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Optimal stochastic scheduling of hydropower-based compensation for combined wind and photovoltaic power outputs

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Listed:
  • Liu, Weifeng
  • Zhu, Feilin
  • Zhao, Tongtiegang
  • Wang, Hao
  • Lei, Xiaohui
  • Zhong, Ping-an
  • Fthenakis, Vasilis

Abstract

This paper examines and quantifies the evolution of the uncertainty in forecasting solar- and wind-based electricity generation compensated with hydroelectric power, based on the forecast uncertainties of the three constituents. We used the generalized martingale model of forecast evolution to separately describe the uncertainties of power outputs of wind and photovoltaic systems in the same region. We then superimposed the separate power outputs to obtain the combined power output from these variable renewable energies (VRE). Furthermore, we developed a stochastic recourse model for optimally scheduling hydropower dispatch to compensate VRE and meet scheduled power demands. We applied the new model to hourly performance data obtained from photovoltaic, wind, and hydropower plants with power outputs of 3.1 GW, 2.7 GW, and 3.3 GW, respectively, in the Yalong River Basin in China. Based on the variance of hourly power outputs during spring days with different weather patterns, we found that the uncertainty of the forecasted combined power output of wind and photovoltaic systems is 46% less than that of the forecasted wind power output, and approximately 2% greater than that of the forecasted photovoltaic power output. After hydropower compensated for the power shortage in the combined VRE power output, the uncertainty of meeting prescheduled hourly demand during each of the considered days was reduced by 90%, compared with that without hydropower compensation. When the forecasts were updated dynamically, the uncertainties of the forecasts of the separate power outputs, of the combined power output, and of the power shortage decreased substantially. Thus, the approach proposed in this study offers a scheduling plan for hydropower compensation of VRE on a daily time scale and can also be used to evaluate the risk of power shortage.

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  • Liu, Weifeng & Zhu, Feilin & Zhao, Tongtiegang & Wang, Hao & Lei, Xiaohui & Zhong, Ping-an & Fthenakis, Vasilis, 2020. "Optimal stochastic scheduling of hydropower-based compensation for combined wind and photovoltaic power outputs," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920310138
    DOI: 10.1016/j.apenergy.2020.115501
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    Cited by:

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    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Zhao, Zhigao & Yang, Jiandong & Huang, Yifan & Yang, Weijia & Ma, Weichao & Hou, Liangyu & Chen, Man, 2021. "Improvement of regulation quality for hydro-dominated power system: quantifying oscillation characteristic and multi-objective optimization," Renewable Energy, Elsevier, vol. 168(C), pages 606-631.
    6. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    7. Ma, Chao & Liu, Lu, 2022. "Optimal capacity configuration of hydro-wind-PV hybrid system and its coordinative operation rules considering the UHV transmission and reservoir operation requirements," Renewable Energy, Elsevier, vol. 198(C), pages 637-653.
    8. Simian Pang & Zixuan Zheng & Fan Luo & Xianyong Xiao & Lanlan Xu, 2021. "Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    9. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Liu, Lu & Lian, Jijian, 2021. "Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems," Applied Energy, Elsevier, vol. 291(C).
    10. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    11. Wang, Cong & Wang, Dekuan & Zhang, Jianming, 2021. "Experimental study on isolated operation of hydro-turbine governing system of Lunzua hydropower station in Zambia," Renewable Energy, Elsevier, vol. 180(C), pages 1237-1247.
    12. Lu, Na & Wang, Guangyan & Su, Chengguo & Ren, Zaimin & Peng, Xiaoyue & Sui, Quan, 2024. "Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties," Applied Energy, Elsevier, vol. 353(PA).
    13. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    14. Gong, Yu & Liu, Pan & Ming, Bo & Li, Dingfang, 2021. "Identifying the effect of forecast uncertainties on hybrid power system operation: A case study of Longyangxia hydro–photovoltaic plant in China," Renewable Energy, Elsevier, vol. 178(C), pages 1303-1321.
    15. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.

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