20 citations · 29 across the 12 of their papers we have counts for
13 papers
Community-to-Vehicle: Integrating Electric Vehicles into Energy Communities -- A Swiss Case Study
Na Li, Dong Liu, Stavros Orfanoudakis +4
The institutional separation between local energy communities and public electric vehicle (EV) charging limits the efficient use of locally generated renewable energy. This paper i…
SAVGO: Learning State-Action Value Geometry with Cosine Similarity for Continuous Control
Stavros Orfanoudakis, Pedro P. Vergara
While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy updates directly in the action…
Learning to Route Electric Trucks Under Operational Uncertainty
Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang +4
Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared…
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…
Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints
Ting Gao, Stavros Orfanoudakis, Nan Lin +3
Balancing policy expressiveness with the exploration-exploitation trade-off is a core challenge in online Reinforcement Learning (RL). While Stochastic Differential Equation (SDE)-…
Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans A. Oliehoek, Peter Palensky +1
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing R…
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…
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…
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…
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…
PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks
Nan Lin, Stavros Orfanoudakis, Nathan Ordonez Cardenas +2
Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks' operation and planning. Therefore, there is a need for scalable algorithms that can provid…
A Novel Multiagent Flexibility Aggregation Framework
Stavros Orfanoudakis, Georgios Chalkiadakis
The increasing number of Distributed Energy Resources (DERs) in the emerging Smart Grid, has created an imminent need for intelligent multiagent frameworks able to utilize these as…