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An integrated multi-objective optimization model for determining the optimal solution in the solar thermal energy system

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  • Kim, Jimin
  • Hong, Taehoon
  • Jeong, Jaemin
  • Lee, Myeonghwi
  • Koo, Choongwan
  • Lee, Minhyun
  • Ji, Changyoon
  • Jeong, Jaewook

Abstract

The STE (solar thermal energy) system is considered an important new renewable energy resource. While various simulations are used as decision-making tools in implementing the STE system, it has a limitation in considering both diverse impact factors and target variables. Therefore, this study aimed to develop an integrated multi-objective optimization model for determining the optimal solution in the STE system. As the optimization algorithm, this study utilizes GA (genetic algorithm) to select optimal STE system solution. Using crossover and mutation, GA investigates optimal STE system solution. The proposed model used GA based on the software program Evolver 5.5. The proposed model presents high available and efficient results as decision-making tools. First, to determine the optimal solution, a total of 30,407,832 possible scenarios were generated by considering various factors in terms of their high availability. Second, in terms of efficiency, an average of 131 s were used to determine the optimal solution out of the previously proposed various scenarios. The proposed model can become a tool for consumers to decide on the optimal solution for the design of the STE system.

Suggested Citation

  • Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Lee, Myeonghwi & Koo, Choongwan & Lee, Minhyun & Ji, Changyoon & Jeong, Jaewook, 2016. "An integrated multi-objective optimization model for determining the optimal solution in the solar thermal energy system," Energy, Elsevier, vol. 102(C), pages 416-426.
    • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:416-426
    DOI: 10.1016/j.energy.2016.02.104
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    2. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    3. Ganjehkaviri, A. & Mohd Jaafar, M.N. & Hosseini, S.E. & Barzegaravval, H., 2017. "Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction," Energy, Elsevier, vol. 119(C), pages 167-177.
    4. Jeongyoon Oh & Taehoon Hong & Hakpyeong Kim & Jongbaek An & Kwangbok Jeong & Choongwan Koo, 2017. "Advanced Strategies for Net-Zero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle," Sustainability, MDPI, vol. 9(12), pages 1-52, December.
    5. Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Choi, Jun-Ki, 2018. "Improving the prediction performance of the finite element model for estimating the technical performance of the distributed generation of solar power system in a building façade," Applied Energy, Elsevier, vol. 215(C), pages 41-53.
    6. Căruțașiu Mihail-Bogdan & Ionescu Constantin & Necula Horia, 2017. "Optimal technical and economic strategy for retrofitting residential buildings in Romania," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 146-156, July.
    7. Cristofari, C. & Carutasiu, M.B. & Canaletti, J.L. & Norvaišienė, R. & Motte, F. & Notton, G., 2019. "Building integration of solar thermal systems-example of a refurbishment of a church rectory," Renewable Energy, Elsevier, vol. 137(C), pages 67-81.
    8. Park, Hyo Seon & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Jeong, Kwangbok, 2016. "A finite element model for estimating the techno-economic performance of the building-integrated photovoltaic blind," Applied Energy, Elsevier, vol. 179(C), pages 211-227.
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    15. Tsvetkov, Nikolay Aleksandrovich & Krivoshein, Ujriy Olegovich & Tolstykh, Aleksandr Vital’yevich & Khutornoi, Andrey Nikolaevich & Boldyryev, Stanislav, 2020. "The calculation of solar energy used by hot water systems in permafrost region: An experimental case study for Yakutia," Energy, Elsevier, vol. 210(C).
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    17. Hong, Taehoon & Kim, Jimin & Lee, Minhyun, 2019. "A multi-objective optimization model for determining the building design and occupant behaviors based on energy, economic, and environmental performance," Energy, Elsevier, vol. 174(C), pages 823-834.
    18. Lee, Minhyun & Hong, Taehoon & Yoo, Hyunji & Koo, Choongwan & Kim, Jimin & Jeong, Kwangbok & Jeong, Jaewook & Ji, Changyoon, 2017. "Establishment of a base price for the Solar Renewable Energy Credit (SREC) from the perspective of residents and state governments in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1066-1080.
    19. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).

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