Search Results (5,424)

This study investigates the transient mode-locking dynamics and parameter-sensitive pulse evolution in a ytterbium-doped mode-locked fiber laser under near-zero net-dispersion conditions. The influence of gain saturation energy E_s, modulation depth T₀, saturation power P_sat, and non-saturable loss P_ns on transient pulse evolution and mode-locking buildup is comparatively analyzed using the complex Ginzburg–Landau equation. Numerical results indicate that increasing the gain saturation energy E_s weakens the gain saturation effect and prolongs the transient buildup process of stable mode locking, while simultaneously promoting intracavity energy accumulation and spectral broadening through enhanced nonlinear phase evolution. Increasing the modulation depth T₀ accelerates mode-locking initiation through enhanced nonlinear transmission contrast, whereas saturation power P_sat mainly affects the transient intracavity energy accumulation process during pulse evolution. Increasing the non-saturable loss P_ns suppresses low-intensity fluctuations during pulse buildup and contributes to faster gain–loss stabilization inside the laser cavity. Under near-zero net-dispersion conditions, stable picosecond pulse evolution with consistent spectral and temporal characteristics is numerically obtained. The present results provide useful physical insight into gain–loss interaction mechanisms and transient dissipative-soliton dynamics in ultrafast fiber lasers. Full article

Co-firing biomass with coal in existing circulating fluidized bed (CFB) boilers is a promising strategy for reducing net CO₂ emissions and utilizing renewable energy. However, the impact of biomass-blending ratio on the complexity of multiphase flow, combustion characteristics, and pollutant formation inside a full-scale CFB boiler is not fully understood yet. This study developed Eulerian–Lagrangian Multiphase Particle-In-Cell (MP-PIC) model and this model was employed to simulate the co-combustion of coal and rice husk in a 72 MW industrial-scale CFB boiler. The pyrolysis kinetics of the coal and biomass were first measured experimentally via thermogravimetric analyzer and integrated into the model via source terms. The experimentally validated model was further used to investigate the effects of biomass-blending ratio (0–40 wt.%) on hydrodynamics, temperature distribution, and gaseous pollutant emissions (NO, SO₂). Results indicated that biomass addition has a negligible impact on the overall particle flow patterns and particle volume fraction distribution in the boiler. However, it significantly lowered the average furnace temperature due to the lower calorific value of biomass. A blending ratio of 40 wt.% biomass yielded the most substantial reduction in pollutant emissions at the outlet, with decreases of 51.99% for CO₂, 15.70% for NO, and 49.50% for SO₂ compared to single coal combustion. This study presents the MP-PIC model as an efficient numerical framework for optimizing co-firing operations and shows that a high ratio of biomass co-firing (40 wt.%) is technically feasible and environmentally advantageous in existing CFB boilers. Full article

China’s road transport, especially private vehicles, has experienced continuous growth in energy consumption and carbon emissions in recent years. Electrification-driven net-zero pathways and their impacts on the power sector have drawn broad concern. Current research insufficiently explores vehicle-to-grid (V2G) advantages and fails to update data and assumptions aligned with the latest policies. This study establishes a provincial bottom-up model to calculate the energy demand and carbon emissions of private vehicles and evaluates decarbonization paths and their impacts on the power sector across different scenarios. Private vehicle ownership will rise first and then fall, hitting around 453 million by 2060. Near-term improvements in energy efficiency combined with the long-term diffusion of new energy vehicles can drive private transport toward net-zero emissions after 2050. Vehicle electrification raises electricity consumption remarkably, whereas V2G effectively mitigates carbon shift and offsets over half of cumulative power generation emissions. Marked regional disparities prevail in vehicle usage and emissions, with eastern China presenting higher values compared with western regions. Decarbonization of road transport is more than just addressing carbon shifting, and V2G facilitates cross-sector coordinated emission reduction. Future research is needed to explore the technical, economic and institutional potential for deepening decarbonization. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. Full article

Adding an upper reservoir to conventional cascade hydropower stations to create pumped-storage systems is an effective strategy for enhancing hydropower regulation capacity and promoting high proportion of variable renewable energy consumption. To leverage the cross-seasonal energy and intra-day power regulation capabilities of such hybrid systems, this paper proposes a multi-scale nested dispatch and benefit assessment method. The coordination principles between pumped storage and cascade hydropower under high variable renewable energy penetration are first analyzed. Subsequently, a dynamic time-of-use electricity pricing mechanism is developed by capitalizing on the temporal characteristics of net load, and a multi-scale nested scheduling model that incorporates grid regulation demands is established. A techno-economic assessment framework is further developed to assess the comprehensive benefits of the pumped-storage retrofitting. The Wujiang Basin case study demonstrates significant benefits: a 4.5% improvement in peak–valley difference reduction, a decrease of 1039 GWh in annual variable renewable energy curtailment (8.8% of the system’s total), and a 30.8% rise in generation benefits. Under wet and dry hydrological years, generation benefits increase by 787 million and 645 million CNY, respectively. These results indicate that implementing pumped-storage retrofitting in cascade hydropower basins with abundant but seasonally uneven inflow can better align grid regulation requirements with project economic viability. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

Used engine oil (UEO) is a hazardous waste stream that poses significant environmental risks when improperly managed. However, its high heating value makes it a promising candidate for energy recovery. In Kuwait, rising vehicle ownership has led to increasing quantities of UEO, while the power sector remains heavily dependent on conventional fossil fuels. Although extensive research has examined UEO treatment methods and combustion characteristics, limited attention has been given to its integration into utility-scale power-generation systems. This study presents a national-scale techno-economic and environmental assessment of using UEO as a supplementary fuel for electricity generation in Kuwait. East Doha Power Station was selected as a representative case study to evaluate fuel-substitution potential and the practicality of integrating UEO into existing power-generation infrastructure. Historical vehicle-registration data were used to estimate UEO generation, and future availability was projected through 2035 based on vehicle-growth trends. The corresponding thermal energy potential, equivalent electricity generation, fuel-displacement capacity, economic benefits, and environmental impacts were subsequently evaluated. The results indicate that annual UEO generation is projected to increase from approximately 181,800 tonnes/year in 2024 to 303,300 tonnes/year in 2035. This quantity corresponds to about 12,126 TJ/year of recoverable thermal energy and an equivalent electricity-generation potential of approximately 1.1 TWh/year (4000 TJ/year), assuming a power-plant efficiency of 33%. The recovered UEO could displace approximately 311,000 tonnes/year of heavy oil or 287,000 tonnes/year of crude oil, with estimated net annual fuel-cost savings of approximately 28–30 million KD. Based on literature-reported emission factors, UEO utilization could reduce combustion-related CO₂ emissions by up to 19.0% and NO_x emissions by up to 45.5% compared with heavy oil. Sensitivity analysis further confirmed the robustness of the findings under a range of recovery and operating conditions. To the best of the authors’ knowledge, this study represents the first comprehensive national-scale assessment of the potential use of UEO for utility-scale power generation in Kuwait. The findings indicate that UEO has the potential to serve as a strategic secondary energy resource that supports waste reduction, fuel conservation, economic savings, and circular-economy objectives. However, practical implementation will require appropriate collection and treatment infrastructure together with further technical validation, pilot-scale demonstration, and regulatory evaluation. Full article

Raising indoor temperature setpoints is widely promoted as a practical way to reduce cooling-related energy demand in commercial buildings, yet its net carbon impact becomes uncertain once behavioral rebound effects are considered. This study develops an integrated carbon-accounting framework to evaluate the climate implications of summer indoor temperature increases of 1–3 °C in U.S. mercantile buildings. The framework combines operational energy savings from reduced cooling demand with consumption-driven emissions arising from longer customer dwell times and increased consumer spending under improved thermal comfort conditions. Carbon outcomes are quantified using sector-level electricity data and the USEEIO emission factor for retail trade. The results reveal a clear imbalance: operational carbon savings range from 0.21 to 0.64 Mt CO₂, whereas consumption-driven emissions range from 3.37 to 21.90 Mt CO₂, yielding a consistently positive net carbon impact of 3.16–21.26 Mt CO₂ across all scenarios. A break-even analysis indicates that only 1.30–3.89 billion USD in additional spending is sufficient to offset the operational savings. The findings remained robust across alternative behavioral and carbon-accounting specifications; a 10,000-iteration Monte Carlo analysis produced positive net carbon impacts in every simulation (median 8.54 Mt CO₂; P(NCI > 0) = 1.00). Overall, the results suggest that temperature-based efficiency measures may overstate their climate benefits when behavioral responses are ignored, highlighting the importance of incorporating rebound effects into building energy assessments and commercial climate policy. Full article

This paper compares two combined methods for modeling correlations among multiple random variables in the state-space sampling approach that is adopted in non-sequential Monte Carlo Simulation. The first method combines the Rank Score (Iman and Conover method) with Latin Hypercube sampling, while the second method combines Kernel Density Estimation with Bayesian Networks to represent dependency between variables. The methods are compared in terms of goodness-of-fit and computational efficiency, using statistical metrics, spatial correlation matrices, and scatter plots. The multiple time series were aggregated into single ones (Total and Net Generation) in order to validate the accuracy of each model in representing multi-dimensional correlated variables. The methods were evaluated for 3 large-dimensional cases based on the actual Brazilian energy system, with 10, 19 and 28 time series of 5-, 3- and 2-year horizons, respectively. The results show that the statistical dependencies of the historical data were accurately captured by both models, with comparable goodness-of-fit, but a higher computational efficiency of the first. Full article

Hydrogen is increasingly recognized as a key energy carrier for decarbonizing hard-to-electrify sectors, yet more than 95% of current global production remains fossil-derived. Biological hydrogen (biohydrogen) produced by dark fermentation (DF), photo-fermentation (PF), or microbial electrolysis cells (MEC) offers the dual advantage of valorizing organic wastes while delivering low-carbon H₂; however, none of these standalone technologies mobilizes more than 25–33% (DF), 40–70% (PF), or 40–60% (MEC) of feedstock organic carbon through H₂-producing oxidation pathways. Most existing reviews compare these pathways on hydrogen yield alone, a metric that conceals where the majority of feedstock carbon and electrons are actually lost and obscures the quantitative rationale for system integration. This review reframes the comparison around carbon and electron flow, explicitly tracking how much input carbon is mobilized through H₂-producing oxidation pathways, how much is retained in volatile fatty acids (VFAs), biomass, or unlinked CO₂, and what happens to the associated electrons. Stoichiometric, mechanistic, and reactor-level evidence is synthesized to show that DF channels only 25–33% of input organic carbon through H₂-yielding decarboxylation on real heterogeneous substrates, with 40–60% retained as residual VFAs and unhydrolyzed solids; PF can recover 60–80% of VFA carbon but is constrained by photon economics and nitrogenase sensitivity; and MEC achieves >85% COD removal only when coupled to an upstream acidogenic stage. Two-stage (DF–PF, DF–MEC) and three-stage (DF–PF–MEC, DF–MEC–AD) configurations are critically evaluated, with theoretical yields separated from experimentally demonstrated performance on real wastes and hidden energy inputs (pretreatment, inter-stage transfer, gas separation, and compression) explicitly accounted for. DF–MEC coupling is identified as the most near-term tractable configuration, achieving 55–70% H₂-pathway carbon mobilization and 80–92% COD removal at an electrical input of 0.9–1.5 kWh/m³ H₂, with levelized hydrogen costs of US$3–5.5/kg under favorable waste-tipping-fee conditions. Multi-stage systems push carbon recovery above 70% but carry unresolved capital, methanogenesis control, and scale-up penalties. This review closes by proposing a standardized ten-descriptor reporting framework including H₂-pathway carbon mobilization (%), cathodic hydrogen recovery (rCAT), net energy recovery (NEB), and LCA carbon intensity under both attributional and consequential boundaries, and demonstrates its backward compatibility by retrospective application to seven studies already in the literature. Research priorities tractable on a 5–10 year horizon are identified, centered on methanogen suppression at pilot scale, real-waste MEC performance, and renewable-electricity coupling. Full article

This paper presents a holistic review of Liquid Organic Hydrogen Carriers (LOHCs), focusing on typology, energy efficiency, techno-economic analyses (TEAs), and life cycle assessments (LCAs). Initially, the study categorizes various LOHC systems documented in existing literature, outlining their chemical structures, catalysts, properties, and main applications. This survey aims to provide a comprehensive understanding of LOHC varieties, making it easier to compare across different types. Next, we explore the efficiency of LOHC systems by reviewing hydrogenation and dehydrogenation energy requirements, catalyst behavior, heat-management constraints, product-separation needs, and net energy storage capabilities. This review also includes reaction stoichiometries, recent catalyst and reactor developments, catalyst-deactivation mechanisms, and heat-integration options for high-temperature dehydrogenation. Finally, our investigation offers a detailed evaluation of TEA and LCA for LOHC systems found in the literature. By exploring economic feasibility and environmental impact, this study presents a complete picture of the sustainability of LOHC deployment. It includes assessments of life-cycle carbon emissions, levelized cost, supply-chain configuration, carrier recyclability, infrastructure compatibility, renewable-electricity capacity factors, and heat-recovery assumptions. Our findings aim to contribute to hydrogen storage and transport research by identifying the most important technical, economic, and environmental trade-offs for LOHC systems. By addressing gaps in recent literature, TEA comparability, and LCA coverage, this paper seeks to advance the development of LOHC technologies and support their broader adoption in a green and sustainable energy landscape. Full article

In response to the contradiction between the insufficient flexibility of nuclear power due to the high proportion of renewable energy grid connection and the increasing demand for system frequency regulations, this paper proposes a coordinated operation model of a nuclear–storage consortium. It uses all-vanadium redox flow batteries as the flexibility transformation solution. Referring to the PJM market and the Guangdong electricity market mechanism, a two-tier optimization model for the nuclear power–energy storage consortium’s participation in the electricity energy/secondary frequency regulation market is constructed. The upper layer optimizes the scale of energy storage configuration, and the lower layer realizes joint clearing based on the Security-Constrained Unit Commitment–Security-Constrained Economic Dispatch (SCUC-SCED) framework. It takes into account the nuclear frequency regulation safety share constraint and energy storage performance coefficient. The case analysis demonstrates that the configuration of 70 MW/70 MWh vanadium redox flow batteries can increase the annualized net income of the consortium by 2.8429 million yuan, mainly by shifting the nuclear power regulation space from the energy market to the high-value frequency regulation market. This study verifies the feasibility of the nuclear–storage synergy model in enhancing market competitiveness while ensuring nuclear safety, providing a quantitative reference for the flexibility transformation of nuclear power and the design of power market mechanisms. Full article

Sustainable EV charging infrastructure is fragmented by proprietary applications, vendor lock-in, and weakly time-differentiated pricing, blunting its contribution to urban-mobility decarbonisation. This paper asks whether an open-protocol, super-app-mediated photovoltaic–storage charging architecture can jointly resolve these three fragmentations under deployed field conditions and what its sustainability profile then looks like. We report a campus photovoltaic–storage microgrid integrating heterogeneous EV chargers under an open, vendor-neutral charging-control protocol with super-app authentication and payment replacing dedicated charging applications and a time-differentiated tariff aligned at the meter-interval level with the underlying utility wholesale rate; the deployment is exercised through a researcher-scheduled commissioning campaign of 13 sessions designed to establish functional correctness across the operating envelope rather than to measure user behaviour. Three results emerge across cross-vendor compatibility, onboarding friction, and grid alignment. First, basic message-level OCPP compatibility is sustained across two charger vendors under a single cloud management system—in sequential single-vendor sessions—including the full charging profile up to near-rated DC peak power. Second, the super-app-mediated workflow, which requires no charging-specific application installation and no new charger-operator account, structurally eliminates the dedicated application installation and the email/SMS/credit-card verification round-trips of conventional onboarding, compressing measured first-use end-to-end interaction to 31 s; relative to reconstructed commercial-operator baselines, this is, to the best of the authors’ knowledge, an order-of-magnitude reduction rather than a controlled benchmark. Third, mid-day energy delivery aligns incidentally with the utility off-peak window, not user-driven demand shifting, while PV-displacement and BESS-discharge contributions to charging are bracketed by scenario rather than being separately metered. The paper’s contribution is therefore a replicable, policy-embedded sustainable charging architecture validated at field scale within the New Taipei Net-Zero Carbon Demonstration Site Programme, with no claim of global novelty; the same architecture is structurally positioned to convert the observed incidental grid-friendliness into a deliberate, user-facing benefit via a hardware-free mid-day-discount redesign. Full article