Probabilistic Ampacity Forecasting for Overhead Transmission Lines

Konstantinou, Theodoros; Savvopoulos, Nikolaos; Hatziargyriou, Nikos D.

doi:10.1109/sest.2019.8849018

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…According to studies by the power industry, many power lines safely operate at 130% of their static-rated capacity for 90% of the year, as shown in Figure 3 [3], [73], [81]. SLR causes transmission lines to underutilize, restrict their full potential and raise their operation costs [56], [74], [77], [82]. While these assumptions are conservative, there are cases wherein the actual ratings based on actual conditions are less than SLR, exposing conductors to the potential for thermal damage or increased sagging [56], [76], [77].…”

Section: A Static Line Ratingmentioning

confidence: 99%

“…It monitored and integrated the DLR system into control systems. Increased wind generation 30% -44%, line capacity increased between 30%-70% compared to SLR, and potentially saving $4 million [74], [76], [76], [119] , [129].…”

Section: Usmentioning

confidence: 99%

“…CIGRE considers the conductor surface's temperature, while IEEE assumes it to be constant along the conductor's length [186]. However, both standards agree that natural and forced convection can reduce the conductor's temperature [74], [77], [187], [188], with wind velocity being the most significant factor. The formulas in both standards depend on wind velocity, but unreliable wind velocity measurements can lead to significant errors in the conductor's ampacity.…”

Section: Computing and Forecasting (Analytical Engine)mentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Abas,

Ab Kadir,

Azis

et al. 2024

IEEE Access

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As the world is pledged towards net zero carbon by 2050, the need for clean and efficient energy transitions is more critical than ever. Optimizing the power grid transfer capacity is crucial for maintaining grid stability and reliability. Ageing infrastructure, population growth, and revolutionary technological developments increase the demand for grid modernization and resilience investments. Climate change and natural disasters highlight the need for adaptive load-shedding schemes. The two possible ways to optimize the grid are an ampacity increase or a voltage increase. While increasing voltage provides the most significant rise in rating, it comes with high investment costs. Out of all the options available, dynamic line rating (DLR) is the most efficient and cost-effective solution. This paper provides a comprehensive review of the optimization of the grid transfer capacity using DLR. The review critically examines different line rating methods, the DLR system, factors that need to be considered before DLR implementation, and its advantages and disadvantages. Also, the review presents the real-world applications and case studies, standards and regulations involved, and current approaches and challenges for implementing DLR in Malaysia. Additionally, we highlight the most commonly used standards to calculate the conductor's ampacity for the steady-state and dynamic state. Moreover, this review work presents how DLR can advance the grid's flexibility, considering its significance for cleaner energy production in the future, challenges related to wind energy power generation, and their mitigations. This work provides a shortcut path for researchers and utilities to understand DLR and as a reference for future research to advance clean energy in response to changing energy needs and climate conditions.

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Section: A Static Line Ratingmentioning

confidence: 99%

Section: Usmentioning

confidence: 99%

Section: Computing and Forecasting (Analytical Engine)mentioning

confidence: 99%

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Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Abas,

Ab Kadir,

Azis

et al. 2024

IEEE Access

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“…In this case, probabilistic prediction methods of the ampacity were developed. In [8], a statistical method was proposed to forecast the probability density function (PDF) of the ampacity based on the numerical weather predictions (NWPs). In [9], historical meteorological data and NWPs were taken as the input of the expectation maximization algorithm to forecast the PDFs of meteorological elements.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction for the ampacity of overhead lines using Quantile Regression Neural Network

Jin

Cai²,

Wang

et al. 2020

E3S Web Conf.

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The ampacity of overhead transmission lines play a key role in power system planning and control. Due to the volatility of the meteorological elements, the ampacity of an overhead line is timevarying. In order to fully utilize the transfer capability of overhead transmission lines, it is necessary to provide system operators with accurate probabilistic prediction results of the ampacity. In this paper, a method based on the Quantile Regression Neural Network (QRNN) is proposed to improve the performance of the probabilistic prediction of the ampacity. The QRNN-based method uses a nonlinear model to comprehensively model the impacts of historical meteorological data and historical ampacity data on the ampacity at predictive time period. Numerical simulations based on the actual meteorological data around an overhead line verify the effectiveness of the proposed method.

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