability and uncertainty, more precisely from the stochasticity of wind power [1,2], of wind power to power system reliability have been reported by researchers and system operators [3]. One way to mitigate the effect of variability and uncertainty of wind power on the optimal power flow (OPF) is to solve the stochastic dynamic ACOPF problem by considering many possible wind power forecasting scenarios, which are generally synthesized using Monte Carlo simulation [4].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

Bai¹,

Lee

2017

Energies

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The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)-a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF' solutions on an IEEE 118-bus system at every hour for 24 h.

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“…As one kind of the rapidly growing renewable energy sources, wind energy has been recognized as an attractive alternative to conventional fossil fuels due to several advantages, including renewability and pollution-free environment [2]. However, wind power is recognized as a stochastic process [3] because of the intermittent and multi-scale characteristics of wind speed fluctuation [4,5]. With the increasing penetration of wind power in electric grids, this presents a number of challenges to power system operation, both technically and economically [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Wang

2018

Energies

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Abstract:With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) is developed for multi-step ahead wind speed prediction. The TPSD including seasonal separation algorithm (SSA), fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) is proposed for the first time to handle the complex and irregular natures of wind speed comprehensively. The FE process is used to capture the useful features of wind speed fluctuations and determine the optimal inputs for a prediction model. The WRELM is employed as a basic predictor for building the prediction model by these selected features. Four real wind speed prediction cases are utilized to evaluate the proposed model, and experimental results verify the effectiveness of the proposed model compared with the benchmark models.

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Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm

Cited by 57 publications

References 55 publications

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

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