1.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Colorado BoulderUS112 papers
- Colorado School of MinesUS59 papers
- King's College LondonGB30 papers
- Lawrence Berkeley National LaboratoryUS28 papers
- Fudan UniversityCN17 papers
- Radboud University NijmegenNL15 papers
- Stanford UniversityUS15 papers
- University of MinnesotaUS15 papers
- University of California, BerkeleyUS14 papers
- Colorado State UniversityUS13 papers
- North Carolina State UniversityUS12 papers
- Princeton UniversityUS12 papers
40 papers · 1 filter
Adaptive Computing for Scale-up Problems
Kevin Patrick Griffin, Hilary Egan, Marc T. Henry de Frahan +11
Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are…
Frequency shaping control for weakly-coupled grid-forming IBRs
Bala Kameshwar Poolla, Yashen Lin, Andrey Bernstein +2
We consider the problem of controlling the frequency of low-inertia power systems via inverter-based resources (IBRs) that are weakly connected to the grid. We propose a novel grid…
Data-Driven Moment-Based Distributionally Robust Chance-Constrained Optimization
Joshua Comden, Ahmed S. Zamzam, Andrey Bernstein
Many stochastic optimization problems include chance constraints that enforce constraint satisfaction with a specific probability; however, solving an optimization problem with cha…
Pricing and Energy Trading in Peer-to-peer Zero Marginal-cost Microgrids
Jonathan Lee, Rodrigo Henriquez-Auba, Bala Kameshwar Poolla +1
Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. I…
Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties
Bai Cui, Ahmed Zamzam, Andrey Bernstein
Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To ex…
Economic Dispatch With Distributed Energy Resources: Co-Optimization of Transmission and Distribution Systems
Xinyang Zhou, Chin-Yao Chang, Andrey Bernstein +2
The increasing penetration of distributed energy resources (DERs) in the distribution networks has turned the conventionally passive load buses into active buses that can provide g…
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
Shuhang Chen, Adithya Devraj, Andrey Bernstein +1
The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts t…
Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks
Manish K. Singh, Sarthak Gupta, Vassilis Kekatos +2
Deep learning for distribution grid optimization can be advocated as a promising solution for near-optimal yet timely inverter dispatch. The principle is to train a deep neural net…
Exactness of OPF Relaxation on Three-phase Radial Networks with Delta Connections
Fengyu Zhou, Ahmed S. Zamzam, Steven H. Low +1
Simulations have shown that while semi-definite relaxations of AC optimal power flow (AC-OPF) on three-phase radial networks with only wye connections tend to be exact, the presenc…
Online Data-Enabled Predictive Control
Stefanos Baros, Chin-Yao Chang, Gabriel E. Colon-Reyes +1
We develop an online data-enabled predictive (ODeePC) control method for optimal control of unknown systems, building on the recently proposed DeePC [1]. Our proposed ODeePC method…
Multi-Level Optimal Power Flow Solver in Large Distribution Networks
Xinyang Zhou, Yue Chen, Zhiyuan Liu +2
Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topolog…
Learning-Accelerated ADMM for Distributed Optimal Power Flow
David Biagioni, Peter Graf, Xiangyu Zhang +3
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) whe…
Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow
Yue Chen, Andrey Bernstein, Adithya Devraj +1
This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit…
Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback
Yi Guo, Xinyang Zhou, Changhong Zhao +3
Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices i…
Matrix Completion Using Alternating Minimization for Distribution System State Estimation
Yajing Liu, April Sagan, Andrey Bernstein +3
This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method whi…
Gradient-Based Multi-Area Distribution System State Estimation
Xinyang Zhou, Zhiyuan Liu, Yi Guo +3
The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system m…
Submodular Optimization Problems and Greedy Strategies: A Survey
Yajing Liu, Edwin K. P. Chong, Ali Pezeshki +1
The greedy strategy is an approximation algorithm to solve optimization problems arising in decision making with multiple actions. How good is the greedy strategy compared to the o…
Sample Complexity of Power System State Estimation using Matrix Completion
Joshua Comden, Marcello Colombino, Andrey Bernstein +1
In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system - a problem known as sample co…
Personalized Optimization with User's Feedback
Andrea Simonetto, Emiliano Dall'Anese, Julien Monteil +1
This paper develops an online algorithm to solve a time-varying optimization problem with an objective that comprises a known time-varying cost and an unknown function. This proble…
Accelerated Voltage Regulation in Multi-Phase Distribution Networks Based on Hierarchical Distributed Algorithm
Xinyang Zhou, Zhiyuan Liu, Changhong Zhao +1
We propose a hierarchical distributed algorithm to solve optimal power flow (OPF) problems that aim at dispatching controllable distributed energy resources (DERs) for voltage regu…
Dispatchable Virtual Oscillator Control for Decentralized Inverter-dominated Power Systems: Analysis and Experiments
Gab-Su Seo, Marcello Colombino, Irina Subotić +3
This paper presents an analysis and experimental validation of dispatchable virtual oscillator control (dVOC) for inverter-dominated power systems. dVOC is a promising decentralize…
Signal-Anticipation in Local Voltage Control in Distribution Systems
Zhiyuan Liu, Seungil You, Xinyang Zhou +2
We consider the signal-anticipating behavior in local Volt/Var control for distribution systems. Such a behavior makes interaction among the nodes a game. We characterize Nash equi…
Hierarchical Distributed Voltage Regulation in Networked Autonomous Grids
Xinyang Zhou, Zhiyuan Liu, Wenbo Wang +3
We propose a novel algorithm to solve optimal power flow (OPF) that aims at dispatching controllable distributed energy resources (DERs) for voltage regulation at minimum cost. The…
A General Framework for Bounding Approximate Dynamic Programming Schemes
Yajing Liu, Edwin Chong, Ali Pezeshki +1
For years, there has been interest in approximation methods for solving dynamic programming problems, because of the inherent complexity in computing optimal solutions characterize…