A stopping-time reward and chance-constrained SoC penalty embedded in an end-to-end learning framework improves battery reachability of target ranges, raises arbitrage profit, and lowers profit variance under volatile prices.
Task-based end-to-end model learning in stochastic optimization
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A co-optimization framework for power system capacity and demand-shaping policies that uses differentiable scenario generation from generative machine learning models.
An end-to-end learning framework for joint building-data-center integrated energy systems improves operational performance 7-9% over predict-then-optimize baselines and cuts total energy cost ~10% via waste-heat recovery.
citing papers explorer
-
Learning Reachability of Energy Storage Arbitrage
A stopping-time reward and chance-constrained SoC penalty embedded in an end-to-end learning framework improves battery reachability of target ranges, raises arbitrage profit, and lowers profit variance under volatile prices.
-
Integrated Investment and Policy Planning for Power Systems via Differentiable Scenario Generation
A co-optimization framework for power system capacity and demand-shaping policies that uses differentiable scenario generation from generative machine learning models.
-
End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
An end-to-end learning framework for joint building-data-center integrated energy systems improves operational performance 7-9% over predict-then-optimize baselines and cuts total energy cost ~10% via waste-heat recovery.