A three-stage adjustable robust optimization framework for energy base leveraging transfer learning

Yuan Gao, Yucan Zhao, Sile Hu, Mustafa Tahir, Wang Yuan and Jiaqiang Yang

Energy, 2025, vol. 319, issue C

Abstract: In pursuit of carbon neutrality, renewable energy exploitation in desert regions offers a compelling alternative to coal-fired power generation. However, managing such energy bases presents challenges, including limited wind and solar data, variable grid tariffs, and renewable energy output uncertainties. This study introduces a Three-Stage Adjustable Robust Optimization (TRARO) framework, integrating a Temporal Convolutional Network with Attention and Gated Recurrent Unit (TCNA-GRU) model for enhanced wind and solar prediction using transfer learning. The TRARO framework addresses uncertainties in energy management through three stages: optimizing capacity configuration, managing power exchanges, and scheduling operations. Simulation results demonstrate significant reductions in photovoltaic and wind turbine capacities by 50 % and 32.26 %, respectively, compared to Two-Stage Robust Optimization, alongside a 41.45 % decrease in grid transaction costs. These findings underscore the economic efficiency and reliability of the TRARO model in addressing uncertainties for large-scale energy bases, offering practical implications for sustainable energy planning.

Keywords: Energy base; Three stage adjustable robust; Transfer learning; Desert regions; Uncertainty (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:319:y:2025:i:c:s0360544225006796

DOI: 10.1016/j.energy.2025.135037

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