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1. 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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2. Sustainability of lettuce production: A comparison of local and centralized food production

   https://doi.org/10.1016/j.jclepro.2023.139224  

   Maynard, Reid; Burkhardt, Jesse; Quinn, Jason C (November 2023, Journal of Cleaner Production)

   Communities are considering local food production in response to the pressing need to reduce food system greenhouse gas (GHG) emissions. However, local food systems can vary considerably in design and operation, including controlled environment agriculture (CEA), which refers to agricultural production that takes place within an enclosed space where environmental conditions, such as temperature, humidity, and light, are precisely controlled. Such systems require a considerable amount of energy and thus emissions; therefore, this study seeks to quantify these environmental impacts to determine how local CEA systems compare to alternative systems. For this study’s methods, we apply life cycle assessment methodology to quantify the cradle-to-storeshelf GHG emissions and water consumption of four lettuce production systems: local indoor plant factory, local greenhouse, local seasonal soil, and conventional centralized production in California with transportation. Using geographically specific inputs, the study estimates the environmental impact of the different production systems including geospatially resolved growth modeling, emissions intensity, and transportation distances. The results include the major finding that baseline CEA systems always have higher GHG emissions (2.6–7.7 kg CO2e kg−1) than centralized production (0.3–1.0 kg CO2e kg−1), though water consumption is significantly less owing to hydroponic efficiency. In contrast, local seasonal soil production generally has a lower GHG impact than centralized production, though water consumption varies by crop yield and local precipitation during growing seasons. Scenario analyses indicate CEA facilities would need to electrify all systems and utilize low-carbon electricity sources to have equivalent or lower GHG impacts than California centralized production plus transportation. We conclude that these results can inform consumers and policy makers that local seasonal production and conventional supply chains are more sustainable than local CEA production in near-term food-energy-water sustainability nexus decision making. 

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3. Wave Power Density Hotspot Distribution and Correlation Pattern Exploration in the Gulf of Mexico

   https://doi.org/10.3390/su14031158  

   Gu, Chengcheng; Li, Hua (February 2022, Sustainability)

   Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns. 

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4. Budget-Constrained Sizing of Renewable and Energy Storage Systems for Farm-Scale Ammonia Production Within the Food-Energy-Water Nexus

   https://doi.org/10.1109/GreenTech58819.2024.10520453  

   Liu, Xuebo; Modarresi, Alex; Kiboma, Lawryn; Wu, Hongyu; Symons, John; Hill, Mary (April 2024, IEEE Green Technologies Conference)

   In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing cleanhybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation. 

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5. Risk-aware electricity dispatch with large-scale distributed renewable integration under climate extremes

   https://doi.org/10.1073/pnas.2426620122  

   Xu, Luo; Zeng, Hongtai; Lin, Ning; Yang, Yue; Guo, Qinglai; Poor, H Vincent (May 2025, Proceedings of the National Academy of Sciences)

   Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks. 

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