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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can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references.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

In the quest for effective solutions to address Environ. Pollut. and meet the escalating energy demands, heterojunction photocatalysts have emerged as a captivating and versatile technology. These photocatalysts have garnered significant interest due to their wide-ranging applications, including wastewater treatment, air purification, CO2 capture, and hydrogen generation via water splitting. This technique harnesses the power of semiconductors, which are activated under light illumination, providing the necessary energy for catalytic reactions. With visible light constituting a substantial portion (46%) of the solar spectrum, the development of visible-light-driven semiconductors has become imperative. Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light. In this comprehensive review, we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media, as well as the remarkable progress made in renewable energy production. Moreover, we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems. Finally, we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain. By unraveling the potential of heterojunction photocatalysts, this review contributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.

Lake ecosystems are extremely sensitive to nitrogen growth, which leads to water quality degradation and ecosystem health decline. Nitrogen depositions, as one of the main sources of nitrogen in water, are expected to change under future climate change scenarios. However, it remains not clear how nitrogen deposition to lakes respond to future meteorological conditions. In this study, a source-oriented version of Community Multiscale Air Quality (CMAQ) Model was used to estimate nitrogen deposition to 263 lakes in 2013 and under three RCP scenarios (4.5, 6.0 and 8.5) in 2046. Annual total deposition of 58.2 Gg nitrogen was predicted for all lakes, with 23.3 Gg N by wet deposition and 34.9 Gg N by dry deposition. Nitrate and ammonium in aerosol phase are the major forms of wet deposition, while NH3 and HNO3 in gas phase are the major forms of dry deposition. Agriculture emissions contribute to 57% of wet deposition and 44% of dry deposition. Under future meteorological conditions, wet deposition is predicted to increase by 5.5% to 16.4%, while dry deposition would decrease by 0.3% to 13.0%. Changes in wind speed, temperature, relative humidity (RH), and precipitation rates are correlated with dry and wet deposition changes. The predicted changes in deposition to lakes driven by meteorological changes can lead to significant changes in aquatic chemistry and ecosystem functions. Apart from future emission scenarios, different climate scenarios should be considered in future ecosystem health evaluation in response to nitrogen deposition.

Energy and buildings 326, 114987 (2025). doi:10.1016/j.enbuild.2024.114987 Published by Elsevier Science, Amsterdam [u.a.]

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.

The required data was provided by Energex. The study received partial funding from the Ministry of Science and Innovation, Spain (Project ID: PID2020-115454GB-C21). Partial support of this work was through the LATENTIA project PID2022-140786NB-C31 of the Spanish Ministry of Science, Innovation and Universities (MICINNU) . This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand ( ) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of where the aleatoric and epistemic uncertainties are quantified using maximum likelihood and Monte Carlo Dropout methodologies. The benchmarks of TCN model include an attention-based, bi-directional, gated recurrent unit, seq2seq, encoder–decoder, recurrent neural networks and natural gradient boosting models. The testing results show that the proposed TCN model attains the lowest relative root mean square error of 5.336-7.547% compared with significantly larger errors for all benchmark models. In respect to the 95% confidence interval using the Diebold–Mariano test statistic and key performance metrics, the proposed TCN model is better than benchmark models, capturing a lower value of total uncertainty, as well as the aleatoric and epistemic uncertainty. The root mean square error and total uncertainty registered for all of the forecast horizons shows that the benchmark models registered relatively larger errors arising from the epistemic uncertainty in predicted electricity demand. The results obtained for TCN, measured by the quality of prediction intervals representing an interval with upper and lower bound errors, registered a greater reliability factor as this model was likely to produce prediction interval that were higher than benchmark models at all prediction intervals. These results demonstrate the effectiveness of TCN approach in electricity demand modelling, and therefore advocates its usefulness in now-casting and forecasting systems.