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Research Papers

Thermo-Economic Optimization of Solar CCHP Using Both Genetic and Particle Swarm Algorithms

[+] Author and Article Information
Sepehr Sanaye

Energy Systems Improvement Laboratory (ESIL),
School of Mechanical Engineering,
Iran University of Science
and Technology (IUST),
Narmak, Tehran 16844, Iran

Hassan Hajabdollahi

Department of Mechanical Engineering,
Vali-e-Asr University of Rafsanjan,
Rafsanjan, Iran
e-mail: Hajabdollahi@iust.ac.ir

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received September 7, 2012; final manuscript received October 30, 2013; published online July 29, 2014. Assoc. Editor: Werner Platzer.

J. Sol. Energy Eng 137(1), 011001 (Jul 29, 2014) (11 pages) Paper No: SOL-12-1221; doi: 10.1115/1.4027932 History: Received September 07, 2012; Revised October 30, 2013

A combined cooling, heating and power generation (CCHP) system is modeled and optimized. The heat demand in this plant can provide by prime mover, backup boiler, and solar panels. Both the genetic algorithm (GA) and particle swarm optimization (PSO) are used to find the maximum of actual annual benefit (AAB) as an objective function. The design parameters or decision variables are capacity of prime mover, their number as well as their partial load (PL), backup boiler and storage tank heating capacity, the number of solar panels, types of electrical and absorption chiller as well as the electric cooling ratio. Both genetic and PSO algorithms are converged with maximum 0.6% difference. As a result, a diesel engine with nominal power of 350 kW combined with 255 solar panels is selected in the optimum situation. In addition, the optimization results show that the advantage of absorption chiller than the electrical chiller due to the extra availability of heat by the prime mover at the warm season in residential area. Finally, the effect of electric cooling ratio, number of solar panels and solar panels investment cost on objective function are investigated and results are reported.

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Figures

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Fig. 1

Schematic of a solar assisted combine cooling, heating, and power generation system

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Fig. 2

Hourly variation of ambient temperature during a year for studied case

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Fig. 3

Hourly demand of electricity, cooling and heating loads during a year for studied case

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Fig. 4

The cost of buying electricity and buying natural gas as a function of electricity and gas consumption

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Fig. 5

Orientation of panel optimum angle and monthly average of received radiation during a year

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Fig. 6

Hourly received radiation in the optimum angle during a year for studied case

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Fig. 7

Power and heat produced by diesel engine versus PL (points indicate the actual data and lines indicate the curve fitting)

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Fig. 8

The sum of energy in Fig. 7

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Fig. 9

Total demand electricity (electricity demand plus electrical chiller), buying electricity, electricity demand load and cogeneration generated electricity during a year in optimum case-for more clarity, the hourly results are replaced by average of results in each 15 days

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Fig. 10

Heating load demand, heat generated by diesel engine, stored heat, heat generated by solar panels and boiler in optimum case; for more clarity, the hourly results are replaced by average of results in each 15 days

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Fig. 11

Variation of AAB versus electric cooling ratio for optimum point

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Fig. 12

Variation of AAB versus the number of solar panels for optimum point

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Fig. 13

Percent of variation in optimum AAB in terms of percent of variation in solar panels investment cost for optimum point

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Fig. 14

Convergence of the objective function versus iterations using GA and PSO for six random runs

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