Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms

Khorramdel, Benyamin; Raoofat, Mahdi

doi:10.1016/j.energy.2012.05.055

Cited by 46 publications

(28 citation statements)

References 29 publications

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“…Though in general stochastic programming is still being initially and progressively introduced into microgrid analyses, see for instance [11,12,13,14], in bulk power systems it has been thoroughly employed in the past; and the need for simulated wind scenario generation has been thus recognized. Moreover, spatial correlation has been recognized as a fundamental concept in determining accurate scenarios [15,16,17,18,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…See for instance [7], where Monte Carlo simulation of parametric (Weibull) distributions had to be employed to account for the uncertainty of wind energy in a unit commitment problem. Or the instance in [11], where Monte Carlo samples were also employed to define an interesting index, from balancing voltage security and loss reduction, which allowed determining the optimal reactive droop coefficients.…”

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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“…The authors in [9] proposed a stochastic programming approach for reactive power scheduling of a microgrid considering the uncertainty of wind power. In [10], the authors presented that fast/emergency reserve can be provided by responsive loads such as residential and small commercial air conditioners.…”

Section: B Literature Reviewmentioning

confidence: 99%

Active distribution networks planning with integration of demand response

Mokryani

2015

Solar Energy

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-This paper proposes a probabilistic method for active distribution networks planning with integration of demand response.Uncertainties related to solar irradiance, load demand and future load growth are modelled by probability density functions. The method simultaneously minimizes the total operational cost and total energy losses of the lines from the point of view of distribution network operators with integration of demand response over the planning horizon considering active management schemes including coordinated voltage control and adaptive power factor control. Monte Carlo simulation method is employed to use the generated probability density functions and the weighting factor method is used to solve the multi-objective optimization problem. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. Index Terms -

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“…The authors in [3] focused later in [4] on the stochastic reactive power market in the presence of volatility of wind power generation in a transmission system. This kind of stochastic reactive power from wind generators was also studied in [5] for a medium-voltage (MV) network. The works in [4,5] used a model for reactive power production from wind turbines based on a detailed induction machine equivalent circuit as presented in [6].…”

Section: Introductionmentioning

confidence: 99%

“…This kind of stochastic reactive power from wind generators was also studied in [5] for a medium-voltage (MV) network. The works in [4,5] used a model for reactive power production from wind turbines based on a detailed induction machine equivalent circuit as presented in [6]. Other researchers [7], however, used a different model based on an electrical model of a wind turbine with a full scale converter.…”

Section: Introductionmentioning

confidence: 99%

On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations

Gabash

2016

Energies

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It has recently been shown that using battery storage systems (BSSs) to provide reactive power provision in a medium-voltage (MV) active distribution network (ADN) with embedded wind stations (WSs) can lead to a huge amount of reverse power to an upstream transmission network (TN). However, unity power factors (PFs) of WSs were assumed in those studies to analyze the potential of BSSs. Therefore, in this paper (Part-I), we aim to further explore the pure reactive power potential of WSs (i.e.

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Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms

Cited by 46 publications

References 29 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

Active distribution networks planning with integration of demand response

On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations

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