Optimization of ice-storage air conditioning system With ASAGA

Author

Mingzhi Zhang ; Yundong Gu

Author_Institution

Sch. of Electr. & Electron. Eng., North China Electr. Power Univ., Beijing, China

fYear

2014

fDate

29-30 Sept. 2014

Firstpage

1042

Lastpage

1046

Abstract

As a distributed energy storage system, ice-storage air conditioning system can not only reduce the cost and improve the efficiency of the existing power system but it can also plays an important role in the demand side management. But how to get the optimal allocation proportion of cooling load between ice storage and chillers still is an unsolved problem. A nonlinear programming is constructed based on the improved model of facilities to achieve the optimization of the ice-storage air conditioning system. Then, an adaptive simulated annealing genetic algorithm (ASAGA) is proposed to solve this nonlinear problem. Finally, the effectiveness of the given facility models and nonlinear program as well as ASAGA are tested by a practical project analysis.

Keywords

air conditioning; cold storage; demand side management; genetic algorithms; nonlinear programming; simulated annealing; ASAGA; adaptive simulated annealing genetic algorithm; chillers; cooling load; cost reduction; demand side management; distributed energy storage system; ice-storage air conditioning system optimization; nonlinear programming; optimal allocation; power system efficiency improvement; Annealing; Cooling; Genetics; Simulated annealing; ASAGA; genetic algorithm; modeling; simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Research and Technology in Industry Applications (WARTIA), 2014 IEEE Workshop on

Conference_Location

Ottawa, ON

Type

conf

DOI

10.1109/WARTIA.2014.6976455

Filename

6976455