Scope of Biogeography Based Optimization for Economic Load Dispatch and Multi-Objective Unit Commitment Problem

2014 ◽  
Vol 3 (4) ◽  
pp. 34-54 ◽  
Author(s):  
Vikram Kumar Kamboj ◽  
S.K. Bath

Biogeography Based Optimization (BBO) algorithm is a population-based algorithm based on biogeography concept, which uses the idea of the migration strategy of animals or other spices for solving optimization problems. Biogeography Based Optimization algorithm has a simple procedure to find the optimal solution for the non-smooth and non-convex problems through the steps of migration and mutation. This research paper presents the solution to Economic Load Dispatch Problem for IEEE 3, 4, 6 and 10-unit generating model using Biogeography Based Optimization algorithm. It also presents the mathematical formulation of scalar and multi-objective unit commitment problem, which is a further extension of economic load dispatch problem.

Author(s):  
Vikram Kumar Kamboj ◽  
S. K. Bath

Biogeography Based Optimization (BBO) algorithm is a population-based algorithm based on biogeography concept, which uses the idea of the migration strategy of animals or other spices for solving optimization problems. Biogeography Based Optimization algorithm has a simple procedure to find the optimal solution for the non-smooth and non-convex problems through the steps of migration and mutation. This research chapter presents the solution to Economic Load Dispatch Problem for IEEE 3, 4, 6 and 10-unit generating model using Biogeography Based Optimization algorithm. It also presents the mathematical formulation of scalar and multi-objective unit commitment problem, which is a further extension of economic load dispatch problem.


Unit Commitment problem (UC) is a large family of mathematical optimization problems usually either match the energy demand at minimum cost or maximize revenues from energy production. This paper proposes a new approach for solving Unit Commitment problem using the EADPSODV technique. In PSODV, the appropriate mutation factor is selected by applying Ant Colony search procedure in which internally a Genetic Algorithm (GA) is employed in order to develop the necessary Ant Colony parameters. In EADPSODV method the advantageous part is that, for determining the most feasible configuration of the control variables in the Unit Commitment. An initial observation and verification of the suggested process is carried on a 10-unit system which is extended to 40-unit system over a stipulated time horizon (24hr). The outcomes attained from the proposed EADPSODV approach indicate that EADPSODV provides effective and robust solution of Unit Commitment.


Sign in / Sign up

Export Citation Format

Share Document