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Demand Response of Integrated Zero-Carbon Power Plant: Model and Method

Author

Listed:
  • Rong Xia

    (State Power Investment Corporation Jiangsu Electric Power Co., Ltd., Nanjing 210008, China)

  • Jun Dai

    (State Power Investment Corporation Jiangsu Electric Power Co., Ltd., Nanjing 210008, China)

  • Xiangjie Cheng

    (Shanghai Power Equipment Research Institute Co., Ltd., Shanghai 200240, China)

  • Jiaqing Fan

    (Shanghai Power Equipment Research Institute Co., Ltd., Shanghai 200240, China)

  • Jing Ye

    (Shanghai Power Equipment Research Institute Co., Ltd., Shanghai 200240, China)

  • Qiangang Jia

    (School of Electrical and Information Engineering, Zhengzhou University, Zhenzhou 450001, China)

  • Sijie Chen

    (Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Qiang Zhang

    (Shanghai Power Equipment Research Institute Co., Ltd., Shanghai 200240, China)

Abstract

An integrated zero-carbon power plant aggregates uncontrollable green energy, adjustable load, and storage energy resources into an entity in a grid-friendly manner. Integrated zero-carbon power plants have a strong demand response potential that needs further study. However, existing studies ignore the green value of renewable energy in power plants when participating in demand response programs. This paper proposed a mathematical model to optimize the operation of an integrated zero-carbon power plant considering the green value. A demand response mechanism is proposed for the independent system operator and the integrated zero-carbon power plants. The Stackelberg gaming process among these entities and an algorithm based on dichotomy are studied to find the demand response equilibrium. Case studies verify that the mechanism activates the potential of the integrated zero-carbon power plant to realize the load reduction target.

Suggested Citation

  • Rong Xia & Jun Dai & Xiangjie Cheng & Jiaqing Fan & Jing Ye & Qiangang Jia & Sijie Chen & Qiang Zhang, 2024. "Demand Response of Integrated Zero-Carbon Power Plant: Model and Method," Energies, MDPI, vol. 17(14), pages 1-15, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3431-:d:1433742
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    References listed on IDEAS

    as
    1. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    2. Kong, Xiangyu & Lu, Wenqi & Wu, Jianzhong & Wang, Chengshan & Zhao, Xv & Hu, Wei & Shen, Yu, 2023. "Real-time pricing method for VPP demand response based on PER-DDPG algorithm," Energy, Elsevier, vol. 271(C).
    3. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    4. Yang, Changhui & Meng, Chen & Zhou, Kaile, 2018. "Residential electricity pricing in China: The context of price-based demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2870-2878.
    5. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    6. Ferreira, R.S. & Barroso, L.A. & Carvalho, M.M., 2012. "Demand response models with correlated price data: A robust optimization approach," Applied Energy, Elsevier, vol. 96(C), pages 133-149.
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