A structure-aware reduction method fixes sparse stable binary commitment variables before MILP solving, preserving feasibility and enabling solver-certified optimality in the restricted feasible region for network-constrained unit commitment.
Unit commitment-a bibliographical survey
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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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Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees
A structure-aware reduction method fixes sparse stable binary commitment variables before MILP solving, preserving feasibility and enabling solver-certified optimality in the restricted feasible region for network-constrained unit commitment.
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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.