IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v364y2024ics0306261924005476.html
   My bibliography  Save this article

Triple-layer optimization of distributed photovoltaic energy storage capacity for manufacturing enterprises considering carbon emissions and load management

Author

Listed:
  • Feng, Ran
  • Wang, Kai
  • Xu, Xu
  • Yu, Zi-Tao
  • Lin, Qingyang

Abstract

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible.

Suggested Citation

  • Feng, Ran & Wang, Kai & Xu, Xu & Yu, Zi-Tao & Lin, Qingyang, 2024. "Triple-layer optimization of distributed photovoltaic energy storage capacity for manufacturing enterprises considering carbon emissions and load management," Applied Energy, Elsevier, vol. 364(C).
  • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924005476
    DOI: 10.1016/j.apenergy.2024.123164
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924005476
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.123164?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. Cervone, A. & Carbone, G. & Santini, E. & Teodori, S., 2016. "Optimization of the battery size for PV systems under regulatory rules using a Markov-Chains approach," Renewable Energy, Elsevier, vol. 85(C), pages 657-665.
    2. Xin-gang, Zhao & Zhen, Wang, 2019. "Technology, cost, economic performance of distributed photovoltaic industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 53-64.
    3. Zhang, Yue-Jun & Liang, Ting & Jin, Yan-Lin & Shen, Bo, 2020. "The impact of carbon trading on economic output and carbon emissions reduction in China’s industrial sectors," Applied Energy, Elsevier, vol. 260(C).
    4. Erdinç, Fatma Gülşen, 2023. "Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness," Applied Energy, Elsevier, vol. 344(C).
    5. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    6. Zhixin Zhang & Min Chen & Teng Zhong & Rui Zhu & Zhen Qian & Fan Zhang & Yue Yang & Kai Zhang & Paolo Santi & Kaicun Wang & Yingxia Pu & Lixin Tian & Guonian Lü & Jinyue Yan, 2023. "Carbon mitigation potential afforded by rooftop photovoltaic in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Chen, Han & Chen, Wenying, 2021. "Status, trend, economic and environmental impacts of household solar photovoltaic development in China: Modelling from subnational perspective," Applied Energy, Elsevier, vol. 303(C).
    8. 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).
    9. Rizqi, Zakka Ugih & Chou, Shuo-Yan & Yu, Tiffany Hui-Kuang, 2023. "Green energy mix modeling under supply uncertainty: Hybrid system dynamics and adaptive PSO approach," Applied Energy, Elsevier, vol. 349(C).
    10. Nguyen, Su & Peng, Wei & Sokolowski, Peter & Alahakoon, Damminda & Yu, Xinghuo, 2018. "Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 228(C), pages 2567-2580.
    11. Jiang, Yanni & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "Electricity trading pricing among prosumers with game theory-based model in energy blockchain environment," Applied Energy, Elsevier, vol. 271(C).
    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. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    2. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Dong, Jingya & Song, Chunhe & Liu, Shuo & Yin, Huanhuan & Zheng, Hao & Li, Yuanjian, 2022. "Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach," Applied Energy, Elsevier, vol. 325(C).
    4. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K. & Yuen, Chau & Smith, David, 2022. "Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    5. Haoran Zhang & Rongxia Zhang & Guomin Li & Wei Li & Yongrok Choi, 2020. "Has China’s Emission Trading System Achieved the Development of a Low-Carbon Economy in High-Emission Industrial Subsectors?," Sustainability, MDPI, vol. 12(13), pages 1-20, July.
    6. Libo Zhang & Qian Du & Dequn Zhou, 2021. "Grid Parity Analysis of China’s Centralized Photovoltaic Generation under Multiple Uncertainties," Energies, MDPI, vol. 14(7), pages 1-19, March.
    7. Liming Deng & Wenjing Shen & Kangkang Xu & Xuhui Zhang, 2024. "An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration," Energies, MDPI, vol. 17(7), pages 1-15, April.
    8. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    9. Liu, Jicheng & Sun, Jiakang & Yuan, Hanying & Su, Yihan & Feng, Shuxian & Lu, Chaoran, 2022. "Behavior analysis of photovoltaic-storage-use value chain game evolution in blockchain environment," Energy, Elsevier, vol. 260(C).
    10. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    11. Min-Hwi Kim & Dong-Won Lee & Deuk-Won Kim & Young-Sub An & Jae-Ho Yun, 2021. "Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community," Energies, MDPI, vol. 14(17), pages 1-17, September.
    12. Du, Hua & Han, Qi & de Vries, Bauke & Sun, Jun, 2024. "Community solar PV adoption in residential apartment buildings: A case study on influencing factors and incentive measures in Wuhan," Applied Energy, Elsevier, vol. 354(PA).
    13. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    14. Jia, Zhijie & Lin, Boqiang, 2020. "Rethinking the choice of carbon tax and carbon trading in China," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    15. Wen-Hsien Tsai & Shang-Yu Lai & Chu-Lun Hsieh, 2023. "Exploring the impact of different carbon emission cost models on corporate profitability," Annals of Operations Research, Springer, vol. 322(1), pages 41-74, March.
    16. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    17. Yang, Yi & Yuan, Zhuqing & Yang, Shengnan, 2022. "Difference in the drivers of industrial carbon emission costs determines the diverse policies in middle-income regions: A case of northwestern China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    18. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).
    19. Wang, Zibo & Yu, Xiaodan & Mu, Yunfei & Jia, Hongjie, 2020. "A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System," Applied Energy, Elsevier, vol. 260(C).
    20. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.

    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:364:y:2024:i:c:s0306261924005476. 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.