A unified decision-focused framework trains VAE, GAN, and diffusion models to generate correlated scenarios for distributionally robust grid dispatch, reducing operational cost by 0.80–2.02% over accuracy-oriented methods.
Stochastic optimization for unit commitment—a review
2 Pith papers cite this work. Polarity classification is still indexing.
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Bayesian neural posterior estimation recovers marginal generation costs from market schedules with credible intervals but shows start-up costs are largely unidentifiable from schedules alone.
citing papers explorer
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Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch
A unified decision-focused framework trains VAE, GAN, and diffusion models to generate correlated scenarios for distributionally robust grid dispatch, reducing operational cost by 0.80–2.02% over accuracy-oriented methods.
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Bayesian Inference for Estimating Generation Costs in Electricity Markets
Bayesian neural posterior estimation recovers marginal generation costs from market schedules with credible intervals but shows start-up costs are largely unidentifiable from schedules alone.