Previous studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to “stress test” a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant “re‐ranking” of weather years in terms of annual wholesale electricity prices (with “good” years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS.
<p>The ongoing global transition to a deeply decarbonized electricity system represents a complex problem. Deep uncertainty in the future pathways of power system capacity expansion and interactions across sectors has led stakeholders to seek out robust methods capable of informing multi-scale, multi-sector tradeoffs among policy pathways within the energy-water-food nexus. In this study, scenario discovery is applied to a large scenario ensemble generated using a global-scale integrated assessment model with a regional focus on Latin America. Scenario discovery is a powerful method for identifying robust, policy-relevant scenarios from large, many-dimensional ensembles of model realizations. Here, ten uncertain sensitivity factors consistent with previous analyses are varied within the model configuration, representing technological costs and efficiencies, advanced electrification, institutional factors, and national climate pledges, among others. The resulting scenario ensemble maps out the impacts of a combinatorial time-evolving uncertainty space defined by these sensitivity factors, using generation mix, electricity cost, energy burden, and energy intensity as power system performance metrics. Additional metrics are utilized to explore cross-sectoral implications of scenarios. The scenario discovery analysis identifies the key global drivers of regional outcomes in Latin America, as well as tradeoffs and synergies regarding climate change mitigation and the future evolution of the Latin American electric power system. Our results underscore the importance of considering coupled systems and the advantages of large-scale scenario ensembles in capacity expansion analyses.</p>
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