Particle swarm optimization for redundant building cooling heating and power system

An optimal and redundant building cooling heating and power (BCHP) system can yield economical savings, but more importantly can save energy as well as reduce the emission of pollutants. This paper presents the energy flow analysis of the conventional separation production (SP) system and the redund...

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Veröffentlicht in:Applied energy Jg. 87; H. 12; S. 3668 - 3679
Hauptverfasser: Wang, Jiangjiang, Zhai, Zhiqiang (John), Jing, Youyin, Zhang, Chunfa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.12.2010
Elsevier
Schriftenreihe:Applied Energy
Schlagworte:
Algorithms
Applied sciences
Building cooling heating and power (BCHP) system
Building cooling heating and power (BCHP) system Particle swarm optimization algorithm (PSOA) Redundant design Energy Economy Environment
Cooling systems
Economic data
Economics
Economy
Electric energy
Electric power generation
Energy
Energy economics
Energy. Thermal use of fuels
Environment
Exact sciences and technology
General, economic and professional studies
General. Economical aspects
Heating
Heating, air conditioning and ventilation
Methodology. Modelling
Optimization
Particle swarm optimization algorithm (PSOA)
Redundant
Redundant design
Searching
Space heating. Hot water
Environment
Economy
Energy
Particle swarm optimization algorithm (PSOA)
Building cooling heating and power (BCHP) system
Redundant design
ISSN:0306-2619, 1872-9118
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Zusammenfassung:An optimal and redundant building cooling heating and power (BCHP) system can yield economical savings, but more importantly can save energy as well as reduce the emission of pollutants. This paper presents the energy flow analysis of the conventional separation production (SP) system and the redundant BCHP system. Four decision variables (the capacity of power generation unit (PGU), the capacity of heat storage tank, the on–off coefficient of PGU and the ratio of electric cooling to cool load) to be optimized are selected in consideration of the design and the operation strategy of BCHP system. An objective function to simultaneously measure the energetic, economical and environmental benefits achieved by BCHP system in comparison to SP system is constructed and maximized. Particle swarm optimization algorithm (PSOA) is employed to search the optimal solutions. A case study of BCHP system with thermal storage unit and hybrid cooling system is presented to ascertain the feasibility and validity of the optimization method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2010.06.021