TY - GEN
T1 - Optimal Dynamic Network Reconfiguration for Power Loss Minimization in Smart Grid Distribution Systems Under Line Disturbances
AU - Haidar Fayumi, Fachry Azca
AU - Putra, Dimas Fajar Uman
AU - Aryani, Ni Ketut
AU - Sidqi, Firas Quthbi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The electricity demand has been increasing in recent years. As a result, significant power losses will occur, resulting in high operational costs and poor power quality. Customer satisfaction will suffer because of the poor power quality. Furthermore, different types of consumers that have different load patterns become a challenge to distribution networks. Another challenge in dealing with distribution networks is the increasing utilisation of renewable energy sources with variable generation. To achieve an ideal and high-quality distribution network, this problem must be solved. Network reconfiguration can solve the problem efficiently. However, static reconfigurations are less capable of handling issues related to load growth, varying consumer load patterns, and fluctuating renewable energy generation. Meanwhile, dynamic reconfiguration has become one effective solution to address the issues and challenges in the distribution network. This research focuses on dynamic reconfiguration using the Binary Particle Swarm Optimization method in a distribution network with three types of load patterns and the integration of renewable energy sources. Four cases are presented to show the impacts of PV installation, the number of intervals in reconfiguration, and the effects of disturbances on the distribution network. The simulation and analysis results of the first and second cases show a reduction in power losses of 12.593 kW and 5.701 kVAR on a distribution network with a higher amount of renewable energy. When comparing the third and second cases, three intervals of dynamic reconfiguration give better results regarding power loss, as it can minimise an additional 12.593 kW and 5.701 kVAR of power losses. Finally, the fourth case shows that the dynamic reconfiguration ensures the availability of electricity for customers even when the distribution network line is under disturbance.
AB - The electricity demand has been increasing in recent years. As a result, significant power losses will occur, resulting in high operational costs and poor power quality. Customer satisfaction will suffer because of the poor power quality. Furthermore, different types of consumers that have different load patterns become a challenge to distribution networks. Another challenge in dealing with distribution networks is the increasing utilisation of renewable energy sources with variable generation. To achieve an ideal and high-quality distribution network, this problem must be solved. Network reconfiguration can solve the problem efficiently. However, static reconfigurations are less capable of handling issues related to load growth, varying consumer load patterns, and fluctuating renewable energy generation. Meanwhile, dynamic reconfiguration has become one effective solution to address the issues and challenges in the distribution network. This research focuses on dynamic reconfiguration using the Binary Particle Swarm Optimization method in a distribution network with three types of load patterns and the integration of renewable energy sources. Four cases are presented to show the impacts of PV installation, the number of intervals in reconfiguration, and the effects of disturbances on the distribution network. The simulation and analysis results of the first and second cases show a reduction in power losses of 12.593 kW and 5.701 kVAR on a distribution network with a higher amount of renewable energy. When comparing the third and second cases, three intervals of dynamic reconfiguration give better results regarding power loss, as it can minimise an additional 12.593 kW and 5.701 kVAR of power losses. Finally, the fourth case shows that the dynamic reconfiguration ensures the availability of electricity for customers even when the distribution network line is under disturbance.
KW - Binary Particle Swarm Optimization (BPSO)
KW - distribution network
KW - dynamic distribution network reconfiguration
KW - line disturbances
KW - power loss reduction
UR - http://www.scopus.com/inward/record.url?scp=85186526303&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427754
DO - 10.1109/ICAMIMIA60881.2023.10427754
M3 - Conference contribution
AN - SCOPUS:85186526303
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 283
EP - 288
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -