.png){kind=icon}
返回
An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-022-06767-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.
Keyword:
Optimization algorithm
Evolutionary computation
Smart grid applications
Renewable energy integration
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
An efficient modified grey wolf optimizer with Levy flight for optimization tasks带有Levy飞行的高效改进的灰狼优化器，用于优化任务
PREAI
分享收藏Kinematic and kinetic differences in the execution of vertical jumps between people with good and poor ankle joint dorsiflexion踝关节背屈良好和不良的人在执行垂直跳跃时的运动学和动力学差异
PREAI
分享收藏Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏Using bias-corrected reanalysis to simulate current and future wind power output使用偏差校正的再分析来模拟当前和未来的风电输出.png)
ENERGY
OAAI
ENERGYIF9.4
OAAI分享收藏HSOS: a novel hybrid algorithm for solving the transient-stability-constrained OPF problemSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏分享收藏
查看更多内容