Self-healing Reconfiguration Strategy for Distribution Networks Considering Distributed Generation Output Uncertainty

As distributed generation are randomly integrated into distribution networks; their output uncertainty adversely affects self-healing reconfiguration. This paper proposes a self-healing reconfiguration strategy for distribution networks that considers the uncertainty of distributed generation output...

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Bibliographic Details
Published in:2024 6th International Conference on Power and Energy Technology (ICPET) pp. 1446 - 1451
Main Authors: Yuji, Yan, Jinghan, He, Mirsaeidi, Sohrab, Meng, Li, Jian, Wang
Format: Conference Proceeding
Language:English
Published: IEEE 12.07.2024
Subjects:
backward-forward power flow
Distributed power generation
Distribution networks
Genetic algorithms
Linear programming
Load modeling
multi-objective function optimization
Optimization
Photovoltaic systems
self-healing reconfiguration
Uncertainty
uncertainty of distributed generation output
Voltage
Wind turbines
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Summary:As distributed generation are randomly integrated into distribution networks; their output uncertainty adversely affects self-healing reconfiguration. This paper proposes a self-healing reconfiguration strategy for distribution networks that considers the uncertainty of distributed generation output. After the fault occurs, the output of photovoltaic and wind turbine is modeled, and the time-varying characteristics of load are also considered. Then, the objective function is to maximize the recovery coefficient of important load, minimize network loss, voltage deviation, and switching action times. The multi-objective optimization approach is employed to construct the self-healing reconfiguration model, which is solved using an improved genetic algorithm. Finally, the IEEE 33-bus distribution system is used to verify the effectiveness of the proposed method.
DOI:10.1109/ICPET62369.2024.10940938