26 citations · 111 across the 28 of their papers we have counts for
28 papers
A Dynamic Capacity Allocation Model for DERs under Non-Firm Connection Agreements
Neda Vahabzad, Kenneth Bruninx, Peter Palensky +1
The growing penetration of distributed energy resources (DERs) intensifies congestion in distribution networks by introducing bidirectional power flows and increasing competition f…
Risk-Based PV-Rich Distribution System Planning Using Generative AI
Habtemariam Aberie Kefale, Weijie Xia, Nanda Kishor Panda +2
Hosting capacity (HC) assessment plays a critical role in distribution system planning under increasing penetration of distributed energy resources (DERs) and associated uncertaint…
Robust Operation of Distribution Networks: Generalized Uncertainty Modelling in Confidence-Level-Based Information Gap Decision
Zhisheng Xiong, Dimitris Boskos, Bo Zeng +2
This paper studies the robust optimal operation of distribution networks (DNs) under renewable generation and load demand uncertainties, seeking an improved trade-off between robus…
Estimating Density Functions for Probabilistic Power Flow Using Invertible Neural Networks
Weijie Xia, James Ciyu Qin, Edgar Mauricio Salazar Duque +4
Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern power systems with high penetrations of renewable generation and flexible loads. Conve…
Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks
Shuyi Gao, Stavros Orfanoudakis, Shengren Hou +2
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time-varying conditions and possi…
SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models
Nan Lin, Yanbo Wang, Jacco Heres +2
Smart meter data is the foundation for planning and operating the distribution network. Unfortunately, such data are not always available due to privacy regulations. Meanwhile, the…
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…
Comparative Analysis of Zero-Shot Capability of Time-Series Foundation Models in Short-Term Load Prediction
Nan Lin, Dong Yun, Weijie Xia +2
Short-term load prediction (STLP) is critical for modern power distribution system operations, particularly as demand and generation uncertainties grow with the integration of low-…
Adaptive Informed Deep Neural Networks for Power Flow Analysis
Zeynab Kaseb, Stavros Orfanoudakis, Pedro P. Vergara +1
This study introduces PINN4PF, an end-to-end deep learning architecture for power flow (PF) analysis that effectively captures the nonlinear dynamics of large-scale modern power sy…
Two-Stage Robust Optimal Operation of Distribution Networks Considering Renewable Energy and Demand Asymmetric Uncertainties
Zhisheng Xiong, Bo Zeng, Peter Palensky +1
This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networ…
Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks
Shengren Hou, Peter Palensky, Pedro P. Vergara
The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, whic…
RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks
Shengren Hou, Shuyi Gao, Weijie Xia +3
Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innov…
EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models
Nan Lin, Peter Palensky, Pedro P. Vergara
High-resolution time series data are crucial for the operation and planning of energy systems such as electrical power systems and heating systems. Such data often cannot be shared…
A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control
Cesar Diaz-Londono, Stavros Orfanoudakis, Pedro P. Vergara +3
Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable a…