A Hybrid Local Search-Genetic Algorithm for Simultaneous Placement of DG Units and Shunt Capacitors in Radial Distribution Systems

Emad Ali Almabsout,Ragab A El-Sehiemy,Osman Nuri Uc An,Oguz Bayat

doi:10.1109/access.2020.2981406

Abstract

Controlling active/reactive power in distribution systems has a great impact on its performance. The placement of distributed generators (DGs) and shunt capacitors (SCs) are the most popular mechanisms to improve the distribution system performance. In this line, this paper proposes an enhanced genetic algorithm (EGA) that combines the merits of genetic algorithm and local search to find the optimal placement and capacity of the simultaneous allocation of DGs/SCs in the radial systems. Incorporating local search scheme enhances the search space capability and increases the exploration rate for finding the global solution. The proposed procedure aims at minimizing both total real power losses and the total voltage deviation in order to enhance the distribution system performance. To prove the proposed algorithm ability and scalability, three standard test systems, IEEE 33 bus, 69 bus, and 119-bus test distribution networks, are considered. The simulation results show that the proposed EGA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literature. Moreover, an economic based cost analysis is provided for light, shoulder and heavy loading levels. It was proven, the proposed EGA leads to significant improvements in the technical and economic points of view.

Highlights

1) SIMULATION RESULTS OF IEEE-33 BUS radial distribution network (RDN) Table 2 reports the simulation results obtained by the proposed enhanced genetic algorithm (EGA) of the first IEEE 33-distribution system for the optimal placement and capacity of variant combinations of distributed generators (DGs) and shunt capacitors (SCs)
In the case of the first combination with (1DG + 1SC), the proposed EGA leads to the total power loss of 51.80 kW with a reduction of 74.44 % compared with 52.73 kW by using Genetic Algorithm (GA) with a reduction of 73.98 %, and
For the second combination with (2DGs + 2SCs), the EGA has the best performance compared with intersect mutation differential evolution (IMDE) [24], Analytic [25] and GA

Summary

Introduction

1) SIMULATION RESULTS OF IEEE-33 BUS RDN Table 2 reports the simulation results obtained by the proposed EGA of the first IEEE 33-distribution system for the optimal placement and capacity of variant combinations of DGs and SCs. In the case of the first combination with (1DG + 1SC), the proposed EGA leads to the total power loss of 51.80 kW with a reduction of 74.44 % compared with 52.73 kW by using GA with a reduction of 73.98 %, and

Objectives

Results

Conclusion