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

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) sto-chastic 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.

Automated summary

This paper proposes a high-renewable portfolio model of energy hub that explores the geothermal-solar-wind multi-energy complementarities. It formulates the problem as a mean-variance approach to deal with forecast uncertainties and optimally determine the energy generation, conversion, and storage candidates.