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- Title
Real-time outage management in active distribution networks using reinforcement learning over graphs.
- Authors
Jacob, Roshni Anna; Paul, Steve; Chowdhury, Souma; Gel, Yulia R.; Zhang, Jie
- Abstract
Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios. Smart grids are characterized by fast-acting, intelligent control while reconfiguration and load shedding are emergency control techniques needed during outages. Here, authors present a graph reinforcement learning approach to manage outage and notably improve resilience in distribution networks.
- Subjects
GRAPH neural networks; POWER distribution networks; DISTRIBUTION management; INTELLIGENT control systems; REINFORCEMENT (Psychology); ENERGY dissipation
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-49207-y