IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v409y2026ics0306261926001467.html

Risk-preference-aware optimal scheduling and profit allocation of load aggregators and charging operators

Author

Listed:
  • Ma, Qianxin
  • Zhao, Zhenyu
  • Han, Ruyue
  • Lin, Shan

Abstract

With the increasing penetration of renewable energy and the ongoing deepening of electricity market liberalization, distributed flexible resources, such as electric vehicles and energy storage systems, are becoming key enablers for enhancing grid regulation capabilities and improving system operational efficiency. However, under current market mechanisms, these resources still suffer from misaligned incentives and insufficient coordination in scheduling, risk sharing, and profit allocation, resulting in their potential flexibility value not being fully exploited. To address these challenges, this paper focuses on the interaction between load aggregators (LAs) and charging operators (COs) and develops a risk-preference-aware framework for optimal scheduling and profit allocation in an LA-CO coupled system. First, a joint probabilistic model of wind and photovoltaic output is established using kernel density estimation and copula theory, and representative generation scenarios are produced via Monte Carlo sampling and K-means clustering. Second, a Stackelberg game-based optimisation model incorporating Conditional Value-at-Risk (CVaR) is formulated to characterise multi-actor strategic responses under risk constraints. Third, an improved weighted Nash bargaining scheme is designed for cooperative profit allocation, in which bargaining weights integrate marginal contribution, revenue contribution, profit growth rate, and output forecast accuracy to ensure both fairness and incentive compatibility. Finally, a case study for the Qingdao region in Shandong Province demonstrates that the proposed model can reduce the system peak-valley difference and imbalance penalty costs by 11.27% and 27.23%, respectively, while increasing the profits of the LA and CO by 24.08% and 19.21%. These results indicate that the proposed approach significantly enhances system operational stability and risk resilience, providing an effective methodological basis for constructing a multi-stakeholder-coordinated, risk-controllable, and renewable-energy-friendly power system.

Suggested Citation

  • Ma, Qianxin & Zhao, Zhenyu & Han, Ruyue & Lin, Shan, 2026. "Risk-preference-aware optimal scheduling and profit allocation of load aggregators and charging operators," Applied Energy, Elsevier, vol. 409(C).
    • Handle: RePEc:eee:appene:v:409:y:2026:i:c:s0306261926001467
    DOI: 10.1016/j.apenergy.2026.127494
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261926001467
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2026.127494?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. Zhang, Juntao & Cheng, Chuntian & Yu, Shen, 2024. "Recognizing the mapping relationship between wind power output and meteorological information at a province level by coupling GIS and CNN technologies," Applied Energy, Elsevier, vol. 360(C).
    2. Xing, Zhikai & He, Yigang, 2023. "Multi-modal multi-step wind power forecasting based on stacking deep learning model," Renewable Energy, Elsevier, vol. 215(C).
    3. Xu, Yijun & Zhang, Xuan & Li, Ji, 2024. "Multiple energy planning in the energy hub considering renewable sources, electric vehicles and management in the daily electricity market with wind multi-objective optimization algorithm," Energy, Elsevier, vol. 309(C).
    4. Huang, Yulong & Deng, Wenyang & Zhang, Kexue & Liu, Mingbo & Liu, Min & Li, Qifeng & Zhang, Yongjun, 2025. "Charge-discharge hierarchical-multi-stage decoupling optimization for large-scale EVs interacting with volt/var control in distribution systems," Applied Energy, Elsevier, vol. 400(C).
    5. Han, Yumeng & Xu, Zipeng & Wang, Chenhui & Huang, Jiguang & Zhang, Heng & Chen, Haiping & Gao, Dan, 2025. "Multi-objective seasonal peak-shaving scheduling optimization for nuclear-powered seawater desalination in electricity spot markets," Energy, Elsevier, vol. 338(C).
    6. Su, Yixin & Wang, Zeyu & Dong, Zhengcheng & Hua, Xiaojun & Ye, Tao & Song, Zida & Shao, Yun, 2025. "Frequency-aware ultra-short-term wind power forecasting using CEEMDAN–VMD–SE and Transformer–GRU networks," Energy, Elsevier, vol. 338(C).
    7. Liang, Yunyang & Pu, Yang & Yu, Mengmeng & Li, Yi-Chang & Jiang, Min & Cui, Lijie & Xiong, Xiaoqin & Jin, Zhong, 2025. "A dual carbon reward mechanism for electric vehicle charging scheduling based on multi-level Stackelberg game," Energy, Elsevier, vol. 337(C).
    8. Sun, Zhuyuan & He, Ye & Wu, Hongbin & Wu, Andrew Y., 2025. "Bi-level planning of electric vehicle charging stations considering charging demand: A Nash bargaining game approach," Energy, Elsevier, vol. 332(C).
    9. Wang, Shaohua & Xu, Jinglei & Cheng, Mengrui & Zhang, Wei, 2025. "Measurement and influencing factors of China’s energy transition level from the perspective of the low-carbon and resilience two-dimensional game," Energy, Elsevier, vol. 338(C).
    10. Zheng, Yanchong & Chen, Yuanyi & Yang, Qiang, 2025. "A Stackelberg-based competition model for optimal participation of electric vehicle load aggregators in demand response programs," Energy, Elsevier, vol. 315(C).
    11. Lei, Xiang & Yu, Hang & Zhong, Jiahao & Shao, Ziyun & Jian, Linni, 2025. "A data-driven robust optimal day-ahead bidding strategy considering V2G operation for distribution-system-side virtual power plant," Energy, Elsevier, vol. 338(C).
    12. Ahmed, Fahim & Ahmad, Shameem & Rahman, Mir Toufikur & Hazari, Md. Rifat & Faiz, Rethwan & Ahmed, Tofael & Karimi, Mazaher, 2026. "A holistic review of electric vehicle charging impacts on power distribution networks: Technical challenges, smart mitigation strategies and future directions," Applied Energy, Elsevier, vol. 402(PB).
    13. Anggraini, Dita & Amelin, Mikael & Söder, Lennart, 2025. "Business models for electric vehicle charging considering grid limitations: An extended review from the European electricity market perspective," Applied Energy, Elsevier, vol. 399(C).
    14. 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).
    15. Yamaguchi, Yohei & Ochi, Yudai & Ohara, Ryotaro & Uchida, Hideaki & Yoshizawa, Shinya & Sakai, Katsuya & Ota, Yutaka & Shimoda, Yoshiyuki, 2025. "Large-scale spatiotemporal modeling of travel behavior, electric vehicle charging demand, and flexibility based on human mobility big data," Energy, Elsevier, vol. 335(C).
    16. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
    17. Gao, Yuhang & Zhang, Mimi & Nasir, Muhammad Ali & Hao, Yan & Wu, Jinghui & Zhang, Lixiao, 2025. "ESG greenwashing behaviour in the electric vehicle supply chain: Insights from evolutionary game theory," International Journal of Production Economics, Elsevier, vol. 289(C).
    18. Shi, Zhihan & Zhang, Guangming & Lu, Chao & Zhou, Xiaoxiong & Lv, Xiaodong, 2025. "Dynamic Spatio-Temporal Graph-Enhanced KANformer for high-fidelity ultra-short-term wind power forecasting," Energy, Elsevier, vol. 337(C).
    19. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    20. Gao, Shuang & Huang, Shengyu & Jin, Xiaolong & Jia, Hongjie & Zhao, Yuming & Tang, Wenjun, 2025. "Cloud-edge collaborative optimal control strategy for virtually aggregated EV charging facilities in a market environment," Energy, Elsevier, vol. 337(C).
    21. Wang, Yanjia & Xie, Da & Zhao, Pengfei & Gu, Chenghong & Wang, Xitian, 2025. "A coordinated optimization strategy for Charging Station siting and EV dispatch based on response costs: A case study of Chicago," Applied Energy, Elsevier, vol. 389(C).
    22. Furqan, Maham, 2026. "Safeguarding equity in energy transition: A global just transition fund (JTF)," Energy Policy, Elsevier, vol. 209(PA).
    23. Liu, Can & Guo, Yuntao & Qian, Xinwu & Li, Xinghua & Liu, Haobing & Zhong, Minghui, 2025. "Understanding spatiotemporal dynamics of V2G participation in megacities: A data-driven study," Applied Energy, Elsevier, vol. 401(PC).
    24. Ju, Liwei & Bai, Xiping & Li, Gen & Gan, Wei & Qi, Xin & Ye, Fan, 2024. "Two-stage robust transaction optimization model and benefit allocation strategy for new energy power stations with shared energy storage considering green certificate and virtual energy storage mode," Applied Energy, Elsevier, vol. 362(C).
    25. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J., 2024. "From user to operator: Rationalizing the charging infrastructure deployment. A case study of Berlin," Applied Energy, Elsevier, vol. 376(PB).
    26. Yang, YeHa & Yang, SoYoung & Moon, HyungBin & Woo, JongRoul, 2024. "Analyzing heterogeneous electric vehicle charging preferences for strategic time-of-use tariff design and infrastructure development: A latent class approach," Applied Energy, Elsevier, vol. 374(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. Zhang, Mingyue & Han, Yang & Wang, Chaoyang & Yang, Ping & Wang, Congling & Zalhaf, Amr S., 2024. "Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data," Applied Energy, Elsevier, vol. 375(C).
    2. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    3. Liu, Lei & Wang, Xinyu & Dong, Xue & Chen, Kang & Chen, Qiuju & Li, Bin, 2024. "Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series," Applied Energy, Elsevier, vol. 374(C).
    4. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    5. Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).
    6. Wang, Tao & Xu, Ye & Qin, Yu & Wang, Xu & Zheng, Feifan & Li, Wei, 2025. "Short-term PV forecasting of multiple scenarios based on multi-dimensional clustering and hybrid transformer-BiLSTM with ECPO," Energy, Elsevier, vol. 334(C).
    7. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
    8. 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).
    9. 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).
    10. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
    11. 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).
    12. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    13. Liu, Shihai & Yang, Ruofei & Liu, Ying, 2026. "New distributionally robust optimization framework and algorithm for energy hub scheduling integrating demand response programs under ambiguous prices and loads," Applied Energy, Elsevier, vol. 405(C).
    14. Sareen, Karan & Panigrahi, Bijaya Ketan & Shikhola, Tushar & Sharma, Rajneesh, 2023. "An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction," Energy, Elsevier, vol. 278(C).
    15. Guoxia Zou, 2022. "The Recognition of Action Idea EEG with Deep Learning," Complexity, John Wiley & Sons, vol. 2022(1).
    16. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    17. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    18. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    19. Luo, Haiwei & Shi, Huifeng & Shi, Qiong & Chen, Xinjie & Duan, Yawen & Yan, Saisai, 2025. "Wind speed forecasting using dual-attention patch-based multiscale transformer," Energy, Elsevier, vol. 340(C).
    20. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(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:appene:v:409:y:2026:i:c:s0306261926001467. 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.