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This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references. Submitted to Applied Energy

Life in soil is a key driver of important ecosystem processes, such as the recycling of carbon and nutrients. In current intensive agricultural soils, however, richness and abundance of many groups of soil organisms are often reduced, which may threaten soil health and sustainable agriculture in the long run. Therefore, a switch to alternative agricultural practices (e.g., minimal tillage) that are less detrimental or even stimulate soil life has been suggested as a way to increase sustainable food production. Although we understand how some of these practices impact specific species or functional groups in soils, it is necessary to get a more complete overview to understand which practices can be used in agriculture to improve soil biodiversity. Here, we present a systematic literature review identifying which practices are studied as alternatives to current, intensive practices for four soil taxonomic groups encompassing a range of trophic groups and functions in the soil ecosystem: nematodes, earthworms, bacteria and fungi. Further, we review how these alternative practices impact the abundance and diversity of these four taxonomic groups, as well as for the 14 functional groups identified and retrieved from the review. We found that a total of 23 alternative agricultural practices, grouped into 10 groups of practices, were studied for the four target taxonomic groups. Three groups of practices, 'fertilization’, ‘soil cover’ and ‘tillage’ were studied for all taxa. In general, alternative agricultural practices had positive impacts on the species richness in the four taxonomic groups and on the abundance of organisms in the functional groups. However, there were some exceptions. For example, organic fertilizers reduced the abundance of epigeic earthworms, while enhancing the abundance of endogeic and anecic earthworms. There was only one alternative practice, i.e., the use of cover crops, that was neutral to positive for the abundance of all functional groups across all taxa. Our review revealed that there are gaps in the literature, as practices that are commonly studied for aboveground biodiversity, such as field margins or flower strips, are not studied well across taxonomic and functional groups and need to be further studied to improve our understanding of the impact of alternative practices on soil life. We conclude that alternative agricultural practices are promising to enhance soil biodiversity. However, as some practices have specific impacts on taxonomic groups in the soil, we may require careful application and combinations of alternative agricultural practices to stimulate multiple groups.

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

CONTEXT: Serious games can be used as a tool for learning, increasing coordination, supporting decision-making processes, and other purposes that can strengthen sustainability transitions. While agriculture is an important corner stone for these transitions, little research has been done on serious games on agricultural and none on the potential link with sustainability issues. OBJECTIVE: This article is a systematic review of published research articles on the use of serious games to address agricultural issues. It aimed to understand how these serious games incorporate or are likely to address sustainability issues. METHODS: The process of the review is described accord to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). After data collection, we conducted a four-step analysis: i) short bibliometric analysis of the corpus, ii) descriptive analysis of the games' characteristics, iii) comprehensive analysis on sustainability based on a framework developed to define what is sustainable agriculture, iv) analysis of assessment of the games. RESULTS AND CONCLUSIONS: Results were based on 237 articles including 182 empirical studies. We showed that the number of articles on serious games in agriculture have recently increased throughout the world. Serious games can reach different goals: i) learning, particularly on specific topics, ii) mediation and co-design, iii) research. Games can be seen as effective means to enable stakeholders to work together. In a context of sustainability transitions, serious games can be used to tackle complex issues. However, more effort must be undertaken to assess the real impact of the game. SIGNIFICANCE: This review confirmed the importance of serious games in agricultural research aiming to enhance sustainability transition. We identified gaps and proposed a research agenda to further work on i) inclusion of the diversity of games, ii) rethinking using of games with possible combinations, iii) opening to broader agricultural productions, iv) assessing the real impact of the games, v) using games for transdisciplinary research.

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.

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation function has a quadratic, strictly dissipative form, for both simplified (constant viscosity) stress tensors and general stress tensors. The proposed expression is not only useful in evaluating energy budgets in turbulent flows, but also in natural convection flows, where it appears in the internal energy equation and is responsible for viscous heating. The viscous dissipation function is such that a consistent total energy balance is obtained: the 'implied' presence as sink in the kinetic energy equation is exactly balanced by explicitly adding it as source term in the internal energy equation. Numerical experiments of Rayleigh-Bénard convection (RBC) and Rayleigh-Taylor instabilities confirm that with the proposed dissipation function, the energy exchange between kinetic and internal energy is exactly preserved. The experiments show furthermore that viscous dissipation does not affect the critical Rayleigh number at which instabilities form, but it does significantly impact the development of instabilities once they occur. Consequently, the value of the Nusselt number on the cold plate becomes larger than on the hot plate, with the difference increasing with increasing Gebhart number. Finally, 3D simulations of turbulent RBC show that energy balances are exactly satisfied even for very coarse grids; therefore, we consider that the proposed discretization forms an excellent starting point for testing sub-grid scale models.

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.

More frequently occurring droughts, related to climate change, lead to reduced growth and loss of vitality in trees. The recent drought of 2018 was extreme, long-lasting and resulted in high evaporative demands due to the concurrent high temperatures. The aim of this study was to compare the drought resilience of nine temperate tree species in the Netherlands, and to determine their responses to the severe drought of 2018 in comparison with five earlier drought events since 1970. To assess drought effects on tree species, we analysed tree-ring series of 678 trees in 45 plots throughout the Netherlands. Resilience indices were calculated based on growth reactions and growth recovery after drought. Furthermore, the impact of drought events on species productivity was quantified. We observed species-specific differences in growth responses to drought timing. All species in nearly all sites responded with growth reductions to drought, except sessile oak (Quercus petraea (Matt.) Liebl.). The most productive species in our study were found to be drought sensitive, with productivity losses of up to 30 % during drought in some sites. Productivity losses were highest on the driest soils. Resilience to the 2018 drought did not differ significantly from other drought years for six out of the nine studied species. However, 77.5 % of the individual trees of all studied species did not fully recover in growth within the following two years. Low post-drought growth remains poorly understood and should be taken into account in future studies to safeguard the health and productivity of the forest under climate change. We consider sessile oak a promising species for future forests in the Netherlands. Based on our results, we provide an outlook on future resilience and growth potential of the species studied under projected climate change for the Netherlands.

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