Round-the-Year Security Analysis With Large-Scale Wind Power Integration

Ciupuliga, A.R.; Gibescu, Madeleine; Pelgrum, E.; Jacobs, P. G. H.; Jansen, Kees; Kling, W.L.

doi:10.1109/tste.2011.2164950

Cited by 15 publications

(4 citation statements)

References 6 publications

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“…With mounting concerns over global warming and fossil fuel depletion, in recent years wind power generation has been greatly increased in the worldwide. However, due to the stochastic behaviors of wind resources cannot be fully predicted, the induced uncertainties significantly influence the power system operations, such as reserve deployment, unit commitment, power dispatch, power system's security and reliability assessments, etc [1][2][3][4][5][6]. Therefore, a thorough uncertainty model for wind power forecasting error is imperative for power system operators and participants, in terms of evaluating the benefits and costs of wind power integration and thus supporting trade-off decisions.…”

Section: Introductionmentioning

confidence: 99%

Wind power forecasting errors modelling approach considering temporal and spatial dependence

Min

Zhou

et al. 2017

J. Mod. Power Syst. Clean Energy

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The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence of prediction errors has done great influence in specific applications, such as multistage scheduling and aggregated wind power integration. In this paper, Pair-Copula theory has been introduced to construct a multivariate model which can fully considers the margin distribution and stochastic dependence characteristics of wind power forecasting errors. The characteristics of temporal and spatial dependence have been modelled, and their influences on wind power integrations have been analyzed. Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.

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Section: Introductionmentioning

confidence: 99%

Wind power forecasting errors modelling approach considering temporal and spatial dependence

Min

Zhou

et al. 2017

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…This procedure needs to be modified when the penetration level of wind energy in a power system is high. The idea is introduced in [6] and further developed in the present paper. In [6], the wind speed series are based on the measured data while the correlation between wind speed series will certainly modify the wind generation pattern.…”

Section: Introductionmentioning

confidence: 99%

“…The idea is introduced in [6] and further developed in the present paper. In [6], the wind speed series are based on the measured data while the correlation between wind speed series will certainly modify the wind generation pattern. This topic can be a major concern especially when the sufficient data is not available on wind speed series of the newly installed wind farms and the future ones.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of load and generation to estimate overload severity is the next stage of bottlenecks identification. A severity function based on the loading duration curve is presented in [6] for bottlenecks ranking. In this severity function, a branch loading median for the overloaded hours is used to compute the overload risk of a branch.…”

Section: Introductionmentioning

confidence: 99%

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Power system security assessment with high wind penetration using the farms models based on their correlation

Mehdizadeh¹,

Ghazi²,

Ghayeni

2018

IET Renewable Power Generation

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Increasing the wind energy penetration in a power system presents some technical challenges to the transmission expansion planning (TEP). In the first stage of TEP, the transmission bottlenecks should be determined and ranked via security assessment. The high penetration of wind energy means a large number of wind farms exist in various geographic areas. It is found that there is a correlation between the wind speed series of different farms. Therefore, integration of the correlated wind farms into security assessment is addressed in this study. Hence, a new model of wind speed prediction is presented based on the correlated autoregressive moving average time series. The copula functions are used to make the predefined correlation among wind speed series. Here a linear correlation using a Gaussian copula is implemented. Then, the risk-based security assessment is performed using the proposed sequential time simulation. The proposed process is applied to the modified IEEE 39-bus test system which comprises 15 wind farms. Then, the transmission bottlenecks are identified and ranked regarding their overload risks. Finally, the impacts of the wind farms correlation on the risk index are investigated and those contingencies which impose the highest influence on this risk are identified.

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New sequential approach based on graph traversal algorithm to investigate cascading outages considering correlated wind farms

Mehdizadeh¹,

Ghazi²,

Ghayeni

2019

IET Generation, Transmission & Distribution

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Round-the-Year Security Analysis With Large-Scale Wind Power Integration

Cited by 15 publications

References 6 publications

Wind power forecasting errors modelling approach considering temporal and spatial dependence

Wind power forecasting errors modelling approach considering temporal and spatial dependence

Power system security assessment with high wind penetration using the farms models based on their correlation

New sequential approach based on graph traversal algorithm to investigate cascading outages considering correlated wind farms

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