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Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system

Author

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  • Huang, Pei
  • Lovati, Marco
  • Zhang, Xingxing
  • Bales, Chris
  • Hallbeck, Sven
  • Becker, Anders
  • Bergqvist, Henrik
  • Hedberg, Jan
  • Maturi, Laura

Abstract

Smart grid is triggering the transformation of traditional electricity consumers into electricity prosumers. This paper reports a case study of transforming an existing residential cluster in Sweden into electricity prosumers. The main energy concepts include (1) click-and-go photovoltaics (PV) panels for building integration, (2) centralized exhaust air heat pump, (3) thermal energy storage for storing excess PV electricity by using heat pump, and (4) PV electricity sharing within the building cluster for thermal/electrical demand (including electric vehicles load) on a direct-current micro grid. For the coupled PV-heat pump-thermal storage-electric vehicle system, a fitness function based on genetic algorithm is established to optimize the capacity and positions of PV modules at cluster level, with the purpose of maximizing the self-consumed electricity under a non-negative net present value during the economic lifetime. Different techno-economic key performance indicators, including the optimal PV capacity, self-sufficiency, self-consumption and levelized cost of electricity, are analysed under impacts of thermal storage integration, electric vehicle penetration and electricity sharing possibility. Results indicate that the coupled system can effectively improve the district-level PV electricity self-consumption rate to about 77% in the baseline case. The research results reveal how electric vehicle penetrations, thermal storage, and energy sharing affect PV system sizing/positions and the performance indicators, and thus help promote the PV deployment. This study also demonstrates the feasibility for transferring the existing Swedish building clusters into smart electricity prosumers with higher self-consumption and energy efficiency and more intelligence, which benefits achieving the ‘32% share of renewable energy source’ target in EU by 2030.

Suggested Citation

  • Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris & Hallbeck, Sven & Becker, Anders & Bergqvist, Henrik & Hedberg, Jan & Maturi, Laura, 2019. "Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s030626191931551x
    DOI: 10.1016/j.apenergy.2019.113864
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    Citations

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    Cited by:

    1. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    2. Sun, Chuyu & Zhao, Xiaoli & Qi, Binbin & Xiao, Weihao & Zhang, Hongjun, 2022. "Economic and environmental analysis of coupled PV-energy storage-charging station considering location and scale," Applied Energy, Elsevier, vol. 328(C).
    3. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
    4. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Georghiou, George E., 2021. "Social arrangements, technical designs and impacts of energy communities: A review," Renewable Energy, Elsevier, vol. 169(C), pages 1138-1156.
    5. Buonomano, Annamaria, 2020. "Building to Vehicle to Building concept: A comprehensive parametric and sensitivity analysis for decision making aims," Applied Energy, Elsevier, vol. 261(C).
    6. Kumar, Gokula Manikandan Senthil & Cao, Sunliang, 2023. "Leveraging energy flexibilities for enhancing the cost-effectiveness and grid-responsiveness of net-zero-energy metro railway and station systems," Applied Energy, Elsevier, vol. 333(C).
    7. Leprince, Julien & Schledorn, Amos & Guericke, Daniela & Dominkovic, Dominik Franjo & Madsen, Henrik & Zeiler, Wim, 2023. "Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities," Applied Energy, Elsevier, vol. 348(C).
    8. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    9. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Xu, Wenya & Wang, Zixuan, 2022. "Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration," Energy, Elsevier, vol. 238(PB).
    10. Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris, 2020. "A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered," Applied Energy, Elsevier, vol. 268(C).
    11. Millar, Michael-Allan & Yu, Zhibin & Burnside, Neil & Jones, Greg & Elrick, Bruce, 2021. "Identification of key performance indicators and complimentary load profiles for 5th generation district energy networks," Applied Energy, Elsevier, vol. 291(C).
    12. Ren, Haoshan & Sun, Yongjun & Norman Tse, Chung Fai & Fan, Cheng, 2023. "Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities," Energy, Elsevier, vol. 274(C).
    13. May, Ross & Huang, Pei, 2023. "A multi-agent reinforcement learning approach for investigating and optimising peer-to-peer prosumer energy markets," Applied Energy, Elsevier, vol. 334(C).
    14. Jouttijärvi, Sami & Lobaccaro, Gabriele & Kamppinen, Aleksi & Miettunen, Kati, 2022. "Benefits of bifacial solar cells combined with low voltage power grids at high latitudes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    15. Zou, Juan & Yang, Xu & Liu, Zhongbing & Liu, Jiangyang & Zhang, Ling & Zheng, Jinhua, 2021. "Multiobjective bilevel optimization algorithm based on preference selection to solve energy hub system planning problems," Energy, Elsevier, vol. 232(C).
    16. Kang, Hyuna & Jung, Seunghoon & Lee, Minhyun & Hong, Taehoon, 2022. "How to better share energy towards a carbon-neutral city? A review on application strategies of battery energy storage system in city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    17. Huang, Pei & Sun, Yongjun & Lovati, Marco & Zhang, Xingxing, 2021. "Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements," Energy, Elsevier, vol. 222(C).
    18. Lovati, Marco & Dallapiccola, Mattia & Adami, Jennifer & Bonato, Paolo & Zhang, Xingxing & Moser, David, 2020. "Design of a residential photovoltaic system: the impact of the demand profile and the normative framework," Renewable Energy, Elsevier, vol. 160(C), pages 1458-1467.
    19. Ren, Haoshan & Ma, Zhenjun & Ming Lun Fong, Alan & Sun, Yongjun, 2022. "Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities," Applied Energy, Elsevier, vol. 319(C).

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