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Neural Computing and Applications

, Volume 31, Issue 3, pp 653–674 | Cite as

A reactive power planning procedure considering iterative identification of VAR candidate buses

  • A. M. Shaheen
  • Ragab A. El-SehiemyEmail author
  • S. M. Farrag
Original Article
  • 209 Downloads

Abstract

This article proposes two-step procedure for solving the reactive power planning (RPP) problem. An iterative method is introduced in the first step to place the additional sources of reactive power and their associated maximum sizes. In the second step, several integrated strategies of differential evolution (DE) are suggested to optimize the RPP variables. Three types of objective function are investigated which aims at minimizing system power losses, minimizing the costs of operation and VAR investment and improving the voltage profile distribution at load buses. The strategies performance is examined on IEEE 30-bus test system and on the West Delta network as a real Egyptian section. The evolution of the system considering the annual growth rate of peak load in the Egyptian system has been taken into consideration at different loading levels. Application of the proposed method is carried out on large-scale power system of 354-bus test system. The strategies robustness and consistency are compared to DE, genetic algorithm and particle swarm optimizer. The proposed two-step procedure using the proposed DE strategy is assessed compared to single-step RPP procedure. Furthermore, its mutation and crossover scales are optimally specified. Simulation outcomes denote that the proposed DE strategy is excessively superior, more powerful and consistent than the other compared optimizers which indicate that the proposed strategy of DE algorithm can be very efficient to solve the RPP. The proposed strategies are proven as alternative solution strategies, especially for large-scale power systems.

Keywords

Reactive power planning problem Annual growth rate DE strategies Control parameter Two-step optimization procedure 

Abbreviations

CMAES

Covariance matrix adaptation evolution strategy

COMs

Classical optimization methods

DE

Differential evolution

DOE

Design of experiment

EP

Evolutionary programming

GA

Genetic algorithm

IM

Iterative method

IP

Interior point

LP

Linear programming

MFLP

Multi-objective fuzzy linear programming

MIP

Mixed integer programming

MNSGA-II

Modified nondominated sorted genetic algorithm-II

MOs

Meta-heuristic optimizers

NLP

Nonlinear programming

PSO

Particle swarm optimizer

QP

Quadratic programming

RGA

Real-coding genetic algorithm

RPP

Reactive power planning

SO

Seeker optimizer

SQP

Sequential quadratic programming

WDN

West Delta network

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • A. M. Shaheen
    • 1
  • Ragab A. El-Sehiemy
    • 2
    Email author
  • S. M. Farrag
    • 3
  1. 1.South Delta Electricity Distribution Company (SDEDCo)TantaEgypt
  2. 2.Electrical Engineering Department, Intelligent Systems Research Group (ISRG), Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt
  3. 3.Electrical Engineering Department, Faculty of EngineeringMenoufiya UniversityShebin El KomEgypt

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