Summary of Imitation Learning For Intra-day Power Grid Operation Through Topology Actions, by Matthijs De Jong et al.
Imitation Learning for Intra-Day Power Grid Operation through Topology Actions
by Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of imitation learning to day-ahead power grid operation through topology actions. The authors investigate the performance of fully-connected neural networks (FCNNs) trained on expert state-action pairs and compare it to rule-based expert agents, including a greedy agent and a more computationally expensive N-1 agent that takes safety considerations into account. The study finds that while FCNN classification accuracy is limited due to class imbalance and overlap, the network performs only slightly worse than expert agents in power system operations. Moreover, hybrid agents that incorporate minimal additional simulations match expert agents’ performance with significantly lower computational cost. These findings suggest that imitation learning holds promise for developing fast and high-performing power grid agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers use artificial intelligence to help humans manage the increasingly complex power grid. They test different types of AI models on day-ahead power grid operation and find that one type of model performs similarly to expert human operators. The authors also show that a combination of AI and minimal additional simulations can be just as effective as more complex expert systems, but at a much lower cost. |
Keywords
» Artificial intelligence » Classification
