Reducing Overall Active Power Loss by Placing Solar and Wind Generators in a Distribution Power System using Wild Horse Optimizer Algorithm

被引:0
|
作者
Minh, Chau Le Thi [1 ]
Duong, Quan [2 ]
机构
[1] Hanoi Univ Sci & Technol, Sch Elect & Elect Engn, Power Grid & Renewable Energy Lab, 1 Dai Co Viet St, Hanoi City, Vietnam
[2] Univ Da Nang, Univ Sci & Technol, Dept Elect Engn, 54 Nguyen Luong Bang St, Da Nang, Vietnam
来源
关键词
capacitor banks; distribution power system; Global Solar Atlas; Global Wind Atlas; load demand variations; overall active power loss; solar generators; wind generators; OPTIMAL CAPACITOR PLACEMENT; ALLOCATION;
D O I
10.5614/j.eng.technol.sci.2024.56.5.2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This study optimized the locations and sizes of wind-based distributed generators (WDGs) and solar photovoltaic-based distributed generators (PVDGs) to reduce the overall active power loss (OAPL) of an IEEE 85-bus distribution power system (DPS). Three meta- heuristic algorithms, including the Wild Horse Optimizer Algorithm (WHOA), the Archimedes Optimization Algorithm (AOA), and the Transient Search Optimization (TSO) algorithm, were applied and compared to each other to identify the most effective method for finding the best value of OAPL. Based on the analysis, WHOA outperformed the other methods in achieving the best value of OAPL according to different criteria. Additionally, the effectiveness of WHOA was compared with previous studies, while WHOA also proved its strength in reducing overall losses, decreasing grid power, and improving voltage profiles. Moreover, the effectiveness of WHOA was tested for a 24-hour period with varying loads and the addition of PVDGs and WDGs. The results indicated that WHOA could successfully determine the optimal positions of both PVDGs and WDGs in Case 3, Case 4.1, and Case 4.2, achieving the optimal value of OAPL in the selected DPS, decreasing grid power utilization, and improving the voltage profile. In conclusion, WHOA proved itself to be an effective optimization tool for dealing with large-scale optimization problems.
引用
收藏
页码:571 / 582
页数:12
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