Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation

Qin, Z. H.; Li, Wenyuan; Xiong, Xiaofu

doi:10.1016/j.apenergy.2013.04.045

Cited by 89 publications

(37 citation statements)

References 22 publications

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“…[10]- [12] or even wind, PV and load data [13], these methods will face difficulties in modelling and may become complicated when the number of wind farms or solar generators increases. For instance, in [12] wind farms and load data are being modelled as three dimensional clusters to keep the dependencies among them.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, the method presented in [14] is capable of keeping the relevance between datasets, as this method utilizes the available capacity probability table, it will become complicated as the number of wind farms increases. Moreover, some of these approaches [13], [14] are applicable in the generation level assessment and haven't addressed the reliability assessment of renewables considering transmission system outages and constraints. On the other hand, sequential method is capable of keeping dependencies.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Cholesky decomposition (CD) technique [103] is one of the popular methods based on the correlation matrix, which is particularly useful for Monte Carlo simulations [104]. However the main drawback of this method is that it can just keep the correlation between normal distributions, while wind power usually does not follow this distribution [13], [74]. Therefore, the Cholesky decomposition is combined with distribution transformation techniques in the sampling process to deal with the correlations between random numbers.…”

Section: Correlationmentioning

confidence: 99%

“…For instance, in Ref. [13] this method is applied to transform normal values into Weibull distributions or in Ref. [74] normal distribution variables ( ) are converted into different distributions such as Weibull, Burr, lognormal and gamma.…”

Section: C) Transformationmentioning

confidence: 99%

“…μ is the average and σ is the standard deviation of the logarithm of random variable. , γ < x < ∞ ; 0 otherwise [13], [85], [90]- [92] Amongst these distribution functions, the Weibull distribution is the most commonly recommended and adopted distribution to describe the distribution of wind speed as this distribution can give a good fit to the measured data [72], [93].…”

Section: Probabilistic Distributionmentioning

confidence: 99%

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Reliability impacts of increased wind generation in the Australian national electricity grid

Mosadeghy¹

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Wind power penetration has been consistently growing and it has been rapidly becoming a significant generation technology in many countries. However, the intermittent and variable nature of wind energy is a major barrier in wind power commitment. Wind speed fluctuations and unpredictability can affect the operation and reliability of power systems. Therefore, the impact of integrating large volume of wind generators on the system reliability needs to be carefully investigated and the reliability contributions of wind farms require to be evaluated for better integration of wind energy sources.Because of intermittency and variability of wind energy, conventional reliability evaluation methods are not applicable and different techniques have been developed to model wind generators.However, most of these methods are time-consuming or may not be able to capture time dependency and correlations between renewable resources and load. Therefore, this research intends to improve the existing reliability methods and proposes a faster and simpler approach. In this approach, wind power and electricity demand are being modelled as time-dependent clusters, which not only can capture their time-dependent attributes, but also is able to keep the correlations between the data sets. To illustrate the effectiveness of this framework, the proposed methodology has been applied to the IEEE reliability test system. In addition, the developed technique is validated by comparing results with the sequential Monte Carlo technique.Due to an increase in the wind power penetration level in Australia, this research also investigates the contribution of wind power in the Australian power system from reliability point of view. In order to calculate the reliability contribution of wind power, the proposed framework is implemented on the national electricity market at two different reliability assessment levels: generation and composite system levels. Moreover, the impacts of strategies such as coordinating hydro units with wind farms on the reliability of wind energy are investigated. Similar to wind energy, photovoltaic (PV) penetration level is increasing in the Australian power system, which can affect not only the reliability of the power system, but also the reliability benefit of wind farms.

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Section: Discussionmentioning

confidence: 99%