IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v256y2026iphs0960148125021524.html

Competitive peak regulation optimization and tracing-based cost allocation model for high-proportion renewable energy power systems

Author

Listed:
  • Gao, Hongjun
  • Yang, Yida
  • Tao, Yuechuan
  • Guo, Minghao
  • Dong, Zhao Yang
  • Liu, Junyong

Abstract

With the continuous expansion of intermittent renewable energy integration into the grid, power systems' peak regulation demand and peak regulation cost steadily increase. Simultaneously, the phenomenon of renewable power curtailment is becoming frequent, driven by increasingly complex factors, such as transmission network constraints and insufficient peak regulation support. The high costs of peak regulation and frequent renewable power curtailment present a serious challenge to the economic operation of power systems. To fully incentivize peak regulation resources (PRRs) and ensure appropriate allocation of responsibility and costs for peak regulation and renewable power curtailment. This paper proposes a competitive peak regulation optimization model for power systems that incorporates PRRs' preferences and the pricing effects of market share. In this model, the independent system operator (ISO) first identifies the peak regulation demand in power systems (including peak regulation periods and required power) and then organizes an iterative bidding process for PRRs, thereby enhancing PRRs’ incentives while simulating a real competitive process. Additionally, an allocation method for peak regulation cost is proposed based on incremental power. For renewable power curtailment cost, two factors, i.e., insufficient PRRs and transmission network constraints, are included in the cost allocation, enabling the attribution and traceability of renewable power curtailment issues. Finally, case studies are conducted based on an improved IEEE 30-bus test system and an improved IEEE 118-bus test system to validate the effectiveness of the proposed peak regulation optimization and cost allocation model.

Suggested Citation

  • Gao, Hongjun & Yang, Yida & Tao, Yuechuan & Guo, Minghao & Dong, Zhao Yang & Liu, Junyong, 2026. "Competitive peak regulation optimization and tracing-based cost allocation model for high-proportion renewable energy power systems," Renewable Energy, Elsevier, vol. 256(PH).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:ph:s0960148125021524
    DOI: 10.1016/j.renene.2025.124488
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125021524
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.124488?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. Wei, F. & Jing, Z.X. & Wu, Peter Z. & Wu, Q.H., 2017. "A Stackelberg game approach for multiple energies trading in integrated energy systems," Applied Energy, Elsevier, vol. 200(C), pages 315-329.
    2. Chen, Yuzhu & Guo, Weimin & Du, Na & Yang, Kun & Wang, Jiangjiang, 2024. "Master slave game-based optimization of an off-grid combined cooling and power system coupled with solar thermal and photovoltaics considering carbon cost allocation," Renewable Energy, Elsevier, vol. 229(C).
    3. Zhu, Yansong & Liu, Jizhen & Hu, Yong & Xie, Yan & Zeng, Deliang & Li, Ruilian, 2024. "Distributionally robust optimization model considering deep peak shaving and uncertainty of renewable energy," Energy, Elsevier, vol. 288(C).
    4. Guo, Qisheng & Wu, Xi & Cai, Hui & Cheng, Liang & Huang, Junhui & Liu, Yichen & Chen, Kangwen, 2024. "Multi-power sources joint optimal scheduling model considering nuclear power peak regulation," Energy, Elsevier, vol. 293(C).
    5. Geng, Xinmin & Zhou, Ye & Zhao, Weiqiang & Shi, Li & Chen, Diyi & Bi, Xiaojian & Xu, Beibei, 2024. "Pricing ancillary service of a Francis hydroelectric generating system to promote renewable energy integration in a clean energy base: Tariff compensation of deep peak regulation," Renewable Energy, Elsevier, vol. 226(C).
    6. Wang, Sen & Li, Fengting & Zhang, Gaohang & Yin, Chunya, 2023. "Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy," Energy, Elsevier, vol. 267(C).
    7. Pan, Yushu & Ju, Liwei & Yang, Shenbo & Guo, Xinyu & Tan, Zhongfu, 2024. "A multi-objective robust optimal dispatch and cost allocation model for microgrids-shared hybrid energy storage system considering flexible ramping capacity," Applied Energy, Elsevier, vol. 369(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. Du, Jialin & Hu, Weihao & Zhang, Sen & Cao, Di & Liu, Wen & Zhang, Zhenyuan & Wang, Daojuan & Chen, Zhe, 2025. "A distributionally robust collaborative scheduling and benefit fallocation method for interconnected microgrids considering tail risk assessment," Applied Energy, Elsevier, vol. 391(C).
    2. Li, Junhui & Yu, Zhenbo & Mu, Gang & Li, Baoju & Zhou, Jiaxu & Yan, Gangui & Zhu, Xingxu & Li, Cuiping, 2024. "An assessment methodology for the flexibility capacity of new power system based on two-stage robust optimization," Applied Energy, Elsevier, vol. 376(PB).
    3. Yiqi Dong & Zuoji Dong, 2023. "Bibliometric Analysis of Game Theory on Energy and Natural Resource," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    4. He, Jiaming & Tan, Qinliang & Lv, Hanyu, 2025. "Data-driven climate resilience assessment for distributed energy systems using diffusion transformer and polynomial expansions," Applied Energy, Elsevier, vol. 380(C).
    5. Ziyu Li & Bangjun Wang, 2024. "A Bibliometric Analysis of Carbon Allowances in the Carbon Emissions Trading Market," Energies, MDPI, vol. 18(1), pages 1-18, December.
    6. Wang, Lixiao & Jing, Z.X. & Zheng, J.H. & Wu, Q.H. & Wei, Feng, 2018. "Decentralized optimization of coordinated electrical and thermal generations in hierarchical integrated energy systems considering competitive individuals," Energy, Elsevier, vol. 158(C), pages 607-622.
    7. Jiang, Hou & Yao, Ling & Lu, Ning & Qin, Jun & Zhang, Xiaotong & Liu, Tang & Zhang, Xingxing & Zhou, Chenghu, 2024. "Exploring the optimization of rooftop photovoltaic scale and spatial layout under curtailment constraints," Energy, Elsevier, vol. 293(C).
    8. Cao, Wenqiang & Yu, Junqi & Ru, Chengyi & Wang, Meng & Wang, Ke, 2025. "A two-stage distributionally robust optimization operation scheduling model for solar PT-PV systems based on integrated load consumption prediction," Energy, Elsevier, vol. 332(C).
    9. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Wang, Lu & Gu, Wei & Wu, Zhi & Qiu, Haifeng & Pan, Guangsheng, 2020. "Non-cooperative game-based multilateral contract transactions in power-heating integrated systems," Applied Energy, Elsevier, vol. 268(C).
    11. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    12. Zhou, Yixing & Hou, Hongjuan & Yan, Haoran & Wang, Xi & Zhou, Rhonin, 2025. "Data-driven distributionally robust stochastic optimal dispatching method of integrated energy system considering multiple uncertainties," Energy, Elsevier, vol. 325(C).
    13. Duan, Jiandong & Gao, Qi & Xia, Yerui & Tian, Qinxing & Qin, Bo, 2024. "MMD-DRO based economic dispatching considering flexible reserve provision from concentrated solar power plant," Energy, Elsevier, vol. 308(C).
    14. Wang, Ziren & Zhang, Yuyuan & Li, Wei, 2026. "A bi-level optimization operation method for hydrogen-enriched integrated energy systems incorporating dynamic carbon emission factors," Renewable Energy, Elsevier, vol. 256(PD).
    15. Wang, Shouxiang & Wang, Shaomin & Zhao, Qianyu & Dong, Shuai & Li, Hao, 2023. "Optimal dispatch of integrated energy station considering carbon capture and hydrogen demand," Energy, Elsevier, vol. 269(C).
    16. Sun, Guoqiang & Wang, Qihui & Chen, Sheng & Li, Qun & Zhang, Ningyu & Wei, Zhinong & Zang, Haixiang, 2025. "Using machine learning to achieve highly efficient solutions of frequency-constrained unit commitment models," Energy, Elsevier, vol. 328(C).
    17. Ren, Xiaoxiao & Wang, Jinshi & Yang, Sifan & Zhao, Quanbin & Jia, Yifan & Ou, Kejie & Hu, Guangtao & Yan, Junjie, 2025. "A novel multi-objective Stackelberg game model for multi-energy dynamic pricing and flexible scheduling in distributed multi-energy system," Energy, Elsevier, vol. 325(C).
    18. Ziqi Liu & Tingting Su & Zhiying Quan & Quanli Wu & Yu Wang, 2023. "Review on the Optimal Configuration of Distributed Energy Storage," Energies, MDPI, vol. 16(14), pages 1-17, July.
    19. Wang, Can & Yan, Chao & Li, Gengfeng & Liu, Shiyu & Bie, Zhaohong, 2020. "Risk assessment of integrated electricity and heat system with independent energy operators based on Stackelberg game," Energy, Elsevier, vol. 198(C).
    20. Ma, Tingshan & Li, Zhengkuan & Lv, Kai & Chang, Dongfeng & Hu, Wenshuai & Zou, Ying, 2024. "Design and performance analysis of deep peak shaving scheme for thermal power units based on high-temperature molten salt heat storage system," Energy, Elsevier, vol. 288(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:256:y:2026:i:ph:s0960148125021524. 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.