A Simplified Risk-Based Method for Short-Term Wind Power Commitment

Karki, Rajesh; Thapa, Suman; Billinton, R.

doi:10.1109/tste.2012.2190999

Cited by 61 publications

(37 citation statements)

References 13 publications

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“…When evaluating the distribution of wind speeds over a short time period, the wind activity is dominated by turbulence and a Gaussian distribution can be used. In particular, bimodal normal densities can represent the offshore wind speed [28][29][30][31]. As shown in Fig.…”

Section: Characterization Of Wind Power Generationmentioning

confidence: 99%

A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

Arriagada

López

et al. 2015

Electric Power Systems Research

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Section: Characterization Of Wind Power Generationmentioning

confidence: 99%

A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

Arriagada

López

et al. 2015

Electric Power Systems Research

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“…The hourly wind data at Toronto Island in Canada obtained from Environment Canada is used to illustrate the wind model. The historical mean (μ) and standard deviation (σ) of wind speed are 17.23 km/h and 9.35 km/h respectively, and the ARMA model for the site is described in [4]. Fig.…”

Section: IImentioning

confidence: 99%

“…The uncertainty of a wind power commitment made using such a deterministic approach is not known. The probability that the actual wind power will be less than the committed value is designated as the "wind power commitment risk (WPCR)" [4]. This section uses the Toronto wind data to illustrate the application of the short term wind speed models described in the previous section to evaluate the WPCR during system operation.…”

Section: Evaluation Of Wind Power Commitment Riskmentioning

confidence: 99%

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Operating risk considerations in wind integrated power systems

Thapa

Karki

Billinton

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Wind is perceived to be the most suitable renewable resource for bulk power generation, and wind power installations are rapidly growing all over the world. The variable nature of wind power is however causing increased challenges in reliable system operation. System operators face considerable difficulties in determining appropriate unit commitment, reserve requirements and in making dispatch decisions to meet anticipated load with minimum operating risk and cost when integrating wind power. There is a need for suitable techniques that evaluate operating risks associated with wind power estimation, and quantify operating reliability associated with unit commitment and operating reserves while incorporating the uncertainties of wind variation. This paper presents operating risk considerations from two perspectives: the wind power commitment risk from the perspective of the wind farm owner, and the unit commitment risk from the perspective of the power system operator. The wind power model for a short future time is created using conditional probability approach based upon knowledge of the initial condition. The presented methods also incorporate the cross correlation of wind speeds between multiple wind farms, and the impacts are illustrated with examples using the IEEE Reliability Test System.

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“…The main drawback of these models is that a large amount of wind speed data is needed to build proper probability distributions of wind speed [82]. Moreover, it is difficult to select an appropriate distribution, and none of these distributions is suitable to represent wind distributions for all wind regimes since different locations may have different distributions [94].…”

Section: Probabilistic Distributionmentioning

confidence: 99%

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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A Simplified Risk-Based Method for Short-Term Wind Power Commitment

Cited by 61 publications

References 13 publications

A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

Operating risk considerations in wind integrated power systems

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

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