Unit commitment by Lagrangian relaxation and genetic algorithms

By Chuan-Ping Cheng, Chih-Wen Liu, Chun-Chang Liu. 2000

521 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/12633f8624a90a6b4eae5953d28523e4e99c6abd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Swapan Kumar Goswami
2. B. Goswami
3. Sarmila Patra
4. K. Kawamoto
5. T. Seki
6. Nobuo Yamashita
7. Machavaram Venkata Chalapathy Rao
8. Dipti Srinivasan

In Management Science and Operations Research

1. K. Kawamoto
2. Machavaram Venkata Chalapathy Rao
3. Dipti Srinivasan
4. Pan Liu
5. B. Goswami
6. Ioannis G. Damousis
7. Anastasios G. Bakirtzis
8. Sarmila Patra

Citations in the corpus

• Finding multiple optimal solutions to optimal load distribution problem in hydropower plant (2012, methodology): “A wide range of optimization methods have been applied to solving the OLD problem, including mixed integer linear programming (MILP) [7–12], mixed integer nonlinear programming [13], dynamic programming (DP) [14–16], Lagrangian relaxation [12,17,18], hierarchical optimization [6,19–23], genetic algo”
• Differential Evolution Algorithm for Solving Unit Commitment with Ramp Constraints (2008, methodology): “[7, 8], tabu search [9], genetic algorithm (GA) [10–12], LR-genetic algorithm (LRGA) [5], ant colony [13], particle swarm optimization (PSO) [14], and evolutionary programming (EP) [15], which are applied to the UC problem.”
• A solution to the unit-commitment problem using integer-coded genetic algorithm (2004, methodology): “However, these heuristics produce suboptimal solutions, and in certain cases, they may require the relaxation of some constraints in order to produce a solution [8], [9].”
• Unit commitment by enhanced adaptive Lagrangian relaxation (2004, methodology): “In addition, the solution quality heavily depends on the initial Lagrangian multipliers [7] and updating Lagrangian multipliers method [7], [17].”
• New Local Search Methods for Improving the Lagrangian-Relaxation-Based Unit Commitment Solution (2010, methodology): “Several methods have been proposed to obtain an approximate solution of the UCP efficiently [17], including priority list methods [18], dynamic programming (DP) [19], branch-andbound methods (B&B) [10], mixed-integer programming (MIP) [8], metaheuristics [13], [16], [21], and Lagrangian relaxation (”
• A novel approach for unit commitment problem via an effective hybrid particle swarm optimization (2006, methodology): “Likewise, the same reason applies for LR [22] and LRGA [38] methods.”
• Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system (2011, methodology): “In this paper, Lagrangian Relaxation with Genetic Algorithm (LRGA) is used to solve the problem.”