A two-stage distributionally robust optimization model for geothermal-hydrogen integrated energy system operation considering multiple uncertainties

Under the dual pressure of the fossil energy crisis and the global low carbon emission target, the utilization of various types of renewable energy has received widespread attention worldwide. In view of this, this study develops a geothermal-hydrogen integrated energy system (integrating geothermal power, power to hydrogen, and energy storage), which can generate “near-zero” CO2 emissions and obtain additional benefits by selling hydrogen. However, random fluctuations in market electricity prices and heat demand severely interfere with the economic, environmental, and reliable operation of the system, hindering its sustainability operation and development. Therefore, this paper proposes a two-stage distributionally robust optimization model considering multiple uncertainties. Specifically, a moment-based ambiguity set is constructed to portray the uncertain distribution of forecasting errors in heat demand and market electricity prices. In the first stage, the daily operating profit of the system is maximized through electricity, hydrogen, and heat scheduling decisions according to the forecast information. In the second stage, the operations of flexibility resources are adjusted from their schedules in the first stage to resist the interference of the “worst-case” distribution in the ambiguity set. Finally, the model is transformed into a mixed-integer linear programming for solution feasibility. Simulations verify that: (1) integrating geothermal and hydrogen energy improves the economic and environmental benefits of system operation; and (2) the model keeps low computational complexity and decision conservativeness, which can realize the benefits of cost-saving, profit improvement, emission reduction, and interference resistance.