DocumentCode :
3576779
Title :
Solving an economic and environmental dispatch problem using evolutionary algorithm
Author :
Zaman, F. ; Sarker, R.A. ; Ray, T.
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, NSW, Australia
fYear :
2014
Firstpage :
1367
Lastpage :
1371
Abstract :
For successful operation of any power system, an effective scheduling of power generation is crucial. In this paper, we consider a power system with two types of generators, thermal and hydro. The characteristics of these generators vary with respect to the cost, emission to the environment, input source, capacity limit, and technological constraints. The mathematical model considering two objectives, such as minimization of the operating cost and minimization of total emissions, for a hydrothermal system is discussed. A solution approach has been proposed, based on evolutionary computation concept, for solving a benchmark problem for both single and bi-objective version of the problem. In the approach, an initial population of solutions is generated based on a heuristic and the population is then evolved using two well-known evolutionary search algorithms. The solutions of our approaches are compared with another approach from the literature. The analysis of the results reveals that the heuristic enhanced the performance of the evolutionary algorithms considered in this paper.
Keywords :
evolutionary computation; hydrothermal power systems; power generation dispatch; power generation economics; search problems; capacity limit; economic dispatch problem; environment emission; environmental dispatch problem; evolutionary computation concept; evolutionary search algorithms; hydro generators; hydrothermal system; input source; mathematical model; power system; technological constraints; thermal generators; Evolutionary computation; Fuels; Optimization; Reservoirs; Sociology; Statistics; Hydrothermal system; infeasibility driven evolutionary algorithm; multiobjective differential evolution; nondominated sorting genetic algorithm-II; scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on
Type :
conf
DOI :
10.1109/IEEM.2014.7058862
Filename :
7058862
Link To Document :
بازگشت