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A mean-variance portfolio optimization approach for high-renewable energy hub

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  • Xu, Da
  • Bai, Ziyi
  • Jin, Xiaolong
  • Yang, Xiaodong
  • Chen, Shuangyin
  • Zhou, Ming

Abstract

This paper proposes a high-renewable portfolio model of energy hub. In this model, geothermal-solar-wind multi-energy complementarities are fully explored based on electrolytic thermo-electrochemical effects of geothermal-to-hydrogen (GTH), which are converted, conditioned, and coupled through energy hub. The proposed high-renewable energy hub portfolio is an intractable optimization problem due to their inherent strong energy couplings and conflicted energy cost/risk. The original problem is thus characterized through the mean-variance approach to explicitly express the risk associated with the forecast uncertainties. The formulated mean-variance portfolio problem is subsequently modeled as a two-stage mixed-integer nonlinear programming (MINLP) stochastic programming to optimally determine appropriate energy generation, conversion, and storage candidates. Numerical studies on a community microgrid are implemented to verify the effectiveness and superiority of the proposed methodology over conventional wind-solar-battery scheme. Simulations results show that the portfolio cost can be reduced by at most 14.9% with a significantly higher operational flexibility.

Suggested Citation

  • Xu, Da & Bai, Ziyi & Jin, Xiaolong & Yang, Xiaodong & Chen, Shuangyin & Zhou, Ming, 2022. "A mean-variance portfolio optimization approach for high-renewable energy hub," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s0306261922011527
    DOI: 10.1016/j.apenergy.2022.119888
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