activity
20182023
most citedReinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

422 citations · 511 across the 15 of their papers we have counts for

collaborators

15 papers

eess.SY2023★ 1 cited

Towards Carbon-Free Electricity: A Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization

Xin Chen, Hungpo Chao, Wenbo Shi +1

This paper introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focu…

math.OC2023★ 3 cited

Carbon-Aware Optimal Power Flow

Xin Chen, Andy Sun, Wenbo Shi +1

To facilitate effective decarbonization of the electric power sector, this paper introduces the generic Carbon-aware Optimal Power Flow (C-OPF) method for power system decision-mak…

eess.SY2023★ 3 cited

Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering

Zijian Feng, Xin Chen, Zijian Lv +2

Deep learning (DL) algorithms have been widely applied to short-term voltage stability (STVS) assessment in power systems. However, transferring the knowledge learned in one power…

math.OC2023★ 2 cited

Continuous-Time Zeroth-Order Dynamics with Projection Maps: Model-Free Feedback Optimization with Safety Guarantees

Xin Chen, Jorge I. Poveda, Na Li

This paper introduces a class of model-free feedback methods for solving generic constrained optimization problems where the specific mathematical forms of the objective and constr…

math.OC2022

Model-Free Feedback Constrained Optimization Via Projected Primal-Dual Zeroth-Order Dynamics

Xin Chen, Jorge I. Poveda, Na Li

In this paper, we propose a model-free feedback solution method to solve generic constrained optimization problems, without knowing the specific formulations of the objective and c…

math.OC2021★ 4 cited

Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters

Xin Chen, Yujie Tang, Na Li

Single-point zeroth-order optimization (SZO) is useful in solving online black-box optimization and control problems in time-varying environments, as it queries the function value…