Journal ArticleDOI
Implementation of a Lagrangian relaxation based unit commitment problem
TLDR
The Lagrangian relaxation methodology has been used for solving the unit commitment problem as discussed by the authors, which is a class of complex combinatorial optimization problems in the power system, where the objective is to obtain an overall least-cost solution for operating the system over the scheduling horizon.Abstract:
The unit commitment problem in a power system involves determining a start-up and shut-down schedule of units to be used to meet the forecasted demand, over a future short term (24-168 hour) period. In solving the unit commitment problem, generally two basic decisions are involved. The "unit commitment" decision involves determining which generating units are to be running during each hour of the planning horizon, considering system capacity requirements including reserve, and the constraints on the start up and shut down of units. The related "economic dispatch" decision involves the allocation of system demand and spinning reserve capacity among the operating units during each specific hour of operation. As these two decisions are interrelated, the unit commitment problem generally embraces both these decisions, and the objective is to obtain an overall least cost solution for operating the power system over the scheduling horizon. The unit commitment problem belongs to the class of complex combinatorial optimization problems. During the past decade a new approach named "Lagrangian Relaxation" has been evolving for generating efficient solutions for this class of problems. It derives its name from the well-known mathematical technique of using Lagrange multipliers for solving constrained optimization problems, but is really a decomposition technique for the solution of large scale mathematical programming problems. The Lagrangian relaxation methodology generates easy subproblems for deciding commitment and generation schedules for single units over the planning horizon, independent of the commitment of other units.read more
Citations
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Journal ArticleDOI
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
TL;DR: This paper presents a detailed overview of the basic concepts of PSO and its variants, and provides a comprehensive survey on the power system applications that have benefited from the powerful nature ofPSO as an optimization technique.
Journal ArticleDOI
A genetic algorithm solution to the unit commitment problem
TL;DR: This paper presents a genetic algorithm (GA) solution to the unit commitment problem using the varying quality function technique and adding problem specific operators, satisfactory solutions to theunit commitment problem were obtained.
Journal ArticleDOI
Unit commitment-a bibliographical survey
TL;DR: In this article, a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of modern unit commitment (UC) problem for past 35 years based on more than 150 published articles.
Journal ArticleDOI
An evolutionary programming solution to the unit commitment problem
TL;DR: The practical implementation of this procedure yielded satisfactory results when the EP-based algorithm was tested on a reported UC problem previously addressed by some existing techniques such as Lagrange relaxation (LR), dynamic programming (DP), and genetic algorithms (GAs).
Journal ArticleDOI
Unit commitment literature synopsis
Gerald B. Sheblé,G.N. Fahd +1 more
TL;DR: Several optimization techniques have been applied to the solution of the thermal unit commitment problem as discussed by the authors, ranging from heuristics such as complete enumeration to the more sophisticated ones such as augmented LaGrangian.
References
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Journal ArticleDOI
Lagrangian Reduction of Search-Range for Large-Scale Unit Commitment
R. Nieva,A. Inda,I. Guillen +2 more
TL;DR: In this article, a new approach capable of producing low-cost solutions in relatively short execution times is presented, which applies Dynamic Programming in successive approximations and a reduced search-range is determined for each successive iteration by means of a Lagrangian technique.
Book
The use of the boxstep method in discrete optimization
TL;DR: The Boxstep method is used to maximize Lagrangean functions in the context of a branch-and-bound algorithm for the general discrete optimization problem.
Related Papers (5)
Towards a more rigorous and practical unit commitment by Lagrangian relaxation
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