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
Link To Document :
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