arXiv cs.AI· Yoon Pyo Lee, Samrendra Roy, Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam·· 2 小时前AI 评分26
面向电力系统规划的自主智能体框架
Autonomous agents for electricity-system planning
AI 导读
一篇 arXiv 论文提出用于电力系统规划的自主智能体框架，由智能体完成方案提议、数值评估、反馈解读、设计修订与最终方案选择，自动执行年度投资-调度优化与代表性馈线验证。该框架在五个电力系统、四个国家的十个系统年场景下评估四十条碳约束轨迹，全部满足物理与年度运行碳预算校验，其中二十八条与参考设计匹配的轨迹超出新场景上限；另附共同 1000 Mt 年度上限下的九十六条轨迹档案。
正文 · 原文
Abstract:Planning electricity systems requires repeated decisions that connect regional resources, investment, operation and network constraints. Here we present an autonomous planning framework that carries prepared regional scenarios through proposal, numerical evaluation, feedback interpretation, redesign and final plan selection. Agents propose investment bounds, storage operating conditions and feeder interventions, interpret numerical feedback, and decide how to revise designs and when to stop. The framework automatically executes annual investment-and-dispatch optimization and representative-feeder verification, applies fixed acceptance criteria and retains the lowest-cost accepted candidate. We evaluate forty carbon-constrained trajectories across ten system-year scenarios in five electricity systems and four countries, using two model deployments and single- and multi-agent architectures. All forty retain candidates satisfying the implemented physical and fixed annual operational carbon-budget checks. Twenty-eight of forty matched reference designs exceed the new scenario ceilings. A separate archive of ninety-six trajectories under a common 1,000 Mt annual ceiling provides matched references and broader regional coverage. The results demonstrate autonomous planning under explicit emissions constraints using public regional data and published institutional demand outlooks.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.05911 [cs.AI] |
| (or arXiv:2611.05911v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05911 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: YoonPyo Lee [view email]
[v1]
Wed, 4 Nov 2026 04:01:54 UTC (1,909 KB)
来源：arXiv cs.AI · arxiv.org