Solving of Optimal Power Flow Problem Including Renewable Energy Resources Using HEAP Optimization Algorithm

被引:39
|
作者
Shaheen, Mohamed A. M. [1 ]
Hasanien, Hany M. [2 ]
Al-Durra, Ahmed [3 ]
机构
[1] Future Univ Egypt, Fac Engn & Technol, Elect Engn Dept, Cairo 11835, Egypt
[2] Ain Shams Univ, Fac Engn, Elect Power & Machines Dept, Cairo 11517, Egypt
[3] Khalifa Univ, Adv Power & Energy Ctr, Dept Elect Engn & Comp Sci, Abu Dhabi 127788, U Arab Emirates
来源
IEEE ACCESS | 2021年 / 9卷
关键词
Optimization; Wind farms; Generators; Fuels; Renewable energy sources; Load flow; Power system stability; Optimal power flow; optimization; power systems; renewable energy;
D O I
10.1109/ACCESS.2021.3059665
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a novel endeavor to use the Heap optimization algorithm (HOA) to solve the problem of optimal power flow (OPF) in the electricity networks. The key objective is to optimize the cost of fuel of the conventional generators under the system limitations. Various scenarios are studied in a later stage considering the addition of the PV panel and/or wind farm with changing load curves during a typical day. The active output power of the generators is selected to be the OPF problem search space. The HOA is employed to get the best solution of the fitness function and provides the corresponding best solutions. The modeling of the heap-based optimizer (HBO) depends on three levels: the relation between the subordinates and the boss, the relation between the same level employees, and the contribution of the employee oneself. The validity of the proposed algorithm is tested for a variety of electric grids, the IEEE 30-bus, IEEE 57-bus and 118 bus networks. These networks are simulated under various scenarios. Real load curves, in this study, are considered to achieve a practical outcome. The simulation outcomes are evaluated and tested. The results indicate that the implemented HOA-based OPF methodology is flexible and applicable compared with that achieved by using the genetic algorithm.
引用
收藏
页码:35846 / 35863
页数:18
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