DocumentCode :
3581066
Title :
Modelization and optimization of multi-type power generators joint scheduling based on improved PSO
Author :
Yifan Zhou ; Wei Hu ; Chengqiu Hong ; Biqin Hu ; Tao Cheng
Author_Institution :
Dept. of Electr. Eng., Tsinghua Univ., Beijing, China
fYear :
2014
Firstpage :
1
Lastpage :
6
Abstract :
This paper presents a multi-type power generators joint scheduling model for power system which is composed of thermal power, hydro power, pumped storage power and wind power. This model has a good accordance with the operation characteristics of each type power generator, security of whole system and abandoned water/wind. Based on the aggregation degree of particles, an improved particle swarm optimization (PSO) algorithm is proposed to solve the model. Four typical particle operations are introduced to overcome the shortcoming of standard PSO such as premature convergence and easily local optimized. Heuristic particle-repairing rules are designed to handle the complex constraints in the model. Taking the IEEE 39-bus and 118-bus system as examples, the simulation results show that the proposed approach has far higher global searching ability and obtains feasible solution of higher quality compared with PSO and GA.
Keywords :
genetic algorithms; particle swarm optimisation; power generation scheduling; power system security; power system simulation; pumped-storage power stations; search problems; thermal power stations; wind power plants; GA; IEEE 118-bus system; IEEE 39-bus system; PSO algorithm; genetic algorithm; global searching ability; heuristic particle-repairing rules; hydropower; multitype power generators joint scheduling model; particle swarm optimization algorithm; power system; pumped storage power; thermal power; wind power; Hydroelectric power generation; Joints; Optimal scheduling; Reservoirs; Sociology; Statistics; Wind power generation; multi-type power generators power system; particle swarm optimization based on aggregation degree (PSO-AD); particle-repairing rules; short-term optimal scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2014 IEEE PES Asia-Pacific
Type :
conf
DOI :
10.1109/APPEEC.2014.7066082
Filename :
7066082
Link To Document :
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