Probabilistic ampacity forecasting for overhead lines using weather forecast ensembles

Ringelband, Tilman; Schäfer, Philipp Matthias; Moser, Albert

doi:10.1007/s00202-012-0244-8

Cited by 60 publications

(34 citation statements)

References 17 publications

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“…The authors in [12] highlight the need to develop DLR forecast models to facilitate its application and present a state-of-art review on the forecasting techniques. Machine learning techniques [13] and ensemble weather forecast [14] are among the most widely used method in the forecasting of DLR. A novel probabilistic DLR forecasting method is developed in [15] to derive the expected value and important percentiles of ratings.…”

Section: B Variables (Written Inmentioning

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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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: B Variables (Written Inmentioning

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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“…As we know, weather forecasting methods are often comprehensive since a single method or model may be unable to get good results [49][50][51]; this is because the atmosphere has nonlinear movement. Atmospheric flows are usually described by a system of nonlinear partial differential equations.…”

Section: Discussionmentioning

confidence: 99%

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Xue

Zhang

et al. 2014

Atmosphere

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Abstract:We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the "fuzzy rule-based neural network", which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the "neural fuzzy inference system", which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the "accurate" OPEN ACCESSAtmosphere 2014, 5 789 prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

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“…In [17], a method to assess the reliability of dynamic thermal rating (DTR) systems is proposed. In [18], a novel method to calculate probability density functions of future ampacity based on probabilistic weather forecasts is presented. In [19], a new modelling method for DTR is presented which is superior in terms of both probability distribution and fitting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Transient Thermal Rating and the Calculation of Risk Level of Transmission Lines

Liu

Yang

et al. 2018

Energies

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Abstract:With the increasing consumption of electric energy, how to improve the capacity of transmission lines within safe margins is an urgent problem to be solved. This paper presents a transient thermal rating method under real-time meteorological conditions. The result of thermal ratings under different conditions shows that this rating approach can significantly increase the capacity of the line. As some of the most critical variables are remaining time and initial temperature, their influence upon the rating is studied. The method of statistical analysis is used to determine the ampacity at different risk levels. The result indicates that with smaller remaining time, the ampacities are larger, and with larger ampacities, the risk of thermal overload is greater. The choice of risk level would heavily affect the values of ampacity.

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Probabilistic ampacity forecasting for overhead lines using weather forecast ensembles

Cited by 60 publications

References 17 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

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Real-Time Transient Thermal Rating and the Calculation of Risk Level of Transmission Lines

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