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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 response

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  • Tan, Zhongfu
  • Wang, Guan
  • Ju, Liwei
  • Tan, Qingkun
  • Yang, Wenhai

Abstract

Conditional value at risk (CVaR) and confidence degree theory are introduced to build scheduling model for VPP connecting with wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs) and incentive-based demand response (IBDR). Latin hypercube sampling method and Kantorovich distance are introduced to construct uncertainties analysis method. A risk aversion scheduling model is proposed with minimum CVaR objective considering maximum operation revenue. The IEEE30 bus system is used as simulation system. Results show: (1) Price-based demand response could realize peak load shifting, ESSs and IBDR could increase operation revenue. (2) Threshold α reflects risk attitude of decision maker, which has strong risk tolerant to gain the excess income with low α. (3) In peak period, decision maker would reduce WPP and PV for avoiding power shortage loss. Otherwise, WPP and PV would be called in priority since system reserve capacity is sufficient. (4) When 0.85≤β < 0.95, the decreasing slope of CVaR value is big, decision maker is sensitive on risk. When β≥0.95, VPP scheduling scheme reach the most conservative, net revenue and CVaR value are ¥8995.34 and ¥18834. Therefore, the proposed model could describe VPP risk and provide decision support tool for decision maker.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:124:y:2017:i:c:p:198-213
    DOI: 10.1016/j.energy.2017.02.063
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    17. Ju, Liwei & Zhang, Qi & Tan, Zhongfu & Wang, Wei & Xin, He & Zhang, Zehao, 2018. "Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy," Energy, Elsevier, vol. 157(C), pages 1035-1052.
    18. Wu, Jiekang & Wu, Zhijiang & Wu, Fan & Tang, Huiling & Mao, Xiaoming, 2018. "CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs," Energy, Elsevier, vol. 143(C), pages 323-336.
    19. Ali Ahmadian & Kumaraswamy Ponnambalam & Ali Almansoori & Ali Elkamel, 2023. "Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning," Energies, MDPI, vol. 16(2), pages 1-17, January.
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    22. S. Muhammad Bagher Sadati & Jamal Moshtagh & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner," Energies, MDPI, vol. 10(11), pages 1-16, October.
    23. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
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    25. 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.

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