An Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies

Wang, Yi; Zhang, Ning; Chen, Kang; Miao, Miao; Shi, Rui; Xia, Qing

doi:10.1109/tpwrs.2017.2755698

Cited by 128 publications

(57 citation statements)

References 38 publications

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“…In the next step, we plan to enhance the modelling framework by explicitly modelling the high-dimensional dependencies among the multiple sources of uncertainty as discussed in [24]. For the long-distance transmission lines, other factors, such as stability constraints and angle limits, may prevent the higher utilization of transfer capability, limiting the benefit of thermal dynamic line rating.…”

Section: Discussionmentioning

confidence: 99%

“…In this case, the cost reduces to 35.1 k£ and only 387 MWh of wind generation are curtailed. Furthermore, previous study in [24] has been carried out to select the single "optimal" quantile of DLR forecast to be used in the dispatch stage under a deterministic fashion. As shown in Fig.4, when higher line rating is used in the dispatch stage, the system dispatch cost reduces due to the increased transfer capability, while the real-time re-dispatch cost would increase in order to deal with the forecasting error in the real-time operation.…”

Section: A Impact Of Operation Strategies On the Benefit Of Dlrmentioning

confidence: 99%

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Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Teng

Dupin

Michiorri

et al. 2018

IEEE Trans. Power Syst.

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Abstract--This paper analyses the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the re-dispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modelling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of DLR in supporting costeffective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyses the impact of different operational strategies, the coordination among multiple flexible technologies and installed capacity of wind generation on the benefits of DLR.

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Section: Discussionmentioning

confidence: 99%

Section: A Impact Of Operation Strategies On the Benefit Of Dlrmentioning

confidence: 99%

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Teng

Dupin

Michiorri

et al. 2018

IEEE Trans. Power Syst.

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“…In the Copula method, the joint cumulative distribution function (CDF) of the forecasted and actual PV unit productions is written as

\begin{matrix} centertrue \\ F (w_{a, 1} \dots w_{a, i} \dots w_{a, I}, w_{f, 1} \dots w_{f, i} \dots w_{f, I}) \\ = C (F (w_{a, 1}) \dots F (w_{a, i}) \dots F (w_{a, I}), F (w_{f, 1}) \dots F (w_{f, i}) \dots F (w_{f, I})) \end{matrix}

where F ( w a , i ) is the marginal CDF of actual power of PV units; F ( w f , i ) is the marginal CDF of forecast power of PV units; and the function C (⋅) is called the Copula function. Essentially, we transform the joint CDF F ( w a ,1 … w a , i … w a , I , w f ,1 … w f , i … w f , I ) to a function of the marginal CDFs F ( w a ,1 ) … F ( w a , i ) … F ( w a , I ), F ( w f ,1 ) … F ( w f , i ) … F ( w f , I ) linked by the Copula function C (⋅) . Similarly, the joint probability density function (PDF) is

\begin{matrix} centertrue \\ f (w_{a, 1} \dots w_{a, i} \dots w_{a, I}, w_{f, 1} \dots w_{f, i} \dots w_{f, I}) \\ = c (F (w_{a, 1}) \dots F (w_{a, i}) \dots F (w_{a, I}), F (w_{f, 1}) \dots F (w_{f, i}) \dots F (w_{f, I})) \end{matrix}

…”

Section: Scenario Generation Of Dersmentioning

confidence: 99%

“…where F (w a,i ) is the marginal CDF of actual power of PV units; F (w f ,i ) is the marginal CDF of forecast power of PV units; and the function C(⋅) is called the Copula function. Essentially, we transform the joint 29 Similarly, the joint probability density function (PDF) is f w a;1 …w a;i …w a;I ; w f ;1 …w f ;i …w f ;…”

Section: Scenario Generation Of Dersmentioning

confidence: 99%

Stochastic battery energy storage scheduling considering cell degradation and distributed energy resources

Tang

Sun

et al. 2019

Int Trans Electr Energ Syst

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Summary The penetration of distributed energy resources (DERs) greatly raises the risk of distribution network operation such as peak shaving and voltage stability. Battery energy storage (BES) has been widely accepted as the most potential application to cope with the challenge of high penetration of DERs. To cope with the uncertainties and variability of DERs, a stochastic BES scheduling model and its algorithm are proposed. The overall economy is achieved by fully considering the cell degradation, DERs, electricity purchasing, and active power losses. The rainflow algorithm‐based cycle counting method is incorporated in the BES scheduling model to capture the cell degradation, greatly extending the expected BES lifetime and achieving a better economy. DER power scenarios are generated to consider the uncertainties and correlations based on the Copula theory. To solve the BES scheduling model, we propose a Lagrangian relaxation‐based algorithm, which has a significantly reduced complexity with respect to the existing techniques. For this reason, the proposed algorithm enables much more scenarios incorporated in the BES scheduling model and better captures the DER uncertainties and correlations. Finally, numerical studies for the day‐ahead BES scheduling in the IEEE 123‐node test feeder are presented to demonstrate the merits of the proposed method. Results show that the actual BES life expectancy of the proposed model has increased to 4.89 times compared with the traditional ones. The problems caused by DERs are greatly alleviated by fully capturing the uncertainties and correlations with the proposed method.

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“…Based on the bivariate and conditional bivariate distributions, the joint distribution can be constructed. The use of vine copulas to tackle power system uncertainty is reported in the previous studies [33][34][35] and probabilistic forecast for multiple wind farms in Wang et al 36 A regular vine can be decomposed to either i. D (drawable)-vine where each node in T j has a degree of at most 2, and conditioning is done sequentially; or…”

Section: Spatio-temporal Modeling Using Vine Copulamentioning

confidence: 99%

A multivariate framework to study spatio‐temporal dependency of electricity load and wind power

2019

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With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter‐spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio‐temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high‐dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k‐means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio‐temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula.

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An Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies

Cited by 128 publications

References 38 publications

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Stochastic battery energy storage scheduling considering cell degradation and distributed energy resources

A multivariate framework to study spatio‐temporal dependency of electricity load and wind power

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