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Despite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces an antifragility indicator. The variability is optimized by favoring upside potential and providing downside protection towards a minimum acceptable performance, while the skewness indicates (anti)fragility. An antifragile design primarily enhances positive outcomes when the uncertainty of the random environment exceeds initial estimations. Hence, it circumvents the issue of underestimating the uncertainty in the operating environment. We applied the methodology to the design of a wind turbine for a community, considering the Levelized Cost Of Electricity (LCOE) as the quantity of interest. The design with optimized variability proves beneficial in 81% of the possible scenarios when compared to the conventional robust design. The antifragile design flourishes (LCOE drops by up to 120%) when the real-world uncertainty is higher than initially estimated in this paper. In conclusion, the framework provides a valid metric for optimizing the variability and detects promising antifragile design alternatives. [ABSTRACT FROM AUTHOR]

Renewable energy is increasingly mobilizing more investment around the globe. However, there has been little attention to evaluating economic and regulatory (E&R) uncertainties, despite their enormous impact on the project cashflows. Consequently, this review analyzes, classifies, and discusses 130 articles dealing with the design of renewable energy projects under E&R uncertainties. After performing a survey and identifying the selected manuscripts, and the few previous reviews on the matter, the following innovative categorization is designed: sources of uncertainty, uncertainty characterization methods, problem formulations, solution methods, and regulatory frameworks. The classification reveals that electricity price is the most considered source of uncertainty, often alone, despite the existence of six other equally influential groups of E&R uncertainties. In addition, real options and optimization arise as the two main approaches researchers use to solve problems in energy system design. Subsequently, the following aspects of interest are discussed in depth: how modeling can be improved, which are the most influential variables, and potential lines of research. Conclusions show the necessity of modeling E&R uncertainties with currently underrepresented methods, suggest several policy recommendations, and encourage the integration of prevailing approaches. [ABSTRACT FROM AUTHOR]

Direct Air Capture (DAC) technologies are gaining interest in the concept of carbon utilization and Power-to-Gas (PtG), as the economic valorization of the CO 2 into methane provides a viable pathway to allow DAC systems to mature. However, research on DAC mainly focuses on isolated systems, and the system performance depends on parameters that are highly uncertain. To study the integration of DAC in PtG, we developed a DAC-PtG model, performed an Energy, Exergy, Economic and Environmental (4E) analysis and implemented uncertainty quantification to consider the uncertain environment. The results illustrate that the DAC-PtG system is autothermal when introducing a two-stage mechanical vapor recompression unit at the DAC outlet. The exergy efficiency ranges between 51.3% and 52.6% within 3 standard deviations, for which the uncertainty is driven by the ambient conditions and the uncertain heat of desorption. The methane issued from DAC-PtG has a lower carbon footprint than fossil methane when the carbon footprint of the electricity supply is below or equal to 0.12 kg˙CO˙2-eq /kWh. The Levelized Cost of Synthetic Natural Gas (LCSNG) ranges between 130 €/ MWh and 744 €/ MWh, following an uncertain electricity price and uncertain expenses related to DAC and electrolysis. Therefore, bulk manufacturing, further maturing of these technologies and more demonstration projects are required to reduce the uncertainty of the LCSNG. Future works will consider intermittent renewable energy sources to supply power. • Integrated direct air capture and CO 2 methanation are investigated. • Exergy efficiency ranges between 51.3% and 52.6% within three standard deviations. • The levelized cost of synthetic natural gas ranges between 130 €/MWh and 744 €/MWh. • Electricity emission intensity below 0.12 kg CO2-eq /kWh results in carbon reduction. • To reduce uncertainty, bulk manufacturing and more pilot projects are needed. [ABSTRACT FROM AUTHOR]