Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning. (arXiv:2101.05317v1 [cs.LG])

As power systems are undergoing a significant transformation with more
uncertainties, less inertia and closer to operation limits, there is increasing
risk of large outages. Thus, there is an imperative need to enhance grid
emergency control to maintain system reliability and security. Towards this
end, great progress has been made in developing deep reinforcement learning
(DRL) based grid control solutions in recent years. However, existing DRL-based
solutions have two main limitations: 1) they cannot handle well with a wide
range of grid operation conditions, system parameters, and contingencies; 2)
they generally lack the ability to fast adapt to new grid operation conditions,
system parameters, and contingencies, limiting their applicability for
real-world applications. In this paper, we mitigate these limitations by
developing a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL
combines the meta strategy optimization together with DRL, and trains policies
modulated by a latent space that can quickly adapt to new scenarios. We test
the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast
adaptation of the meta-trained DRL polices with latent variables to new
operating conditions and scenarios using the proposed method and achieve
superior performance compared to the state-of-the-art DRL and model predictive
control (MPC) methods.

Source: https://arxiv.org/abs/2101.05317

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