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

Fei Teng (1) , Romain Dupin (2) , Andrea Michiorri (2) , Georges Kariniotakis (2) , Yanfei Chen (1) , Goran Strbac (1)

1 Imperial College London
2 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques

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.

Mots clés: Dynamic line rating; probabilistic forecasting; stochastic programming; wind generation; Index Terms—Dynamic line rating; probabilistic forecasting; stochastic programming; wind generation

Domaines: Sciences de l'ingénieur [physics]; Energétique

Fei Teng, Romain Dupin, Andrea Michiorri, Georges Kariniotakis, Yanfei Chen, et al.. Understanding the Benefits of Dynamic Line Rating under Multiple Sources of Uncertainty. IEEE Transactions on Power Systems, 2018, 33 (3), pp.3306 - 3314. ⟨10.1109/TPWRS.2017.2786470⟩. ⟨hal-01686328⟩