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

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

Vikram Kumar Kamboj (Department of Electrical Engineering, Punjab Technical University, Jalandhar, Punjab, India) and S.K. Bath (Department of Electrical Engineering, GZSCET PTU Campus, Punjab, India)
Copyright: © 2014 |Pages: 21
DOI: 10.4018/ijeoe.2014100103
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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.
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Economic Load Dispatch Problem Formulation

The objective of economic load dispatch of electric power generation is to schedule the committed generating unit outputs so as to meet the load demand at minimum operating cost while satisfying all units and operational constraints of the power system. The economic dispatch problem is a constrained optimization problem and it can be mathematically expressed as follows (Dhillon & Kothari, 2010):

(1) subject to:
  • 1.

    The energy balance equation:


  • 2.

    The inequality constraints:


where, ijeoe.2014100103.m04 and ijeoe.2014100103.m05 are cost coefficients:

  • ijeoe.2014100103.m06 - is Load Demand;

  • ijeoe.2014100103.m07 - is power transmission Loss;

  • ijeoe.2014100103.m08 - is the number of generation buses;

  • ijeoe.2014100103.m09 - is real power generation and will act as decision variable.

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