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

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

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.

Suggested Citation

  • Gao, Yuan & Zhao, Yucan & Hu, Sile & Tahir, Mustafa & Yuan, Wang & Yang, Jiaqiang, 2025. "A three-stage adjustable robust optimization framework for energy base leveraging transfer learning," Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:energy:v:319:y:2025:i:c:s0360544225006796
    DOI: 10.1016/j.energy.2025.135037
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