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Ahmed S. Zamzam
Researcher at National Renewable Energy Laboratory
Publications - 52
Citations - 899
Ahmed S. Zamzam is an academic researcher from National Renewable Energy Laboratory. The author has contributed to research in topics: Optimization problem & Artificial neural network. The author has an hindex of 14, co-authored 52 publications receiving 501 citations. Previous affiliations of Ahmed S. Zamzam include Nile University & University of Texas at Austin.
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Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
Ahmed S. Zamzam,Kyri Baker +1 more
TL;DR: A machine learning approach to optimize the real-time operation of electric power grids finds feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps, resulting in a significant decrease in computational burden for grid operators.
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Optimal Water–Power Flow-Problem: Formulation and Distributed Optimal Solution
TL;DR: In this article, the authors formalize an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures.
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Physics-Aware Neural Networks for Distribution System State Estimation
TL;DR: In this paper, the authors proposed a neural network architecture that utilizes the structure of the power grid to reduce the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting separability of the estimation problem.
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Beyond Relaxation and Newton–Raphson: Solving AC OPF for Multi-Phase Systems With Renewables
TL;DR: The Feasible Point Pursuit - Successive Convex Approximation algorithm is leveraged, a powerful approach for general nonconvex quadratically constrained quadratic programs, to identify feasible and optimal AC OPF solutions in challenging scenarios where existing methods may fail.
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Data-Driven Learning-Based Optimization for Distribution System State Estimation
TL;DR: In this article, a shallow neural network is trained to initialize Gauss-Newton with historical or simulation-derived data to map the available measurements to a point in the neighborhood of the true latent states (network voltages).