Comparative Analysis in DG Installation Scheme for Resilience Enhancement

This paper presents a comparative analysis of the Distributed Generation (DG) scheme for resilience enhancement. This study models categories of hurricanes as disruptive events, considering data on the fragility of transmission towers concerning wind speeds. The simulation involves generating sustai...

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Bibliographic Details
Published in:2024 IEEE 4th International Conference in Power Engineering Applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024
Main Author: Zakaria F.B.; Musirin I.B.; Aminudin N.B.; Johari D.B.; Shaaya S.A.; Ibrahim N.F.B.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191752247&doi=10.1109%2fICPEA60617.2024.10498681&partnerID=40&md5=421e9c288565960e6a04b85ac82315c1
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Summary:This paper presents a comparative analysis of the Distributed Generation (DG) scheme for resilience enhancement. This study models categories of hurricanes as disruptive events, considering data on the fragility of transmission towers concerning wind speeds. The simulation involves generating sustained winds corresponding to different categories of hurricanes, following the Saffir-Simpson Hurricane scale. The transmission power system will encounter power outages when the transmission tower collapses. The installation of DG is one of the suitable efforts to alleviate this phenomenon where it is used as a compensating device to improve power grid resilience. In this study, the Evolutionary Programming (EP) and Artificial Immune System (AIS) optimization techniques are used to determine sizing and strategic locations for the placement of multiple DG units for loss control in the power system. The resilience status of the system is also observed. The proposed optimization techniques are validated on the IEEE 30-Bus Reliability Test System (RTS) under varying loads. Verification was conducted through a comparison of optimization outcomes obtained from EP and AIS techniques. The findings illustrate the effectiveness of these algorithms in significantly reducing total loss and improving the resilience of the tested system. © 2024 IEEE.
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DOI:10.1109/ICPEA60617.2024.10498681