Endogenous Technology Learning in TEMOA-Europe

Energy system optimization modeling tools provide insights for the evaluation of energy strategies minimizing the total cost of the integrated supply-demand system. In the classical formulation, costs for the different energy technologies are assigned as parameters, with the possibility to account for cost reductions just depending on elapsed time, independently from the effective rate of adoption of a technology (exogenous learning). This work adopts the TEMOA-Europe model instance to implement a nonlinear endogenous technology learning algorithm. Endogenous learning changes the unit investment cost acting according to assigned learning rates. The case study examined here considers a net-zero emissions scenario by 2050 analyzing the effects of endogenous learning on electricity generation, hydrogen production technologies and cars. The results highlight large differences in the development of those sectors interested by the application of learning rate when comparing the exogenous and the endogenous learning runs, despite negligible effects on the overall system and on the increase in computational cost.