Development of a methodology to evaluate technology-specific discount rates for energy system optimization models

The urgency to develop policy strategies to support clean energy investments to tackle the current climate and socio-economic challenges is significantly increasing the interest in energy system models, which aim to assess the effectiveness of possible energy transition policies in pursuing climate targets. Among these tools, energy system optimization models (ESOMs) are widely used to find the least-cost configuration of the technological network which allows to satisfy the final energy service demands, in a certain region and along a certain time horizon. Bottom-up ESOMs rely on a detailed techno-economic characterization of the technologies included in the energy system. While the cost trajectories for both well-established and innovative technologies are defined according to the available literature, technology-specific discount rates, also called hurdle rates, are often based on educated guesses. In some models, high hurdle rates are adopted to represent the barriers for investments in innovative, high-risk energy projects. This work is devoted to the establishment of a rigorous methodology to define hurdle rates for technologies typically composing the Reference Energy Systems of ESOMs. in ESOMs. The economic parameters influencing the hurdle rate were identified and assigned to the single technologies typically composing the Reference Energy System. The proposed methodology was then applied to the open-source TEMOA-Italy model instance, representative of the Italian Energy System. The results given by TEMOA-Italy were compared with those coming from the previous version of the database in which the hurdle rates were assigned to few technologies based on assumptions and/or educated guesses. It came out that a variation of the hurdle rates does not always significantly affect the technology competition, but they enrich the analysis by considering risks not accounted before. The business-as-usual (BAU) examined scenario with updated hurdle rates was then used in the subsequent steps of this study as a benchmark to compare the impact of new policies. Indeed, the parameters elaborated to evaluate the hurdle rates were revised in the light of the guidelines of the EU Taxonomy. This work focuses on EU Taxonomy Mitigation Technology Screening Criteria (TSC) which refers mainly on emission activities and electricity consumption of technologies. Although no noteworthy differences from the BAU scenario were revealed, the role of Taxonomy becomes interesting when combined with decarbonisation scenarios.