Refrigeration and air-conditioning systems are vital to the global economy but contribute significantly to greenhouse gas emissions by using high-global-warming potential synthetic refrigerants. As regulatory frameworks like the Montreal Protocol, the Kigali Amendment, and the EU’s F-gas Regulations tighten, the industry faces a mandatory transition toward environmentally benign alternatives. This perspective paper evaluates the technological and environmental implications of replacing synthetic fluids with natural refrigerants, specifically ammonia, carbon dioxide, and hydrocarbons. A comparative assessment reveals that natural refrigerants offer superior thermodynamic efficiency, zero ozone depletion potential, and ultra-low global warming potential. While technologies like transcritical CO2 and low-charge ammonia systems may involve higher initial capital costs, they increasingly achieve life cycle cost parity through improved energy performance and regulatory stability. The analysis further explores advanced cycle configurations, such as ejectors and expanders, which mitigate efficiency losses. The transition to natural refrigerants is presented as a technologically feasible and environmentally friendly strategy to mitigate the risk that rising cooling demands further accelerate climate change. Ultimately, natural refrigerants are expected to become the default global standard within the shortest feasible timeframe, with policy, industry, and research aligned to support and accelerate this transition.

The rapid growth of wind and solar energy poses new challenges to safe and reliable system operation. Effectively characterizing and generating high-risk wind and photovoltaic (PV) power output scenarios is therefore essential for system risk assessment and preventive dispatch and control. However, existing scenario generation methods either rely on predefined probability distributions or focus narrowly on extreme output levels, failing to comprehensively reflect system-level operational risk induced by renewable energy. To this end, a power system optimal dispatch model and a flexibility indicator system mainly incorporating system ramping and transmission margins are established. Thereafter, analytic hierarchy process (AHP) and the entropy weight method (EWM) are used to fuse indicators into a quantitative operational risk index. Historical wind and PV scenarios are evaluated through the dispatch model to generate risk-labeled samples, based on which a conditional generative adversarial network (cGAN) is trained to produce wind and PV power output scenarios with specified risk levels. Case studies verify that the risk labels constructed can effectively guide the subsequent conditional generation model and scenarios corresponding to a given risk level can be effectively generated by the model.

Achieving synergy between the digital economy and energy efficiency is pivotal for realizing high-quality development under the “Dual Carbon” targets. However, traditional econometric methods struggle to capture the complex nonlinear and spatio-temporal dependencies inherent in this relationship. To address this issue, this study develops a two-stage framework using Chinese provincial panel data. It combines LightGBM/CatBoost and SHAP for critical factor identification, and employs STGNN for capturing nonlinear and spatial correlation patterns, to systematically decode the driving mechanisms of the digital economy on energy efficiency. The results reveal three key findings: (1) Complex Nonlinearity: The impact manifests in distinct U-shaped, inverted U-shaped, and weak correlation patterns, accompanied by significant spatial clustering. (2) Structural Heterogeneity: The dimensions of the digital economy show differential associations with energy efficiency. Industrial digitization and infrastructure are associated with more direct improvements in efficiency, whereas digital industrialization functions primarily through indirect technological supply. (3) Spatial Correlation Pattern: Higher levels of digital development correspond to higher local energy efficiency and are linked to positive predicted adjustments in neighboring regions, with notable regional heterogeneity. Combining machine learning-based feature selection with deep learning-based spatiotemporal modeling provides a scientific basis for formulating location-specific digital economy strategies and coordinated energy-saving policies.

Large-scale renewable energy bases increasingly employ automatic voltage control (AVC) to coordinate heterogeneous reactive-power resources. The resulting voltage regulation process inherently involves sampling, communication delay, and nonlinear device characteristics, which may induce nontraditional voltage oscillations and stability degradation that cannot be adequately captured by conventional continuous-time or small-signal analysis. This paper proposes a discrete-time nonlinear voltage stability analysis framework for renewable energy bases with multi-reactive-power-resource coupling under AVC-based coordinated control. The voltage regulation dynamics are formulated as a discrete-time nonlinear closed-loop system by incorporating sampled AVC actions, delayed voltage feedback, and nonlinear voltage–reactive-power coupling. An incremental system representation is constructed, and a strong-contraction-based stability criterion is derived using sector-bounded nonlinearity descriptions and linear matrix inequalities, providing a sufficient condition for global voltage convergence without local linearization. Extensive numerical studies are conducted on a representative renewable energy base with parallel and series coupling topologies. A total of 2916 randomized configurations are evaluated. The proposed criterion achieves consistency rates exceeding 96% for the parallel topology and 99% for the series topology when compared with time-domain simulations, while the probability of dangerous misjudgment remains below 1%. Scenario-based simulations further demonstrate that coupling topology plays a critical role in shaping voltage stability behaviors, and state-space analysis further supports the observed stability behaviors. These results indicate that nonlinear strong contraction offers an effective and practical stability notion for AVC-based voltage regulation in renewable energy bases.

Conventional coal mine reserve estimation methods are misaligned with modern production. This study develops a lifecycle, production-oriented framework for dynamic reserve accounting, including a strip inversion algorithm for mined faces and an improved GIS grid method for unmined faces, and a classification-coding system for coal pillar resources in remaining spaces. A case study on the Huainan Zhujidong Mine verifies the framework: the inverse calculations for the mined-faces yield of extracted coal were 9.20–14.30% lower than those of traditional methods, aligning better with reality; unmined-face predictions achieved relative errors of 1.91% (reserves) and 2.99% (extraction); remaining-space pillar resources were inventoried into five categories and 13 blocks (8.873 Mt). The framework supports accurate reserve accounting and refined decision-making and can be applied to similar coal mines.

Single-phase current source (SCS) converters typically necessitate a bulky inductor to mitigate DC current fluctuations. Furthermore, open circuit conditions must be avoided in these systems. Active power decoupling (APD) offers an effective solution by eliminating double-line frequency ripple power without a bulky inductor. However, it introduces additional power losses due to the extra devices. To address this issue, this paper proposes an efficient and reliable bridgeless power factor correction (PFC) APD converter. A diode is incorporated into the converter to construct new current paths. The new paths decrease the number of semiconductor devices to lower conduction losses and provide an inherent freewheeling path for DC current to enhance system reliability. Through a specific modulation strategy, the utilization of new paths is maximized, and switching losses are reduced. This paper begins by describing the operating modes of the proposed converter. It then introduces a specific modulation strategy. A detailed analysis of power losses is presented. Finally, a 360 W prototype is constructed, and experimental results demonstrate an efficiency improvement of 2.5%.

Airfoil fin Printed Circuit Heat Exchangers (PCHEs) offer significant advantages in reducing flow resistance, promoting turbulence, and enhancing heat transfer performance due to their discrete fin configuration. However, compared with conventional continuous-channel structures, the geometric discontinuities and sharp trailing edges introduced by discrete fins tend to induce severe stress concentration at the fin roots, resulting in a more complex structural response. In this study, a PCHE core with NACA0020 airfoil fins is investigated. Finite element analysis combined with a sequential one-way thermo-structural coupling approach is conducted to characterize the fins’ stress and deformation behavior under high temperature and pressure. The plate region is evaluated based on the linear elastic stress criteria specified in ASME Boiler and Pressure Vessel Code Section III, while localized yielding regions such as the fin roots are assessed using an equivalent plastic strain indicator. Results indicate that the structural response of the PCHE core is dominated by pressure loading under the investigated operating conditions with ΔT = 18 °C and ΔP = 12.05 MPa, whereas thermal stress caused by constrained thermal expansion mainly modifies local stress distributions and has a limited effect on global deformation. Owing to the discontinuous support provided by discrete airfoil fins, the fin roots act as the primary load-transfer path and sustain higher stress levels. The maximum von Mises stress is observed at the trailing edge of the fin root on the high-pressure side, while the largest deformation occurs in the unsupported plate region and is governed by bending. Parametric analysis indicates that, within the investigated parameter range, a fully staggered fin arrangement promotes more uniform load distribution and exhibits the most favorable structural response. In contrast, increasing the fin chord length and relative thickness reduces the overall load-carrying capacity of the core. Finally, a power-law predictive correlation for the maximum total plate deformation was developed, showing that the parameter influence on plate structural response follows the order horizontal pitch (Lh) > vertical pitch (Lv) > channel etching depth (Le) > staggered pitch (Ls). In contrast, normalized sensitivity analysis of the maximum fin-root von Mises stress shows the order staggered pitch (Ls) > horizontal pitch (Lh) > vertical pitch (Lv) > channel etching depth (Le), indicating that global plate deformation and local fin-root response are governed by different structural mechanisms.

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a 1.9% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises.

The transition to low-emission road transport in Europe depends not only on the growth of plug-in electric vehicle (PEV) uptake, but also on the timely expansion of publicly accessible charging infrastructure. This article provides a descriptive and diagnostic assessment of the relationship between electrification dynamics and public charging infrastructure development in Europe. The analysis combines a long-run descriptive window (2015–2024, with 2025 treated separately as a scenario observation) and a core diagnostic window (2020–2024) for which a consistent proxy of potential infrastructure strain—plug-in vehicles per public recharging point (VPP)—is available. The results show a strong increase in PEV share in new registrations, from 1.0% in 2015 to 20.92% in 2024, while the number of public recharging points rose from 67,064 to 900,000 over the same period. In the core sample, VPP declined from 15.24 in 2020 to 13.92 in 2024, which is consistent with a catch-up phase in infrastructure deployment after 2021. At the same time, the short-window relationship between PEV share, infrastructure scale and average CO2 emissions of newly registered cars remains weak and unstable, indicating the role of additional structural factors. The article contributes a transparent, replicable indicator-based framework for describing infrastructure strain in aggregate European data. In policy terms, the findings support a shift from simple point-count targets toward functionally and spatially differentiated infrastructure planning, including interoperability, power structure, and accessibility in underserved areas.

With the increasing penetration of wind and solar renewable energy into modern power systems, grids exhibit ‘dual-high’ (i.e., a high proportion of both renewable energy and power electronic devices) and ‘dual-low’ (i.e., low equivalent rotational inertia and low short-circuit capacity) structural characteristics. This leads to critical challenges, notably insufficient short-circuit capacity, declining voltage and frequency stability, and weakened system damping. To address the stability requirements of new power systems, this study proposes and systematically investigates a variable-speed synchronous condenser based on AC excitation technology. The research encompasses the operational principles, starting mechanisms, and control strategies of the device, with a particular focus on analyzing its stator-flux-oriented vector control method and active–reactive power decoupling regulation mechanism. By independently adjusting the frequency, amplitude, and phase of the AC excitation on the rotor side, the system achieves a millisecond-level dynamic reactive power response, rapid frequency support, and self-starting capability without the need for external starting devices. To validate the effectiveness of the theoretical analysis and engineering practicality, this study presents grid-connected operational tests using a 3600 kVar engineering prototype at a wind farm. The test results demonstrate that the variable-speed synchronous condenser performs excellently in speed regulation, dynamic reactive power response, and primary frequency modulation. It effectively provides short-circuit capacity, enhances system damping, and significantly improves the voltage and frequency stability of power grids with high penetration of renewable energy. This study offers innovative technical pathways and empirical evidence for constructing a stability support system that meets the developmental needs of new power systems. It holds significant theoretical value and engineering guidance for promoting the smooth transition of power grids from synchronous machine-dominated to power electronics-based architectures.