IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i17p3299-d261334.html
   My bibliography  Save this article

Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration

Author

Listed:
  • Tianliang Wang

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Xin Jiang

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Yang Jin

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Dawei Song

    (State Grid Henan Economic Research Institute, Zhengzhou 450052, China)

  • Meng Yang

    (State Grid Henan Economic Research Institute, Zhengzhou 450052, China)

  • Qingshan Zeng

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

As the installed capacity of wind power increases rapidly, how to promote wind power curtailment (WPC) integration has become a concern. The surface and underlying causes of wind power curtailment are insufficient peaking capability of the power system and imperfect peaking compensation mechanisms, respectively. Therefore, this paper proposes a peaking compensation mechanism uniting supply side and demand side to enhance system peaking capability. Firstly, through incentive and fairness analysis, the interest relationship of peaking subjects is researched based on game theory, and the peaking contribution on supply/demand side is quantified by Pearson correlation coefficients. Secondly, based on clustering analysis, the potential of system peaking providers are explored adequately, supply-side thermal units are divided into deep peaking clusters, and demand-side demand response (DR) resources are integrated into virtual peaking plants (VPP). Accordingly, a two-stage wind-thermal-VPP coordination optimization model is built to dispatch peaking providers. Furtherly, a two-layer peaking compensation allocation method considering peaking contribution and peaking enthusiasm is proposed to encourage peaking providers and mitigate “combination explosion”. Simulation results indicate that the proposed mechanism effectively promotes the enthusiasm of union peaking and the integration of WPC.

Suggested Citation

  • Tianliang Wang & Xin Jiang & Yang Jin & Dawei Song & Meng Yang & Qingshan Zeng, 2019. "Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration," Energies, MDPI, vol. 12(17), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3299-:d:261334
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/17/3299/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/17/3299/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. He, Yongxiu & Xu, Yang & Pang, Yuexia & Tian, Huiying & Wu, Rui, 2016. "A regulatory policy to promote renewable energy consumption in China: Review and future evolutionary path," Renewable Energy, Elsevier, vol. 89(C), pages 695-705.
    2. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    3. Monyei, C.G. & Adewumi, A.O., 2017. "Demand Side Management potentials for mitigating energy poverty in South Africa," Energy Policy, Elsevier, vol. 111(C), pages 298-311.
    4. Craparo, E.M. & Sprague, J.G., 2019. "Integrated supply- and demand-side energy management for expeditionary environmental control," Applied Energy, Elsevier, vol. 233, pages 352-366.
    5. Zhang, Sufang & Jiao, Yiqian & Chen, Wenjun, 2017. "Demand-side management (DSM) in the context of China's on-going power sector reform," Energy Policy, Elsevier, vol. 100(C), pages 1-8.
    6. Siqing Sheng & Qing Gu, 2019. "A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response," Energies, MDPI, vol. 12(9), pages 1-26, May.
    7. Dong, Changgui & Qi, Ye & Dong, Wenjuan & Lu, Xi & Liu, Tianle & Qian, Shuai, 2018. "Decomposing driving factors for wind curtailment under economic new normal in China," Applied Energy, Elsevier, vol. 217(C), pages 178-188.
    8. Seungmi Lee & Jinho Kim, 2018. "Analytical Assessment for System Peak Reduction by Demand Responsive Resources Considering Their Operational Constraints in Wholesale Electricity Market," Energies, MDPI, vol. 11(12), pages 1-15, November.
    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. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    2. Wang, Bo & Deng, Nana & Li, Haoxiang & Zhao, Wenhui & Liu, Jie & Wang, Zhaohua, 2021. "Effect and mechanism of monetary incentives and moral suasion on residential peak-hour electricity usage," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    4. Ali, Muhammad Rizwan & Shafiq, Muhammad, 2021. "Revealing expert perspectives on challenges to electricity Demand-Side Management in Pakistan: An application of Q-Methodology," Utilities Policy, Elsevier, vol. 70(C).
    5. Li, Sitao & Zhang, Sufang & Andrews-Speed, Philip, 2019. "Using diverse market-based approaches to integrate renewable energy: Experiences from China," Energy Policy, Elsevier, vol. 125(C), pages 330-337.
    6. Oveis Abedinia & Mehdi Bagheri, 2021. "Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning," Energies, MDPI, vol. 14(10), pages 1-18, May.
    7. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    8. Giacomo Falchetta & Nicolò Stevanato & Magda Moner-Girona & Davide Mazzoni & Emanuela Colombo & Manfred Hafner, 2020. "M-LED: Multi-sectoral Latent Electricity Demand Assessment for Energy Access Planning," Working Papers 2020.09, Fondazione Eni Enrico Mattei.
    9. Jiansheng You & Guohan Ding & Liyuan Zhang, 2022. "Heterogeneous Dynamic Correlation Research among Industrial Structure Distortion, Two-Way FDI and Carbon Emission Intensity in China," Sustainability, MDPI, vol. 14(15), pages 1-23, July.
    10. 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).
    11. Dirk Johan van Vuuren & Annlizé L. Marnewick & Jan Harm C. Pretorius, 2021. "A Financial Evaluation of a Multiple Inclination, Rooftop-Mounted, Photovoltaic System Where Structured Tariffs Apply: A Case Study of a South African Shopping Centre," Energies, MDPI, vol. 14(6), pages 1-26, March.
    12. Alassi, Abdulrahman & Bañales, Santiago & Ellabban, Omar & Adam, Grain & MacIver, Callum, 2019. "HVDC Transmission: Technology Review, Market Trends and Future Outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 530-554.
    13. Mughees, Neelam & Jaffery, Mujtaba Hussain & Mughees, Anam & Ansari, Ejaz Ahmad & Mughees, Abdullah, 2023. "Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids," Applied Energy, Elsevier, vol. 342(C).
    14. Ming Tang & Jian Wang & Xiaohua Wang, 2020. "Adaptable Source-Grid Planning for High Penetration of Renewable Energy Integrated System," Energies, MDPI, vol. 13(13), pages 1-26, June.
    15. Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).
    16. Asante, Dennis & He, Zheng & Adjei, Nana Osae & Asante, Bismark, 2020. "Exploring the barriers to renewable energy adoption utilising MULTIMOORA- EDAS method," Energy Policy, Elsevier, vol. 142(C).
    17. Shuangquan Liu & Yanxuan Huang & Yue Wang & Qizhuan Shao & Han Zhou & Jinwen Wang & Cheng Chen, 2023. "Incentive Mechanisms to Integrate More Renewable Energy in Electricity Markets in China," Energies, MDPI, vol. 16(18), pages 1-16, September.
    18. Dejian Yu & Sun Meng, 2018. "An overview of biomass energy research with bibliometric indicators," Energy & Environment, , vol. 29(4), pages 576-590, June.
    19. Liu, Yang & Jiang, Zhigao & Guo, Bowei, 2022. "Assessing China’s provincial electricity spot market pilot operations: Lessons from Guangdong province," Energy Policy, Elsevier, vol. 164(C).
    20. Yuanxin Liu & FengYun Li & Xinhua Yu & Jiahai Yuan & Dong Zhou, 2018. "Assessing the Credit Risk of Corporate Bonds Based on Factor Analysis and Logistic Regress Analysis Techniques: Evidence from New Energy Enterprises in China," Sustainability, MDPI, vol. 10(5), pages 1-21, May.

    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:gam:jeners:v:12:y:2019:i:17:p:3299-:d:261334. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.