Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot
  Adaption via Contextual Meta Graph Reinforcement Learning
Bairong Deng Tao Yu Zhenning Pan Xuehan Zhang Yufeng Wu Qiaoyi Ding
Reinforcement learning is an emerging approaches to facilitate multi-stage
sequential decision-making problems. This paper studies a real-time multi-stage
stochastic power dispatch considering multivariate uncertainties. Current
researches suffer from low generalization and practicality, that is, the
learned dispatch policy can only handle a specific dispatch scenario, its
performance degrades significantly if actual samples and training samples are
inconsistent. To fill these gaps, a novel contextual meta graph reinforcement
learning (Meta-GRL) for a highly generalized multi-stage optimal dispatch
policy is proposed. Specifically, a more general contextual Markov decision
process (MDP) and scalable graph representation are introduced to achieve a
more generalized multi-stage stochastic power dispatch modeling. An upper
meta-learner is proposed to encode context for different dispatch scenarios and
learn how to achieve dispatch task identification while the lower policy
learner learns context-specified dispatch policy. After sufficient offline
learning, this approach can rapidly adapt to unseen and undefined scenarios
with only a few updations of the hypothesis judgments generated by the
meta-learner. Numerical comparisons with state-of-the-art policies and
traditional reinforcement learning verify the optimality, efficiency,
adaptability, and scalability of the proposed Meta-GRL.