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Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model's decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6\% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

Climate change, along with infectious diseasespathogens notably Batrachochytrium dendrobatidis (Bd), B. salamandrivorans (Bsal), Ranavirus, and PerkinseaPerkinsus, continue to devastate global amphibian populations, contributing to the greatest vertebrate extinctions of the Anthropocene. These pathogens, primarily favoring cooler, subtropical conditions, demonstrate a significant overlap in their climatic niches, thus affecting a broad range species. Here, we aim to explore the role of global warming and other climatic factors in the dispersal and evolution of these pathogens and to predict the future implications for amphibian populations worldwide. Given the limitations of data availability We conducted a thorough analysis of the climatic niche conservatism (NC) and evolution (CNE) of these pathogens using the currently available distributional data, including our own. We used , We engaged in a comprehensive analysis of the climatic niche conservatism (NC) and evolution (CNE) of these pathogens, utilizing predictive models to anticipate potential shifts in their future distribution and evaluate the capacity for CNE in response to climate change. We show that Bd and Bsal are likely to experience a total reduction in their current potential distributions by 2040, while Ranavirus and PerkinseaPerkinsus may expand their distributions. Interestingly, CNE has played a significant role in influencing the climatic niches of Bd and Bsal, with lineage dependent variations. However, there was no strong correlation found between virulence of Bd and its climatic niche. On the contrary, ranaviruses Ranaviruses and PerkinseaPerkinsus showed evidence of sporadic and recent CNE. Moreover, the emergence of lineages adapted to warmer climates suggests an ongoing CNE and a potential evolutionary response to climate change. With increased infection risk, particularly for Asian amphibians (from Ranavirus and PerkinseaPerkinsus), and the vulnerability of the southern hemisphere (except Bsal) due to limited prior exposure, this study underscores the urgent need for close monitoring and preventive measures, including stringent biosecurity protocols such as risk analysis and pre-border pathogen screening. Our study provides a critical framework for international collaboration and guideline development for amphibian trade, while contributing to the deeper dialogue on mitigating impacts of climate change on wildlife diseases.

Existing modeling approaches for long-duration energy storage (LDES) are often based either on an oversimplified representation of power system operations or limited representation of storage technologies, e.g., evaluation of only a single application. This manuscript presents an overview of the challenges of modeling LDES technologies, as well as a discussion regarding the capabilities and limitations of existing approaches. We used two test power systems with high shares of both solar photovoltaics- and wind (70% - 90% annual variable renewable energy shares) to assess LDES dispatch approaches. Our results estimate that better dispatch modeling of LDES could increase the associated operational value by 4% - 14% and increase the standard capacity credit by 14% - 34%. Thus, a better LDES dispatch could represent significant cost saving opportunities for electric utilities and system operators. In addition, existing LDES dispatch modeling approaches were tested in terms of both improved system value (e.g., based on production cost and standard capacity credit) and scalability (e.g., based on central processing unit time and peak memory usage). Both copper plate and nodal representations of the power system were considered. Although the end volume target dispatch approach, i.e., based on mid-term scheduling, showed promising performance in terms of both improved system value and scalability, there is a need for robust and scalable dispatch approaches for LDES in transmission-constrained electric grids. Moreover, more research is required to better understand the optimal operation of LDES considering extreme climate/weather events, reliability applications, and power system operational uncertainties. Comment: 45 pages, 16 figures, Submitted to Renewable and Sustainable Energy Reviews

The current body of research on the gender-energy nexus has largely concentrated on the effects of energy poverty within households, highlighting the impact on women in domestic settings. Nonetheless, women entrepreneurs engaged in productive activities are also pivotal in adopting new energy technologies. This second version of the dataset incorporates significant updates, presenting raw and processed data from 40 face-to-face interviews conducted across multiple African countries, now including Nigeria, which was previously excluded. The dataset focuses on micro and small-sized enterprises with at least one female owner, offering a more unified and comprehensive sample to assess energy access among women entrepreneurs in Africa and explore the potential for renewable energy adoption. The data collection through semi-structured, face-to-face interviews occurred between February and October 2024. The interviews followed a predetermined questionnaire designed to collect quantitative and qualitative data. A new notes section explains the main methods and references used in the dataset. Distinctions are also made between primary and secondary data for appliance power ratings, ensuring transparency in cases where secondary data supplements gaps. This version includes updated technical data, such as time-of-use information for appliances, enhancing the dataset's strength in providing technical insights. Key components of the dataset include: - Socio-economic factors: Enterprise location, ISIC division and industry sector classification, main production goods, gender-based ownership structures, enterprise formality (based on registration), year of establishment or business start, enterprise size (number of employees), profit margins, and business challenges related to the owner's gender. - Energy access characteristics: Type of energy carriers used, subapplications, energy supply shortages, energy consumption levels, type, number, power rating of appliances used, temperature requirements, time-of-use data, and energy expenditure. - Potential for renewable energy adoption: Type and amount of process waste, perceived barriers and drivers for renewable energy adoption, willingness to invest in new technologies, and preferred financing methods for such technologies. This updated dataset provides enhanced value for researchers, policymakers, and practitioners aiming to understand the energy access landscape for women entrepreneurs in Africa. It offers a robust foundation for developing targeted interventions that promote gender equity in energy access and encourage renewable energy adoption.

Climate models encode our collective knowledge about the climate system and are among the best tools available for estimating past and future climate change. However, in response to greenhouse gas forcing, climate models exhibit a large intermodel spread in various aspects of the climate system, adding considerable uncertainty to future climate projections. This dissertation introduces a series of conceptual models and frameworks to understand the behavior of climate models under greenhouse gas forcing and, consequently, Earth's changing climate. A simple statistical model is used to explain and constrain the intermodel spread in Arctic sea ice projections across climate models. The probability of encountering seasonally ice-free conditions in the twenty-first century is also explored by systematically constraining components of the statistical model with observations. A conceptual framework is introduced to understand controls on the strength and structure of the Atlantic meridional overturning circulation (AMOC) in climate models. This framework is used to explain why climate models suggest the present-day and future AMOC strength are related. This framework, in conjunction with observations, implies modest twenty-first-century AMOC weakening. A simple energy budget framework is used to examine precipitation over a wide range of climates simulated by climate models. It is shown that in extremely hot climates, global-mean precipitation decreases despite increasing surface temperatures because of increased atmospheric shortwave absorption from water vapor, which limits energy available for surface evaporation. These results have large implications for understanding weathering rates in past climates as well as Earth's climate during the Hadean and Archaean eons. Finally, a framework is introduced to reconcile two different approaches for quantifying the effect of climate feedbacks on surface temperature change. The framework is used to examine the influence of clouds on Arctic amplification in a climate model and an energy balance model. This work introduces an important non-local mechanism for Arctic amplification and shows that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic warming. This dissertation advances our understanding of various aspects of Earth's changing climate and provides a series of conceptual frameworks that can be used to further constrain the behaviour of climate models in response to external forcing.

The study of disease ecology aims to understand the complex interactions among hosts, environments, and pathogens which result in a final disease outcome. An area of research that has been expanded within this field in recent years is the impact of climate change and global warming. Climate change impacts are of particular concern as the alterations of a host or pathogen’s physiology to more variable or warm environments have been found to be highly influential of disease outcomes in many disease systems. To understand the influence of climate change on disease systems, researchers have assessed the thermal responses of a given pathogen or host in constant laboratory conditions, which may be difficult to relate to more complex, natural environments, or variable field conditions that may be difficult to disentangle direct cause and effect of individual environmental factors on physiological traits. A primary focus of this dissertation is to incorporate the complexities of variable temperatures predicted with climate change conditions in experimental evolution that can assess the implications of climate change on a pathogen known as Batrachochytrium dendrobatidis (Bd) and the resulting disease outcomes within the chytridiomycosis system. In the first chapter of this dissertation, I conduct a literature review of the impact climate change may have on disease systems and the role that temperature has on the thermal biology and adaptive potential of pathogens and hosts within a given disease system. In the second chapter, I assess and establish the characteristics of thermal biology for multiple isolates of Bd that will be used in later chapters. In the third chapter, I use the knowledge of the thermal biology of the isolate from New Mexico to understand patterns of seasonal infection intensity observed in the field. In the last chapter, I assess the physiological responses and adaptive potential of previously studied isolates within this dissertation when experimentally evolved to climate change simulations.