Wind speed simulation in wind farms for steady-state security assessment of electrical power systems

Feijóo, Andrés; Cidrás, J.; Dornelas, J.L.G.

doi:10.1109/60.815109

Cited by 127 publications

(60 citation statements)

References 3 publications

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“…Moreover, spatial correlation has been recognized as a fundamental concept in determining accurate scenarios [15,16,17,18,19,20,21,22]. Mostly when as for instance is reported in [23,24,25,26] (and as we later justify) the correlation among close wind generatorsthose of a microgrid-is not negligibly but really strong.…”

Section: Introductionmentioning

confidence: 99%

“…Some authors have proposed that uncorrelated Monte Carlo samples of wind speed can be transformed into correlated samples by means of a Cholesky factorization of the covariance matrix [15,19,20]. It is a relatively computationally inexpensive and simple method, which nevertheless has been already reported to have some problems with the production of unreal negative wind speeds.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Díaz

Casielles

Coto

2014

International Journal of Electrical Power & Energy Systems

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In clusters of wind generators spread over small geographic areas, the spatial correlation of wind power production is strong. Simulation of joint power production in such cases-such as for instance for determining the available power in a microgrid-is flawed if the correlation is not properly defined.Several methods have been proposed in the literature for producing scenarios of correlated samples; mostly focused on wind speed. In this paper we analyze three popular choices: classical Monte Carlo (with correlation induced by Cholesky factorization), Latin Hypercube Sampling (with correlation induced by rank sorting), and the recent copula theory. We put together a variety of statistical tools to transform an uncorrelated multivariate sample into a correlated one; and supplement other works by introducing a detailed definition of the wind power distribution and by expanding the Archimedean copula analysis to dimensions beyond the bivariate case analyzed in some related works.We analyze a year of wind production of 210 wind site from NREL data base. We cluster them to give a view of prospective microgrids, and employ several statistical techniques to measure the adequacy of the simulated samples to the original measured data.Our results show that, for generation in small geographic areas, the higher the number of generators, the better the wind power dependence structure is described by LHS. On the contrary, copulas-Gumbel or Gaussian for two-and three-dimensional problems, and Gaussian for higher dimensions-are better suited for representing correlated wind speed. The results are different when the generators are spread over large geographic areas.Compared with LHS endowed with rank sorting for inducing correlation, copula theory is in some sense cumbersome to apply for modeling and simulating wind power data. However, simulations can be performed in prospective microgrids in small geographical areas with larger accuracy by means of LHS if wind power is analyzed rather than wind speed. This advantage is lost for large distances or when small number of generators is considered.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Díaz

Casielles

Coto

2014

International Journal of Electrical Power & Energy Systems

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“…Normally, the long-time wind speed follows Weibull distribution; the illumination intensity follows Beta distribution [27][28]. The corresponding parameter of Weibull and Beta distribution is assumed.…”

Section: Resultsmentioning

confidence: 99%

Optimal Coordination of Intermittent Distributed Generation with Probabilistic Power Flow

Xing¹,

Cheng²,

Zhang³

2015

Journal of Electrical Engineering and Technology

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-This paper analyzes multiple active management (AM) techniques of active distribution network (ADN), and proposes an optimal coordination model of intermittent distributed generation (IDG) accommodation considering the timing characteristic of load and IDG. The objective of the model is to maximize the daily amount of IDG accommodation under the uncertainties of IDG and load. Various active management techniques such as IDG curtailment, on-load tap changer (OLTC) tap adjusting, voltage regulator (VR) tap adjusting, shunt capacitors compensation and so on are fully considered. Genetic algorithm and Primal-Dual Interior Point Method (PDIPM) is used for the model solving. Point estimate method is used to simulate the uncertainties. Different scenarios are selected for the IDG accommodation capability investigation under different active management schemes. Finally a modified IEEE 123 case is used to testify the proposed accommodation model, the results show that the active management can largely increase the IDG accommodation and penetration.

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“…It has been demonstrated by a large number of experiments that the stochastic wind speed in most regions approximately follows Weibull distribution, which can be formulated as follows [11]:…”

Section: The Stochastic Model Of Distributed Wind Power Generationmentioning

confidence: 99%

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

TANG¹,

Jiekang²,

Wu³

et al. 2018

Turk J Elec Eng & Comp Sci

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This paper introduces a stochastic second-order cone programming (SSCOP) approach to solve the distributed generation coordination problem considering the uncertainty of electric vehicle charging in distribution networks. To minimize the total power loss in the distribution system, the problem is formulated to coordinate the output power of distributed generations (DGs). Two stages are presented to solve the optimization problem: the first stage is to optimize the output power of distributed generators without electric vehicle charging in the distribution system, and the second stage is to optimize the output power of distributed generators according to the stochastic increased load due to the uncertainty of electric vehicle charging. The proposed approach is tested on 69-node and 118-node large-scale distribution systems. The simulated results demonstrate the feasibility and effectiveness of SSCOP.

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Wind speed simulation in wind farms for steady-state security assessment of electrical power systems

Cited by 127 publications

References 3 publications

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Optimal Coordination of Intermittent Distributed Generation with Probabilistic Power Flow

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

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