A floating-point genetic algorithm for solving the unit commitment problem

DOI10.1016/j.ejor.2005.10.071zbMath1123.90310OpenAlexW1965178198MaRDI QIDQ877631

Chuangyin Dang, Minqiang Li

Publication date: 3 May 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2005.10.071

zbMATH Keywords

unit commitmentelectrical power generationdynamic genetic strategyfloating-point genetic algorithmgenerators scheduling and economic dispatch

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (7)

A modified differential evolution algorithm for the solution of a large-scale unit commitment problem ⋮ A hybrid biased random key genetic algorithm approach for the unit commitment problem ⋮ A polynomial-time algorithm with tight error bounds for single-period unit commitment problem ⋮ A memory simulated annealing method to the unit commitment problem with ramp constraints ⋮ A strengthened mixed-integer linear programming formulation for combined-cycle units ⋮ Unit commitment scheduling using binary differential evolution algorithm ⋮ Two-stage network constrained robust unit commitment problem

Uses Software

• Genocop

Cites Work

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• Unit commitment in power generation -- a basic model and some extensions
• A hybrid neural network and simulated annealing approach to the unit commitment problem
• Short-Term Scheduling of Thermal-Electric Generators Using Lagrangian Relaxation

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