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Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator

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  • Gao, Xiang
  • Chan, Ka Wing
  • Xia, Shiwei
  • Zhou, Bin
  • Lu, Xi
  • Xu, Da

Abstract

Renewable energy producers such as wind power producers (WPP) and electric vehicle (EV) aggregators are playing an increasingly important role in the electricity market as their large capacity could strategically influence the electricity price. This paper proposes a bi-level stochastic optimization model of offering strategy for an aggregated WPP-EV hybrid power plant (HPP) as a price maker in the day-ahead (DA) market while considering the uncertainties of the energy production and spot price in the real-time (RT) market. While the HPP's profits is maximized in the upper level of the proposed model with the use of conditional-value-at-risk (CVaR) to manage the risk of expected revenues, the social welfare from the perspective of the grid is maximized in the lower level. The formulated bi-level model is first transformed into a single-level mathematical program with equilibrium constraints (MPEC) and then further transformed into a mixed integer linear programming (MILP) problem for solution. Simulation results have demonstrated the effectiveness of the proposed HPP model with strategically bidding price to increase profits and reduce volatility of profits by considering the risk-metric.

Suggested Citation

  • Gao, Xiang & Chan, Ka Wing & Xia, Shiwei & Zhou, Bin & Lu, Xi & Xu, Da, 2019. "Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator," Energy, Elsevier, vol. 177(C), pages 183-191.
    • Handle: RePEc:eee:energy:v:177:y:2019:i:c:p:183-191
    DOI: 10.1016/j.energy.2019.04.048
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Li, Bingkang & Zhao, Huiru & Wang, Xuejie & Zhao, Yihang & Zhang, Yuanyuan & Lu, Hao & Wang, Yuwei, 2022. "Distributionally robust offering strategy of the aggregator integrating renewable energy generator and energy storage considering uncertainty and connections between the mid-to-long-term and spot elec," Renewable Energy, Elsevier, vol. 201(P1), pages 400-417.
    3. Hu, Xiaobin & Li, Ying & Xie, Xiangdong, 2019. "A study on a U-shaped piezoelectric coupled beam and its corresponding ingenious harvester," Energy, Elsevier, vol. 185(C), pages 938-950.
    4. Zhu, Ziqing & Wing Chan, Ka & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2021. "Real-Time interaction of active distribution network and virtual microgrids: Market paradigm and data-driven stakeholder behavior analysis," Applied Energy, Elsevier, vol. 297(C).
    5. Lu, Xi & Xia, Shiwei & Gu, Wei & Chan, Ka Wing & Shahidehpour, Mohammad, 2021. "Two-stage robust distribution system operation by coordinating electric vehicle aggregator charging and load curtailments," Energy, Elsevier, vol. 226(C).
    6. İskeceli, Bilge Dilara & Kayakutlu, Gulgun & Daim, Tugrul U. & Shaygan, Amir, 2020. "Optimization of battery and wind technologies: Case of power deviation penalties," Technology in Society, Elsevier, vol. 63(C).
    7. Li, Qirui & Yang, Zhifang & Yu, Juan & Li, Wenyuan, 2023. "Impacts of previous revenues on bidding strategies in electricity market: A quantitative analysis," Applied Energy, Elsevier, vol. 345(C).
    8. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    9. Giulia Maesano & Giuseppe Di Vita & Gaetano Chinnici & Gioacchino Pappalardo & Mario D'Amico, 2020. "The Role of Credence Attributes in Consumer Choices of Sustainable Fish Products: A Review," Sustainability, MDPI, vol. 12(23), pages 1-18, November.
    10. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).

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