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Thermal energy storage (TES) plays a key role in concentrating solar power (CSP) plants by enhancing dispatchability and improving overall system efficiency. This study presents a comparative techno-economic analysis of three TES configurations integrated into CSP plants: (i) the conventional two-tank molten salt system, (ii) a phase change material (PCM)-based system with a cascade arrangement, and (iii) a concrete-based system. While technical performance simulations indicate similar annual energy production across all cases, significant differences emerge in economic viability. The PCM TES system demonstrates the lowest levelized cost of electricity (LCoE) at $14.35/kWh, leveraging its high energy density and reduced material requirements, despite lower efficiency (93 % compared to 99 % in molten salt). Conversely, the concrete TES system, while capable of extended discharge at partial loads, incurs higher parasitic losses and investment costs, resulting in a higher LCoE ($16.16/kWh). The cost analysis further highlights the cost-performance quotient (CPQ) as a valuable metric for assessing TES competitiveness, with PCM exhibiting the most favorable CPQ of $1.04/kWh. These findings underscore the necessity of integrating economic assessments into TES selection for CSP plants. Moreover, the study identifies opportunities for cost reduction in concrete TES through optimized modular designs and improved material formulations. This work provides valuable insights for policymakers, engineers, and industry stakeholders aiming to enhance the financial feasibility of next-generation CSP storage solutions. This study receives funding from the Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) through the PCI2020-120695-2 project and the European Union “NextGenerationEU”/PRTR“. CSP-ERA.NET is supported by the European Commission within the EU Framework Programme for Research and Innovation Horizon 2020 (Cofund ERA-NET Action, N° 838311). This work was partially funded by the Ministerio de Ciencia e Innovación de España TED2021-132216A-I00 funded by MCIN/AEI/10.13039/501100011033 and the European Union by NextGenerationEU/PRTR. This work was partially funded by the Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación (AEI) (PID2021-123511OB-C31 – MCIN/AEI/10.13039/501100011033/FEDER, UE) and (RED2022-134219-T). This work is partially supported by ICREA under the ICREA Academia programme. The authors from University of Lleida would like to thank the Departament de Recerca i Universitats of the Catalan Government for the quality accreditation given to their research group (2021 SGR 01615). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia.

The training process of learning-based distribution system state estimation (DSSE) methods relies on accurate state variables, which typically contain unknown noise and outliers in practice. To this end, this paper proposes an adaptive noise-resistant graphical learning-based DSSE method considering the impact of inaccurate state variables. Specifically, two global-scanning graph jumping connection networks are first developed to capture the regression rules between measurements and state variables considering the structure constraints. To mitigate the negative impact caused by inaccurate labels, a collaborative learning framework is further developed, within which Gaussian mixture model-based discriminators are employed to adaptively select clean samples in each mini-batch. These allow the method to obtain robustness against noisy state labels in historical data, as well as anomalous measurements during online operations. Comparative tests show the superiority of the proposed method in tackling abnormal data in both the training and test phases.

AbstractWe assessed hepatocellular carcinoma (HCC) risk associated with smoking and alcohol consumption and their interactions, using both questionnaire data and objective serum biomarkers. Information on smoking and alcohol consumption was collected at baseline from 450,112 participants of the EPIC cohort, among whom 255 developed HCC after a median follow‐up of 14 years. In a nested case–control subset of 108 HCC cases and 108 matched controls, known biomarkers of smoking (cotinine, nicotine) and habitual alcohol consumption (2‐hydroxy‐3‐methylbutyric acid) were annotated from untargeted metabolomics features. Multivariable‐adjusted hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were computed, and multiplicative and additive interaction parameters were calculated. Compared to never smokers, current smokers had a higher HCC risk (HR = 2.46, 95% CI = 1.77–3.43) dose‐dependently with the number of cigarettes smoked per day (Ptrend <.001). Compared to light drinkers, HCC risk was higher in former (HR = 3.20, 95% CI = 1.70–6.03), periodically heavy (HR = 1.98, 95% CI = 1.11–3.54), and always heavy (HR = 5.51, 95% CI = 2.39–12.7) drinkers. Higher HCC risk was also observed in the highest versus the lowest tertiles of cotinine (OR = 4.88, 95% CI = 1.52–15.70), nicotine (OR = 5.80, 95% CI = 1.33–25.30) and 2‐hydroxy‐3‐methylbutyric acid (OR = 5.89, 95% CI = 1.33–26.12). Questionnaire‐assessed smoking and alcohol exposures did not demonstrate an HCC risk interaction at the multiplicative (MI = 0.88, 95% CI = 0.40–1.96) or additive (RERI = 0.71, 95% CI = −10.1 to 23.6; attributable proportion = 0.17, 95% CI = −0.52 to 1.16; synergy index = 1.27, 95% CI = 0.98–1.66) scales. Similar analyses with cotinine, nicotine, and 2‐hydroxy‐3‐methylbutyric acid also did not show interactions between smoking and alcohol consumption on HCC risk. Smoking and alcohol consumption are strong independent risk factors for HCC and do not appear to synergistically impact its risk, but larger studies are needed.

Short-term load forecasting (STLF) plays an important role in real-time decision-making and management of the power system while is still a challenging task. Considering that sleep improves brain memories and cognitive processes, this paper explores a approach of integrating biological mechanisms to reduce information loss of networks, and hence proposes a sleep-induced network (SI-Net) by analogy for achieving high-performance STLF. Firstly, through mimicking the sleep process, a multi-level bionic flowchart of the SI-Net is designed to integrate the gated, attention, parallel, cooperative, and asynchronous mechanisms, which not only encode features from coarse to fine but also enhance the fitting capability at the feature layer. Secondly, through imitating the brain memory paths during sleep, the primary and secondary memory paths are designed to update and store information, respectively, and their independence and collaboration avoid information loss in the SI-Net. Thirdly, the loss function constructed by the Gaussian kernel makes nonlinear errors linearly separable in the high-dimensional space, being beneficial to train the SI-Net. The experiments with real-world load datasets are performed and the results show that the SI-Net outperforms 15 baselines and presents high accuracy and stability. Bionically-inspired ideas are promising to design high-performance forecasting networks for energy systems.

ABSTRACTThe primary production of fjords across the Arctic and Subarctic is undergoing significant transformations due to the climatically driven retreat of glaciers and ice sheets. However, the implications of these changes for upper trophic levels remain largely unknown. In this study, we employ both bulk and compound‐specific stable isotope analyses to investigate how shifts at the base of fjord food webs impact the carbon and energy sources of consumers. Focusing on two rapidly changing fjords in Southern Greenland, we used the migratory Arctic char as an indicator species, sampling populations along environmental gradients within the fjords, building upon the assumption that char populations feed primarily close to their natal stream, thereby integrating a dietary gradient. Our analysis of bulk stable isotopes in Arctic char tissue confirmed this premise, revealing a consistent change in resource use from the outer to the inner fjord, which nonetheless served as preferred feeding grounds. Essential amino acid analysis further indicated shifts in carbon and nitrogen sources, consistent with changes in nutrient use near glacier inputs characterized by low turbidity and high iron levels. Notably, these changes in the source of primary production were associated with shifts in trophic positions and the transfer of polyunsaturated fatty acids, with Arctic char in glacier‐influenced inner fjords feeding at lower trophic level (size‐corrected) and accumulating higher levels of high‐quality docosahexaenoic acid (DHA). These findings highlight the usefulness of new analytical tools in revealing that glacial retreat can substantially alter food web dynamics, enhancing both carbon flow and the nutritional quality of fish in fjord ecosystems. The two Southern Greenland fjords studied could represent the future of other fjords, where retreating glaciers become land‐terminating and glacial inputs decrease. Our study underscores the critical role of glacier dynamics in affecting high‐level consumers, such as salmonids, with implications for fjords globally.

This paper presents a theoretical comparison of power swing characteristics between conventional synchronous generator (SG)-based systems and grid-following-voltage source converter (GFL-VSC) systems. Unlike conventional SG-based systems where the swing dynamics are characterized by the change of physical angle between different power sources, the swing characteristics in GFL-VSC systems are influenced by the control dynamic of the VSC, and the loss of synchronization is characterized by the divergence of the output angle of the phase-locked loop, which is control-dependent. Building on these insights, this paper further explores the performance and efficacy of impedance-based swing-detecting schemes at GFL-VSC systems. To validate the correctness of the comprehensive analysis, different simulations are carried out using the PSCAD/EMTDC platform, both in single-machine and multi-machine systems.

Scenario analysis plays a central role in estimating how global changes affect the relationships linking ecosystem conditions and functioning to human needs. This is particularly true for agroecosystems, which are pivotal to ensure sustainable land planning, ecological management and food security strategies. Soils are key providers of multiple ecosystem services (ES) in agroecosystems but they are very sensitive to global drivers such as changes in climate, land use and cover. How agroecosystems should achieve sustainability, through optimizing soil capacity to supply ES while limiting the occurrence of threats, is a priority of EU policy agendas. Nevertheless, there is currently a lack of a comprehensive framework of scenario-based approaches to assess changes in soil ES (SES) and soil threats (ST). As a part of the project SERENA funded by the European Joint Program on Agricultural Soil Management, this study aims to: i) understand how drivers of global change are commonly studied in the scientific literature; ii) identify how some SES and ST are assessed in scenario-based approaches; iii) provide a preliminary discussion on how soil properties are represented in these approaches. Through a systematic review of 230 published articles related to seven SES and ten ST, this study highlights that not all SES and ST are considered with the same frequency and geographic distribution in scenario-based approaches. Despite a great methodological variability in the assessment and mapping of SES and ST, dominant methodological trends can be identified. SES are mapped more frequently than ST and, specific SES appear more disposed to spatially explicit assessments than others. Due to its novelty and complexity, research on this topic is limited to a small subset of ST or SES and projections of the combined impacts of climate, land use and management changes on multiple ST and SES should be a scientific priority to help policy makers.