A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch

Wang, Xiaoting; Liu, Rong-Peng; Wang, Xiaozhe; Hou, Yunhe; Bouffard, François

doi:10.1109/tpwrs.2021.3114083

Cited by 20 publications

(16 citation statements)

References 8 publications

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“…Refer to [15] Section II for the formulation of PTTC assessment. The second case presents the ANCOVA indices estimation for the economic dispatch (ED) problem considered in [20]. In contrast to the first case, we use realworld data from the NREL's Western Wind Data Set [21], which exhibits unknown distribution types and potentially complicated correlations.…”

Section: Methods 2 Pce Nt or Pce Rt Ancova Indices Estimation And Unc...mentioning

confidence: 99%

“…To make matters more challenging, Y (the ED cost) is multimodal. See Appendix in [20] for the formulation of ED problem. The UQLab toolbox is adopted to build the PCE-based models [22] [23].…”

Section: Methods 2 Pce Nt or Pce Rt Ancova Indices Estimation And Unc...mentioning

confidence: 99%

“…We consider the IEEE 118-bus system integrated with a 20-node gas system for the stochastic ED problem. Refer to Section IV [20] for the detailed system configuration. There are 120 random inputs (i.e., wind generator outputs), and Y is the ED cost.…”

Section: B Case 2: Stochastic Economic Dispatch Problemmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control

Wang¹,

Liu²,

Wang³

et al. 2024

IEEE Trans. Circuits Syst. II

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In this letter, we compare three polynomial chaos expansion (PCE)-based methods for ANCOVA (ANalysis of CO-VAriance) indices based global sensitivity analysis for correlated random inputs in two power system applications. Surprisingly, the PCE-based models built with independent inputs after decorrelation may not give the most accurate ANCOVA indices, though this approach seems to be the most correct one and was applied in [1] in the field of civil engineering. In contrast, the PCE model built using correlated random inputs directly yields the most accurate ANCOVA indices for global sensitivity analysis. Analysis and discussions about the errors of different PCE-based models will also be presented. These results provide important guidance for uncertainty management and control in power system operation and security assessment.

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Section: Methods 2 Pce Nt or Pce Rt Ancova Indices Estimation And Unc...mentioning

confidence: 99%

Section: Methods 2 Pce Nt or Pce Rt Ancova Indices Estimation And Unc...mentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control

Wang¹,

Liu²,

Wang³

et al. 2024

IEEE Trans. Circuits Syst. II

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“…One such method to quantify the Uncertainty in modern power systems is using Monte Carlo simulations, which are computationally expensive. As measurement devices such as phasor measurement units are prevalent in the network, various data-driven methods have gained growing attention in the literature (see, Wang (2020), Wang et al (2021)).…”

Section: Case Study 3: Stochastic Power Flowmentioning

confidence: 99%

Robust Hyperparameter Estimation in Gaussian Process Regression Model

Algikar

Mili

2023

Preprint

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A stochastic process mathematically models the random behavior of systems and naturally occurring phenomena. A popular process is a Gaussian process which assumes a multivariate Gaussian distribution for the response variables. The predictive distributions of target variables can be explicitly obtained by using conditioning and inference techniques. The mean function and covariance function of the predictive distributions are parameterized whose estimates can be obtained by maximizing the marginal likelihood of the training responses or their posterior distribution. Unfortunately, these estimates are not robust to outliers that are often present in the observational training data sets. To overcome this problem, we develop a robust data-driven process based on the Schweppe-type Huber-generalized maximum likelihood (SHGM) estimator to bound the influence of all types of outliers on the estimates while exhibiting a high statistical efficiency at the Gaussian and thick-tail distributions. The weights utilized by the SHGM estimator are calculated using projection statistics, which are robust distances of the data points determined by the row vectors of the design matrix. These weights make the estimator robust to both vertical outliers and bad leverage points while not downweighting good leverage points. We show that the proposed robust model is able to handle up to 25 percent of outliers among the data set. The proposed method is illustrated by two real-world examples in which the performance of the proposed method is assessed against the standard Gaussian process regression.

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“…Several techniques have been proposed for modeling the uncertainty arising in OPF problems including robust optimization (Ben-Tal, El Ghaoui, and Nemirovski 2009;Lorca and Sun 2015), chance constrained optimization (Ben-Tal, El Ghaoui, and Nemirovski 2009;Cheng et al 2018;Shapiro, Dentcheva, and Ruszczyński 2014;Liu and Xu 2010;Zhang, Gatsis, and Giannakis 2013), surrogate modeling (Safta et al 2017;Hu et al 2021;Wang et al 2022), and two-or multi-stage stochastic programming using scenarios (Shapiro, Dentcheva, and Ruszczyński 2014;Birge and Louveaux 2011;King et al 2018;Reynolds et al 2020;Sigler et al 2020). While each of these stochastic methods are viable in certain contexts and have different computational scaling characteristics, all of them can become computationally intractable when considering large interconnection regions, e.g., with O 10 4 buses.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Panda¹,

King²,

Maack³

et al. 2022

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A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch

Cited by 20 publications

References 8 publications

A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control

A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control

Robust Hyperparameter Estimation in Gaussian Process Regression Model

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

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