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Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system

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  • Zhang, Yi
  • Cheng, Chuntian
  • Cai, Huaxiang
  • Jin, Xiaoyu
  • Jia, Zebin
  • Wu, Xinyu
  • Su, Huaying
  • Yang, Tiantian

Abstract

It is popular to combine the hydropower plant with the wind and solar power plants to supply electricity, and the joint is termed as the hydro-wind-solar renewable energy supply system (RESS). The long-term optimal operation of the RESS is a challenging task, due to the nature of different spatial and temporal variabilities associated with renewable energy resources, and the significant operation uncertainties due to the changing natural environment. To address the task, the stochastic model predictive control (MPC), based on probabilistic forecasting and rolling stochastic optimization, is designed and implemented for the long-term operations of the RESSs in Yunnan province, China. The paper tests out the system efficiencies for different penetration levels of wind and solar power, and finds out that (1) the Long Short-Term Memory performs best among candidate point prediction algorithms; (2) the stochastic MPC considering the correlations among renewable resources helps the RESS operate with a higher efficiency; (3) the large-scale hydropower plant has great potential to offset the effects of seasonal uncertainties and demand-generation mismatch, but cannot completely avoid the electricity shortage enabled by unexpected scenarios of renewable resources.

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  • Zhang, Yi & Cheng, Chuntian & Cai, Huaxiang & Jin, Xiaoyu & Jia, Zebin & Wu, Xinyu & Su, Huaying & Yang, Tiantian, 2022. "Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922005128
    DOI: 10.1016/j.apenergy.2022.119134
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