AI 中文总结
该研究针对电力系统规划任务，复现当前最优Agentic AI并评估其能力，发现其仅能解决部分低复杂度问题，明确了能力升级需求，强调需采用严格测试与可复现基准评估Agentic AI进展。
AI 中文摘要
AI驱动的信息基础设施（尤其是数据中心）的快速扩张，正给电力系统带来前所未有的压力，且加速了新资产与电网互联的步伐。由于 bulk 输电扩建进展缓慢，新负荷与发电设施越来越多地在现有电网约束内部署。亟需 Agentic AI 实现众多重复性连接流程的自动化，但它在复杂任务与大规模系统上的成熟度尚未得到系统验证。我们复现了电力系统规划领域 Agentic AI 的当前最优水平，并针对涵盖6个任务复杂度等级、4种电网规模的结构化节点规划问题集对其评估。研究发现，在部分测试电网规模下仅能解决最低的2个复杂度等级问题，并明确了缩小该差距所需的特定能力升级。采用更严格的测试协议与可复现的评估基准，对评估 Agentic AI 的真实进展及运行就绪度至关重要。
英文摘要
The rapid expansion of AI-driven information infrastructure, particularly data centers, is placing unprecedented pressure on power systems and accelerating the pace at which new assets must interconnect with the grid. As bulk transmission expansion rolls out slowly, new loads and generation are increasingly deployed within existing network constraints. Agentic AI is urgently needed to automate the numerous and repetitive connection processes, but its maturity has not been systematically validated on complex tasks and large-scale systems. We replicate the current state of the art in agentic AI for power systems planning and evaluate it against a structured suite of nodal planning problems spanning six levels of task complexity and four grid scales. We find that only the two lowest complexity levels are solvable on some of the test grid sizes, and identify the specific capability upgrades required to close this gap. Adopting stricter testing protocols and reproducible evaluation benchmarks is essential for assessing both genuine progress and the operational readiness of agentic AI.
Comments5 pages, 1 figure, 2 tables
