Multiple objective reactive power planning using genetic algorithms.

摘要

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.;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 VAr source allocation schemes that allow a system to operate safely under an assumed load growth.;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.