RestoreBench: Can AI Agents Restore Power Flow Convergence?

• URL: http://arxiv.org/abs/2609.00384v1
• Date: Mon, 31 Aug 2026 21:15:19 GMT
• Title: RestoreBench: Can AI Agents Restore Power Flow Convergence?
• Abstract summary: Diagnosing and resolving non-convergent power flow cases is a promising yet largely unexplored application.<n>We introduce a benchmark that evaluates these capabilities across multiple LLMs and three architectures.<n>The evaluation covers two power grids and 46 cases per grid, each requiring one or more corrective actions to restore convergence.
• Score: 2.4326395001229053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and iterative planning. Diagnosing and resolving non-convergent power flow cases is a promising yet largely unexplored application, as it requires engineering judgment, experimentation, and decision-making within constrained action spaces. We introduce a benchmark that evaluates these capabilities across multiple LLMs and three architectures: \emph{chatbot}, \emph{single agent}, and \emph{multi-agent} systems. The evaluation covers two power grids and 46 cases per grid, each requiring one or more corrective actions to restore convergence. The benchmark defines the simulation environment, observation and action spaces, and evaluation metrics, providing a reproducible foundation for developing agentic AI systems for power system planning and operation. The code is available at https://github.com/Mansutti081/RestoreBench

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