Meta-learning and proximal policy optimization driven two-stage emergency allocation strategy for multi-energy system against typhoon disasters

To achieve resilience improvement of the multi-energy system against typhoon disasters, this study designs a novel two-stage optimization framework that considers the emergency allocation of distributed resources under typhoon disasters to fully exploit the potential of distributed resources for resilience enhancement. Firstly, we formulate the emergency allocation of distributed resources as a Markov decision process. Then, a meta-learning-driven proximal policy optimization method is utilized to solve it. Different from that the existing reinforcement learning methods always ignore the unpredictable change caused by typhoon and keep multi-energy system dynamics invariant, limiting its control performance. The proposed method embeds meta-learning to fine-tune the pre-trained allocation policy to new tasks with high adaptability and few interactions. Finally, comparison results with other benchmark methods are carried out and shows that the proposed method can learn the appropriate resource allocation policy for multi-energy system and achieve better resilience enhancement, yielding fast application efficiency and good generalization ability for emergency fault conditions.