5 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…
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…
Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search
Fabio Pavirani, Jonas Van Gompel, Seyed Soroush Karimi Madahi +2
The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the el…