Wan, Xu;
Sun, Mingyang;
Chen, Boli;
Chu, Zhongda;
Teng, Fei;
(2023)
AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems.
In: Williams, Brian and Chen, Yiling and Neville, Jennifer, (eds.)
Proceedings of the AAAI Conference on Artificial Intelligence.
(pp. pp. 5294-5302).
AAAI: Washington, D.C., USA.
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Abstract
With the increasing penetration of inverter-based renewable energy resources, deep reinforcement learning (DRL) has been proposed as one of the most promising solutions to realize real-time and autonomous control for future carbon-neutral power systems. In particular, DRL-based frequency control approaches have been extensively investigated to overcome the limitations of model-based approaches, such as the computational cost and scalability for large-scale systems. Nevertheless, the real-world implementation of DRLbased frequency control methods is facing the following fundamental challenges: 1) safety guarantee during the learning and decision-making processes; 2) adaptability against the dynamic system operating conditions. To this end, this is the first work that proposes an Adaptive and Safe-Certified DRL (AdapSafe) algorithm for frequency control to simultaneously address the aforementioned challenges. In particular, a novel self-tuning control barrier function is designed to actively compensate the unsafe frequency control strategies under variational safety constraints and thus achieve guaranteed safety. Furthermore, the concept of meta-reinforcement learning is integrated to significantly enhance its adaptiveness in non-stationary power system environments without sacrificing the safety cost. Experiments are conducted based on GB 2030 power system, and the results demonstrate that the proposed AdapSafe exhibits superior performance in terms of its guaranteed safety in both training and test phases, as well as its considerable adaptability against the dynamics changes of system parameters.
| Type: | Proceedings paper |
|---|---|
| Title: | AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems |
| Event: | Thirty-Seventh AAAI Conference on Artificial Intelligence |
| Location: | Washington DC |
| Dates: | 7 Feb 2023 - 14 Feb 2023 |
| ISBN-13: | 978-1-57735-880-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1609/aaai.v37i4.25660 |
| Publisher version: | https://doi.org/10.1609/aaai.v37i4.25660 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | APP: Energy, Environment & Sustainability, ML: Applications, ML: Reinforcement Learning Algorithms, PEAI: Safety, Robustness & Trustworthiness |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10160010 |
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