26 citations · 111 across the 28 of their papers we have counts for
10 papers · 1 filter
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…
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
Weijie Xia, Chenguang Wang, Peter Palensky +1
Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g.,…
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking
Stavros Orfanoudakis, Cesar Diaz-Londono, Yunus E. Yılmaz +2
As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions…
On future power system digital twins: A vision towards a standard architecture
Wouter Zomerdijk, Peter Palensky, Tarek AlSkaif +1
The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world…