Computationally Efficient Safe Reinforcement Learning for Power Systems
Abstract
We propose a computationally efficient approach to safe reinforcement learning (RL) for frequency regulation in power systems with high levels of variable renewable energy resources. The approach draws on set-theoretic control techniques to craft a neural network-based control policy that is guaranteed to satisfy safety-critical state constraints, without needing to solve a model predictive control or projection problem in real time. By exploiting the properties of robust controlled-invariant polytopes, we construct a novel, closed-form "safety-filter" that enables end-to-end safe learning using any policy gradient-based RL algorithm. We then apply the safety filter in conjunction with the deep deterministic policy gradient (DDPG) algorithm to regulate frequency in a modified 9-bus power system, and show that the learned policy is more cost-effective than robust linear feedback control techniques while maintaining the same safety guarantee. We also show that the proposed paradigm outperforms DDPG augmented with constraint violation penalties.
Cite
@article{arxiv.2110.10333,
title = {Computationally Efficient Safe Reinforcement Learning for Power Systems},
author = {Daniel Tabas and Baosen Zhang},
journal= {arXiv preprint arXiv:2110.10333},
year = {2022}
}
Comments
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