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Optimal Sizing of Hydro-PV-Pumped Storage Integrated Generation System Considering Uncertainty of PV, Load and Price

Author

Listed:
  • Jichun Liu

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Jianhua Li

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Yue Xiang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Shuai Hu

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

Effectively using complementary property of various renewable energy sources by an integrated generation system is a concerned study. Considering the uncertainty of PV, spot price and load, the sizing of integrated generation system with combined market operation is a difficult problem. Based on the above uncertain factors, the sizing method of hydro-PV-pumped storage integrated generation system is proposed in this paper. The output characteristic model is established by utilizing the wide range of hydropower output and flexible schedulability of pumped storage. Then, the volatility ratio of the power exchange curve and load tracking coefficient are the evaluation index of the complementary effect, which ensures the stable and smooth output curve. Further, the uncertainty models of PV, spot price and load are established, considering that, the operation of the power generation system is optimized in the day-ahead market, and the predictive deviation of PV and load is balanced in a real-time market. Based on the above factors, the sizing model of the hydro-PV-pumped storage integrated energy system was proposed based on economics and complementary index. Finally, a case study was undertaken, the sensitivity analysis of the economy and complementarity indices was carried out, as the complementarity index is improved, the economy would be down. Further, the optimal sizing of hydro-PV-pumped storage integrated generation system was obtained, hydropower was a total of 165 MW, PV was 100 MW and pumped storage was 50 MW, that could ensure the economy while meeting the complementary index, the effectiveness of the model proposed was verified.

Suggested Citation

  • Jichun Liu & Jianhua Li & Yue Xiang & Shuai Hu, 2019. "Optimal Sizing of Hydro-PV-Pumped Storage Integrated Generation System Considering Uncertainty of PV, Load and Price," Energies, MDPI, vol. 12(15), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:3001-:d:254561
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    References listed on IDEAS

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    1. Zhang, Hongxuan & Lu, Zongxiang & Hu, Wei & Wang, Yiting & Dong, Ling & Zhang, Jietan, 2019. "Coordinated optimal operation of hydro–wind–solar integrated systems," Applied Energy, Elsevier, vol. 242(C), pages 883-896.
    2. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    3. Sun, Kaiqi & Li, Ke-Jun & Pan, Jiuping & Liu, Yong & Liu, Yilu, 2019. "An optimal combined operation scheme for pumped storage and hybrid wind-photovoltaic complementary power generation system," Applied Energy, Elsevier, vol. 242(C), pages 1155-1163.
    4. Ming, Bo & Liu, Pan & Guo, Shenglian & Zhang, Xiaoqi & Feng, Maoyuan & Wang, Xianxun, 2017. "Optimizing utility-scale photovoltaic power generation for integration into a hydropower reservoir by incorporating long- and short-term operational decisions," Applied Energy, Elsevier, vol. 204(C), pages 432-445.
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    5. Yan Lu & Jing Xiang & Pengyun Geng & Huimin Zhang & Lili Liu & Haoran Wang & Jiajie Kong & Mingli Cui & Yan Li & Cheng Zhong & Tiantian Feng, 2023. "Coupling Mechanism and Synergic Development of Carbon Market and Electricity Market in the Region of Beijing–Tianjin–Hebei," Energies, MDPI, vol. 16(4), pages 1-22, February.

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