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Data-Driven Chance-Constrained Schedule Optimization of Cascaded Hydropower and Photovoltaic Complementary Generation Systems for Shaving Peak Loads

Author

Listed:
  • Yang Li

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Feng Wu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Xudong Song

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Linjun Shi

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Keman Lin

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Feilong Hong

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

The coordinated scheduling of cascade hydropower with photovoltaic (PV) power stations can significantly improve the utilization rate of delivery transmission lines. However, the inherent uncertainty associated with photovoltaic (PV) forecasts challenges the reliable and economic operation of the complementary energy system. Against this background, in this paper, a day-ahead, chance-constrained scheduling for cascaded hydro–photovoltaic complementary generation systems (CHPSs) considering the transmission capacity is proposed. Firstly, the uncertainty of PV forecast errors is simulated by a probability density function fitted using kernel density estimation with historical sampling data. Then, a chance-constrained optimization model considering peak-shaving demands of the receiving-end power grid is developed to determine the day-ahead optimal schedules of CHPSs. Also, complex hydraulic coupling and unit operation constraints of cascade hydropower are considered in the proposed model. To deal with the nonlinear and stochastic constraints, an efficient linearization method is adopted to transform the proposed model into a mixed-integer linear programming (MILP) problem. Finally, the effectiveness and feasibility are verified by case studies. The results show that the day-ahead schedule optimized by the proposed method can fully balance peak-shaving and photovoltaic accommodation while considering photovoltaic output uncertainty.

Suggested Citation

  • Yang Li & Feng Wu & Xudong Song & Linjun Shi & Keman Lin & Feilong Hong, 2023. "Data-Driven Chance-Constrained Schedule Optimization of Cascaded Hydropower and Photovoltaic Complementary Generation Systems for Shaving Peak Loads," Sustainability, MDPI, vol. 15(24), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16916-:d:1301749
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    References listed on IDEAS

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