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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.

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Citations
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Journal ArticleDOI

Feasible Modified Subgradient Method for Solving the Thermal Unit Commitment Problem as a New Approach

TL;DR: In this article, a new approach, feasible modified subgradient (F-MSG) method which does not require finding an unconstrained global minimum of the Lagrangian function and knowing an optimal value of the problem under consideration in order to update dual variables at the each iteration, is firstly used for solving the thermal unit commitment (UC) problem.
Proceedings ArticleDOI

Risk constrained unit commitment considering uncertainty of wind power and load

TL;DR: Because of the great stochastic of wind power, it is difficult to forecast it accurately and the forecasting result can not be applied to Unit Commitment directly, so a dispersed probability distribution model is built and Gaussian function fitting is introduced in the Lagrangian Relaxation to handle the Unit commitment with Wind Power.
Proceedings ArticleDOI

Relativity Pheromone Updating Strategy in Ant Colony Optimization for Constrained Unit Commitment Problem

TL;DR: The adoption of RPUS not only enhances the search convergence of EACO, but also provides relatively pheromone information that is suitably exploited for a good guidance of search process.
Journal ArticleDOI

Optimal Power Pooling for a Multiple Area Power System through PSO

TL;DR: In this article, the authors used particle swarm optimization (PSO) and its variants to solve the valve-point effect problem in a multiple area power system, where power can be transferred from one area to other to improve the load factor, reliability, security and economics of the power system.
Book ChapterDOI

Economic Dispatch (ED) and Unit Commitment Problems (UCP): Formulation and Solution Algorithms

TL;DR: In this paper, the objectives function for ED and UCP was formulated and the system and unit constraints for generating solutions were proposed for generating an initial solution to the economic dispatch problem.
References
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Journal ArticleDOI

The Lagrangian Relaxation Method for Solving Integer Programming Problems

TL;DR: This paper is a review of Lagrangian relaxation based on what has been learned in the last decade and has led to dramatically improved algorithms for a number of important problems in the areas of routing, location, scheduling, assignment and set covering.
Journal ArticleDOI

Validation of subgradient optimization

TL;DR: It is concluded that the “relaxation” procedure for approximately solving a large linear programming problem related to the traveling-salesman problem shows promise for large-scale linear programming.
Journal ArticleDOI

A dual-based procedure for uncapacitated facility location

TL;DR: This approach has obtained and verified optimal solutions to all the Kuehn-Hamburger location problems in well under 0.1 seconds each on an IBM 360/91 computer, with no branching required.
Journal ArticleDOI

An Applications Oriented Guide to Lagrangian Relaxation

Marshall L. Fisher
- 01 Apr 1985 - 
TL;DR: This tutorial provides a practical guide to the use of Lagrangian relaxation and an on-line computerized routing and scheduling optimizer.
Journal ArticleDOI

Towards a more rigorous and practical unit commitment by Lagrangian relaxation

TL;DR: A mathematically based, systematic and generally applicable procedure to search for a reserve-feasible dual solution for power system generator unit commitment, giving reliable performance and low execution times.
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