Decomposition approaches for block-structured chance-constrained programs with application to hydro-thermal unit commitment

DOI10.1007/s00186-014-0478-5zbMath1306.93079OpenAlexW2133903253MaRDI QIDQ486936

Wim van Ackooij

Publication date: 19 January 2015

Published in: Mathematical Methods of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00186-014-0478-5

zbMATH Keywords

stochastic programmingdecomposition methodsoperations researchchance constraint programminghydro-thermal unit commitment

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Large-scale problems in mathematical programming (90C06) Stochastic programming (90C15) Production models (90B30) Stochastic scheduling theory in operations research (90B36) Optimal stochastic control (93E20) Decomposition methods (49M27)

Related Items

Second-order differentiability of probability functions ⋮ A comparison of four approaches from stochastic programming for large-scale unit-commitment ⋮ Level bundle methods for constrained convex optimization with various oracles ⋮ A solution algorithm for chance-constrained problems with integer second-stage recourse decisions ⋮ (Sub-)differentiability of probability functions with elliptical distributions ⋮ Non-smooth DC-constrained optimization: constraint qualification and minimizing methodologies ⋮ Large-scale unit commitment under uncertainty: an updated literature survey ⋮ Nonanticipative duality, relaxations, and formulations for chance-constrained stochastic programs ⋮ A strongly convergent proximal bundle method for convex minimization in Hilbert spaces ⋮ Regularized decomposition of large scale block-structured robust optimization problems ⋮ Convexity and optimization with copulæ structured probabilistic constraints ⋮ Large-scale unit commitment under uncertainty ⋮ Nonlinear chance-constrained problems with applications to hydro scheduling

Uses Software

• PLCP
• SQPlab

Cites Work

• Level bundle methods for constrained convex optimization with various oracles
• Divide to conquer: decomposition methods for energy optimization
• Auxiliary problem principle and decomposition of optimization problems
• On probabilistic constraints induced by rectangular sets and multivariate normal distributions
• Variable metric bundle methods: From conceptual to implementable forms
• Hydro-electric unit commitment subject to uncertain demand
• Lagrangean relaxation. (With comments and rejoinder).
• Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences
• Large-scale unit commitment under uncertainty
• Joint chance constrained programming for hydro reservoir management
• Comparison of bundle and classical column generation
• A primal-proximal heuristic applied to the French unit-commitment problem
• Level bundle methods for oracles with on-demand accuracy
• Constrained Bundle Methods for Upper Inexact Oracles with Application to Joint Chance Constrained Energy Problems
• Solving Nonlinear Single-Unit Commitment Problems with Ramping Constraints
• Lectures on Stochastic Programming
• On Convergence of an Augmented Lagrangian Decomposition Method for Sparse Convex Optimization
• The Supporting Hyperplane Method for Unimodal Programming
• Numerical optimization. Theoretical and practical aspects. Transl. from the French
• Stochastic Lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty
• Capacity planning model for a multipurpose water reservoir with target-priority operation
• Unit commitment by augmented Lagrangian relaxation: testing two decomposition approaches
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item