Cohort: 3

Members: Sheng Yang, Jia Tang，Tao Chen ，Quanqi Chen, Zhen Dai,Xueqing Song

Problem: Critical power transmission routes can become overloaded when renewable generation, extreme weather, or grid topology changes shift electricity flows. Previously, operators relied heavily on manual checks, on-site experience, and repeated calculations within short decision windows, which made responses slower, more conservative, and harder to scale.

Approach: The system continuously reads minute-by-minute grid data, forecasts, and network conditions. A reinforcement learning agent recommends safe adjustments to generators, storage assets, or controllable loads. Each recommendation is checked against operating constraints, tested through fast simulation, and reviewed by human operators before execution.

Result: At the Guangxi control center, severe overloads were reduced by approximately 90%. Response speed improved by roughly threefold, with strategies generated in seconds, while also delivering cost savings.