papers

Publications (29)

math.OC2017

The role of convexity on saddle-point dynamics: Lyapunov function and robustness

Ashish Cherukuri, Enrique Mallada, Steven Low +1

This paper studies the projected saddle-point dynamics associated to a convex-concave function, which we term saddle function. The dynamics consists of gradient descent of the sadd…

cs.GT2014

The Role of a Market Maker in Networked Cournot Competition

Subhonmesh Bose, Desmond Cai, Steven Low +1

We study the role of a market maker (or market operator) in a transmission constrained electricity market. We model the market as a one-shot networked Cournot competition where gen…

eess.SY2015

Solving the power flow equations: a monotone operator approach

Krishnamurthy Dvijotham, Steven Low, Michael Chertkov

The AC power flow equations underlie all operational aspects of power systems. They are solved routinely in operational practice using the Newton-Raphson method and its variants. T…

cs.DS2024

Risk-Sensitive Online Algorithms

Nicolas Christianson, Bo Sun, Steven Low +1

We study the design of risk-sensitive online algorithms, in which risk measures are used in the competitive analysis of randomized online algorithms. We introduce the CVaR-com…

eess.SY2018

Distributed Optimal Frequency Control Considering a Nonlinear Network-Preserving Model

Zhaojian Wang, Feng Liu, John Z. F. Pang +2

This paper addresses the distributed optimal frequency control of power systems considering a network-preserving model with nonlinear power flows and excitation voltage dynamics. S…

math.OC2015

Convexity of Energy-Like Functions: Theoretical Results and Applications to Power System Operations

Krishnamurthy Dvijotham, Steven Low, Michael Chertkov

Power systems are undergoing unprecedented transformations with the incorporation of larger amounts of renewable energy sources, distributed generation and demand response. All the…

eess.SY2026

Decentralized Parametric Stability Certificates for Grid-Forming Converter Control

Verena Häberle, Xiuqiang He, Linbin Huang +2

We propose a decentralized framework to analytically guarantee the small-signal stability of future power systems with grid-forming converters. Our approach leverages dynamic loop-…

eess.SY2026

Data-Driven Successive Linearization for Optimal Voltage Control

Yiwei Dong, Wenqi Cui, Han Xu +2

Power distribution systems are increasingly exposed to large voltage fluctuations driven by intermittent renewable generation and time varying loads (e.g., electric vehicles and st…

eess.SY2014

Decentralized Primary Frequency Control in Power Networks

Changhong Zhao, Steven Low

We augment existing generator-side primary frequency control with load-side control that are local, ubiquitous, and continuous. The mechanisms on both the generator and the load si…

eess.SY2021

Stability Constrained Reinforcement Learning for Real-Time Voltage Control

Yuanyuan Shi, Guannan Qu, Steven Low +2

Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world powe…

math.OC2018

Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

Xinyang Zhou, Masoud Farivar, Zhiyuan Liu +2

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. In this paper we investigate…

eess.SY2024

Uncertainty-Aware Capacity Expansion for Real-World DER Deployment via End-to-End Network Integration

Yiyuan Pan, Yiheng Xie, Steven Low

The deployment of distributed energy resource (DER) devices plays a critical role in distribution grids, offering multiple value streams, including decarbonization, provision of an…

eess.SY2025

Leveraging Predictions in Power System Voltage Control: An Adaptive Approach

Wenqi Cui, Yiheng Xie, Steven Low +2

High variability of solar PV and sudden changes in load (e.g., electric vehicles and storage) can lead to large voltage fluctuations in the distribution system. In recent years, a…

eess.SY2026

Synchrophasors and Synchrowaveforms for the Distribution Grid: The SoCal 28-Bus Dataset

Yiheng Xie, Lucien Werner, Kaibo Chen +3

We provide an open-access dataset of phasor & waveform measurement units (PMUs/WMUs) of a real-world electrical distribution network. The network consists of diverse sets of genera…

eess.SY2017

Breaking Diversity Restriction: Distributed Optimal Control of Stand-alone DC Microgrids

Zhaojian Wang, Feng Liu, Ying Chen +2

Stand-alone direct current (DC) microgrids may belong to different owners and adopt various control strategies. This brings great challenge to its optimal operation due to the diff…

eess.SY2024

Fast and Reliable Contingency Screening with Input-Convex Neural Networks

Nicolas Christianson, Wenqi Cui, Steven Low +2

Power system operators must ensure that dispatch decisions remain feasible in case of grid outages or contingencies to prevent cascading failures and ensure reliable operation. How…

math.OC2015

Distributed Algorithm for Optimal Power Flow on Unbalanced Multiphase Distribution Networks

Qiuyu Peng, Steven Low

The optimal power flow (OPF) problem is funda- mental in power distribution networks control and operation that underlies many important applications such as volt/var control and d…

eess.SY2023

Inverse Power Flow Problem

Ye Yuan, Steven Low, Omid Ardakanian +1

This paper formulates an inverse power flow problem which is to infer a nodal admittance matrix (hence the network structure of a power system) from voltage and current phasors mea…

math.OC2019

System Level Synthesis

James Anderson, John C. Doyle, Steven Low +1

This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how…

eess.SY2016

Event Detection and Localization in Distribution Grids with Phasor Measurement Units

Omid Ardakanian, Ye Yuan, Roel Dobbe +3

The recent introduction of synchrophasor technology into power distribution systems has given impetus to various monitoring, diagnostic, and control applications, such as system id…

cs.AI2020

Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks

Andreas Venzke, Guannan Qu, Steven Low +1

This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems a…

cs.ET2011

Optimal Inverter VAR Control in Distribution Systems with High PV Penetration

Masoud Farivar, Russell Neal, Christopher Clarke +1

The intent of the study detailed in this paper is to demonstrate the benefits of inverter var control on a fast timescale to mitigate rapid and large voltage fluctuations due to th…

cs.LG2022

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Xin Chen, Guannan Qu, Yujie Tang +2

With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexi…

eess.SY2015

A differential analysis of the power flow equations

Krishnamurthy Dvijotham, Michael Chertkov, Steven Low

The AC power flow equations are fundamental in all aspects of power systems planning and operations. They are routinely solved using Newton-Raphson like methods. However, there is…

math.OC2020

Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach

Guannan Qu, Chenkai Yu, Steven Low +1

Model-free learning-based control methods have seen great success recently. However, such methods typically suffer from poor sample complexity and limited convergence guarantees. T…

eess.SY2023

Tractable Identification of Electric Distribution Networks

Ognjen Stanojev, Lucien Werner, Steven Low +1

The identification of distribution network topology and parameters is a critical problem that lays the foundation for improving network efficiency, enhancing reliability, and incre…

cs.LG2022

Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control

Tongxin Li, Ruixiao Yang, Guannan Qu +3

Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approx…

eess.SY2013

Design and Stability of Load-Side Primary Frequency Control in Power Systems

Changhong Zhao, Ufuk Topcu, Na Li +1

We present a systematic method to design ubiquitous continuous fast-acting distributed load control for primary frequency regulation in power networks, by formulating an optimal lo…

math.OC2018

Running Primal-Dual Gradient Method for Time-Varying Nonconvex Problems

Yujie Tang, Emiliano Dall'Anese, Andrey Bernstein +1

This paper considers a nonconvex optimization problem that evolves over time, and addresses the synthesis and analysis of regularized primal-dual gradient methods to track a Karush…