Korean Journal of Air-Conditioning and Refrigeration Engineering (설비공학논문집)

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
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Comparative Study on Size Optimization of a Solar Water Heating System in the Early Design Phase Using a RETScreen Model with TRNSYS Model Optimization

RETScreen 모델이용 태양열온수시스템 초기설계단계 설계용량 최적화기법의 TRNSYS 모델과의 비교분석

  • Lee, Kyoung-Ho (Solar Energy Department, Korea Institute of Energy Research)
  • Received : 2013.10.14
  • Published : 2013.12.10
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Abstract

This paper describes a method for size optimization of the major design variables for solar water heating systems at the stage of concept design. The widely used RETScreen simulation tool was used for optimization. Currently, the RETScreen tool itself does not provide a function for optimization of the design parameters. In this study, an optimizer was combined with the software. A comparative study was performed to evaluate the RETScreen-based approach with the case study of a solar heating system in an office building. The optimized results using the RETScreen model were compared to previously published results with the TRNSYS model. The objective function of the optimization is the life-cycle cost of the system. The optimized design results from the RETScreen model showed good agreement with the optimized TRNSYS results for the solar collector area and storage volume, but presented a slight difference for the collector slope angle in terms of the converged direction of the solutions. The energy cost, life-cycle cost, and thermal performance regarding collector efficiency, system efficiency, and solar fraction were compared as well, and the RETScreen model showed good agreement with the TRNSYS model for the conditions of the base case and optimized design.

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References

  1. TRNSYS, University of Wisconsin at Madison, Solar Energy Laboratory.
  2. EnergyPlus, U.S. Department of Energy.
  3. Kalogirou, S., 2004, Optimization of solar systems using artificial neural-networks and genetic algorithms, Applied Energy Vol. 77, pp. 383-405. https://doi.org/10.1016/S0306-2619(03)00153-3
  4. Lima, J. B. A., Prado, R. T. A., and Taborianski, V. M., 2006, Optimization of tank and flat-plate collector of solar water heating system for single-family households to assure economic efficiency through the TRNSYS program, Renewable Energy, Vol. 31, pp. 1581-1595. https://doi.org/10.1016/j.renene.2005.09.006
  5. Ko, M. J., Choi, D. S., Chang, J. D., and Kim, Y. S., 2010, Optimizing the life cycle cost of a solar water heating system in an office building through simulation, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 22, No. 12, pp. 859-866.
  6. Ko, M. J., Choi, D. S., Chang, J. D., and Kim, Y. S., 2011, Energy performance variation of solar water heating system by LCC optimization in an office building, Journal of the Korean Solar Energy Society, Vol. 31, No. 2, pp. 89-98. https://doi.org/10.7836/kses.2011.31.2.089
  7. RETScreen, Natural Resource Canada, http://www.retscreen.net.
  8. Lee, K. H., Lee, D. W., Baek, N. C., Kwon, H. M., and Lee, C. J., 2012, Preliminary determination of optimal size for renewable energy resources in buildings using RETScreen, Energy, Vol. 47, pp. 83-96. https://doi.org/10.1016/j.energy.2012.08.040
  9. Duffie, J. and Beckman, W., 2006, Solar engineering of thermal processes, Third edition, Wiley.