Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach

This paper presents an application of genetic algorithms (GA) for solving the long‐term power generation expansion planning (PGEP) problem, a highly constrained nonlinear discrete optimization problem. The problem is formulated into a mixed integer nonlinear programming (MINLP) program that determin...

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Bibliographic Details
Published in:International journal of energy research Vol. 30; no. 2; pp. 81 - 99
Main Authors: Sirikum, Jiraporn, Techanitisawad, Anulark
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01.02.2006
Wiley
Subjects:
Applied sciences
emission modelling
Energy
Energy. Thermal use of fuels
Exact sciences and technology
genetic algorithms
Installations for energy generation and conversion: thermal and electrical energy
mixed integer nonlinear programming
power generation expansion planning
Thermal power plants
Costs
power generation expansion planning
Power production
Thermal power plant
Non linear programming
genetic algorithms
Modeling
Optimization
Case study
mixed integer nonlinear programming
emission modelling
Genetic algorithm
Heuristic method
Models
Planning
Expansion
ISSN:0363-907X, 1099-114X
Online Access:Get full text
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Summary:This paper presents an application of genetic algorithms (GA) for solving the long‐term power generation expansion planning (PGEP) problem, a highly constrained nonlinear discrete optimization problem. The problem is formulated into a mixed integer nonlinear programming (MINLP) program that determines the most economical investment plan for additional thermal power generating units over a planning horizon, subject to the requirements of power demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations. Computational results show that the GA‐based heuristic method can solve the PGEP problem effectively and more efficiently at a significant saving in runtime, when compared with a commercial optimization package. Copyright © 2005 John Wiley & Sons, Ltd.
Bibliography:ArticleID:ER1125
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ISSN:0363-907X
1099-114X
DOI:10.1002/er.1125