Multiple objective reactive power planning using genetic algorithms

Small, Steven M. (2007) Multiple objective reactive power planning using genetic algorithms. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (3MB)

Abstract

Increased load demand can severely deteriorate the performance of a power system. Reactive compensation allocation is a common method to allow a power system to return to an acceptable performance level for an expected load increase. The reactive power planning problem (RPP) is used to determine the optimal placement of reactive devices for a set of objectives. The RPP is a large scale, multi-objective, highly constrained and partially discrete optimization problem that is very difficult to solve. -- Heuristic optimization techniques have been used as a means to solve difficult optimization problems including many power system optimization problems. Heuristic techniques based on evolutionary strategies have been used to solve RPPs as they overcome many of the difficulties with classical optimization techniques. However, new multi-objective evolutionary computational techniques have shown the ability to consider an optimization problem's objectives independently for the determination of Pareto-optimal solutions. -- A popular multi-objective evolutionary strategy called the Non-Dominated Sorting Genetic Algorithm II (NSGAII) is applied to a series of multi-objective RPP case studies in this research. The results from the case studies presented show that the tool is able to determine feasible, non-dominated V Ar source allocation schemes that allow a system to operate safely under an assumed load growth.

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