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Urban virtual power plant operation optimization with incentive-based demand response

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  • Zhou, Kaile
  • Peng, Ning
  • Yin, Hui
  • Hu, Rong

Abstract

Urban virtual power plant (VPP) shows great potential in alleviating urban power shortage or power supply-demand imbalance. This study proposes a bi-layer optimization model considering incentive-based demand response (IDR) to optimize the operation scheduling of urban VPP. The bi-layer optimization model includes the lower-layer IDR model and the upper-layer urban VPP optimal operation scheduling model. In the lower-layer IDR model, user satisfaction, comfort and preference are considered. Users mainly participate in the scheduling of urban VPP by IDR. The demand response resources obtained by urban VPP through providing incentives for users can effectively alleviate the energy supply pressure. In the upper-layer urban VPP optimal operation scheduling model, the scheduling plans for generating units and trading plans for the electricity wholesale market can be determined. Through upper-layer scheduling, it can not only meet the energy demand of users, but also minimize the urban VPP operation cost. The experimental results show that the proposed bi-layer optimization model for urban VPP operation considering IDR can achieve urban energy resources optimal allocation and support urban energy supply-demand balance, on the premise of ensuring economic benefits.

Suggested Citation

  • Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223020947
    DOI: 10.1016/j.energy.2023.128700
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    References listed on IDEAS

    as
    1. Wang, Jian & Ilea, Valentin & Bovo, Cristian & Xie, Ning & Wang, Yong, 2023. "Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework," Energy, Elsevier, vol. 278(PB).
    2. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    3. 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.
    4. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    5. Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
    6. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    7. 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.
    8. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    9. 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.
    10. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.
    11. Liu, Hong & Zhao, Yue & Gu, Chenghong & Ge, Shaoyun & Yang, Zan, 2021. "Adjustable capability of the distributed energy system: Definition, framework, and evaluation model," Energy, Elsevier, vol. 222(C).
    12. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    13. Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
    14. Qin, Haosen & Yu, Zhen & Li, Tailu & Liu, Xueliang & Li, Li, 2023. "Energy-efficient heating control for nearly zero energy residential buildings with deep reinforcement learning," Energy, Elsevier, vol. 264(C).
    15. Tascikaraoglu, A. & Erdinc, O. & Uzunoglu, M. & Karakas, A., 2014. "An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units," Applied Energy, Elsevier, vol. 119(C), pages 445-453.
    16. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2017. "An interactive cooperation model for neighboring virtual power plants," Applied Energy, Elsevier, vol. 200(C), pages 273-289.
    17. Yu, Biying & Sun, Feihu & Chen, Chen & Fu, Guanpeng & Hu, Lin, 2022. "Power demand response in the context of smart home application," Energy, Elsevier, vol. 240(C).
    18. 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).
    19. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    20. Hlalele, Thabo G. & Zhang, Jiangfeng & Naidoo, Raj M. & Bansal, Ramesh C., 2021. "Multi-objective economic dispatch with residential demand response programme under renewable obligation," Energy, Elsevier, vol. 218(C).
    21. 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).
    22. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    23. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    24. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    25. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
    26. Yu, Mengmeng & Lu, Renzhi & Hong, Seung Ho, 2016. "A real-time decision model for industrial load management in a smart grid," Applied Energy, Elsevier, vol. 183(C), pages 1488-1497.
    27. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    28. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    29. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
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