1 citations · 1 across the 11 of their papers we have counts for
11 papers
Control-Oriented Scenario Tree Construction through Reinforcement Learning
Fabio Pavirani, Bert Claessens, Pierre Pinson +1
Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sa…
Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels
Burak Karabulut, Olayiwola Arowolo, Carlo Manna +2
Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to interm…
Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times
Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi +1
The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on…
S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty
Fabio Pavirani, Bert Claessens, Pierre Pinson +1
Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance, optimizing the dispatch of…
Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding
Celle Hendrickx, Fabio Pavirani, Chris Develder
The growing share of Renewable Energy Sources (RES) in modern power systems increases both grid imbalances and frequency deviations, reinforcing the need for ancillary services suc…
Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Burak Karabulut, Carlo Manna, Chris Develder
Fault location in distribution grids is critical for reliability and minimizing outage durations. Yet, it remains challenging due to partial observability, given sparse measurement…
Neural Network-Assisted Model Predictive Control for Implicit Balancing
Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens +1
In Europe, balance responsible parties can deliberately take out-of-balance positions to support transmission system operators (TSOs) in maintaining grid stability and earn profit,…
Herd Behavior in Decentralized Balancing Models: A Case Study in Belgium
Max Bruninx, Seyed Soroush Karimi Madahi, Timothy Verstraeten +3
In a decentralized balancing model, Balance Responsible Parties (BRPs) are encouraged by the Transmission System Operator (TSO) to deviate from their schedule to help the system re…
Model Predictive Control-Guided Reinforcement Learning for Implicit Balancing
Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens +1
In Europe, profit-seeking balance responsible parties can deviate in real time from their day-ahead nominations to assist transmission system operators in maintaining the supply-de…
Generalization of Graph Neural Network Models for Distribution Grid Fault Detection
Burak Karabulut, Carlo Manna, Chris Develder
Fault detection in power distribution grids is critical for ensuring system reliability and preventing costly outages. Moreover, fault detection methodologies should remain robust…
Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Toon Van Puyvelde, Mehran Zareh, Chris Develder
In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains…