46 citations · 155 across the 26 of their papers we have counts for
29 papers
Unsupervised Learning for Equitable DER Control
Zhenyi Yuan, Guido Cavraro, Ahmed S. Zamzam +1
In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsu…
Equitable Networked Microgrid Topology Reconfiguration for Wildfire Risk Mitigation
Yuqi Zhou, Ahmed Zamzam, Andrey Bernstein
The increasing number of wildfires in recent years consistently challenges the safe and reliable operations of power systems. To prevent power lines and other electrical components…
Solving Decision-Dependent Games by Learning from Feedback
Killian Wood, Ahmed Zamzam, Emiliano Dall'Anese
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the…
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning
Patrick Emami, Xiangyu Zhang, David Biagioni +1
In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced…
Managing Wildfire Risk and Promoting Equity through Optimal Configuration of Networked Microgrids
Sofia Taylor, Gabriela Setyawan, Bai Cui +2
As climate change increases the risk of large-scale wildfires, wildfire ignitions from electric power lines are a growing concern. To mitigate the wildfire ignition risk, many elec…
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Daniel Tabas, Ahmed S. Zamzam, Baosen Zhang
Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swa…
Enabling Grid-Aware Market Participation of Aggregate Flexible Resources
Bai Cui, Ahmed Zamzam, Andrey Bernstein
Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. With…
Multi-Area Distribution System State Estimation via Distributed Tensor Completion
Yajing Liu, Ahmed S. Zamzam, Andrey Bernstein
This paper proposes a model-free distribution system state estimation method based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting…
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
David Biagioni, Xiangyu Zhang, Dylan Wald +4
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environm…
OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
Trager Joswig-Jones, Kyri Baker, Ahmed S. Zamzam
Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of discipl…
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…
Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks
Minh-Quan Tran, Ahmed S. Zamzam, Phuong H. Nguyen
Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a…
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…
PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
Faisal M. Almutairi, Aritra Konar, Ahmed S. Zamzam +1
Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing ene…
Novel Region of Attraction Characterization for Control and Stabilization of Voltage Dynamics
Bai Cui, Ahmed Zamzam, Guido Cavraro +1
In this paper, we study the monitoring and control of long-term voltage stability considering load tap-changer (LTC) dynamics. We show that under generic conditions, the LTC dynami…
Substation-Level Grid Topology Optimization Using Bus Splitting
Yuqi Zhou, Ahmed S. Zamzam, Andrey Bernstein +1
Operations of substation circuit breakers are important for maintenance needs and topology reconfiguration in power systems. Bus splitting is one type of topology change where the…
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…
Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition
Ahmed S. Zamzam, Yajing Liu, Andrey Bernstein
As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. This paper uses unique tr…
Cell-Edge Detection via Selective Cooperation and Generalized Canonical Correlation
Mohamed Salah Ibrahim, Ahmed S. Zamzam, Aritra Konar +1
Improving the uplink quality of service for users located around the boundaries between cells is a key challenge in LTE systems. Relying on power control, existing approaches throt…
GRATE: Granular Recovery of Aggregated Tensor Data by Example
Ahmed S. Zamzam, Bo Yang, Nicholas D. Sidiropoulos
In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples. This problem is motivated by several applicatio…
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…
Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
Ahmed Zamzam, Kyri Baker
In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps o…
Energy Storage Management via Deep Q-Networks
Ahmed S. Zamzam, Bo Yang, Nicholas D. Sidiropoulos
Energy storage devices represent environmentally friendly candidates to cope with volatile renewable energy generation. Motivated by the increase in privately owned storage systems…
Physics-Aware Neural Networks for Distribution System State Estimation
Ahmed S. Zamzam, Nicholas D. Sidiropoulos
The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the ini…