Using stochastic dual dynamic programming to design long-term operation policy of hydro-wind-solar energy systems considering multiple coupled uncertainties and end-of-year carryover storage

Hydropower with reservoirs is increasingly important for balancing seasonal variability of growing variable renewable energy (VRE) through its reservoir regulation capability. However, the coupling of the seasonal variability and randomness of VRE with the stochastic nature of inflows makes it extremely challenging to manage long-term hydropower operations related to generation decisions within the current scheduling periods and future end-of-year carryover storage control. To address these challenges, we propose a stochastic dual dynamic programming-based framework for designing long-term hydro-wind-solar complementary operation policies. Inflow and VRE output uncertainties are captured by two different approaches: Markov chain and AutoRegressive Moving Average. These approaches enable the integration of stage-wise dependent randomness into the stochastic decision-making process. Model reconstruction techniques based on Disjunctive Programming are proposed to transform stage-wise nonlinear models into linear ones. Subsequently, Benders cuts families are constructed to constrain the feasible decision space related to hydropower operation and stochastic parameters , while managing the end-of-year carryover storage requirement. Case studies of a large-scale hydro-wind-solar energy system in China indicate that the proposed framework can derive effective complementary operation policies considering future reservoir storage management requirements under multiple coupled uncertainties. Real simulation results indicate that the framework can effectively enhance channel utilization by leveraging hydropower flexibility to support VRE integration, with the monthly average channel utilization rate exceeding 80 %. Besides, hydro-wind-solar complementary operation policies with varying end-of-year carryover storage requirements can be designed, with lower storage requirements trending to enhance hydropower output in a hydro-wind-solar complementary mode. • An SDDP-based framework for long-term hydro-wind-solar complementary operation. • Addresses multiple uncertainties and end-of-year carryover storage requirements. • Derives long-term hydro-wind-solar complementary operation policy. • Enhances transmission channel utilization by leveraging hydropower flexibility.

Authors

Publication Details

Journal
Applied Energy
Published
2025-05-15
DOI
https://doi.org/10.1016/j.apenergy.2025.126072
Citations
13
Primary Topic
Water resources management and optimization
Type
article
Field-Weighted Citation Impact
14.89
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Using stochastic dual dynamic programming to design long-term operation policy of hydro-wind-solar energy systems considering multiple coupled uncertainties and end-of-year carryover storage

Lingzhi Yan, Zhipeng Zhao, Shubing Cai, Xiaoyu Jin et al.
13 citations
Applied Energy
Water resources management and optimization
14.89
article

Using stochastic dual dynamic programming to design long-term operation policy of hydro-wind-solar energy systems considering multiple coupled uncertainties and end-of-year carryover storage

Lingzhi Yan, Zhipeng Zhao, Shubing Cai, Xiaoyu Jin, Chuntian Cheng
article en
13 citations

Abstract

Hydropower with reservoirs is increasingly important for balancing seasonal variability of growing variable renewable energy (VRE) through its reservoir regulation capability. However, the coupling of the seasonal variability and randomness of VRE with the stochastic nature of inflows makes it extremely challenging to manage long-term hydropower operations related to generation decisions within the current scheduling periods and future end-of-year carryover storage control. To address these challenges, we propose a stochastic dual dynamic programming-based framework for designing long-term hydro-wind-solar complementary operation policies. Inflow and VRE output uncertainties are captured by two different approaches: Markov chain and AutoRegressive Moving Average. These approaches enable the integration of stage-wise dependent randomness into the stochastic decision-making process. Model reconstruction techniques based on Disjunctive Programming are proposed to transform stage-wise nonlinear models into linear ones. Subsequently, Benders cuts families are constructed to constrain the feasible decision space related to hydropower operation and stochastic parameters , while managing the end-of-year carryover storage requirement. Case studies of a large-scale hydro-wind-solar energy system in China indicate that the proposed framework can derive effective complementary operation policies considering future reservoir storage management requirements under multiple coupled uncertainties. Real simulation results indicate that the framework can effectively enhance channel utilization by leveraging hydropower flexibility to support VRE integration, with the monthly average channel utilization rate exceeding 80 %. Besides, hydro-wind-solar complementary operation policies with varying end-of-year carryover storage requirements can be designed, with lower storage requirements trending to enhance hydropower output in a hydro-wind-solar complementary mode. • An SDDP-based framework for long-term hydro-wind-solar complementary operation. • Addresses multiple uncertainties and end-of-year carryover storage requirements. • Derives long-term hydro-wind-solar complementary operation policy. • Enhances transmission channel utilization by leveraging hydropower flexibility.

Applied EnergyVol. 393
Affordable and clean energy
Openalex Percentile: Top 1%
Water resources management and optimization
14.89
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