IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v306y2022ipas0306261921012782.html
   My bibliography  Save this article

Automated computational design method for energy systems in buildings using capacity and operation optimization

Author

Listed:
  • Iijima, Fuyumi
  • Ikeda, Shintaro
  • Nagai, Tatsuo

Abstract

Research has been conducted previously to reduce the energy consumption and costs of energy systems by focusing on the optimization of building designs. However, the operation method significantly affects the results of the design optimization. Therefore, the aim of this study was to optimize the operation of thermal energy storage, which is particularly difficult, and the load dispatch to the heat source equipment. The optimization was achieved using a hybrid strategy involving three methods: epsilon differential evolution with random jumping (εDE-RJ) for design optimization, dynamic programming for heat storage tank optimization, and Lagrange's multiplier method for load dispatch optimization. Based on this method, 28% and 8% reductions were achieved in the life cycle costs associated with operation and design optimization, as compared to those incurred using the conventional method and the design optimization approach without operation optimization, respectively. The results show that optimal systems can be realized by simultaneously optimizing the operation and design of energy systems.

Suggested Citation

  • Iijima, Fuyumi & Ikeda, Shintaro & Nagai, Tatsuo, 2022. "Automated computational design method for energy systems in buildings using capacity and operation optimization," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012782
    DOI: 10.1016/j.apenergy.2021.117973
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921012782
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117973?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ferrara, Maria & Della Santa, Francesco & Bilardo, Matteo & De Gregorio, Alessandro & Mastropietro, Antonio & Fugacci, Ulderico & Vaccarino, Francesco & Fabrizio, Enrico, 2021. "Design optimization of renewable energy systems for NZEBs based on deep residual learning," Renewable Energy, Elsevier, vol. 176(C), pages 590-605.
    2. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
    3. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    4. Gabrielli, Paolo & Fürer, Florian & Mavromatidis, Georgios & Mazzotti, Marco, 2019. "Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis," Applied Energy, Elsevier, vol. 238(C), pages 1192-1210.
    5. Falke, Tobias & Krengel, Stefan & Meinerzhagen, Ann-Kathrin & Schnettler, Armin, 2016. "Multi-objective optimization and simulation model for the design of distributed energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1508-1516.
    6. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Particle swarm optimization for redundant building cooling heating and power system," Applied Energy, Elsevier, vol. 87(12), pages 3668-3679, December.
    7. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    8. Yokoyama, Ryohei & Shinano, Yuji & Wakayama, Yuki & Wakui, Tetsuya, 2019. "Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method," Energy, Elsevier, vol. 181(C), pages 782-792.
    9. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.
    10. Yokoyama, Ryohei & Shinano, Yuji & Taniguchi, Syusuke & Wakui, Tetsuya, 2019. "Search for K-best solutions in optimal design of energy supply systems by an extended MILP hierarchical branch and bound method," Energy, Elsevier, vol. 184(C), pages 45-57.
    11. Takanori Ida, Kayo Murakami, and Makoto Tanaka, 2016. "Electricity demand response in Japan: Experimental evidence from a residential photovoltaic power-generation system," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 1).
    12. Pruitt, Kristopher A. & Braun, Robert J. & Newman, Alexandra M., 2013. "Evaluating shortfalls in mixed-integer programming approaches for the optimal design and dispatch of distributed generation systems," Applied Energy, Elsevier, vol. 102(C), pages 386-398.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lyu, Chenghao & Zhang, Yuchen & Bai, Yilin & Yang, Kun & Song, Zhengxiang & Ma, Yuhang & Meng, Jinhao, 2024. "Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage," Applied Energy, Elsevier, vol. 363(C).
    2. Jinho Shin & Jihwa Jung & Jaehaeng Heo & Junwoo Noh, 2022. "A Decision-Making Model for Optimized Energy Plans for Buildings Considering Peak Demand Charge—A South Korea Case Study," Energies, MDPI, vol. 15(15), pages 1-22, August.
    3. Guo, Jiacheng & Liu, Zhijian & Wu, Xuan & Wu, Di & Zhang, Shicong & Yang, Xinyan & Ge, Hua & Zhang, Peiwen, 2022. "Two-layer co-optimization method for a distributed energy system combining multiple energy storages," Applied Energy, Elsevier, vol. 322(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xia, Tian & Huang, Wujing & Lu, Xi & Zhang, Ning & Kang, Chongqing, 2020. "Planning district multiple energy systems considering year-round operation," Energy, Elsevier, vol. 213(C).
    2. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    3. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    4. Wang, Jiangjiang & Sui, Jun & Jin, Hongguang, 2015. "An improved operation strategy of combined cooling heating and power system following electrical load," Energy, Elsevier, vol. 85(C), pages 654-666.
    5. Prada, A. & Gasparella, A. & Baggio, P., 2018. "On the performance of meta-models in building design optimization," Applied Energy, Elsevier, vol. 225(C), pages 814-826.
    6. Deng, Yan & Liu, Yicai & Zeng, Rong & Wang, Qianxu & Li, Zheng & Zhang, Yu & Liang, Heng, 2021. "A novel operation strategy based on black hole algorithm to optimize combined cooling, heating, and power-ground source heat pump system," Energy, Elsevier, vol. 229(C).
    7. Luqing Zhang & Aikang Chen & Han Gu & Xitian Wang & Da Xie & Chenghong Gu, 2019. "Planning of the Multi-Energy Circular System Coupled with Waste Processing Base: A Case from China," Energies, MDPI, vol. 12(20), pages 1-17, October.
    8. Ahn, Hyeunguk & Rim, Donghyun & Freihaut, James D., 2018. "Performance assessment of hybrid chiller systems for combined cooling, heating and power production," Applied Energy, Elsevier, vol. 225(C), pages 501-512.
    9. Deng, Yan & Zeng, Rong & Liu, Yicai, 2022. "A novel off-design model to optimize combined cooling, heating and power system with hybrid chillers for different operation strategies," Energy, Elsevier, vol. 239(PB).
    10. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    11. Wang, Jiangjiang & Liu, Yi & Ren, Fukang & Lu, Shuaikang, 2020. "Multi-objective optimization and selection of hybrid combined cooling, heating and power systems considering operational flexibility," Energy, Elsevier, vol. 197(C).
    12. Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2017. "Development of an optimization based design framework for microgrid energy systems," Energy, Elsevier, vol. 140(P1), pages 340-351.
    13. Tao Zhang & Minli Wang & Peihong Wang & Junyu Liang, 2020. "Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty," Energies, MDPI, vol. 13(14), pages 1-17, July.
    14. Ahn, Hyeunguk & Freihaut, James D. & Rim, Donghyun, 2019. "Economic feasibility of combined cooling, heating, and power (CCHP) systems considering electricity standby tariffs," Energy, Elsevier, vol. 169(C), pages 420-432.
    15. Hu, Mengqi, 2015. "A data-driven feed-forward decision framework for building clusters operation under uncertainty," Applied Energy, Elsevier, vol. 141(C), pages 229-237.
    16. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Das, Barun K. & Al-Abdeli, Yasir M. & Kothapalli, Ganesh, 2018. "Effect of load following strategies, hardware, and thermal load distribution on stand-alone hybrid CCHP systems," Applied Energy, Elsevier, vol. 220(C), pages 735-753.
    18. Zhou, Yuan & Ma, Yanpeng & Wang, Jiangjiang & Lu, Shuaikang, 2021. "Collaborative planning of spatial layouts of distributed energy stations and networks: A case study," Energy, Elsevier, vol. 234(C).
    19. Lei, Yunkai & Hou, Kai & Wang, Yue & Jia, Hongjie & Zhang, Pei & Mu, Yunfei & Jin, Xiaolong & Sui, Bingyan, 2018. "A new reliability assessment approach for integrated energy systems: Using hierarchical decoupling optimization framework and impact-increment based state enumeration method," Applied Energy, Elsevier, vol. 210(C), pages 1237-1250.
    20. Li, Longxi & Mu, Hailin & Gao, Weijun & Li, Miao, 2014. "Optimization and analysis of CCHP system based on energy loads coupling of residential and office buildings," Applied Energy, Elsevier, vol. 136(C), pages 206-216.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012782. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.