Long-term optimization scheduling of cascade hydropower stations with wind and photovoltaic energy integration

Key Points

The proposed method achieves a 5.63% improvement in total output, showcasing effective integration of renewable sources.
Optimized scheduling also guarantees a minimum periodic output of 4.41%, ensuring energy reliability amid fluctuations.
Utilizing a dual-objective stochastic optimization framework, this approach enhances multi-energy system scheduling in hydropower management.
The findings highlight the potential for integrating diverse energy sources, pointing to broader implications for sustainable power generation.

Abstract

Abstract This study models temporal correlations of hydro-wind-solar resources using LHS-Cholesky decomposition, generating scenarios reduced via probabilistic distance for computational efficiency. A dual-objective stochastic optimization framework for the long-term multi-energy complementary system scheduling (LMCS) problem maximizes long-term power generation while guaranteeing minimum periodic output. The proposed orthogonal design and multi-population search framework-based evolutionary (OMPE) algorithm integrates orthogonal design, multi-population search, and constraint handling, achieving 5.63% and 4.41% improvements in total and minimum outputs versus benchmarks. Validation confirms orthogonal initialization, recombination operators, and dominance rules enhance performance, while scenario quantity regulates economic-risk tradeoffs.

Long-term optimization scheduling of cascade hydropower stations with wind and photovoltaic energy integration

Key Points

Abstract

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