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1. Geophysical constraints on the reliability of solar and wind power in the United States

   https://doi.org/10.1039/c7ee03029k  

   Shaner, Matthew R.; Davis, Steven J.; Lewis, Nathan S.; Caldeira, Ken (January 2018, Energy & Environmental Science)

   We analyze 36 years of global, hourly weather data (1980–2015) to quantify the covariability of solar and wind resources as a function of time and location, over multi-decadal time scales and up to continental length scales. Assuming minimal excess generation, lossless transmission, and no other generation sources, the analysis indicates that wind-heavy or solar-heavy U.S.-scale power generation portfolios could in principle provide ∼80% of recent total annual U.S. electricity demand. However, to reliably meet 100% of total annual electricity demand, seasonal cycles and unpredictable weather events require several weeks’ worth of energy storage and/or the installation of much more capacity of solar and wind power than is routinely necessary to meet peak demand. To obtain ∼80% reliability, solar-heavy wind/solar generation mixes require sufficient energy storage to overcome the daily solar cycle, whereas wind-heavy wind/solar generation mixes require continental-scale transmission to exploit the geographic diversity of wind. Policy and planning aimed at providing a reliable electricity supply must therefore rigorously consider constraints associated with the geophysical variability of the solar and wind resource—even over continental scales. 

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2. Geophysical constraints on the reliability of solar and wind power worldwide

   https://doi.org/10.1038/s41467-021-26355-z  

   Tong, Dan; Farnham, David J.; Duan, Lei; Zhang, Qiang; Lewis, Nathan S.; Caldeira, Ken; Davis, Steven J. (December 2021, Nature Communications)

   null (Ed.)

   Abstract If future net-zero emissions energy systems rely heavily on solar and wind resources, spatial and temporal mismatches between resource availability and electricity demand may challenge system reliability. Using 39 years of hourly reanalysis data (1980–2018), we analyze the ability of solar and wind resources to meet electricity demand in 42 countries, varying the hypothetical scale and mix of renewable generation as well as energy storage capacity. Assuming perfect transmission and annual generation equal to annual demand, but no energy storage, we find the most reliable renewable electricity systems are wind-heavy and satisfy countries’ electricity demand in 72–91% of hours (83–94% by adding 12 h of storage). Yet even in systems which meet >90% of demand, hundreds of hours of unmet demand may occur annually. Our analysis helps quantify the power, energy, and utilization rates of additional energy storage, demand management, or curtailment, as well as the benefits of regional aggregation. 

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3. Modeling and Evaluating Beneficial Matches between Excess Renewable Power Generation and Non-Electric Heat Loads in Remote Alaska Microgrids

   https://doi.org/10.3390/su14073884  

   Bolt, Grace; Wilber, Michelle; Huang, Daisy; Sambor, Daniel J.; Aggarwal, Srijan; Whitney, Erin (April 2022, Sustainability)

   Many Alaska communities rely on heating oil for heat and diesel fuel for electricity. For remote communities, fuel must be barged or flown in, leading to high costs. While renewable energy resources may be available, the variability of wind and solar energy limits the amount that can be used coincidentally without adequate storage. This study developed a decision-making method to evaluate beneficial matches between excess renewable generation and non-electric dispatchable loads, specifically heat loads such as space heating, water heating and treatment, and clothes drying in three partner communities. Hybrid Optimization Model for Multiple Electric Renewables (HOMER) Pro was used to model potential excess renewable generation based on current generation infrastructure, renewable resource data, and community load. The method then used these excess generation profiles to quantify how closely they align with modeled or actual heat loads, which have inherent thermal storage capacity. Of 236 possible combinations of solar and wind capacity investigated in the three communities, the best matches were seen between excess electricity from high-penetration wind generation and heat loads for clothes drying and space heating. The worst matches from this study were from low penetrations of solar (25% of peak load) with all heat loads. 

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4. Exploring sustainable electricity system development pathways in South America’s MERCOSUR sub-region

   Chowdhury, A.F.M. Kamal (September 2023, Energy strategy reviews)

   We explore sustainable electricity system development pathways in South America’s MERCOSUR sub-region under a range of techno-economic, infrastructural, and policy forces. The MERCOSUR sub-region includes Argentina, Brazil, Chile, Uruguay, and Paraguay, which represent key electricity generation, consumption, and trade dynamics on the continent. We use a power system planning model to co-optimize investment and operations of generation, storage, and transmission facilities out to 2050. Our results show that, under business-as-usual conditions, wind and solar contribute more than half of new generation capacity by 2050, though this requires substantial expansion of natural gas-based capacity. While new hydropower appears to be less cost-competitive, the existing high capacity of hydropower provides critically important flexibility to integrate the wind and solar and to avoid further reliance on more expensive or polluting resources (e.g., natural gas). Over 90% emission cut by 2050 could be facilitated mostly by enhanced integration (predominantly after 2040) of wind, solar, and battery storage with 11%–28% additional cost, whereas enhanced expansion of hydropower reduces the cost of low-carbon transition, suggesting trade-off opportunities between saving costs and environment in selecting the clean energy resources. Achieving high emission reduction goals will also require enhanced sub-regional electricity trade, which could be mostly facilitated by existing interconnection capacities. 

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5. Stochastic Model Predictive Control-Based Electric Taxi Fleet Coordination under Solar Power Uncertainty

   https://doi.org/10.1145/3744748  

   Yuan, Yukun; Jia, Mian; Zhao, Yue; Lin, Shan (October 2025, ACM Transactions on Cyber-Physical Systems)

   As electric vehicles (EVs) gradually replace fuel vehicles and provide transportation services in cities, e.g., electric taxi fleets, solar-powered charging stations with energy storage systems have been deployed to provide charging services for EV fleets. The mixture of solar-powered and traditional charging stations brings efficiency challenges to charging stations and reliability challenges to power systems. In this article, we explore e-taxis’ mobility and charging demand flexibility to co-optimize service quality of e-taxi fleets and system cost of charging infrastructures, e.g., solar power under-utilization and reliability issues of power distribution networks due to reverse power flow. We propose SAC, an e-taxi coordination framework to dispatch e-taxis for charging or serving passengers under spatial-temporal dynamics of renewable energy and passenger mobility, which integrates the renewable power generation estimation from a forecast system. Moreover, we extend our design to a stochastic Model Predictive Control problem to handle the uncertainty of solar power generation, aiming to fully utilize generated solar power. Our data-driven evaluation shows that SAC significantly outperforms existing solutions, enhancing the usage rate of solar power by up to 172.6%, while maintaining e-taxi service quality with very small overhead, i.e., reducing the supply-demand ratio by 2.2%. 

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