Energy labeling converts otherwise difficult-to-observe product energy performance into standardized and comparable information, thereby reshaping market incentives for energy-efficient production; however, whether these incentives translate into more effective utilization of firms’ quasi-fixed productive capacity remains insufficiently understood. This study examines the impact of the China Energy Label (CEL) on firm-level capacity utilization using 41,106 firm-year observations for 3862 Chinese A-share listed firms from 2001 to 2024. Exploiting the staggered inclusion of product categories in the CEL catalogue, we estimate a multi-period difference-in-differences model with firm and year fixed effects, with capacity utilization (CU) measured using a stochastic frontier production function. We also conduct a battery of identification and robustness checks, including event-study tests, the Sun–Abraham estimator, and leave-one-batch-out analyses. The results show that CEL increases CU by approximately 1.2 percentage points under a specification that accounts for time-varying city- and industry-level shocks. The positive effect persists across a range of identification and robustness checks. Mechanism analyses provide suggestive evidence that CEL is positively associated with green managerial cognition, breakthrough innovation, and internationalization. These findings are consistent with these factors serving as potential channels through which CEL may improve CU. The effect is more pronounced among state-owned and loss-making firms. Overall, the findings indicate that energy labeling can improve production-side efficiency by promoting more effective use of firms’ existing productive capacity.
Conventional methods for determining shale oil content are costly and time-consuming when large numbers of samples must be analyzed. This study developed and evaluated a general regression neural network (GRNN) to predict shale oil content using a compiled global dataset comprising 94 oil-shale observations. Following predictor-redundancy analysis, eight variables were retained as model inputs: analytical moisture, ash, total sulfur, and the elemental composition of kerogen (C, H, S, N, and O). The selected NeuralTools (NT) GRNN yielded R2 = 0.950, RMSE = 2.02 percentage points, and MAE = 1.30 percentage points for the 75 training observations. For the 19-observation hold-out testing subset, the corresponding values were R2 = 0.871, RMSE = 2.35 percentage points, and MAE = 1.95 percentage points. Repeated 10-fold cross-validation using an independently implemented standard GRNN produced a pooled out-of-fold (R2) of 0.595, RMSE of 5.47 percentage points, and MAE of 3.81 percentage points, demonstrating sensitivity to data partitioning. Under identical repeated cross-validation partitions, multiple linear regression provided lower mean RMSE and MAE than the independently implemented GRNN. Ash was the dominant predictor in the selected NT model. The results support the use of the GRNN as a preliminary screening and sample-prioritization tool but also demonstrate the importance of robust validation when modeling relatively small and heterogeneous geological datasets.
Accurate daily forecasts of thermal generation, renewable generation, hydropower, and imported electricity are essential for spot-market trading and day-ahead scheduling. However, short training histories, incomplete operational information, and distinct target drivers constrain accuracy and generalizability. To address these challenges, this study proposes a Delta-enhanced, validation-guided framework for configuring target-specific forecasting pathways. The Delta representation captures inter-day changes in thermal and hydropower generation. For the internal renewable-generation target, a regional target-day weather-forecast interface is applied retrospectively. It aggregates 13 hierarchical regional forecast columns for each of 24 weather variables and incorporates six calendar variables. For each target, chronological validation selects an admissible pathway defined by forecast-origin information, target representation, predictor family, optional temporal representation, and output rule. Once selected, it is refitted and fixed for subsequent forecasting. The evaluation covers four internal targets from a regional power system in China and six national-scale targets from France. The 97-day evaluation yielded R2 values of 0.8084, 0.7337, 0.7782, and 0.5212 for thermal generation, renewable generation, hydropower, and imported electricity, respectively. The frozen thermal and renewable pathways reduced RMSE by 23.2% and 50.9%, respectively, relative to Persistence, a baseline that carries the previous-day value forward. These findings support target-specific forecasting under data constraints.
This study presents the development and validation of a predictive 0D/1D combustion model for a high-performance direct injection hydrogen spark-ignition engine, explicitly incorporating thermo-diffusive instability (TDI) effects through a physics-based formulation derived from linear flame stability theory and DNS-informed scaling laws. A laminar flame speed correlation obtained from detailed chemical kinetics is enhanced via an instability growth-rate model and embedded within an entrainment-based turbulent combustion framework, coupled with an Extended Zeldovich mechanism for NOx prediction. The model is validated against experiments covering 2000–7500 rpm, loads up to 28 bar IMEP-H, and relative air-to-fuel ratio between 1.2 and 3.0. The results demonstrate that instability-aware modeling is essential for accurate ultra-lean combustion prediction, where conventional approaches severely underpredict burning rates. Across the full operating map, combustion phasing is predicted within ±3 CAD and NOx with an average error of 20%, enabling robust system-level simulation and virtual calibration of high-performance hydrogen engines.
Accurate pre-construction carbon accounting for power equipment is critical for grid decarbonization, yet a standardized methodology applicable to transmission and substation projects remains lacking. This study develops a pre-construction carbon accounting methodology for transmission and substation equipment and applies it to a 220 kV project in China, employing localized emission factors and investment-based screening to identify nine key equipment categories. The analysis quantifies carbon emissions in raw material extraction and manufacturing stages, yielding 23 154 tCO2. Results show that substation equipment, despite its compact footprint, accounts for 35% (8087 tCO2) of total emissions—disproportionately high relative to the 30 km transmission line. Emissions are highly concentrated: aluminum conductor steel-reinforced (ACSR), gas-insulated switchgear (GIS), and steel towers collectively contribute nearly 90%, with aluminum dominating ACSR and GIS emissions due to its high emission intensity (28.5 tCO2/t). Sensitivity and scenario analyses identify aluminum and steel emission factors and GIS aluminum mass as the dominant uncertainty sources. The low-carbon scenario demonstrates a 34.7% abatement potential (8024 tCO2) through hydropower-based aluminum, direct-reduced iron–electric arc furnace (DRI–EAF) steel, and compact GIS design, while the equipment-level emission hierarchy remains stable across all scenarios. These findings underscore that material decarbonization and compact design, pursued synergistically, offer high-impact pathways for pre-construction emission reduction.
The main goal of this review, based on selected papers, is to analyze various types of batteries, proton exchange membrane fuel cells, and hybrid solutions in the operation of unmanned aerial vehicles. We discuss the advantages and limitations of current battery solutions, with a particular emphasis on lithium-polymer and lithium-ion batteries, as well as current hybrid solutions and fuel cell technologies in the design of unmanned aerial vehicles for military applications, or more specifically, for relay drones. This article is completed by a discussion of current energy management strategies in the operation of these types of aircraft. Selected examples of possible energy storage types and their impact on relay drone design are presented and an indication of future directions for the development of these technologies.
With the large-scale integration of Electric Vehicles (EVs) into distribution systems, the spatiotemporal uncertainty of charging loads and the interplay between user charging behavior and network operational constraints present new challenges to the safe and economical operation of the power system. To address the insufficient coordination among flexibility characterization, distributed optimization, and user-side responses, this paper proposes a closed-loop collaborative dispatch strategy. The strategy integrates flexibility aggregation, endogenous dynamic pricing, and user charging-station selection behavior. Firstly, a three-tier collaborative architecture comprising the Distribution System Operator (DSO), Electric Vehicle Aggregators (EVAs), and EV users is established, with rolling updates implemented using Model Predictive Control (MPC). A flexible aggregation model is developed based on set operations of vehicle-level constraints, dynamically calculating power boundaries and energy feasibility domains. Furthermore, a distributed coordinated optimization model between the DSO and multiple EVAs is established and solved via the Alternating Direction Method of Multipliers (ADMM) under privacy-preserving conditions. By analyzing the correlation between ADMM dual variables and the marginal value of network constraints, a Distribution Locational Marginal Pricing (DLMP) -inspired dynamic price signal—endogenous to the optimization—is constructed to guide spatial reallocation of charging loads. Joint simulations based on an IEEE 33-node distribution network and the Sioux Falls transport network demonstrate that the proposed strategy reduces 24 h network losses from 9.05 MWh (uncoordinated) to 8.41 MWh, lowers user total costs from 22,200 yuan to 7100 yuan, and eliminates voltage limit violations (duration reduced from 1.50 h to 0), while exhibiting good distributed solution performance and closed-loop control capability.
This study presents a new improved primal–dual interior point method associated with a generation scaling factor (PDIPM–GSF) for congestion-aware optimal power flow (OPF) in power systems with renewable integration in a deregulated electricity market. The proposed framework combines the robustness of the primal–dual interior point method with an adaptive generation scaling strategy that adjusts generator outputs according to transmission line flow sensitivities, enabling early congestion mitigation, improved power flow feasibility, and accurate determination of locational marginal prices (LMPs). The proposed approach is evaluated on the IEEE 30-bus test system and a real 114-bus Algerian power network. The results demonstrate that the proposed framework can reduce generation costs and improve social profit under both single-sided and double-sided market operation through enhanced coordination between generation, demand, and congestion management. The impact of integrating wind energy is analyzed under different levels of wind energy penetration in the first IEEE 30-bus test system, showing that the integration of wind energy can not only improve social welfare and save generation costs but also lower transmission losses. Additionally, the results show that the placement of wind farms can improve the performance of the proposed PDIPM–GSF framework in terms of congestion management and reduction in LMPs at important nodes. For the 114-bus Algerian transmission network, renewable energy integration further demonstrates the capability of the proposed framework to reduce transmission losses and generation costs while improving overall market performance. These results demonstrate that the proposed PDIPM–GSF framework provides an effective smart optimization approach for renewable-integrated power systems by improving congestion management, facilitating large-scale WE integration, enhancing market efficiency, and ensuring reliable LMP computation. The proposed method, therefore, represents a practical and efficient solution for supporting future renewable energy transition and competitive electricity market operation in Algeria and other renewable-rich power systems.
A reduced-order, mass- and energy-conserving combustion-capacity model was developed to represent premixed and mixing-controlled heat release in a direct-injection diesel engine. Unlike conventional reduced-order formulations that prescribe heat-release shape or correlate heat release directly with fitted functions, the proposed framework first determines the fuel mass physically capable of combustion from the simultaneous availability of injected fuel and stoichiometric air. Fuel prepared during the ignition-delay period forms a finite premixed reservoir that is consumed through an analytical depletion law. After the start of combustion, the remaining fuel and entrained air define a continuously evolving mixing-controlled combustion capacity consumed through a finite-rate formulation. Explicit fuel-allocation and conservation constraints prevent double-counting between the two combustion stages and limit cumulative heat release to the available fuel chemical energy. The model was evaluated using experimentally derived apparent heat-release-rate histories at six engine speeds from 1200 to 2200 rpm and brake torques of 150 and 200 N·m. The premixed combustion-rate coefficient was determined for each operating condition from the corresponding average fuel-injection pressure difference, while the mixing-controlled coefficient was maintained constant. Across the 12 operating conditions, the root-mean-square error (RMSE) ranged from 0.0055 to 0.0224 kJ/°CA, the mean absolute error (MAE) from 0.0032 to 0.0113 kJ/°CA, and R2 from 0.446 to 0.968. The absolute peak heat-release-rate error remained below 3% for 10 of the 12 cases. The results demonstrate that the proposed framework reproduces the principal apparent heat-release characteristics while maintaining explicit separation and conservation of the premixed and mixing-controlled fuel reservoirs.
Anaerobic digestion (AD) plays a central role in renewable energy generation and sustainable waste management. However, the operation of AD systems remains challenging due to the complex interactions among microbial communities, substrate variability, and non-linear process dynamics. Traditional monitoring and control approaches often fail to anticipate disturbances or maintain optimal conditions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities to model, predict, and control AD processes by leveraging high-resolution sensor data and data-driven algorithms. This review synthesizes current progress in ML- and AI-based approaches for prediction, optimization, and intelligent control of AD, with particular emphasis on data processing pipelines, neural network architectures, soft sensors, digital twins, and explainable AI.
This study presents a 3D computational fluid dynamics (CFD) investigation of methane and methanol combustion in a medium-speed, large-bore marine spark-ignition engine equipped with a pre-chamber (PC) ignition system. Simulations were performed in ANSYS Forte at 100%, 80%, and 20% engine load. Methane operation with an active PC was used as the reference configuration, while methanol was investigated with both active and passive PC. A preliminary injection-timing analysis was conducted for the active methanol configuration to obtain a near-stoichiometric and sufficiently homogeneous mixture inside the PC at spark timing (ST). The results show that active methanol operation promotes earlier heat release, shorter combustion duration, and higher thermal efficiency than methane operation. The active PC generates stronger turbulent reacting jets and ensures more robust combustion than the passive configuration. Methanol also considerably reduces NOx emissions because of its lower initial and combustion temperatures. However, active methanol operation increases CO emissions, particularly at low load, because of incomplete oxidation associated with low temperatures, mixture inhomogeneity, and possible spray–wall interaction. The passive PC further reduces NOx and CO emissions but causes delayed combustion, lower thermal efficiency, higher fuel consumption, and tank-to-wake CO2 emissions than the active methanol configuration.
This study investigates CO2–brine–rock interactions in Entrada Sandstone from the San Juan Basin under reservoir-relevant pressure and temperature conditions. A core plug from 8313 to 8315 ft was exposed to CO2-saturated synthetic formation brine with a salinity of 16,601 ppm, followed by static aging. Pre- and post-experiment analyses included porosity and permeability measurements, ICP-MS, SEM-EDS, XRD, CT imaging, and petrographic thin-section analysis. Permeability decreased from 4.22 to 1.60 mD, corresponding to a 62.08% reduction, whereas porosity decreased from 12.37% to 11.75%, a change interpreted as within experimental uncertainty. Effluent chemistry showed increases in Ca2+, Mg2+, K+, Si, Sr, and Mn, consistent with carbonate cement dissolution and feldspar/silicate alteration under CO2-acidified brine conditions. XRD analysis of suspended effluent solids identified quartz, montmorillonite, and illite–montmorillonite mixed-layer clays, indicating fines mobilization. These results suggest that permeability impairment was governed primarily by pore–throat blockage from mobilized clay particles, with possible contribution from secondary carbonate redistribution. The findings emphasize the importance of cement composition and clay mineralogy in evaluating injectivity risks for CO2 storage in clay-bearing sandstone reservoirs.
Liquid organic hydrogen carriers (LOHCs) offer a safe and scalable solution for hydrogen storage and transportation, overcoming many of the limitations of conventional compressed and liquefied hydrogen systems. Among the available LOHCs, the dibenzyltoluene/perhydro-dibenzyltoluene (DBT/18H-DBT) pair is particularly attractive due to its high theoretical hydrogen storage capacity (6.2 wt.%) and favourable handling characteristics. This study investigates the hydrogenation of DBT over a cost-effective 13 wt.% Ni/Al2O3 catalyst using a multiphysics modelling approach developed in COMSOL Multiphysics. A zero-dimensional (0D) kinetic model, assessed against available experimental data, was employed to investigate the effects of temperature and hydrogen pressure on conversion, intermediate formation, and selectivity, and was subsequently extended to a two-dimensional (2D) predictive modelling framework to evaluate reactor behaviour under continuous-flow conditions. The simulations identified an optimum operating window of 505–515 K and 2 MPa, achieving complete DBT conversion and 95–97% selectivity towards the fully hydrogenated product, 18H-DBT, while higher pressures provided only marginal additional benefits. The 2D reactor model further predicted a final 18H-DBT selectivity of 96.8%, confirming the suitability of these conditions for continuous hydrogenation. The developed modelling framework provides valuable insight into reactor-scale performance and offers a robust tool for the design and optimisation of efficient, economically viable LOHC hydrogenation systems based on nickel catalysts.
Small modular reactors (SMRs) offer firm low-carbon heat and power, and data centers concentrate large, continuous electrical and cooling loads. Transparent tools for screening their thermal integration are scarce. To the authors’ knowledge, this paper develops the first steady-state thermodynamic framework that couples SMR steam extraction, solar thermal input, and recovered data-center liquid-cooling heat through a single mixing-tank thermal bus serving both an absorption chiller and an organic Rankine cycle (ORC). The framework’s novelty is in its focus on the structural level rather than the component level. Two consistency requirements are built into its equations. First, the data-center control volume closes exactly, so that recovered heat reduces the residual cooling demand and heat removal equals IT dissipation. Second, delivered cooling is credited identically in every configuration compared, so that apparent gains cannot arise from asymmetric accounting. The framework identifies the governing mechanism of the architecture: a small 120 °C extraction stream (1.01% of core thermal power at the activation bound) unlocks the larger 70 °C recovered stream, which cannot drive the chiller alone. At the margin-constrained design point (2.93% extraction), direct liquid recovery removes 20.9 MWth, absorption cooling serves the remaining 16.2 MWth, and net electricity is 5.8 MWe above the all-electric reference. An itemized estimate places the integration-specific parasitic loads at 0.6–1.5 MWe (central value 1.0 MWe), which reduces the increment over the liquid-cooled non-integrated reference from +0.5 MWe (gross) to approximately −0.5 MWe (net). A 20,000-sample Monte Carlo analysis across seven uncertain parameters shows the net-of-parasitics gain over the all-electric reference is positive with 92% probability (median +4.1 MWe), while the increment over the non-integrated reference is positive with only 29% probability. A compact exergy inventory attributes 7.1 MW of destruction to the recovery train (process heat exchanger 2.0, mixing 1.2, ORC 2.0, chiller 2.0). The architecture’s robust value therefore lies in thermally driven cooling and the productive use of recovered heat, not in net energy. The framework is a screening tool rather than a validated plant model; a companion study populates it with published plant, climate, and equipment data.
As a core component of carbon capture, utilization and storage (CCUS), CO2 geological storage is critical to China’s carbon neutrality strategy. However, current assessments of China’s CO2 geological storage potential suffer from substantial numerical discrepancies, and single evaluation methods lack sufficient reliability, constraining the scientific formulation of national CCUS planning. Addressing these deficiencies, this study proposes a comprehensive evaluation framework to integrate multi-source results and derive a robust quantitative range of national CO2 storage potential. A weighted calculation method covering four criterion layers and 15 evaluation factors is developed in this work. From four perspectives of data authority, temporal validity, methodological reliability and regional comprehensiveness, nine representative evaluation datasets are systematically scored to determine their respective comprehensive weights. Combined with the weighted average model, China’s national CO2 geological storage potential is quantified at 1786–2669 Gt. This multi-source fusion result avoids the inherent limitations of single-method evaluation. This study achieves the effective integration of heterogeneous multi-source evaluation data, resolving the inconsistency in existing national potential assessments. The estimate, while robust under the present validation, remains subject to inherent uncertainties and should therefore be interpreted with due caution in supporting CCUS decision-making.
This study proposes a physics-proxy-residual-guided cross-attention ensemble neural network (PGAE-NN) for oil-immersed transformer bushing insulation-condition assessment and early warning. Eight core indicators are selected from twelve candidates via Pearson correlation and Fisher discriminant analyses, with four insulation levels defined with reference to IEEE Std C57.104-2019. LightGBM, 1D-CNN, and Transformer Encoder serve as heterogeneous base learners for statistical, local temporal, and global temporal features. A cross-attention meta-learner fuses their outputs by penalizing predictions that deviate from Arrhenius thermal-aging and Fick moisture-migration proxy residuals. A piecewise regularization strategy and a classification-precursor dual-task objective further enhance degradation-stage adaptivity and early warning. Validation uses 23,400 accelerated-aging samples from four 110 kV bushings under four typical defects. PGAE-NN achieves 96.14% test accuracy (F1 = 0.9613; AUC = 0.9835) and 96.36% ± 0.54% five-fold cross-validation accuracy, outperforming PSO-SVM and Traditional Stacking by 7.99 and 2.69 percentage points, respectively. The precursor-warning F1 reaches 0.923, and ablation studies confirm the meta-learner, dual physics constraints, and dual-task design contribute 1.82, 1.46, and 1.11 percentage points, respectively. The proxy residual under severe conditions drops by 44.9%, demonstrating that physics-guided fusion constrains predictions within physically consistent boundaries.
This study examines growing tensions between household energy security and climate and energy policy objectives in Poland. It aims to analyze household attitudes towards the decarbonization of heating, particularly the phase-out of solid fuels and gas, and to assess perceptions of alternative heat sources as a means of ensuring energy security. The analysis draws on a survey of representatives of 508 households in five municipalities in the Małopolska Voivodeship, characterized by diverse heating structures. The quantitative study was complemented by 18 in-depth interviews with local government representatives. The results indicate low public acceptance of heating decarbonization amid growing energy uncertainty. Respondents recognized the importance of diversifying heat sources and supported retaining a solid-fuel boiler, regardless of its class, as an emergency backup. Energy security was associated more strongly with autonomy, independence, and freedom of choice than with low-carbon technologies. Solid-fuel heating was perceived as strengthening household resilience to supply disruptions and crises. The findings highlight the need to consider household energy security, particularly energy costs and resilience, when designing energy policies and regulatory instruments such as ETS2 (EU Emissions Trading System 2), especially in municipalities with socio-economic and energy conditions similar to those of the municipalities included in the study.
Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems.
The high penetration of renewable energy has posed significant challenges to frequency regulation, primarily due to the scarcity of traditional regulation resources in these regions. To address this, a novel secondary frequency regulation control method utilizing voltage-sensitive flexible load (VSFL) clusters is proposed. The strategy leverages the voltage-active power coupling characteristics of composite loads. By regulating the load voltage, composite loads under 110/10 kV substations are transformed into controllable loads, which are then aggregated to collaborate with Automatic Generation Control (AGC) units in SFR. To tackle the inherent uncertainty in load regulation capacity, a load control signal correction strategy based on Tube-based Model Predictive Control (T-MPC) is designed. This strategy effectively compensates for calculation errors in control objectives caused by load uncertainty, achieving optimal control with minimal cost. Simulation results demonstrate that the proposed scheme enables the large-scale mining and flexible utilization of load-side regulation resources. Furthermore, the T-MPC approach exhibits superior robustness and frequency regulation performance compared to traditional MPC methods.
High-pressure premixed H2/O2 combustion in closed vessels involves short chemical time scales, strong compression-wave feedback, and repeated end-wall reflections, making the onset of deflagration-to-detonation transition (DDT) highly sensitive to gas-dynamic–chemical coupling. This review focuses on DDT developing from the acceleration of an initially premixed flame in closed, high-pressure H2/O2 systems. Studies of H2/air, diluted mixtures, and open or semi-closed configurations are considered only where they provide relevant mechanistic or methodological insight, and their applicability to closed, high-pressure, undiluted H2/O2 conditions is assessed explicitly. The review synthesizes confined flame acceleration, induction-time gradients, coherent energy release, and shock–flame interaction together with H2/O2 chemical kinetics, compressible reactive-flow modeling, multidimensional simulations, and model validation. The reviewed evidence indicates that the transition is governed by the coupled evolution of flame-area growth and compression waves, restructuring of the induction-time field, local energy release, and the establishment of persistent shock–reaction coupling. Initial pressure and temperature, mixture distribution, ignition strategy, geometry, vessel scale, and thermal boundaries influence this process by modifying intrinsic reaction scales, wave phasing, reflection paths, and heat and momentum losses. Numerical predictions remain sensitive to chemical kinetics, transport treatment, numerical dissipation, wall modeling, dimensionality, mesh resolution, and the definition of the DDT event. Accordingly, DDT identification based only on peak pressure or a short interval of near-CJ wave velocity can be ambiguous, whereas multi-observable validation using pressure, wave or flame position, reaction-zone information, and coupling persistence provides a more reliable basis for model assessment. The principal remaining gap is the limited availability of complete-process, three-dimensional experiment–simulation datasets for fully closed vessels containing high-pressure, undiluted H2/O2.
