“20th PSCC 2018 papers submission and review platform

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Optimal Power Flow Based on Genetic Algorithms and Clustering Techniques

Optimal power flow problem has been studied extensively
for the past decades. Two approaches for solving the
problem have been distinguished: mathematical programming
and evolutionary algorithms. The first is fast but is not converging to a global optimum for every case. The second ones are robust but time-consuming. This paper proposes a method that combines both approaches to eliminate their flaws and take advantage of their benefits. The method uses properties of genetic algorithms to group their chromosomes around optimums in the search space. The centers of these groups are identified by clustering techniques and furthermore used as initial points for gradient based search methods. At the end, the proposed method finds global optimum and its closest local optimums. Continuous Newton-Raphson method is used to overcome ill-conditioned points in search space when calculating power flows. The proposed method is compared against similar methods showing considerable improvement.


Stefan Stankovic    
Royal Institute of Technology KTH

Lennart Söder    
Royal Institute of Technology KTH


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