Search Results (1,268)

Against the backdrop of China’s dual-carbon targets and the construction of a new-type power system, the coordinated development of the electricity market, carbon market, and tradable green certificate (TGC) market is critical for energy system optimization. Based on 2024 cross-sectional data from 30 Chinese provinces, this study employs an entropy-weighted coupling-coordination model and an obstacle-degree model to evaluate the coupling coordination level of the three markets and identify regional bottlenecks. The results show that: (1) The national average coupling coordination degree is 0.581 (ranging from 0.480 to 0.747), with 10 provinces reaching the “favourably balanced” level, 17 provinces at the “barely balanced” level, 3 provinces at the “slightly unbalanced” level, and no province achieving the “superiorly balanced” level. (2) Regionally, the coupling coordination degree exhibits an “east-high, west-low” pattern, and most provinces display a “high coupling but low coordination” pattern. (3) The underdevelopment of the TGC market is a nationwide issue, characterized by dual structural problems on both the supply and demand sides. This study provides quantitative evidence and empirical support for enhancing the synergy of the electricity–carbon–TGC markets and promoting regionally differentiated policy formulation. Full article

To address long-duration, high-impact extreme events, this study investigates resilience enhancement optimization dispatching for hydro–wind–solar power systems under continuous multi-day extreme scenarios. A mathematical model is constructed with the resilience objective of minimizing the average load deviation percentage and the economic objective of maximizing the total power generation of the system, while considering constraints such as water balance. The solution steps are provided in this paper. A case study of the Laxiwa hydropower station and nearby wind and photovoltaic power stations demonstrates the following: (1) The compensatory regulation capability of hydropower can be leveraged to enhance power system resilience under continuous multi-day extreme scenarios, and there is a trade-off between resilience and economic objectives. (2) The ability of hydropower to enhance power system resilience is limited by several factors, such as installed capacity, existing reservoir storage, minimum output constraints, and available storage capacity, making it insufficient to fully prevent issues like power shortage, the curtailment of renewable energy, and water spillage. (3) The impact of extreme wind and solar power outputs on the power system exhibits a cumulative effect under continuous multi-day extreme scenarios, and in concurrent scenarios, there is a certain offsetting effect between the impacts of under- and over-generation. This paper provides technical support and a reference for optimizing resilience-oriented scheduling and exploring mechanisms in hybrid hydro–wind–solar power systems under extreme conditions. Full article

The presence of multiple uncertainties in integrated electricity–heat energy systems (E-HIES) poses significant challenges to system dispatch. To achieve an effective balance between economy and robustness, this paper proposes an optimal scheduling method based on fuzzy chance-constrained Information Gap Decision Theory (Fuzzy-IGDT), accounting for uncertainties in wind power output, photovoltaic output, electrical load, and thermal load. The method employs trapezoidal fuzzy numbers to model the four types of uncertain variables and constructs a fuzzy robust model (F-RM) for conservative decision-makers and a fuzzy opportunity model (F-OM) for aggressive decision-makers. An Adaptive Step Ratio (ASR) optimization method is then developed to solve the proposed models. Case studies demonstrate the effectiveness of the proposed methodology. Results show that: compared with conventional IGDT, pure fuzzy and stochastic programming, Fuzzy-IGDT simultaneously optimizes economy, stability and reliability: daily operating cost is reduced by 12.7%, the standard deviation of cost volatility shrinks by 34.5%, and the loss-of-load probability is only 0.3%. Relative to the traditional Weighted Offset Coefficient (WOC) method, ASR directly coordinates the deviation ratios of multiple variables through its step-ratio mechanism, cutting system risk cost by 21.3%, raising solution efficiency by 42%, and improving convergence stability by a factor of 3.8. This research provides new theoretical support and practical tools for optimal scheduling of E-HIES under multiple uncertainties. Full article

As thermal power units in China shift toward serving as flexible regulation sources in new-type power systems, accurately assessing the axial thrust of steam turbine regulating stages under off-design conditions has become critical. This paper employs numerical methods to investigate the axial thrust characteristics and nozzle structural optimization of the regulating stage under off-design conditions (VWO, THA, 75% THA, 50% THA). Steady-state results reveal significant deviations in the interstage hub forces predicted by 3D simulations compared with those from the conventional 1D formula under partial admission, prompting a correction. Unsteady results show that reducing the partial admission degree intensifies flow unsteadiness, increasing rotor blade axial force fluctuation from 1175 N (VWO) to 2057 N (50% THA). In terms of structural optimization, compared with not increasing the nozzle angle, increasing the nozzle angle by 2° reduces the total axial force on the regulating stage by 7.3%; compared with not extending the inlet guide arc segment, extending its length by 40 mm increases the axial force on the rotor blade by 1.6%, but decreases the maximum amplitude from 323.9 to 249.9. Based on these findings, the optimization direction for the nozzle structure is proposed. Full article

This paper presents a generalized epidemic modeling framework based on g-tempered Caputo fractional derivatives with discrete time delays. The proposed approach incorporates nonlocal memory effects, nonlinear temporal scaling, and delayed epidemiological responses within a unified mathematical structure. The introduction of the nonlinear time transformation $g\left(t\right)$ and the tempering parameter $\lambda$ eliminates the unrealistic infinite-memory behavior associated with classical power-law kernels while simultaneously introducing new challenges related to parameter identifiability and inverse problems. We investigate the structural properties of the resulting dynamical systems and show that the associated inverse problem is inherently ill-posed. To illustrate the practical implications of these results, the framework is applied to a delayed SIQR epidemiological model. Numerical simulations are performed using a generalized L1-type scheme adapted to delayed fractional histories, and a multi-phase parameter estimation procedure is proposed to address the ill-posedness of the reconstruction problem. The results demonstrate the ability of the model to capture both short- and long-term memory effects in epidemic evolution while highlighting the challenges of statistical identifiability in generalized fractional systems. Full article

The clean and low-carbon transition of new-type power systems imposes increasingly stringent demands on green technology innovation among enterprises along the power industry chain. Identifying the drivers and potential remedies for green innovation bubble can offer China-originated solutions to the sustainable development of the global power sector. This paper focuses on Chinese power industry chain enterprises over the period 2016–2023. Drawing on the AMO framework, a three-dimensional analytical framework encompassing ability, motivation, and opportunity is developed. Double machine learning (DDML) is employed to perform benchmark regression and causal identification. Subsequently, gradient boosting trees (GBT) combined with SHAP interpretability analysis are applied to uncover nonlinear relationships and heterogeneous transmission pathways among key variables. The results indicate that energy-saving policies and green financial policies significantly inhibit the formation of the green innovation bubble in power industry chain enterprises. Specifically, these policies curb the green innovation bubble via three channels: an innovation incentive management mechanism, a peer imitation and convergence mechanism, and an industrial chain technology spillover mechanism. Upstream enterprises exhibit greater sensitivity to direct regulatory measures and backward technology spillovers from energy-saving and green finance policies, whereas midstream enterprises are more reliant on peer carbon emission pressure. The findings are validated through cross-verification among DDML, mechanism analysis, and interpretable analysis. The results provide empirical evidence and policy implications for optimizing energy-saving and green finance policies and for precisely deflating the green innovation bubble. Full article

The development of a new-type power system requires addressing the long-timescale imbalance between electricity supply and demand caused by the high penetration of wind and solar energy, which places higher demands on the secure and stable operation of power systems. Conventional single-type energy storage cannot simultaneously satisfy short-term power regulation and medium- to long-term energy balancing requirements. Therefore, coordinated optimal allocation of multi-type energy storage is necessary. This study investigates the optimal capacity allocation of short- and long-duration energy storage in high-renewable-penetration power grids to improve renewable energy accommodation, enhance system flexibility, and optimize life-cycle cost. A mathematical model of a Multi-Type Energy Storage Coupled System (MTESCS) considering both power and energy balance is first established, together with a life-cycle economic model. Then, a source-load time-series reduction method based on Ward’s method is adopted to preserve the original temporal trends while reducing optimization complexity, and an optimal capacity allocation model is developed with the objective of minimizing system life-cycle cost. Finally, different storage configuration scenarios are constructed for comparative analyses under various renewable energy penetration levels. Results show that the proposed MTESCS can effectively improve renewable energy accommodation and economic performance, providing useful support for system design and engineering applications. Full article

Hydro-wind-solar hybrid power systems have become a mainstream configuration for new-type power systems. However, the high proportion of power-electronics-interfaced generation alters system inertia and damping characteristics, leading to complex high-dimensional frequency dynamics and severe stability challenges. This paper investigates the frequency response mechanism and Hopf bifurcation characteristics of the aggregated frequency response model for hydro-wind-solar hybrid power systems. First, primary frequency response models for hydropower, wind power, and photovoltaic (PV) generation are established under a small-signal analysis framework. On this basis, a tenth-order nonlinear dynamic model of the integrated system is constructed by considering hydraulic nonlinearities, virtual inertia control of wind power, and reserve-based frequency regulation of PV systems. Then, Hopf bifurcation theory is applied to analyze stability and oscillatory instability mechanisms of the high-dimensional system. The bifurcation conditions are derived via high-dimensional Jacobian matrix analysis and Routh-Hurwitz criterion, with supplementary normal form calculation and first Lyapunov coefficient derivation to identify the supercritical/subcritical nature of the bifurcation. Finally, numerical simulations under both small and large disturbances validate the theoretical analysis. The results demonstrate that variations in key control parameters may induce Hopf bifurcation, leading the high-dimensional system from a stable equilibrium to sustained low-frequency oscillations. The findings provide insights and practical guidance for stable operation and parameter tuning of hydro-wind-solar hybrid power systems. Full article

To address the key problems of low-frequency oscillation and insufficient regulation accuracy at the Point of Common Coupling (PCC) in hydro–wind–photovoltaic hybrid systems, which are caused by the randomness of wind and photovoltaic output, the water-hammer effect of hydropower units, and multi-source power coupling, a joint control strategy based on Fractional-Order Proportional Integral Derivative (FOPID) and Co-evolutionary Multi-objective Particle Swarm Optimization (CMOPSO) is proposed. First, a small-signal transfer function model of the system covering photovoltaic inverters, doubly fed induction generators (DFIGs), hydropower units and voltage-source converter-based high-voltage direct current (VSC-HVDC) converter stations is established to accurately characterize the water-hammer effect and multi-source dynamic coupling characteristics. Second, a Caputo-type FOPID controller is designed. Compared with traditional integer-order controllers with limited tuning flexibility, the FOPID controller utilizes its five degrees of freedom to address specific multi-source coupling challenges. This precisely compensates for the non-minimum phase lag caused by the water-hammer effect in hydropower units via the fractional derivative link, and effectively smooths the impact of stochastic wind–solar fluctuations on PCC voltage through the memory characteristics of the fractional integral link. This multi-parameter regulation mechanism prevents a trade-off between response speed and overshoot suppression, achieving effective decoupling of complex multi-source dynamic interactions. Third, a dual-objective optimization framework with the Integral of Time-weighted Absolute Error (ITAE) and Oscillatory Disturbance Risk Index (ODRI) as the objectives is constructed. The multi-population co-evolution mechanism of the CMOPSO algorithm is adopted to solve the Pareto-optimal solution set, realizing the coordinated optimization of dynamic response accuracy and oscillation instability risk. Finally, comparative simulations are carried out on the Simulink platform with traditional PI/FOPI controllers and optimization algorithms such as Multi-objective Particle Swarm Optimization based on the Decomposition/Simple Indicator-Based Evolutionary Algorithm (MPSOD/SIBEA). The results show that the proposed strategy can effectively suppress low-frequency oscillations in the range of 0~30 Hz. Compared with the traditional PI controller, the PCC voltage overshoot is reduced by more than 40%, the oscillation decay time is shortened by 33%, the ITAE and ODRI indices are decreased by 12.58% and 2.47%, respectively, and the stability of DC bus voltage is significantly improved. Its robustness and comprehensive control performance are superior to existing methods, providing an efficient and stable control scheme for power electronics-dominated complex new energy grid-connected systems. Full article

Grid-forming converters, with their voltage-source characteristics, can independently provide voltage support and thus have become a critical supporting technology for new-type power systems. However, they suffer from overcurrent risks and insufficient voltage support capability during grid faults. To overcome these shortcomings, this paper proposes an adaptive transient-voltage support strategy for grid-forming PMSG wind turbines based on DC capacitor-voltage synchronization. First, the inertia synchronization and autonomous-voltage support mechanisms of such grid-forming wind turbines are analyzed. Second, based on power-flow equations and the grid-forming topology, key factors affecting the grid-connected voltage during faults are identified, and an adaptive voltage-support strategy using fuzzy control is developed. Finally, a grid-forming wind power system is modeled on the PSCAD/EMTDC platform, where the proposed strategy raises the minimum PCC voltage to 0.62 p.u. and increases reactive power injection by 0.13 p.u. under a 70% deep sag, successfully fulfilling low-voltage ride-through requirements. Full article

Faced with the challenges of cross-cultural communication of public opinion in emergency events, traditional sentiment recognition methods struggle to accurately capture the complex semantics under multi-lingual and multi-symbol systems. This paper takes the powerful 7.7-magnitude earthquake that struck Myanmar in 2025 as a case study. It constructs a multi-dimensional public opinion annotation framework that integrates four types of semantic information—time, space, subject, and sentiment—by extracting data from multi-source textual materials, including social media, news reports, and government announcements. Building on this foundation, we design an improved Model-Agnostic Meta-Learning (MAML) model that incorporates cultural features to enhance sentiment recognition performance in low-resource cross-linguistic scenarios. Experimental results show that the model outperforms traditional methods in terms of sentiment classification accuracy, cultural semantic deviation rate and metaphor recognition ability. Furthermore, the research reveals the coupling mechanism of public opinion communication of “cultural modulation–agenda game”, and clarifies the influence paths and weight distributions among multiple subjects. The research results provide theoretical support and practical paths for improving the governance capacity of cross-border public opinion in emergency events and the construction of multilingual monitoring models. Full article

Natural disasters and extreme meteorological events are primary causes of unplanned outages in modern power systems. Existing early warning methods suffer from insufficient non-linear feature extraction, severe class imbalance, and limited minority-class recall under single-classifier architectures. This paper proposes a seven-class meteorological fault early warning framework that integrates a sparse autoencoder (SAE), a G1–entropy composite weighting scheme, SMOTE oversampling, and a soft-voting BP–XGBoost ensemble. A leakage-free experimental protocol confines SMOTE exclusively to the training partition, eliminating data contamination from evaluation. Validated on 1955 fault records from a regional grid in East China covering 110 kV, 220 kV, and 500 kV voltage levels (2013–2022), the proposed framework achieved 96.42% accuracy and a 97.46% macro F1-score on the held-out test set, outperforming SVM (72.68%), Random Forest (89.31%), LSTM (81.47%), 1D-CNN (85.38%), and LightGBM (92.15%). Ablation experiments confirmed that SMOTE and G1–entropy weighting contributed macro F1 gains of 8.34 and 6.91 percentage points, respectively, while removing the XGBoost branch degraded accuracy by 28.25%. Temporal validation on 2019–2022 records yielded 91.57% accuracy, confirming temporal generalization. Error analysis further revealed that bidirectional misclassification between lightning damage and wind damage, rooted in shared atmospheric instability signatures, constitutes the dominant residual error source, providing theoretical guidance for future threshold optimization strategies. Full article

Across modern Asia and many other regions, artificial intelligence is transforming religious life in diverse and profound ways. Robot priests chant sutras at Japanese Buddhist temples, AI-powered apps offer personalised coaching in Quranic recitation to millions of Muslims, and bereaved families consult algorithm-generated avatars of the deceased in China. They are neither merely tools for instrumental use nor channels for transmitting pre-existing religious authority. Instead, they create new forms of religious content, new types of spiritual encounters for religious users, and new structures of authority. This paper argues that understanding these phenomena requires theoretical innovation beyond simply applying existing concepts to new domains. Drawing on Actor–Network Theory, algorithmic culture studies, and scholarship on Asian religious traditions, the paper proposes the theoretical framework of generative charisma, theorising how AI systems gain religious authority through three interconnected mechanisms: captivation by generation, intimacy trust through personalisation, and oscillating enchantment. It also highlights accountability as a structural issue that needs critical discussion regarding governance. The paper demonstrates the framework’s usefulness by examining AI recitation coaching in Islamic practice and AI grief avatars in Chinese Buddhist mourning, showing its relevance across different religious traditions and technological forms. Full article

The common root characteristic of CO₂ and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO₂ and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O₂/CO₂ cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO₂ and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO₂ emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement. Full article

With the advancement of new-type power systems and smart grids, the structure of power protection and control systems has become increasingly complex, and their reliability exhibits dynamic evolution, multi-factor coupling, and full life cycle characteristics. Against this background, this paper presents a review of the evolution of reliability analysis methods for power protection and control systems. Early research has focused on parametric modeling based on statistical data and structural logic combination analysis, establishing a static reliability analysis framework grounded in the relationship between component failure probability and system structure. Subsequently, to characterize temporal process features such as state transitions, fault dependencies, and maintenance recovery, dynamic modeling methods such as state-space models and dynamic fault trees were developed and applied. In recent years, with the continuous accumulation of full life cycle operational data, multi-source information fusion and data-driven technologies have gradually been introduced into reliability research, promoting the expansion of the analysis framework from stage-based evaluation to full-process evolutionary modeling. On this basis, the modeling concepts, applicable scenarios, and inherent limitations of different methods are summarized and compared. Furthermore, the development trend of an integrated reliability analysis system that deeply combines mechanism models with data-driven methods is discussed, aiming to provide a theoretical foundation for the improvement of reliability analysis systems. Full article