Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
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Optimization Design of Solar Water Heating System based on Economic Evaluation Criterion using a Genetic Algorithm

유전알고리즘 이용 경제적 평가기준에 따른 태양열급탕시스템 최적화 설계에 관한 연구

  • 최두성 (청운대학교 건축설비소방학과) ;
  • 고명진 ((주)나비환경설비컨설턴트) ;
  • 박광태 ((주)CNI엔지니어링)
  • Received : 2016.08.31
  • Accepted : 2016.10.17
  • Published : 2016.10.30
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Abstract

To assure maximum economic benefits and the energy performance of solar water heating systems, the proper sizing of components and operating conditions need to be optimized. In recent years, a number of studies to design optimally solar water heating systems have been tried. This paper presents a design method for optimizing the various capacity-related and installation-related design variables based on life cycle cost using a genetic algorithm. The design variables considered in this study included the types and numbers of solar collector and auxiliary heaters; the types of storage tanks and heat exchangers; the solar collector slope; mass flow rates of the fluid on the hot and cold sides. The suggested method was applied for optimizing a solar water heating system for an elementary school in Seoul, South Korea. In addition, the effectiveness of the proposed optimization method was assessed by analyzing the obtained optimal solutions of six case studies, each of which was simulated with different solar fractions. It is observed that a trade-off between the equipment cost and the energy cost results in an optimal design that yields the lowest life cycle cost. Therefore, it could be helpful to apply the optimal solar water heating system by comparing the various design solutions obtained by using the optimization method instead of the engineer's experience and intuition.

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References

  1. Balusamy, T. and Sadhishkumar, S., Performance improvement in solar water heating systems-A review. Renewable Sustainable Energy Review, Vol. 37, pp. 191-198, 2014. https://doi.org/10.1016/j.rser.2014.04.072
  2. Islam, M. R., Sumathy, K., and Khan, S. U., Solar water heating systems and their market trends. Renewable Sustainable Energy Review, Vol. 17, pp. 1-25, 2013. https://doi.org/10.1016/j.rser.2012.09.011
  3. Kulkarni, G. N., Kedare, S. B., and Bandyopadhyay, S., Determination of design space and optimization of solar water heating systems. Solar Energy, Vol. 81, No. 8, pp. 958-968, 2007. https://doi.org/10.1016/j.solener.2006.12.003
  4. Loomans, M. and Visser, H., Application of the genetic algorithm for optimization of large solar hot water systems. Solor Energy, Vol. 72, No. 5, pp. 427-439, 2002. https://doi.org/10.1016/S0038-092X(02)00020-8
  5. Kalogirou, S. A., Optimization of solar systems using artificial neural-networks and genetic algorithms. Applied Energy, Vol. 77, No. 4, pp. 383-405, 2004. https://doi.org/10.1016/S0306-2619(03)00153-3
  6. Atia, D. M., Fahmy, F. H., Ahmed, N. M., and Dorrah, H. T., Optimal sizing of a solar water heating system based on a genetic algorithm for an aquaculture system, Mathematical and Computer Modelling, Vol. 55, No. 3-4, pp. 1436-1449, 2012. https://doi.org/10.1016/j.mcm.2011.10.022
  7. Ko, M. J., Analysis and optimization design of a solar water heating system based on life cycle cost using a genetic algorithm, Energies, Vol. 8, No. 10, pp. 11380-11403. 2015. https://doi.org/10.3390/en81011380
  8. Ko, M. J., A novel design method for optimizing an indirect forced circulation solar water heating system based on life cycle cost using a genetic algorithm, Energies, Vol. 8, No. 10, pp. 11592-11617, 2015. https://doi.org/10.3390/en81011592
  9. Henderson, H., Huang, Y. J., and Parker, D., Residential equipment part load curve for use in DOE-2: Technical report LBNL-42175, Lawrence Berkeley National Laboratory, USA, 1999.
  10. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, Chichester, Wiley, 2009.
  11. Konak, A., Coit, D. W., and Smith, A. E., Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety, Vol. 91, No. 9, pp. 992-1007, 2006. https://doi.org/10.1016/j.ress.2005.11.018
  12. Deru, M., Field, K., Studer, D., Benne, K. Griffith, B., Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Yazdanian, M., Huang, J., and Crawley, D., U.S. Department of energy commercial reference building models off the national building stock, National Renewable Energy Laboratory, USA, 2011.
  13. Baughn, J. W. and Young, M. F., The calculated performance of a solar hot water system for a range of collector flow rates. Solar energy, Vol. 32, pp. 303-305, 1984. https://doi.org/10.1016/S0038-092X(84)80048-1
  14. Hobbi, A. and Siddiqui, K., Optimal design of a forced circulation solar water heating system for a residential unit in cold climate using TRNSYS. Solar energy, Vol. 83, pp. 700-714, 2009. https://doi.org/10.1016/j.solener.2008.10.018
  15. Kim, Y. D., Thu, K., Bhatia, H. K., and Bhatia, C. S., Thermal analysis and performance optimization of a solar hot water plant with economic evaluation. Solar energy, Vol. 86, pp. 1378-1395, 2012. https://doi.org/10.1016/j.solener.2012.01.030