A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem

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.

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

“…Test Network References C1 C2 C3 C4 C5 C6 C7 C8 C9 OF1 X X -------38 bus-Roy-Billinton Test System [8] X -X -X X ---IEEE 30, 57, 118 and 300 bus [10,27,29] X -X -X ----IEEE 33 bus [13] X X X X -----IEEE 33 and 94 bus [14,15] X X X -X X ---IEEE 30 bus [21] X X X -X ----IEEE 33 and 85 bus [25,28] X X X X X ----IEEE 33 and 119 bus [5] OF2 X --------IEEE 10, 23 and 34 bus [12] X -X -X ----IEEE 22, 69, 85 and 141 bus [32] OF3 X -X -X X ---IEEE 30, 57, 118 and 300 bus [10,27] X X X -X X ---IEEE 30 bus [21] OF4 X -X ---X -X IEEE 10, 33 and 69 bus [16,17,22,26] X -X ----X -IEEE 10, 15 and 34 bus [19] X -X ------IEEE 30 and 85 bus [20] X -X ------IEEE 33, 34, 69 and 85 bus [23,27] X -X -X -X -X IEEE 85 and 118 bus [24] OF5 X ---X ----IEEE 28-bus [11] X --------IEEE 9-bus [30] OF6 X -X --X X --IEEE 30 bus [18] X -X -X X X --IEEE 30, 57 and 118 bus [27] X -X -X ----IEEE 30, 118 and 300 bus [29] Energies 2019, 12, 4239 4 of 36…”

“…An overview about the metaheuristics used for the problem of capacitor banks allocation is made in the following, highlighting their specific purpose. The OCBA solution for power losses or cost minimization is obtained using a genetic algorithm in [10,11], a fuzzy technique in [12] and an artificial neural network in [9]. Regarding the metaheuristics, a significant number of papers consider the joule loss minimization, voltage bus improvement, and total cost minimization.…”

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

“…There were a huge number of optimization algorithms that were applied for the ORPD problem and resulted in good solutions. These methods are the evolutionary programming method (EPM) [1], the modified hybrid evolutionary programming method (MHEPM) [2], differential evolution (DE) [3,4], a combination of iterative method and differential evolutionary method (CIDE) [5], the improved differential evolutionary method (IDE) [6], the adaptive genetic algorithm (AGA) [7], the self-adaptive real coded genetic algorithm (SARCGA) [8], the particular genetic algorithm (PGA) [9], the particle swarm optimization (PSO) with inertia weight factor (WPSO) [10], PSO with an aging leader and challengers (PSO-ALC) [11], PSO with improved pseudo-gradient search (PSO-IPGS) [12], and PSO hybridize with imperialist competitive algorithm (PSO-ICA) [13]. In [1], EPM has successfully dealt with an IEEE 30-bus system for the case of reducing active power losses objective.…”

“…GA has suffered from the main shortcoming of easily falling into ineffective search zones with low quality solutions owing to the use of roulette-wheel selection. For that reason, many variants of GA have been developed by improving techniques of GA or by combing GA and other methods [7][8][9]. In [9], particularly the genetic algorithm (PGA), together with a novel constraint handling strategy, have been combined for finding optimal solutions for the ORPD problem.…”

“…For that reason, many variants of GA have been developed by improving techniques of GA or by combing GA and other methods [7][8][9]. In [9], particularly the genetic algorithm (PGA), together with a novel constraint handling strategy, have been combined for finding optimal solutions for the ORPD problem. PGA has been supported by applying two penalty function approaches and its solution search ability has been tested on IEEE 30, 57, 118 and 300-bus power systems for performance comparisons.…”