5 citations · 11 across the 6 of their papers we have counts for
6 papers
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…
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…
Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing
Xue Yao, Shengren Hou, Serge P. Hoogendoorn +1
Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This chall…
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…
Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling
Hou Shengren, Edgar Mauricio Salazar, Pedro P. Vergara +1
Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due…