A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

Abstract

In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.

Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems

“…It was proved that the quasi-opposite number is closer than its opposite number to the optimal solution. This concept was used to generate the initial population for some algorithms [40][41][42]. However, the QOT will be used in this paper not only at the initialization stage, but also inside PODESCA's main loop.…”

“…where X i O ∈ a i , b i are the opposite points in d [39,42]. Then, evaluate the resulted PIS again using the OF.…”

“…Therefore, ten BTFs are utilized in this paper in order to study PODESCA's performance. Those BTFs are: f 1 : Ackley, f 2 : Griewank, f 3 : Rastrigin, f 4 : Levy, f 5 : Perm 0, d, β, f 6 : Sum squares, f 7 : Rotated hyper-ellipsoid, f 8 : Power sum, f 9 : Rosenbrock, and f 10 : Dixon-Price, where their detailed formulas and all needed data can be found in [42,44]. The proposed algorithm's results are also compared with those from six other novel algorithms; PSO, GA, ABC, firefly algorithm (FA), DE, and SCA.…”

“…Remark 1. The QOT has been employed to enhance the exploration and search ability of some algorithms by generating their initial populations as in [40][41][42]. However, this concept is applied at the initialization stage as well as inside the main loop of the proposed PODESCA in this paper.…”

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

“…Many optimization techniques have been applied based on these three objective functions. These techniques are the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA), a novel stochastic fractal search algorithm (SFSA), genetics algorithm (GA), a comprehensive teaching learning-based optimization (CTLBO) technique, ant-lion optimization algorithm (ALOA), improved Harris Hawks optimizer (IHHO), and multi-objective improved Harris Hawks (MOIHHO) [51][52][53][54][55][56][57][58].…”