Thermoeconomic optimization of small size central air conditioner

Zhang, G.Q.; Wang, L.; Liu, Long; Wang, Z.

doi:10.1016/j.applthermaleng.2003.10.009

Cited by 10 publications

(6 citation statements)

References 1 publication

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“…1) that allow refrigerating or heating an environment, depending on the requirements [29]. This dual operation is due to the equipment capacity to extract heat from one place to transfer it through a flow to another zone.…”

Section: Modeling Of the Equipment Operationmentioning

confidence: 99%

Genetic algorithm model to control peak demand to defer capacity investment

Alamos

Rudnick

2012

2012 IEEE Power and Energy Society General Meeting

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This paper formulates and develops a peak demand control tool for electric systems within the framework of direct load control plans. This tool allows defining a load dispatch centre for central air conditioning systems in commercial buildings, hence allowing a measured control of peak demand for such pieces of equipment, which are known for their important influence in the end customers' consumption and for the correlation their demand curve has with the system demand curve during summer months. Traditionally, this type of application has been developed in the field of demand management; however, the high energy consumption growth rates have taken electric firms to analyze their application on the system expansion planning, hence deferring, or even preventing, the need to invest in capacity to supply the demand during peak periods. The generic model presented herein is evaluated in an actual urban substation, characterized by a predominant commercial consumption, by the contribution of the air conditioning systems in the substation loads, and by the problems present in its capacity to expand; model that is solved through advanced genetic algorithm techniques.Index Terms-Demand Side Management, direct load control, peak demand control, genetic algorithms, air conditioner. I.

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Section: Modeling Of the Equipment Operationmentioning

confidence: 99%

Genetic algorithm model to control peak demand to defer capacity investment

Alamos

Rudnick

2012

2012 IEEE Power and Energy Society General Meeting

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“…In the studies, various methods were discussed for decreasing energies and environmental hazard based on thermodynamics and fluid dynamics, and optimal conditions were found experimentally [1][2][3][4][5][6][7][8]. Recently, theoretical and numerical approaches were also given [9][10][11][12]. In the systems presented in the above papers, the cooling systems were discussed mainly.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, air conditioners with small energy loss and low environmental hazard are required, and a number of interesting studies have been reported for building air conditioners and automobile air conditioners [1][2][3][4][5][6][7][8][9][10][11][12]. In the studies, various methods were discussed for decreasing energies and environmental hazard based on thermodynamics and fluid dynamics, and optimal conditions were found experimentally [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

High energy efficiency desiccant assisted automobile air-conditioner and its temperature and humidity control system

Nagaya¹,

Senbongi²,

Li³

et al. 2006

Applied Thermal Engineering

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“…That is, they are typically based on nominal or estimated values for all the input data considered in the analysis 1359 [5,[12][13][14][15][16][17]. This means that the key parameters that influence the optimization task are assumed to be perfectly known in advance, so the only situation assessed in the study is the most likely one.…”

Section: Introductionmentioning

confidence: 99%

Economic performance optimization of an absorption cooling system under uncertainty

Gebreslassie

Guillén‐Gosálbez

Jiménez

et al. 2009

Applied Thermal Engineering

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a b s t r a c tMany of the strategies devised so far to address the optimization of energy systems are deterministic approaches that rely on estimated data. However, in real world applications there are many sources of uncertainty that introduce variability into the decision-making problem. Within this general context, we propose a novel approach to address the design of absorption cooling systems under uncertainty in the energy cost. As opposed to other approaches that optimize the expected performance of the system as a single objective, in our method the design task is formulated as a stochastic bi-criteria non-linear optimization problem that simultaneously accounts for the minimization of the expected total cost and the financial risk associated with the investment. The latter criterion is measured by the downside risk, which avoids the need to define binary variables thus improving the computational performance of the model. The capabilities of the proposed modeling framework and solution strategy are illustrated in a case study problem that addresses the design of a typical absorption cooling system. Numerical results demonstrate that the method presented allows to manage the risk level effectively by varying the area of the heat exchangers of the absorption cycle. Specifically, our strategy allows identifying the optimal values of the operating and design variables of the cycle that make it less sensitive to fluctuations in the energy price, thus improving its robustness in the face of uncertainty.

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Thermoeconomic optimization of small size central air conditioner

Cited by 10 publications

References 1 publication

Genetic algorithm model to control peak demand to defer capacity investment

Genetic algorithm model to control peak demand to defer capacity investment

High energy efficiency desiccant assisted automobile air-conditioner and its temperature and humidity control system

Economic performance optimization of an absorption cooling system under uncertainty

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