activity
20242026
most citedStability Constrained Optimization in High IBR-Penetrated Power Systems-Part I: Constraint Development and Unification

2 citations · 3 across the 3 of their papers we have counts for

collaborators

8 papers

eess.SY20261 cited

Stability Constrained Optimization in High IBR-Penetrated Power Systems-Part II: Constraint Validation and Applications

Zhongda Chu, Fei Teng

Multiple operational constraints of power system stability are derived analytically and reformulated into Second-Order Cone (SOC) form through a unification method in Part I of thi…

eess.SY20262 cited

Stability Constrained Optimization in High IBR-Penetrated Power Systems-Part I: Constraint Development and Unification

Zhongda Chu, Fei Teng

Conventional power system optimization framework is becoming less reliable and efficient due to the stability issues brought by the ever-increasing inverter-interfaced renewable pe…

eess.SY2026

Impedance-Based VSC Unit Commitment with STATCOM Support under High IBG Penetration

Aoun Abbas, Zhongda Chu, Charalambos Konstantinou

The large-scale replacement of synchronous machines with inverter-based generation (IBG) introduces critical challenges to both voltage and frequency stability. This work builds on…

eess.SY2026

Learning-Augmented Power System Operations: A Unified Optimization View

Wangkun Xu, Zhongda Chu, Fei Teng

With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic…

eess.SY2025

Headroom as A Grid Service in Software-Defined Power Grids: A Peak-to-Peak Control Design Approach

Zhongda Chu, Fei Teng

To address system frequency challenges driven by the integration of renewable generation, advanced control strategies are designed at the device level to provide effective frequenc…

math.OC2025

Data-Driven Adjustable Robust Optimization

Xiaoxing Ren, Alessio Moreschini, Zhongda Chu +2

In this paper, we develop a two-stage data-driven approach to address the adjustable robust optimization problem, where the uncertainty set is adjustable to manage infeasibility ca…