A fuzzy goal programming method to solve congestion management problem using genetic algorithm
DOI:
https://doi.org/10.31181/dmame1902040bKeywords:
Congestion Management; Fuzzy Goal Programming; Genetic Algorithm; Membership Function; Overload Alleviation; Particle Swarm OptimizationAbstract
The objective of this work is to present a priority-based fuzzy goal programming (FGP) method for solving the congestion management (CM) problem in electric power transmission lines by employing genetic algorithm (GA). To formulate the model for this problem, membership functions which are associated with the fuzzy model goals are converted into membership goals by assigning highest membership value (unity) as goal level and adding under- and over-deviational variables to each of them. In solution process, a GA computational scheme is addressed within the framework of FGP model to achieve aspired goal levels of goals according to their priorities in imprecise environment. The standard IEEE 30-Bus 6-Generator test system is taken as a case example to show the effectiveness of the approach. A comparison of model solution is also compared with solution of another approach studied previously.
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