The Effect of Temporal Discretization on Dynamic Thermal Line Rating

Bartoň, Tomáŝ; Musilek, Michal; Musílek, Petr

doi:10.1109/epe51172.2020.9269231

Cited by 2 publications

(1 citation statement)

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“…Transmission lines are designed and rated at maximum conductor temperatures that are representative of worst-case scenarios and assumed to be static or varied only seasonally. In reality, line temperatures vary with ambient temperature, solar irradiance, precipitation and notably wind speed and direction [11]. Compared to static line rating (SLR), dynamic thermal line rating (DTLR) takes the real-life temperature changes into account to estimate the maximum ampacity, or the highest current the line can carry at the actual operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating

et al. 2022

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It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.

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Section: Introductionmentioning

confidence: 99%