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Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

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  • Basnet, Manjur R.
  • Bryan, Jacob A.
  • Dana, Seth J.
  • Meek, Aiden S.
  • Wang, Hailei
  • Talbot, Paul

Abstract

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value.

Suggested Citation

  • Basnet, Manjur R. & Bryan, Jacob A. & Dana, Seth J. & Meek, Aiden S. & Wang, Hailei & Talbot, Paul, 2024. "Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets," Applied Energy, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:appene:v:375:y:2024:i:c:s0306261924014880
    DOI: 10.1016/j.apenergy.2024.124105
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    References listed on IDEAS

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