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

Vadim Avkhimenia, Matheus Gemignani, Tim Weis and Petr Musilek ()
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Vadim Avkhimenia: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Matheus Gemignani: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Tim Weis: Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Petr Musilek: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Energies, 2022, vol. 15, issue 23, 1-15

Abstract: 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.

Keywords: deep reinforcement learning; multi-agent system; demand response; load forecasting; dynamic line rating; linear programming; battery degradation; battery capacity sizing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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