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Many studies have demonstrated the feasibility of fully renewable power systems. Yet the future costs of key technologies are highly uncertain, and little is known about the robustness of a renewable power system to these uncertainties. To analyze it, we build 315 cost scenarios by varying the costs of key technologies and we model the optimal renewable power system for France, simultaneously optimizing investment and dispatch. We add to the literature by studying a consecutive 18-years weather period; by testing all combinations of technology costs rather than changing them one-at-a-time; and by calculating the regret from optimizing the energy mix on the basis of cost assumptions that do not materialize. Our results indicate that the cost of a 100% system is not that sensitive to uncertainty. Admittedly, the optimal energy mix is highly sensitive to cost assumptions: across our scenarios, the installed capacity in PV, onshore wind and power-togas varies by a factor of 5, batteries and offshore wind even more. However, in every scenario the total production and storage cost is similar to, or lower than the current cost. This indicates that renewable technologies will become by and large substitutable. Moreover, even if the energy mix is optimized based on cost assumptions which turn out to be wrong, the extra cost is low: 4% in average and less than 9% in 95% of the scenarios. Keywords: Power system modelling, Variable renewables, Electricity storage, Robust decision making, 1. INTRODUCTION According to Article 4.1 of the Paris Agreement, the Parties shall endeavor to rapidly reduce greenhouse gas emissions in order to achieve a balance between anthropogenic emissions by [...]

Climate change and evolving water management practices may have a profound impact on hydropower generation. While hydrological models have been widely used to assess these effects, they often present some limitations. A major challenge lies in the modeling of release decisions for hydropower reservoirs, which result from intricate trade-offs, involving power sector dispatch, competing water uses and the spatial allocation of power generation within the grid. To address this gap, this study introduces a novel demand-based approach for integrating hydropower within the routing module of land surface models. First, hydropower infrastructures are placed in coherence with the hydrological network and links are built between hydropower plants and their supplying reservoirs to explicitly represent water transfers built for hydropower generation. Then, coordinated dam operation is simulated by distributing a prescribed electric demand to be satisfied by hydropower over the different power plants on the power grid, while considering the operational constraints associated with the multipurpose nature of most dams. To validate our approach, this framework is implemented within the water transport scheme of a land surface model and assessed with the case study of the French electrical system. We drive the model with a high-resolution atmospheric reanalysis and prescribe the observed national hydropower production as the total power demand to be met by hydropower infrastructures. By comparing the simulated evolution of the stock in reservoirs to the observations, we find that the model simulates realistic operations of reservoirs and successfully satisfies hydropower production demands over the entire period. We highlight the roles of uncertainties in estimated precipitation and of the limited knowledge of hydropower networks on the estimation of production. Finally, we show that such an integration of hydropower operations in the model improves the simulations of river discharges in mountainous catchments affected by hydropower. [ABSTRACT FROM AUTHOR]