5 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
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…
Tensor Power Flow Formulations for Multidimensional Analyses in Distribution Systems
Edgar Mauricio Salazar Duque, Juan S. Giraldo, Pedro P. Vergara +3
In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of power flows in…
A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
Shengren Hou, Edgar Mauricio Salazar Duque, Peter Palensky +1
The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and re…
Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm
Hou Shengren, Pedro P. Vergara, Edgar Mauricio Salazar Duque +1
The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operati…