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Risk-Aware Scheduling for Maximizing Renewable Energy Utilization in a Cascade Hydro–PV Complementary System

Author

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  • Yan Liu

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

  • Xian Zhang

    (Beijing Power Exchange Center, Beijing 100032, China)

  • Ziming Ma

    (Beijing Power Exchange Center, Beijing 100032, China)

  • Wenshi Ren

    (State Grid Sichuan Economic Research Institute, Chengdu 610041, China)

  • Yangming Xiao

    (State Grid Sichuan Economic Research Institute, Chengdu 610041, China)

  • Xiao Xu

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

  • Youbo Liu

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

  • Junyong Liu

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

Abstract

With the increasing integration of variable renewables, cascade hydro–photovoltaic (PV) systems face growing challenges in scheduling under PV output uncertainty. This paper proposes a risk-aware bi-level scheduling model based on the Information Gap Decision Theory (IGDT) to maximize renewable energy utilization while accommodating different risk preferences. The upper level optimizes the uncertainty horizon based on the decision-maker’s risk attitude (risk-neutral, opportunity-seeking, or risk-averse), while the lower level ensures operational feasibility under corresponding deviations in the PV and hydropower schedule. The bi-level model is reformulated into a single-level mixed-integer linear programming (MILP) problem. A case study based on four hydropower plants and two photovoltaic (PV) clusters in Southwest China demonstrates the effectiveness of the model. Numerical results show that the opportunity-seeking strategy (OS) achieves the highest total generation (68,530.9 MWh) and PV utilization (102.2%), while the risk-averse strategy (RA) improves scheduling robustness, reduces the number of transmission violations from 38 (risk-neutral strategy) to 33, and increases the system reserve margin to 20.1%. Compared to the conditional value-at-risk (CVaR) model, the RA has comparable robustness. The proposed model provides a flexible and practical tool for risk-informed scheduling in multi-energy complementary systems.

Suggested Citation

  • Yan Liu & Xian Zhang & Ziming Ma & Wenshi Ren & Yangming Xiao & Xiao Xu & Youbo Liu & Junyong Liu, 2025. "Risk-Aware Scheduling for Maximizing Renewable Energy Utilization in a Cascade Hydro–PV Complementary System," Energies, MDPI, vol. 18(12), pages 1-32, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3109-:d:1677743
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    References listed on IDEAS

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