Abstract

The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity and location of DGs. For this purpose, an objective function was defined, which takes into account the fault current and the levelized cost of energy, and a modified particle swarm optimization method was applied. Then, we analyzed a case of a single line-to-ground fault with a test feeder (i.e., the IEEE 30 bus system) with no DGs connected, as well as a case where the DGs are optimally connected. The effect of the optimally allocated DGs on the system was analyzed. We discuss an optimal layout method that takes the economic efficiency of the DG installation into account.

Highlights

  • Classical unsymmetrical fault analysis is based on node equations and sequence network and was presented by Kimbark in 1946 [1]

  • Modern distributed generators (DGs) have an ability to reduce the burden by injecting active power and reactive power based on power electronics [5]

  • Modern DG installationcost optimization methods based on supervisory control and data acquisition (SCADA), hardware-in-the-loop [9], and power-flow management systems [10] were presented by researchers

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Summary

Introduction

Classical unsymmetrical fault analysis is based on node equations and sequence network and was presented by Kimbark in 1946 [1]. These methods were presented by Anderson and Kundur [2,3]. Many modern fault analysis and control methods that are model or signal based [4] are presented by many researchers. Modern DG installationcost optimization methods based on supervisory control and data acquisition (SCADA), hardware-in-the-loop [9], and power-flow management systems [10] were presented by researchers. This guideline suggests connecting DGs as current sources. The fault current should be taken into account for the problem of DG installation optimization

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