Lightning attachment procedure optimization algorithm for nonlinear non-convex short-term hydrothermal generation scheduling

被引:17
作者
Mohamed, Maha [1 ]
Youssef, Abdel-Raheem [1 ]
Kamel, Salah [2 ,4 ]
Ebeed, Mohamed [3 ]
机构
[1] South Valley Univ, Fac Engn, Dept Elect Engn, Qena, Egypt
[2] Aswan Univ, Fac Engn, Dept Elect Engn, Aswan 81542, Egypt
[3] Sohag Univ, Fac Engn, Dept Elect Engn, Sohag, Egypt
[4] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing, Peoples R China
关键词
Short-term hydrothermal scheduling; Non-convex optimization problem; Lightning attachment procedure optimization; Valve point loading effect; CODED GENETIC ALGORITHM; CHAOTIC DIFFERENTIAL EVOLUTION; PARTICLE SWARM OPTIMIZATION; BEE COLONY ALGORITHM; SEARCH ALGORITHM; SYSTEMS; HYBRID;
D O I
10.1007/s00500-020-04936-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Short-term hydrothermal scheduling (STHS) is considered an important problem in the field of power system economics. The solution of this problem gives the hourly output of power generation schedule of the available hydro and thermal power units, which leads to minimization of the total fuel cost of thermal units for a given period of a time. The optimal generation of STHS is considered as a complicated and nonlinear optimization problem with a set of equality and inequality constraints such as the valve point loading effect of thermal units, the power transmission loss and the load balance. This paper proposes lightning attachment procedure Optimization (LAPO) algorithm for solving the nonlinear non-convex STHS optimization problem in order to minimize the operating fuel cost of thermal units with satisfying the operating constraints of the system. The performance of LAPO algorithm is validated using three different test systems considering the valve point loading effects of thermal units and the power transmission losses. The obtained results prove the effectiveness and superiority of LAPO algorithm for solving the STHS problem compared with other well-known optimization techniques.
引用
收藏
页码:16225 / 16248
页数:24
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