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Identifying the effect of forecast uncertainties on hybrid power system operation: A case study of Longyangxia hydro–photovoltaic plant in China

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  • Gong, Yu
  • Liu, Pan
  • Ming, Bo
  • Li, Dingfang

Abstract

Power system operations are unavoidably influenced by forecast uncertainties. The forecast uncertainty's effect on operations was quantified in individual systems, however, was seldom investigated in a hybrid system. This study aims to quantify the effect of forecast uncertainties on the hybrid hydro–photovoltaic system operation. First, the operation model is built considering streamflow and photovoltaic forecast uncertainties with long- and short-time scales. Second, total release uncertainty is determined from the sum of squares for releases under different forecast scenarios. Finally, a variance partitioning method is used to divide the total release uncertainty into the uncertainties induced by individual forecast errors. Longyangxia hybrid hydro–photovoltaic power plant from China was selected as a case study. Results provide following insights: (1) long-term inflow forecast uncertainty accounts for 98.89% of the contribution to the long-term release uncertainty, while photovoltaic uncertainty accounts for 1.11%; (2) long-term inflow forecast uncertainty still dominates the short-term release uncertainty, with the contribution of 62.06%; and (3) the total release uncertainty decreases in long-term hybrid operation compared with the reservoir operation, indicating the complementary relationship between long-term hydropower and photovoltaic power. These implications highlight the importance of improving forecast accuracies for varied energy sources at different time scales.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1303-1321
    DOI: 10.1016/j.renene.2021.06.104
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    Cited by:

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    3. Liu, Yuan & Ji, Changming & Wang, Yi & Zhang, Yanke & Jiang, Zhiqiang & Ma, Qiumei & Hou, Xiaoning, 2023. "Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models," Energy, Elsevier, vol. 273(C).
    4. Jiang, Jianhua & Ming, Bo & Liu, Pan & Huang, Qiang & Guo, Yi & Chang, Jianxia & Zhang, Wei, 2023. "Refining long-term operation of large hydro–photovoltaic–wind hybrid systems by nesting response functions," Renewable Energy, Elsevier, vol. 204(C), pages 359-371.
    5. Yang, Zhikai & Liu, Pan & Cheng, Lei & Liu, Deli & Ming, Bo & Li, He & Xia, Qian, 2021. "Sizing utility-scale photovoltaic power generation for integration into a hydropower plant considering the effects of climate change: A case study in the Longyangxia of China," Energy, Elsevier, vol. 236(C).
    6. Jiang, Jianhua & Ming, Bo & Huang, Qiang & Guo, Yi & Shang, Jia’nan & Jurasz, Jakub & Liu, Pan, 2023. "A holistic techno-economic evaluation framework for sizing renewable power plant in a hydro-based hybrid generation system," Applied Energy, Elsevier, vol. 348(C).

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