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

•A thermodynamic network is formulated to model the electrolytic thermo-electrochemical effects.•Geothermal-solar-wind 100% renewable complementarities are proposed for multi-energy supplies.•A mean-variance portfolio scheme is developed to determine appropriate energy generation, conversion, and storage candidates.•The energy risks of high-renewable portfolio are considered. 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.