DocumentCode
1866387
Title
Multi-objective economic-emission optimal load dispatch using bacterial foraging algorithm
Author
Farhat, I.A. ; El-Hawary, M.E.
Author_Institution
Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
fYear
2012
fDate
April 29 2012-May 2 2012
Firstpage
1
Lastpage
5
Abstract
The optimal economic-emission dispatch problem (EED) is addressed in this paper considering the environmental aspects. To solve this multi-objective problem, a modified bacterial foraging algorithm (MBFA) is implemented. In addition to minimizing the cost function, the minimization of NOx, SO2 and CO2 gaseous emissions is also considered using the weighted-sum method. The BFA is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The BFA has been successfully used to tackle small scale optimization problems. However, when applied to larger constrained problems, it shows poor convergence properties. To overcome these difficulties, due to the complexity and high-dimensionality of the search space of the EED problem, significant modifications are introduced. The MBFA is applied to obtain the optimal or near optimal load dispatch and capture the trade-off set of solutions.
Keywords
air pollution control; carbon compounds; evolutionary computation; load dispatching; nitrogen compounds; power system economics; sulphur compounds; CO2; E. coli bacteria; EED problem; MBFA; NOx; SO2; convergence property; evolutionary optimization technique; gaseous emission minimization; modified bacterial foraging algorithm; multiobjective economic-emission optimal load dispatch; multiobjective problem; small scale optimization problems; Cost function; Economics; Fuels; Heuristic algorithms; Linear programming; Microorganisms; Bacterial foraging optimization; Multi-objective optimization; economic-emission dispatch;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical & Computer Engineering (CCECE), 2012 25th IEEE Canadian Conference on
Conference_Location
Montreal, QC
ISSN
0840-7789
Print_ISBN
978-1-4673-1431-2
Electronic_ISBN
0840-7789
Type
conf
DOI
10.1109/CCECE.2012.6334860
Filename
6334860
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