Abstract

This paper presents an alternative constraint handling approach within a specialized genetic algorithm (SGA) for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear single-objective optimization problem aiming at minimizing power losses while keeping network constraints. The proposed constraint handling approach is based on a product of sub-functions that represents permissible limits on system variables and that includes a specific goal on power loss reduction. The main advantage of this approach is the fact that it allows a straightforward verification of both feasibility and optimality. The SGA is examined and tested with the recommended constraint handling approach and the traditional penalization of deviations from feasible solutions. Several tests are run in the IEEE 30, 57, 118 and 300 bus test power systems. The results obtained with the proposed approach are compared to those offered by other metaheuristic techniques reported in the specialized literature. Simulation results indicate that the proposed genetic algorithm with the alternative constraint handling approach yields superior solutions when compared to other recently reported techniques.

Highlights

  • The optimal reactive power dispatch (ORPD) consists of scheduling available reactive power sources so that operational constraints are met while optimizing a given objective function

  • X are approximately zero, meaning that the than the other metaheuristics, especially when using F ; at a expense of higher solution found meets the operational constraints defined for this system, which is not always the the values obtained with the specialized genetic algorithm (SGA) for Pf(x) and V(x) are approximately zero, case for the other reported metaheuristics

  • The values obtained with the SGA for Pf(x) and zero, which means that the solution found meets all operational constraints

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Summary

Introduction

The optimal reactive power dispatch (ORPD) consists of scheduling available reactive power sources so that operational constraints are met while optimizing a given objective function (typical minimization of power losses or voltage deviation from a desired level). The ORPD plays an important role on the economic and secure operation of power systems It is a complex combinatorial optimization problem involving a nonlinear objective function, nonlinear constraints and a mixture of continuous and discrete control variables [1]. Initial attempts to approach the ORPD problem resorted to linear programming [2], nonlinear programming [3], quadratic programming [4], interior point methods [5], Newton based method [6], dynamic programming [7] and mixed integer programming [8] These techniques are computationally fast they do not perform well when dealing with non-convex problems and discrete variables. They tend to converge to local minima and have difficulties handling a large number of decision variables

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