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.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A progressive integrality outer-inner approximation framework solves large-scale AC unit commitment problems faster and more robustly than commercial solvers on 200- and 500-bus networks.
A supervised transformer regression model predicts DSO responses to P2P trades on a modified IEEE 33-bus system, enabling local feasibility checks and improved market efficiency.
citing papers explorer
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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.
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Warm-Startable Progressive Integrality Outer-Inner Approximation for AC Unit Commitment with Conic Formulation
A progressive integrality outer-inner approximation framework solves large-scale AC unit commitment problems faster and more robustly than commercial solvers on 200- and 500-bus networks.
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Grid-Aware Peer-to-Peer Energy Trading: A Learning-Augmented Framework
A supervised transformer regression model predicts DSO responses to P2P trades on a modified IEEE 33-bus system, enabling local feasibility checks and improved market efficiency.