Integrated transmission expansion and PMU planning considering dynamic thermal rating in uncertain environment

Madadi, Sajad; Mohammadi‐Ivatloo, Behnam; Tohidi, Sajjad

doi:10.1049/iet-gtd.2019.0728

Cited by 13 publications

(2 citation statements)

References 29 publications

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“…Existing literature related to DTR is mainly focused on the application in transmission networks to support centralized renewable energy integration. Relevant studies have explored the optimization methods of transmission network dispatching ( Teng et al., 2018 ; Mohamed et al., 2020 ) and planning ( Zhan et al., 2018 ; Madadi et al., 2020 ) with the DTR of overhead lines. The results show that the application of DTR can effectively alleviate congestion, support renewable energy integration, and reduce operating cost and network investment.…”

Section: Introductionmentioning

confidence: 99%

Improving distributed PV integration with dynamic thermal rating of power distribution equipment

Wang

Kang

et al. 2022

iScience

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Section: Introductionmentioning

confidence: 99%

Improving distributed PV integration with dynamic thermal rating of power distribution equipment

Wang

Kang

et al. 2022

iScience

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“…To overcome the shortcomings of the STLR, dynamic TLR (DTLR) is proposed in which the thermal condition of OHL can be monitored. Thus, DTLR unlocks the additional capacity headroom of current OHLs in a secure way, thereby addressing network congestion and postponing/eliminating the need for transmission expansion [6]. As a significant additional benefit, DTLR facilitates the delivery of highly variable and uncertain power from renewable energy systems (RESs) to the end-users due to perceptible correlation between RES generation and the additional capacity provided by the DTLR.…”

Section: Introductionmentioning

confidence: 99%

Secure Probabilistic Prediction of Dynamic Thermal Line Rating

Safari¹,

Mazhari

Chung

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Accurate short-term prediction of overhead line (OHL) transmission ampacity can directly affect the efficiency of power system operation and planning. Any overestimation of the dynamic thermal line rating (DTLR) can lead to the lifetime degradation and failure of OHLs, safety hazards, etc. This paper presents a secure yet sharp probabilistic model for the hour-ahead prediction of the DTLR. The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR. The model is based on an augmented deep learning architecture that makes use of a wide range of predictors, including historical climatology data and latent variables obtained during DTLR calculation. Furthermore, by introducing a customized cost function, the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing the deviations of the predicted DTLRs from the actual values. The proposed probabilistic DTLR is developed and verified using recorded experimental data. The simulation results validate the superiority of the proposed DTLR compared with the state-of-the-art prediction models using well-known evaluation metrics.

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Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating

Akhlaghi

Moravej²,

Bagheri³

2022

Energy

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Integrated transmission expansion and PMU planning considering dynamic thermal rating in uncertain environment

Cited by 13 publications

References 29 publications

Improving distributed PV integration with dynamic thermal rating of power distribution equipment

Improving distributed PV integration with dynamic thermal rating of power distribution equipment

Secure Probabilistic Prediction of Dynamic Thermal Line Rating

Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating

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