activity
20242026
most citedOn the Choice of Loss Function in Learning-based Optimal Power Flow

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2026

Expanding the Transient Stability Region of Attraction of Networked Grid-Interactive Inverters: A Probabilistic Active Learning Framework

Zhong Liu, Jialin Zheng, Junjie Qin +1

The continuous integration of inverter-based resources makes transient stability analysis increasingly important for power system modernization, in light of the intricate dynamics…

eess.SY2025

The Role of Flexible Connection in Accelerating Load Interconnection in Distribution Networks

Nan Gu, Ge Chen, Junjie Qin

This paper investigates the role of flexible connection in accelerating the interconnection of large loads amid rising electricity demand from data centers and electrification. Fle…

eess.SY2025

Federated Aggregation of Demand Flexibility

Yifan Dong, Ge Chen, Junjie Qin

This paper proposes a federated framework for demand flexibility aggregation to support grid operations. Unlike existing geometric methods that rely on a static, pre-defined base s…

eess.SY2024★ 1 cited

On the Choice of Loss Function in Learning-based Optimal Power Flow

Ge Chen, Junjie Qin

We analyze and contrast two ways to train machine learning models for solving AC optimal power flow (OPF) problems, distinguished with the loss functions used. The first trains a m…

math.OC2024

Neural Risk Limiting Dispatch in Power Networks: Formulation and Generalization Guarantees

Ge Chen, Junjie Qin

Risk limiting dispatch (RLD) has been proposed as an approach that effectively trades off economic costs with operational risks for power dispatch under uncertainty. However, how t…