Reinforcement Learning-Based Approach for EMT Automation of Large-Scale PV Plants

Conference · Mon Jul 01 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/PESGM51994.2024.10689173· OSTI ID:3002913

Xia, Qianxue ^[1]; Kurte, Kuldeep ^[2]; Debnath, Suman ^[1]

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1. ORNL
2. Reliance Jio

In the pursuit of efficient and precise modeling of large-scale power systems, particularly utility-scale photovoltaic (PV) plants, Electromagnetic Transient (EMT) simulations play a crucial role. As utility-scale PV plants increase in size and complexity, traditional computational methods become inadequate, necessitating more advanced techniques. This paper highlights the progressive efforts made to accelerate EMT simulations. A novel continuous reinforcement learning (RL) strategy is explored to automate the differentiation and categorization of stiff and non-stiff differential algebraic equations (DAEs). The use of stiff and non-stiff integration methods applied to relevant parts of the DAEs assists with the speed-up of the simulations. The paper details the data acquisition, development and offline training of the RL model, leading to its validation that demonstrates a high precision in optimizing simulation methods. The proposed RL promises to significantly enhance the efficacy of EMT simulations, offering a robust framework for the future of power system analysis.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 3002913
• Country of Publication: United States
• Language: English