IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v237y2024ipas096014812401601x.html

Multi-time scale optimal configuration of user-side energy storage considering demand perception

Author

Listed:
  • Wang, Haibing
  • Wang, Fengxia
  • Han, Dong
  • Sun, Weiqing

Abstract

The promotion of user-side energy storage is a pivotal initiative aimed at enhancing the integration capacity of renewable energy sources within modern power systems. However, there is a notable absence of systematic research exploring the optimal configuration of energy storage tailored to diverse user needs and scenarios. In this study, a multi-time scale optimal configuration approach for user-side energy storage is introduced, which takes into account demand perception. Initially, the behavioral patterns of large-scale electricity consumers are deeply studied, and the discriminant index system for user-side energy storage configurations is established, leveraging the interrelationships among various indicators to discern demand patterns. Subsequently, the uncertainties across multiple time scales are combined and the full lifecycle operational scenarios are captured through clustering techniques. By integrating various profit models, including peak-valley arbitrage, demand response, and demand management, the goal is to optimize economic efficiency throughout the system's lifespan. Consequently, a multi-time scale user-side energy storage optimization configuration model that considers demand perception is constructed. This framework enables a comparative analysis of energy storage capacity allocation across different users, assessing its economic impact, and thus promoting the commercialization of user-side energy storage.

Suggested Citation

  • Wang, Haibing & Wang, Fengxia & Han, Dong & Sun, Weiqing, 2024. "Multi-time scale optimal configuration of user-side energy storage considering demand perception," Renewable Energy, Elsevier, vol. 237(PA).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pa:s096014812401601x
    DOI: 10.1016/j.renene.2024.121533
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812401601X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121533?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. Li, Jinwei & Ma, Rongjiang & Deng, Mengsi & Cao, Xiaoling & Wang, Xicheng & Wang, Xianlin, 2024. "A comparative study of clustering algorithms for intermittent heating demand considering time series," Applied Energy, Elsevier, vol. 353(PA).
    2. Gao, Mingfei & Han, Zhonghe & Zhang, Ce & Li, Peng & Wu, Di & Li, Peng, 2023. "Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage," Energy, Elsevier, vol. 277(C).
    3. Zhou, Yuan & Wang, Jiangjiang & Yang, Mingxu & Xu, Hangwei, 2023. "Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology," Applied Energy, Elsevier, vol. 349(C).
    4. Uti, Mat Nizam & Md Din, Ami Hassan & Yusof, Norhakim & Yaakob, Omar, 2023. "A spatial-temporal clustering for low ocean renewable energy resources using K-means clustering," Renewable Energy, Elsevier, vol. 219(P2).
    5. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    6. Gao, Xinjia & Wu, Xiaogang & Xia, Yinlong & Li, Yalun, 2024. "Life extension of a multi-unit energy storage system by optimizing the power distribution based on the degradation ratio," Energy, Elsevier, vol. 286(C).
    7. Dong, Haiyan & Fu, Yanbo & Jia, Qingquan & Zhang, Tie & Meng, Dequn, 2023. "Low carbon optimization of integrated energy microgrid based on life cycle analysis method and multi time scale energy storage," Renewable Energy, Elsevier, vol. 206(C), pages 60-71.
    8. Jani, Hardik K. & Kachhwaha, Surendra Singh & Nagababu, Garlapati & Das, Alok, 2022. "Temporal and spatial simultaneity assessment of wind-solar energy resources in India by statistical analysis and machine learning clustering approach," Energy, Elsevier, vol. 248(C).
    9. Zhou, Kaile & Wei, Shuyu & Yang, Shanlin, 2019. "Time-of-use pricing model based on power supply chain for user-side microgrid," Applied Energy, Elsevier, vol. 248(C), pages 35-43.
    10. Li, Ke & Yang, Fan & Wang, Lupan & Yan, Yi & Wang, Haiyang & Zhang, Chenghui, 2022. "A scenario-based two-stage stochastic optimization approach for multi-energy microgrids," Applied Energy, Elsevier, vol. 322(C).
    11. Xiang, Yue & Tang, Qingwei & Xu, Weiting & Hu, Shuai & Zhao, Pengfei & Guo, Jingrong & Liu, Junyong, 2024. "A multi-factor spatio-temporal correlation analysis method for PV development potential estimation," Renewable Energy, Elsevier, vol. 223(C).
    12. Ma, Kai & Zhang, Rencai & Yang, Jie & Song, Debao, 2023. "Collaborative optimization scheduling of integrated energy system considering user dissatisfaction," Energy, Elsevier, vol. 274(C).
    13. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    14. Xia, Yuanxing & Xu, Qingshan & Chen, Lu & Du, Pengwei, 2022. "The flexible roles of distributed energy storages in peer-to-peer transactive energy market: A state-of-the-art review," Applied Energy, Elsevier, vol. 327(C).
    15. Ding, Yixing & Xu, Qingshan & Hao, Lili & Xia, Yuanxing, 2023. "A Stackelberg Game-based robust optimization for user-side energy storage configuration and power pricing," Energy, Elsevier, vol. 283(C).
    16. Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
    17. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
    18. Wen, Songkang & Li, Yanting & Su, Yan, 2022. "A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations," Renewable Energy, Elsevier, vol. 198(C), pages 155-168.
    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. Haozhi Zhang & Haibing Wang, 2025. "Research on Energy Storage Planning and Operation for New Energy in the Substitute Power Product Market," Sustainability, MDPI, vol. 17(5), pages 1-23, February.
    2. Zhao, Manli & Zhang, Xinhua & Hueng, C. James, 2025. "The user-side energy storage investment under subsidy policy uncertainty," Applied Energy, Elsevier, vol. 386(C).
    3. Zhang, Zhi & Chen, Yanbo & Ma, Tianyang & Tian, Haoxin & Liu, Jingyu & Zhou, Ming & Wang, Wei, 2025. "Multi-type energy storage expansion planning: A review for high-penetration renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(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. Jiang, Meihui & Xu, Zhenjiang & Zhu, Hongyu & Hwang Goh, Hui & Agustiono Kurniawan, Tonni & Liu, Tianhao & Zhang, Dongdong, 2024. "Integrated demand response modeling and optimization technologies supporting energy internet," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    2. Yang, Hui & Chen, Jiajia & Li, Yuanzheng, 2025. "Two-layer iterative optimization for enhanced electricity–carbon pricing to promote renewable integration and load smoothing with multi-type prosumer," Energy, Elsevier, vol. 336(C).
    3. Yin, Boyi & Zhu, Wenjiang & Tang, Cheng & Wang, Can & Xu, Xinhai, 2025. "Hierarchical optimal scheduling of IES considering SOFC degradation, internal and external uncertainties," Applied Energy, Elsevier, vol. 381(C).
    4. Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
    5. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    6. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    7. Wu, Chun & Chen, Xingying & Hua, Haochen & Yu, Kun & Gan, Lei & Shen, Jun & Ding, Yi, 2024. "Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade," Applied Energy, Elsevier, vol. 358(C).
    8. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    9. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    10. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
    11. Hanwen Wang & Xiang Li & Haojun Hu & Yizhou Zhou, 2024. "Distributed Dispatch and Profit Allocation for Parks Using Co-Operative Game Theory and the Generalized Nash Bargaining Approach," Energies, MDPI, vol. 17(23), pages 1-19, December.
    12. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    13. Liu, Jiejie & Li, Yao & Ma, Yanan & Qin, Ruomu & Meng, Xianyang & Wu, Jiangtao, 2023. "Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy," Energy, Elsevier, vol. 285(C).
    14. Wu, Tianyu & Han, Fengwu & Zhao, Yunlong & Yu, Zishuo, 2025. "A decarbonization-oriented and uncertainty-aware energy management strategy for multi-district integrated energy systems with fair peer-to-peer trading," Energy, Elsevier, vol. 323(C).
    15. Xiaozhi Gao & Han Xiao & Shiwei Xu & Hsiung-Cheng Lin & Pengyu Chang, 2024. "What Is the Optimal Solution for Scheduling Multiple Energy Systems? Overview and Analysis of Integrated Energy Co-Dispatch Models," Energies, MDPI, vol. 17(18), pages 1-25, September.
    16. Liu, Xingtao & Tang, Qinbin & Feng, Yitian & Lin, Mingqiang & Meng, Jinhao & Wu, Ji, 2023. "Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment," Applied Energy, Elsevier, vol. 351(C).
    17. Sun, Yanwei & Li, Ying & Wang, Run & Ma, Renfeng, 2025. "Dynamic geospatial modeling of solar energy expansion potential in China: Implications for national-level optimization of solar photovoltaic plant layouts," Energy, Elsevier, vol. 333(C).
    18. Su, Xin & Zhang, Qian & Lu, YangDong & Hao, RuiYi & Qin, TianXi & Li, ChunYan & Huang, ShengWei & Bi, KeFan, 2025. "Study on multi-energy scheduling strategy considering dynamic energy prices and low-carbon demand response," Renewable Energy, Elsevier, vol. 247(C).
    19. Li, Hao & Chen, Chao, 2025. "Lithium-ion battery SOH prediction based on multi-dimensional features and multi-model feature selector," Energy, Elsevier, vol. 331(C).
    20. Li, Jianfang & Jia, Li & Zhou, Chengyu, 2024. "Probability density function based adaptive ensemble learning with global convergence for wind power prediction," Energy, Elsevier, vol. 312(C).

    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:237:y:2024:i:pa:s096014812401601x. 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.