The large-scale integration of wind and solar power is essential for power-sector decarbonization, but their variability creates persistent challenges for forecasting, reserve allocation, storage scheduling, curtailment, and emission-aware operation. This review synthesizes recent advances in statistical, deep-learning, transformer, foundation-model, probabilistic, and physics-informed renewable forecasting, with emphasis on physical consistency, uncertainty, computational requirements, real-time applicability, and system-level operational value. We propose the Physics-Informed Large Model Carbon-Aware (PILM-CA) framework, a conceptual architecture linking physical renewable dynamics, physics-informed large-model forecasting, grid-operational decision support, and carbon-aware optimization. Unlike accuracy-centric forecasting frameworks, PILM-CA explicitly represents the pathway through which forecast uncertainty can affect commitment, reserves, storage, fossil backup, curtailment, and consequent carbon intensity. Published evidence indicates that these effects are system-dependent rather than universal; therefore, the framework distinguishes evidence-supported components from conceptual cross-layer relationships requiring operational validation. Probabilistic forecasting metrics, extreme-event resilience, digital-twin deployment, technology readiness, and climate adaptation are also critically examined. The review identifies research priorities for scalable, physically coherent, uncertainty-aware forecasting that supports both decarbonization and resilience in high-renewable power systems.
Limited substrate availability and competing metabolic reactions constrain photo-fermentative hydrogen production (PFHP) from lignocellulosic biomass. This study evaluated cobalt-iron–nitrogen-doped biochar (Co-Fe-NBC) for enhancing PFHP from corn straw by combining material characterization, abiotic electrochemical analysis and batch fermentation. Experiments used pure Rhodopseudomonas palustris with 6 g corn straw per reactor and 253 mL starting liquid under continuous illumination for 84 h. Co-Fe-NBC exhibited a BET surface area of 291.81 m2/g and scan-rate-dependent current responses in abiotic electrode measurements. At 20 mg/L, cumulative hydrogen production reached 567.65 ± 25.35 mL/reactor, compared with 165.95 ± 4.94 mL/reactor in the unamended control, representing a 242.06 % increase across three complete experiments (mean ± sample standard deviation). Production normalized to added corn straw was 94.61 ± 4.22 mL/g, and the maximum mean 12 h hydrogen-production rate reached 21.24 ± 0.96 mL/h. Increased reducing-sugar concentrations and changes in soluble products, pH and oxidation–reduction potential accompanied hydrogen enhancement; at 36 h, the triphenyl tetrazolium chloride reduction response and NAD+/NADH ratio were 24.73 μg/mL and 1.33, compared with 13.21 μg/mL and 0.83 in the control. These findings support Co-Fe-NBC as an effective additive for enhancing corn-straw PFHP and identify accompanying changes in substrate availability and cellular redox indicators. Future work should clarify the contributions of hydrolysis and material-cell redox interactions and assess metal leaching, toxicity, reuse and long-term performance.
Battery management systems (BMSs) are evolving from threshold-based supervisory units and isolated predictive modules toward intelligent, closed-loop decision architectures capable of diagnosis, prognosis, optimization, and autonomous control. This review examines how agentic artificial intelligence extends conventional AI-enabled BMS research by integrating state awareness, risk-informed reasoning, goal-driven planning, and safety-constrained action across cell-, pack-, and system-level operations. Rather than merely cataloguing prior studies, the review compares major groups of works spanning data-driven state estimation, degradation modeling, reinforcement learning-based control, hybrid physics-informed methods, digital twins, and emerging multi-agent strategies, highlighting their respective strengths, operational assumptions, scalability limits, interpretability, and deployment readiness. The analysis shows that prediction-oriented approaches remain dominant, while comparatively fewer studies achieve robust closed-loop autonomy under uncertainty, aging, thermal coupling, and heterogeneous usage conditions. To address this gap, the paper proposes the Agentic Battery Management Stack (A-BMS), a six-layer framework linking multimodal sensing, health inference, predictive risk assessment, decision intelligence, autonomous control execution, and human oversight. The review concludes by identifying key barriers and research priorities for trustworthy, adaptive, and certifiable autonomous battery management.
Converter-dominated power systems with high penetration of inverter-based resources exhibit fast, nonlinear, and multi-timescale dynamics that introduce new stability modes and operational risks beyond the scope of traditional assessment techniques. In recent years, artificial intelligence and machine learning methods have been widely applied to small-signal, transient, voltage, and frequency stability prediction using simulation data and wide-area measurements. Although many studies report high predictive accuracy, the literature remains fragmented across stability categories, data regimes, and model classes, and typically emphasizes point prediction rather than uncertainty, regime transitions, and control-oriented early-warning. This review presents a systematic and literature-rich synthesis of AI-driven stability assessment approaches for converter-dominated power systems, covering learning architectures, feature representations, graph-based methods, and real-time monitoring integration. Key gaps are identified in uncertainty-aware prediction, cross-timescale stability coupling, interpretability for control action, and operational risk quantification. To address these limitations, a novel AI-Based Stability Risk Envelope Framework is proposed, linking predictive models with uncertainty bounds, operating-region tracking, and decision-triggered early-warning mechanisms for dynamics-informed grid operation.
The removal of nitrogen species, particularly nitrates, is critically important for environmental remediation and the production of renewable energy. Converting nitrogen in wastewater into ammonia is an alternative to the Haber-Bosch process, which constitutes an effective and economical method for addressing nitrate contamination in source waters. This process simultaneously facilitates the reclamation of nitrogen as a renewable and promising energy resource. This approach not only decreases dependence on energy-consuming procedures but is also environmentally friendly. Copper-based electrocatalysts for the nitrogen/nitrate reduction reaction (NRR) have been widely studied, yet a comprehensive understanding of the relationships between their preparation, characterization, and catalytic activity remains incomplete. In this review, we aim to provide a structured summary of synthetic approaches for Cu-based electrodes using both physical and chemical methods, highlighting the structure–activity correlations between synthesis strategies and catalyst performance. We also present a thorough overview of advanced characterization techniques, including structural, morphological, compositional, electrochemical, and in situ/operando studies, to identify active sites, reaction intermediates, and degradation pathways. The electrocatalytic performance of Cu-based electrodes in NRR is critically analyzed and compared, with emphasis on the effects of morphology, surface composition, electrolyte selection, and pH on activity and selectivity. Methods for quantifying ammonia, such as colorimetry, NMR, GC, and ion chromatography, are reviewed to ensure benchmarking and reproducibility. Despite these advances, practical application remains limited due to challenges such as the trade-off between selectivity and activity, catalytic deactivation, and competition from the hydrogen evolution reaction (HER). This work aims to provide a consistent and rational framework for screening Cu-based systems toward highly efficient and cost-effective ammonia electrosynthesis.
