Generation Scheduling with Large-Scale Wind Farms using Grey Wolf Optimization

被引:1
作者
Saravanan, R. [1 ]
Subramanian, S. [1 ]
Dharmalingam, V. [2 ]
Ganesan, S. [1 ]
机构
[1] Annamalai Univ, Dept Elect Engn, Annamalainagar, Tamil Nadu, India
[2] Pandian Saraswathi Yadav Engn Coll, Dept Elect & Elect Engn, Sivaganga, Tamil Nadu, India
关键词
Grey wolf optimization; Generation scheduling; Wind power; PARTICLE SWARM OPTIMIZATION; UNIT COMMITMENT PROBLEM; LAGRANGIAN-RELAXATION; ECONOMIC-DISPATCH; ALGORITHM; SEARCH; HYBRID; POWER;
D O I
10.5370/JEET.2017.12.4.1348
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.
引用
收藏
页码:1348 / 1356
页数:9
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