26 citations · 111 across the 28 of their papers we have counts for
11 papers · 1 filter
Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks
Dong Liu, Juan S. Giraldo, Peter Palensky +1
Centralised reinforcement learning (RL) for voltage magnitude regulation in distribution networks typically involves numerous agent-environment interactions and power flow (PF) cal…
Reinforcement Learning to Initialize Newton-Raphson for AC Power Flow with Quantum Annealing-Based Environment Updates
Zeynab Kaseb, Matthias Moller, Lindsay Spoor +4
The Newton-Raphson (NR) method is widely used for solving power flow (PF) equations due to its quadratic convergence. However, its performance deteriorates under poor initializatio…
Performance Comparison of Gate-Based and Adiabatic Quantum Computing for AC Power Flow Problem
Zeynab Kaseb, Matthias Moller, Peter Palensky +1
We present the first direct comparison between gate-based quantum computing (GQC) and adiabatic quantum computing (AQC) paradigms for solving the AC power flow (PF) equations. The…
A data-driven approach for topology correction in low voltage distribution networks with PVs
Dong Liu, Sander Timmerman, Yu Xiang +3
Most existing phase balancing and topology reconfiguration problems are formulated as mixed-integer optimization problems that depend on network topologies~\cite{10098964,11017695,…
Optimal Droop Control Strategy for Coordinated Voltage Regulation and Power Sharing in Hybrid AC-MTDC Systems
Hongjin Du, Tuanku Badzlin Hashfi, Rashmi Prasad +3
With the growing integration of modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems, there is an increasing need for control strategie…
Power flow and optimal power flow using quantum and digital annealers: a computational scalability analysis
Zeynab Kaseb, Matthias Moller, Pedro P. Vergara +1
This study further explores reformulating power flow (PF) analysis as a discrete combinatorial optimization problem, proposed in our earlier study using the Adiabatic Quantum Power…
Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems
Zeynab Kaseb, Rahul Rane, Aleksandra Lekic +4
Quantum computing has emerged as a promising computational paradigm to address unresolved challenges in the modeling and control of modern power systems. However, most existing stu…
A Framework for Solving Continuous Energy and Power System Problems using Adiabatic Quantum Computing
Zeynab Kaseb, Matthias Moller, Peter Palensky +1
The increasing scale and nonlinearity of modern energy and power system problems pose significant challenges to classical numerical solvers. In parallel, advances in quantum and qu…
Model-Free Privacy Preserving Power Flow Analysis in Distribution Networks
Dong Liu, Juan S. Giraldo, Peter Palensky +1
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). How…
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks
Stavros Orfanoudakis, Peter Palensky, Pedro P. Vergara
Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL)…
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments
Stavros Orfanoudakis, Nanda Kishor Panda, Peter Palensky +1
Reinforcement Learning (RL) methods used for solving real-world optimization problems often involve dynamic state-action spaces, larger scale, and sparse rewards, leading to signif…