IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i1p411-d1312264.html

Optimal Planning of Urban Building-Type Integrated Energy Systems Considering Indoor Somatosensory Comfort and PV Consumption

Author

Listed:
  • Guangzeng You

    (Power Grid Planning and Construction Research Center, Yunnan Power Grid Co., Ltd., Kunming 130022, China)

  • Peng Sun

    (Power Grid Planning and Construction Research Center, Yunnan Power Grid Co., Ltd., Kunming 130022, China)

  • Yi Lei

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China)

  • Donghui Zhang

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China)

  • Haibo Li

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China)

Abstract

Building energy consumption is the main urban energy consumption component, which mainly serves people-centered work and living energy demands. Based on the physical requirements of humans in urban buildings and to determine the comfortable body temperature in each season, this paper establishes a sizing optimization model of building-type integrated energy systems (IES) for sustainable development, where the cooling and heating loads required to maintain indoor somatosensory body comfort temperature are calculated. Depending on the external energy price, internal power balance, and other constraints, the model develops an optimal sizing and capacity-planning method of energy conversion and storage unit in a building-type IES with PV generation. The operating principle is described as follows: the PV generation is fully consumed, a gas engine satisfies the electric and thermal base load requirements, and the power system and a heat pump supply the remaining loads. The gas price, peak-valley electricity price gap, and heat-to-power ratio of gas engines are considered important factors for the overall techno-economic analysis. The developed method is applied to optimize the economic performance of building-type IES and verified by practical examples. The results show that using the complementary characteristics of different energy conversion units is important to the overall IES cost. A 300 kW building photovoltaic system can reduce the gas engine capacity from 936.7 kW to 854.7 kW, and the annual cost can be approximately reduced from 7.82 million to 7.50 million RMB yuan.

Suggested Citation

  • Guangzeng You & Peng Sun & Yi Lei & Donghui Zhang & Haibo Li, 2024. "Optimal Planning of Urban Building-Type Integrated Energy Systems Considering Indoor Somatosensory Comfort and PV Consumption," Sustainability, MDPI, vol. 16(1), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:1:p:411-:d:1312264
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/411/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/411/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Westner, Günther & Madlener, Reinhard, 2012. "Investment in new power generation under uncertainty: Benefits of CHP vs. condensing plants in a copula-based analysis," Energy Economics, Elsevier, vol. 34(1), pages 31-44.
    2. Oh, Si-Doek & Lee, Ho-Jun & Jung, Jung-Yeul & Kwak, Ho-Young, 2007. "Optimal planning and economic evaluation of cogeneration system," Energy, Elsevier, vol. 32(5), pages 760-771.
    3. Mei, Jun & Xia, Xiaohua & Song, Mengjie, 2018. "An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 221(C), pages 450-463.
    Full references (including those not matched with items on IDEAS)

    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. Ronald Bernstein & Reinhard Madlener, 2011. "Responsiveness of Residential Electricity Demand in OECD Countries: A Panel Cointegation and Causality Analysis," FCN Working Papers 8/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Marjolein Harmsen - van Hout & Gaurav Ghosh & Reinhard Madlener, 2013. "The Impact of Green Framing on Consumers’ Valuations of Energy-Saving Measures," FCN Working Papers 7/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Lozano, Miguel A. & Ramos, Jose C. & Serra, Luis M., 2010. "Cost optimization of the design of CHCP (combined heat, cooling and power) systems under legal constraints," Energy, Elsevier, vol. 35(2), pages 794-805.
    4. He, Deqiang & Teng, Xiaoliang & Chen, Yanjun & Liu, Bin & Wang, Heliang & Li, Xianwang & Ma, Rui, 2022. "Energy saving in metro ventilation system based on multi-factor analysis and air characteristics of piston vent," Applied Energy, Elsevier, vol. 307(C).
    5. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    6. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    7. Ondeck, Abigail & Edgar, Thomas F. & Baldea, Michael, 2017. "A multi-scale framework for simultaneous optimization of the design and operating strategy of residential CHP systems," Applied Energy, Elsevier, vol. 205(C), pages 1495-1511.
    8. Kang Qian & Tong Lv & Yue Yuan, 2021. "Integrated Energy System Planning Optimization Method and Case Analysis Based on Multiple Factors and A Three-Level Process," Sustainability, MDPI, vol. 13(13), pages 1-22, July.
    9. Mirko M. Stojiljković & Mladen M. Stojiljković & Bratislav D. Blagojević, 2014. "Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics," Energies, MDPI, vol. 7(12), pages 1-28, December.
    10. Dahlgren, Eric & Leung, Tim, 2015. "An optimal multiple stopping approach to infrastructure investment decisions," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 251-267.
    11. Basrawi, Firdaus & Ibrahim, Thamir K. & Habib, Khairul & Yamada, Takanobu & Daing Idris, Daing Mohamad Nafiz, 2017. "Techno-economic performance of biogas-fueled micro gas turbine cogeneration systems in sewage treatment plants: Effect of prime mover generation capacity," Energy, Elsevier, vol. 124(C), pages 238-248.
    12. Bartela, Łukasz & Skorek-Osikowska, Anna & Kotowicz, Janusz, 2015. "An analysis of the investment risk related to the integration of a supercritical coal-fired combined heat and power plant with an absorption installation for CO2 separation," Applied Energy, Elsevier, vol. 156(C), pages 423-435.
    13. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "Optimal design of distributed energy resource systems coupled with energy distribution networks," Energy, Elsevier, vol. 85(C), pages 433-448.
    14. Oh, Si-Doek & Kim, Ki-Young & Oh, Shuk-Bum & Kwak, Ho-Young, 2012. "Optimal operation of a 1-kW PEMFC-based CHP system for residential applications," Applied Energy, Elsevier, vol. 95(C), pages 93-101.
    15. Jeon, Jongug & Lee, Sunil & Hong, Daehie & Kim, Yongchan, 2010. "Performance evaluation and modeling of a hybrid cooling system combining a screw water chiller with a ground source heat pump in a building," Energy, Elsevier, vol. 35(5), pages 2006-2012.
    16. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    17. Eardley, Scott & Choi, Jun-Ki & Hong, Taehoon & An, Jongbaek, 2024. "Decarbonization potential of regional combined heat and power development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Stojiljković, Mirko M. & Ignjatović, Marko G. & Vučković, Goran D., 2015. "Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization," Energy, Elsevier, vol. 92(P3), pages 420-434.
    19. Liu, Minzhang & Zhu, Chunguang & Zhang, Huan & Zheng, Wandong & You, Shijun & Campana, Pietro Elia & Yan, Jinyue, 2019. "The environment and energy consumption of a subway tunnel by the influence of piston wind," Applied Energy, Elsevier, vol. 246(C), pages 11-23.
    20. Yılmaz, Sebnem & Selim, Hasan, 2013. "A review on the methods for biomass to energy conversion systems design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 420-430.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:16:y:2024:i:1:p:411-:d:1312264. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.