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Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant

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  • Kong, Xiangyu
  • Xiao, Jie
  • Wang, Chengshan
  • Cui, Kai
  • Jin, Qiang
  • Kong, Deqian

Abstract

With the development of energy internet and power market, the operation regulation and pricing mechanism of traditional virtual power plants are improved to adapt to the new environment. In this paper, a bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant is proposed to provide a framework for solving the interest distribution between operators and optimal scheduling problems of multiple-operator virtual power plant. An operator power allocation and internal electricity price formation method based on bidding equilibrium is proposed in the upper level, which introduces the fluctuation cost coefficient to express the influence of the uncertainty of renewable energy power generation on the bidding process. A multi-time scale optimal scheduling method combining scheduling model and adjustment strategy is established in the lower level. A default penalty mechanism in the scheduling model is used to ensure that operators provide the electricity allocated from the bidding process and considering the influence of demand response based on internal electricity price on adjustment strategy. Simulation results show that the proposed method can realize the optimal distribution of operators’ power generation and form the internal electricity price that reflects the internal supply and demand level of virtual power plant. Besides, it can reduce the impact of uncertainty on dispatching results and improve the application range of virtual power plant to enhance the competitiveness of virtual power plant in market transactions.

Suggested Citation

  • Kong, Xiangyu & Xiao, Jie & Wang, Chengshan & Cui, Kai & Jin, Qiang & Kong, Deqian, 2019. "Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant," Applied Energy, Elsevier, vol. 249(C), pages 178-189.
    • Handle: RePEc:eee:appene:v:249:y:2019:i:c:p:178-189
    DOI: 10.1016/j.apenergy.2019.04.130
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    1. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    2. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    3. Zou, Peng & Chen, Qixin & Xia, Qing & He, Guannan & Kang, Chongqing & Conejo, Antonio J., 2016. "Pool equilibria including strategic storage," Applied Energy, Elsevier, vol. 177(C), pages 260-270.
    4. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    5. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    6. 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.
    7. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    8. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
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