- Volume 28 Issue 7
In recent years, heat source systems which have a principal effect on the performance of buildings are difficult to design optimally as a great number of design factors and constraints in large and complicated buildings need to be considered. On the other hand, it is necessary to design an optimum system combination and operation planning for energy efficiency considering Life Cycle Cost (LCC). This study suggests a multi-level and multi-objective optimization method to minimize both LCC and investment cost using a genetic algorithm targeting an office building which requires a large cooling load. The optimum method uses a two stage process to derive the system combination and the operation schedule by utilizing the input data of cooling and heating load profile and system performance characteristics calculated by dynamic energy simulation. The results were assessed by Pareto analysis and a number of Pareto optimal solutions were determined. Moreover, it was confirmed that the derived operation schedule was useful for operating the heat source systems efficiently against the building energy requirements. Consequently, the proposed optimization method is determined by a valid way if the design process is difficult to optimize.
Optimization;Genetic algorithm;Life cycle cost;Pareto analysis
- Ooka, R. and Komamura, K., 2009, Optimal design method for building energy systems using genetic algorithms, Building and Environment, Vol. 44, No. 7, pp. 1538-1544. https://doi.org/10.1016/j.buildenv.2008.07.006
- Kayo, G. and Ooka, R., 2010, Building energy system optimizations with utilization of waste heat from cogenerations by means of genetic algorithm, Energy and Buildings, Vol. 42, No. 7, pp. 985-997. https://doi.org/10.1016/j.enbuild.2010.01.010
- Seo, J. H., Ooka, R., Kim, J. T., and Nam, Y. J., 2014, Optimization of the HVAC system design to minimize primary energy demand, Energy and Building, Vol. 76, pp. 102-108. https://doi.org/10.1016/j.enbuild.2014.02.034
- Kong, D. S., Jang, Y. S., and Huh, J. H., 2014, A multiobjective optimization method for energy system design considering initial cost and primary energy consumption, Korean Journal of Air-Conditioning and Refrigerating Engineering, Vol. 26, No. 8, pp. 357-365. https://doi.org/10.6110/KJACR.2014.26.8.357
- Hafez, O. and Bhattacharya, K., 2012, Optimal planning and design of a renewable energy based supply system for microgrids, Renewable Energy, Vol. 45, pp. 7-15. https://doi.org/10.1016/j.renene.2012.01.087
- Rao, S. S. 2011, Engineering Optimization : theory and practice, 4th ed., pp. 726-737.
Paek, N. S., 2011, Evaluating economy and environmental load of heat pump hot water system with
$CO_2$refrigerant by LCC and $LCCO_2$analyses, MS thesis, Hanyang University, Seoul, Korea.
- Yu, M. G., Cho, J. H., and Nam, Y., 2015, Feasibility study of the energy supply system for horticulture facility using dynamic energy simulation, Korean Journal of Ecological Architecture and Environment, Vol. 15, No. 1, pp. 103-109.
- Jang, S. H., 2004, Evolutionary Multi-Objective Optimization Algorithms using Pareto Dominance Rank and Density Weighting, The KIPS Transcations : Part B, Vol. 11B, No. 2, pp. 213-220. https://doi.org/10.3745/KIPSTB.2004.11B.2.213
- Park, C. B., 2011, A study on the application of low energy cooling systems in office building, the graduate school of chung-ang university, doctor degree.
- Study on the Optimum Design Method of Heat Source Systems with Heat Storage Using a Genetic Algorithm vol.9, pp.10, 2016, https://doi.org/10.3390/en9100849
Supported by : 한국에너지기술평가원(KETEP)