Wind farm electrical power production model for load flow analysis

Segura-Heras, Isidoro; Escrivá-Escrivá, Guillermo; Alcázar-Ortega, Manuel

doi:10.1016/j.renene.2010.09.007

Cited by 32 publications

(17 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: 70%

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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 70%

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

View full text Add to dashboard Cite

show abstract

“…These predictions are employed for optimal operation policies and operative costs [3,4], load balancing [1,5], site and capacity planning [6,7], and unit commitment for electricity markets [1e3]. Tipically, wind farm energy production is estimated using a fixed weighted measure of the wind farm's nominal power and forecasts from historical atmospheric data [8,9]. Further, it has been stated that wind speed is one (if not the most) important variable related to wind power generation [10].…”

Section: Introductionmentioning

confidence: 99%

Wind speed forecasting for wind farms: A method based on support vector regression

2016

View full text Add to dashboard Cite

a b s t r a c tIn this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, M exico, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5e23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.

show abstract

“…wind power, which affect power system can be predicted and modeled using efficient methods [3,4]. Furthermore, in systems with high penetration of wind power, spatial and temporal correlation of wind power should be considered for extra accurate and reliable results [5,6].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

et al. 2016

View full text Add to dashboard Cite

a b s t r a c tIncreasing penetration of wind power in power systems causes difficulties in system planning due to the uncertainty and non dispatchability of the wind power. The important issue, in addition to uncertain nature of the wind speed, is that the wind speeds in neighbor locations are not independent and are in contrast, highly correlated. For accurate planning, it is necessary to consider this correlation in optimization planning of the power system. With respect to this point, this paper presents a probabilistic multiobjective optimal power flow (MO-OPF) considering the correlation in wind speed and the load. This paper utilizes a point estimate method (PEM) which uses Nataf transformation. In reality, the joint probability density function (PDF) of wind speed related to different places is not available but marginal PDF and the correlation matrix is available in most cases, which satisfy the service condition of Nataf transformation. In this paper biogeography based optimization (BBO) algorithm, which is a powerful optimization algorithm in solving problems including both continuous and discrete variables, is utilized in order to solve probabilistic MO-OPF problem. In order to demonstrate performance of the method, IEEE 30-bus standard test case with integration of two wind farms is examined. Then the obtained results are compared with the Monte Carlo simulation (MCS) results. The comparison indicates high accuracy of the proposed method.

show abstract

Wind farm electrical power production model for load flow analysis

Cited by 32 publications

References 7 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

Wind speed forecasting for wind farms: A method based on support vector regression

Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

Contact Info

Product

Resources

About