A modified NSGA approach for optimal sizing and allocation of distributed resources and battery energy storage system in distribution network. (2021)

• Record Type: Journal Article
• Authors: Siddique, Rabbia; Raza, Safdar; Mannan, Abdul; Khalil, Linta; Alwaz, Nashitah; Riaz, Mughees
• Is Part Of: Materials today. Volume 47:Suppement(2021)
• Page Start: S102
• Page End: S109
• Publication Date: 2021
• Subjects: Battery energy storage system -- Depth of discharge -- Distributed Energy Resources -- Modified NSGA -- Particle Swarm optimization -- Voltage regulation; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.05.669
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The integration of distributed energy resources (DERs) causes fluctuation in system voltage, thus obstructing the voltage regulation and cause efficiency issue in performance of electrical network. This obstruction can overcome by proper sizing and allocation of DERs and battery energy storage system which in returns improves the performance of electrical power network. In this work, the optimal allocation of battery energy storage system and DERs has been proposed to reduce the voltage regulation issues. For this purpose, the modified non-sorting dominated genetic algorithm (NSGA) is used to optimally allocate and size the DERs and battery energy storage system. Furthermore, the results are compared with particle swarm optimization (PSO) in terms of power losses, voltage regulation and stability. It has been found that by utilizing modified non-sorting dominated genetic algorithm, the size of DER reduces and voltage profile improves as compared to particle swarm optimization (PSO). The simulations for particular modified NSGA have been performed on MATLAB software by keeping in mind the IEEE 33 bus system.