IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v335y2025ics0360544225035376.html

Electricity data valuation and game pricing for enhanced DRL-based economic dispatch

Author

Listed:
  • Liu, Ziming
  • Huang, Bonan
  • Wang, Rui
  • Tian, Jiaqi
  • Du, Pengbo
  • Sun, Qiuye

Abstract

To address the problem of acquiring high-quality data required for AI-based economic dispatch in data trading, this paper proposes a blockchain-enabled electricity data trading framework, which incorporates a comprehensive market mechanism that includes data valuation, pricing, and trading. The platform aims to facilitate the discovery of high-quality data in market transactions and enable the sharing of economic dispatch data. First, a novel valuation method is proposed to effectively assess the value attributes of electricity data, considering uncertainty, integrity, and timeliness in power system dispatching. On this basis, the market clearing price of electricity data, guided by the data valuation, is obtained through a game-theoretic approach that takes into account the supply and demand of the electricity data. Furthermore, transactions are completed using smart contracts, eliminating the need for third-party data servicers and effectively preventing data privacy leaks and realizing data trading without trust. Simulations are provided to illustrate the effectiveness of the proposed framework and mechanisms.

Suggested Citation

  • Liu, Ziming & Huang, Bonan & Wang, Rui & Tian, Jiaqi & Du, Pengbo & Sun, Qiuye, 2025. "Electricity data valuation and game pricing for enhanced DRL-based economic dispatch," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225035376
    DOI: 10.1016/j.energy.2025.137895
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225035376
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.137895?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. Zhu, Xu & Sun, Yuanzhang & Yang, Jun & Dou, Zhenlan & Li, Gaojunjie & Xu, Chengying & Wen, Yuxin, 2022. "Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses," Energy, Elsevier, vol. 251(C).
    2. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    3. Lyu, Lin & Yang, Xinran & Xiang, Yue & Liu, Junyong & Jawad, Shafqat & Deng, Runqi, 2020. "Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis," Energy, Elsevier, vol. 198(C).
    4. Sun, Xilei & Fu, Jianqin & Yang, Huiyong & Xie, Mingke & Liu, Jingping, 2023. "An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control," Energy, Elsevier, vol. 269(C).
    5. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "A two-stage energy management for heat-electricity integrated energy system considering dynamic pricing of Stackelberg game and operation strategy optimization," Energy, Elsevier, vol. 244(PA).
    6. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(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. Ma, Siyuan & Mi, Yang & Shi, Shuai & Li, Dongdong & Xing, Haijun & Wang, Peng, 2024. "Low-carbon economic operation of energy hub integrated with linearization model and nodal energy-carbon price," Energy, Elsevier, vol. 294(C).
    2. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    3. Li, Bingbing & Wang, Kang & Zhang, Hao & Ben, Wei & Liu, Zhijun & Zhuang, Weichao & Yin, Guodong & Chen, Boli, 2025. "A globally tuned load-leveling strategy for energy management of hybrid electric vehicles," Energy, Elsevier, vol. 336(C).
    4. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    5. Ma, Yixiang & Yu, Lean & Zhang, Guoxing & Lu, Zhiming & Wu, Jiaqian, 2023. "Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling," Renewable Energy, Elsevier, vol. 219(P1).
    6. Habib, Salman & El-Ferik, Sami & Gulzar, Muhammad Majid & Chauhdary, Sohaib Tahir & Ahmad, Hasnain & Ahmed, Emad M., 2025. "Optimizing integrated energy systems with a robust MISOCP model and C&CG algorithm for enhanced grid efficiency and profitability," Energy, Elsevier, vol. 318(C).
    7. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    8. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    9. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(C).
    10. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    11. Petros Tzallas & Alexios Papaioannou & Asimina Dimara & Napoleon Bezas & Ioannis Moschos & Christos-Nikolaos Anagnostopoulos & Stelios Krinidis & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2025. "MAS-DR: An ML-Based Aggregation and Segmentation Framework for Residential Consumption Users to Assist DR Programs," Sustainability, MDPI, vol. 17(4), pages 1-33, February.
    12. 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 interactions, uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 288(C).
    13. Shi, Zhengyu & Wu, Libo & Zhou, Yang, 2023. "Predicting household energy consumption in an aging society," Applied Energy, Elsevier, vol. 352(C).
    14. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    15. Gheouany, Saad & Ouadi, Hamid & El Bakali, Saida, 2024. "Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code," Energy, Elsevier, vol. 306(C).
    16. Adil, Muhammad & Mahmud, M.A. Parvez & Kouzani, Abbas Z. & Khoo, Sui Yang, 2024. "Three-stage energy trading framework for retailers, charging stations, and electric vehicles: A game-theoretic approach," Energy, Elsevier, vol. 301(C).
    17. Zhang, Zhenwei & Wang, Chengfu & Wu, Qiuwei & Dong, Xiaoming, 2024. "Optimal dispatch for cross-regional integrated energy system with renewable energy uncertainties: A unified spatial-temporal cooperative framework," Energy, Elsevier, vol. 292(C).
    18. Do-Hyeon Ryu & Ryu-Hee Kim & Seung-Hyun Choi & Kwang-Jae Kim & Young Myoung Ko & Young-Jin Kim & Minseok Song & Dong Gu Choi, 2020. "Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    19. Sun, Xilei & Zhou, Feng & Fu, Jianqin & Liu, Jingping, 2024. "Experiment and simulation study on energy flow characteristics of a battery electric vehicle throughout the entire driving range in low-temperature conditions," Energy, Elsevier, vol. 292(C).
    20. Wang, Shuhan & Yao, Kun & Guo, Wei & Xu, Xiangyang & Liu, Yiqiang & Qian, Pengfei & Zhao, Junwei & Dong, Peng, 2025. "Approximate optimal energy management strategy for multi-speed series-parallel PHEV integrating global prediction and real-time control," Energy, Elsevier, vol. 335(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:energy:v:335:y:2025:i:c:s0360544225035376. 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/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.