IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v242y2025ics0960148125001533.html
   My bibliography  Save this article

Statistical analysis for estimating the optimized battery capacity for roof-top PV energy system

Author

Listed:
  • Zhang, Yuhang
  • Zhang, Yi
  • Zheng, Bo
  • Cui, Hongzhi
  • Qi, He

Abstract

Calculating optimized battery capacity for building rooftop photovoltaic (PV) consumption efficiently can benefit to decision making and policy implementation. Optimization for battery calculation requires speciality, but optimized battery capacities can be fitted based on similarities between buildings. This study aims to proposed a two-stage battery allocation model to efficiently estimate battery capacity for building PV consumption. An integer linear programming model has been constructed in the first stage to allocate battery for buildings. After optimizing part of buildings’ battery capacities, quadratic polynomial regression analysis has been applied in the second stage to estimate optimized battery capacity using building information for the rest of buildings. Current scenario with electricity feed-in tariff, future scenario without feed-in tariff and baseline scenario have been considered for scenario analysis to compare method performance. 53,016 buildings in Pingshan district, Shenzhen, China have been used as a case study. For current and future scenarios, optimized battery capacity can be fitted by floor number and building area, the R2 score is higher than 0.94. Compared with current scenario, future scenario increases 5.81 % optimized battery capacity and 2.58 % annual cost. The proposed method can reduce nearly 100 % computation time with only 3.3 % accuracy loss compared with optimization method.

Suggested Citation

  • Zhang, Yuhang & Zhang, Yi & Zheng, Bo & Cui, Hongzhi & Qi, He, 2025. "Statistical analysis for estimating the optimized battery capacity for roof-top PV energy system," Renewable Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:renene:v:242:y:2025:i:c:s0960148125001533
    DOI: 10.1016/j.renene.2025.122491
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125001533
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.122491?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

    for a different version of it.

    References listed on IDEAS

    as
    1. T Ladhari & M K Msakni & A Allahverdi, 2012. "Minimizing the total completion time in a two-machine flowshop with sequence-independent setup times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(4), pages 445-459, April.
    2. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    3. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    4. Zhang, Yuhang & Zhang, Yi & Yi Zhang, & Zhang, Chengxu, 2022. "Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data," Energy, Elsevier, vol. 261(PB).
    5. Chen, Xi & Liu, Zhongbing & Wang, Pengcheng & Li, Benjia & Liu, Ruimiao & Zhang, Ling & Zhao, Chengliang & Luo, Songqin, 2023. "Multi-objective optimization of battery capacity of grid-connected PV-BESS system in hybrid building energy sharing community considering time-of-use tariff," Applied Energy, Elsevier, vol. 350(C).
    6. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    7. Wei, Tianxi & Zhang, Yi & Zhang, Yuhang & Miao, Rui & Kang, Jian & Qi, He, 2024. "City-scale roof-top photovoltaic deployment planning," Applied Energy, Elsevier, vol. 368(C).
    8. Yingyue Li & Hongjun Li & Rui Miao & He Qi & Yi Zhang, 2023. "Energy–Environment–Economy (3E) Analysis of the Performance of Introducing Photovoltaic and Energy Storage Systems into Residential Buildings: A Case Study in Shenzhen, China," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    9. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Jeoung, Jaewon & Hong, Taehoon, 2024. "Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    10. Liu, Jiangyang & Liu, Zhongbing & Wu, Yaling & Chen, Xi & Xiao, Hui & Zhang, Ling, 2022. "Impact of climate on photovoltaic battery energy storage system optimization," Renewable Energy, Elsevier, vol. 191(C), pages 625-638.
    11. Bingham, Raymond D. & Agelin-Chaab, Martin & Rosen, Marc A., 2019. "Whole building optimization of a residential home with PV and battery storage in The Bahamas," Renewable Energy, Elsevier, vol. 132(C), pages 1088-1103.
    12. Mousavi, Shadi Bashiri & Ahmadi, Pouria & Adib, Mahdieh & Izadi, Ali, 2023. "Techno-economic assessment of an efficient liquid air energy storage with ejector refrigeration cycle for peak shaving of renewable energies," Renewable Energy, Elsevier, vol. 214(C), pages 96-113.
    13. Thaker, Jayesh & Höller, Robert, 2024. "Hybrid model for intra-day probabilistic PV power forecast," Renewable Energy, Elsevier, vol. 232(C).
    14. Tassenoy, Robin & Couvreur, Kenny & Beyne, Wim & De Paepe, Michel & Lecompte, Steven, 2022. "Techno-economic assessment of Carnot batteries for load-shifting of solar PV production of an office building," Renewable Energy, Elsevier, vol. 199(C), pages 1133-1144.
    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. Gao, Yuan & Hu, Zehuan & Yamate, Shun & Otomo, Junichiro & Chen, Wei-An & Liu, Mingzhe & Xu, Tingting & Ruan, Yingjun & Shang, Juan, 2025. "Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems," Applied Energy, Elsevier, vol. 384(C).
    2. Panagiotis Michailidis & Iakovos Michailidis & Elias Kosmatopoulos, 2025. "Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations," Energies, MDPI, vol. 18(7), pages 1-40, March.
    3. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Application-oriented assessment of grid-connected PV-battery system with deep reinforcement learning in buildings considering electricity price dynamics," Applied Energy, Elsevier, vol. 364(C).
    4. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
    5. Wenya Xu & Yanxue Li & Guanjie He & Yang Xu & Weijun Gao, 2023. "Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control," Energies, MDPI, vol. 16(13), pages 1-19, June.
    6. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
    7. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    8. Li, Yonggang & Su, Yaotong & Zhang, Yuanjin & Wu, Weinong & Xia, Lei, 2024. "Two-layered optimal scheduling under a semi-model architecture of hydro-wind-solar multi-energy systems with hydrogen storage," Energy, Elsevier, vol. 313(C).
    9. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    10. Zhang, Zhonglian & Yang, Xiaohui & Li, Moxuan & Deng, Fuwei & Xiao, Riying & Mei, Linghao & Hu, Zecheng, 2023. "Optimal configuration of improved dynamic carbon neutral energy systems based on hybrid energy storage and market incentives," Energy, Elsevier, vol. 284(C).
    11. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    12. Idiano D'Adamo & Alessandro Dell'Aguzzo & Marco Pruckner, 2024. "Residential photovoltaic and energy storage systems for sustainable development: An economic analysis applied to incentive mechanisms," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(1), pages 84-100, February.
    13. Behzadi, Amirmohammad & Duwig, Christophe & Ploskic, Adnan & Holmberg, Sture & Sadrizadeh, Sasan, 2024. "Application to novel smart techniques for decarbonization of commercial building heating and cooling through optimal energy management," Applied Energy, Elsevier, vol. 376(PA).
    14. Beata Piotrowska & Daniel Słyś, 2023. "Analysis of the Life Cycle Cost of a Heat Recovery System from Greywater Using a Vertical “Tube-in-Tube” Heat Exchanger: Case Study of Poland," Resources, MDPI, vol. 12(9), pages 1-17, August.
    15. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik, 2025. "PV Generation Prediction Using Multilayer Perceptron and Data Clustering for Energy Management Support," Energies, MDPI, vol. 18(6), pages 1-16, March.
    16. Liao, Chenxin & Miyata, Shohei & Qu, Ming & Akashi, Yasunori, 2025. "Year-round operational optimization of HVAC systems using hierarchical deep reinforcement learning for enhancing indoor air quality and reducing energy consumption," Applied Energy, Elsevier, vol. 390(C).
    17. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    18. Liu, Jia & Chen, Xi & Yang, Hongxing & Li, Yutong, 2020. "Energy storage and management system design optimization for a photovoltaic integrated low-energy building," Energy, Elsevier, vol. 190(C).
    19. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    20. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).

    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:eee:renene:v:242:y:2025:i:c:s0960148125001533. 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.journals.elsevier.com/renewable-energy .

    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.