Search Results (11,520)

Accurate and efficient short-term load forecasting is crucial for the secure and stable operation and scheduling of power grids. Addressing the inability of traditional Transformer-based prediction models to capture symmetric correlations between different feature sequences and their susceptibility to multi-scale feature influences, this paper proposes a short-term power distribution network load forecasting model based on an enhanced Komodo Mlipir Algorithm (KMA)—Multivariate Variational Mode Decomposition (MVMD)-Crossformer. Initially, the KMA is enhanced with chaotic mapping and temporal variation inertia weighting, which strengthens the symmetric exploration of the solution space. This enhanced KMA is integrated into the parameter optimization of the MVMD algorithm, facilitating the decomposition of distribution network load sequences into multiple Intrinsic Mode Function (IMF) components with symmetric periodic characteristics across different time scales. Subsequently, the Multi-variable Rapid Maximum Information Coefficient (MVRapidMIC) algorithm is employed to extract features with strong symmetric correlations to the load from weather and date characteristics, reducing redundancy while preserving key symmetric associations. Finally, a power distribution network short-term load forecasting model based on the Crossformer is constructed. Through the symmetric Dimension Segmentation (DSW) embedding layer and the Two-Stage Attention (TSA) mechanism layer with bidirectional symmetric correlation capture, the model effectively captures symmetric dependencies between different feature sequences, leading to the final load prediction outcome. Experimental results on the real power distribution network dataset show that: the Root Mean Square Error (RMSE) of the proposed model is as low as 14.7597 MW, the Mean Absolute Error (MAE) is 13.9728 MW, the Mean Absolute Percentage Error (MAPE) reaches 4.89%, and the coefficient of determination (R²) is as high as 0.9942. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article

Accurate modelling of near surface wind speeds is essential for robust resource assessment, turbine design, and grid integration. This study presents a unified framework comparing four candidate marginal distributions—Weibull, Gamma, Lognormal, and Generalised Extreme Value (GEV)—across 21 years of daily observations from 11 sites in New South Wales and southern Queensland, Australia. Parameters are estimated by maximum likelihood, with L-moments used when numerical fitting fails. Univariate goodness-of-fit is evaluated via information criteria (Akaike Information Criterion, AIC; Bayesian Information Criterion, BIC) and distributional tests (Anderson–Darling, Cramér–von Mises, Kolmogorov–Smirnov). To capture spatial dependence, we fit an 11-dimensional regular vine (“R-vine”) copula to the probability-integral-transformed data, selecting pair-copula families by AIC and estimating parameters by sequential likelihood. A composite score (70% univariate, 30% copula) ranks distributions per location. Results demonstrate that Lognormal best matches central behaviour at most sites, Weibull remains competitive for bulk modelling, Gamma often excels in moderate tails, and GEV best represents extremes. All turbine yield results presented are illustrative, showing how statistical choices impact energy estimates; they should not be interpreted as operational forecasts. In a case study, 5000 joint simulations from the top-two models drive IEC V90 and E82 power curves, revealing up to 10% variability in annual energy yield due solely to marginal choice. This workflow provides a replicable template for comprehensive wind resource and load hazard analysis in complex terrains. Full article

Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies. Full article

This paper examines artificial intelligence (AI) bias in power systems applications through systematic analysis of three critical use cases: load forecasting, predictive maintenance, and ontology matching for system interoperability. While AI solutions show great potential for addressing complex power system challenges, they face adoption barriers due to biases that compromise fairness, reliability, and operational performance. Our investigation demonstrates how different bias types—including data representation, algorithmic, and sampling biases—manifest in power systems contexts, directly affecting grid efficiency, resource allocation, and socioeconomic equity across the electrical power and energy domain. For each use case, we provide quantitative evidence of bias impact and propose targeted mitigation strategies that emphasize data diversity, ensemble methods, explainable AI techniques, and fairness-aware algorithms. By establishing a comprehensive taxonomy of bias types relevant to power systems and developing practical mitigation frameworks, this work bridges the critical gap between abstract bias concepts and real-world power system applications. The resulting framework provides a structured approach for developing equitable, robust AI systems that align with power systems’ operational requirements while accelerating the responsible adoption of AI in safety-critical infrastructure. Full article

Determining appropriate spatio-temporal scales for monitoring migratory shorebirds is challenging. Effective surveys must detect population trends without excessive or insufficient sampling, yet many programs lack formal evaluations of survey effectiveness. Using data from 2012 to 2019 on Louisiana’s barrier islands (Whiskey, west Raccoon, east Raccoon, and Trinity), we assessed how spatial and temporal scales influence population trend inference for piping plovers (Charadrius melodus). Point count data were aggregated to grid sizes from 50 to 200 m and analyzed using Bayesian dynamic occupancy models. We found occupancy and colonization estimates varied by spatial resolution, with space–time autocorrelation common across scales. Smaller islands (east and west Raccoon) yielded higher trend detection power due to better detectability, while larger islands (Trinity and Whiskey) showed lower power. Detectability, more than sampling frequency, drove trend inference. Models incorporating spatial autocorrelation outperformed traditional Frequentist approaches but showed poorer fit at coarser scales. These findings underscore how matching analytical scale to ecological processes and selecting appropriate models can influence predictions. Power analysis revealed that increasing survey frequency may improve inference, especially in low-detectability areas. Overall, our study highlights how careful scale selection, model diagnostics, and survey design can enhance monitoring efficiency and support long-term conservation of migratory shorebirds. Full article

Despite extensive research, indoor localization techniques remain an open problem, with Bluetooth Low Energy (BLE) continuing to be a dominant technology even in the presence of ultrawideband and Bluetooth 5.1. This study proposes a novel approach for indoor mobile device localization using BLE. Unlike traditional methods relying on the Received Signal Strength Indicator (RSSI), this technique employs spatial signal coverage analysis from multi-power Bluetooth emulators, with data collected by an array of receivers. These coverage patterns form a probability grid, which is processed to accurately determine the mobile device’s location. The method accounts for the intrinsic properties of antennas and the operational ranges of multiple beacon emulators, thereby enhancing localization precision. By utilizing receiver range data rather than RSSI, localization outcomes demonstrate greater consistency. Static measurements show an average error of 1.83 m, a median error of 1.73 m, and a mode error of 2.35 m. In dynamic settings, a moving robot exhibited a measurement error of 3.6 m for 70% of samples and 4.6 m for 94% of samples. This solution is currently being implemented to track attendees at trade fairs, providing metrics to inform stand rental pricing and insights for optimizing stand distribution to encourage visitor exploration. Full article

Under the twin imperatives of climate change mitigation and sustainable development, achieving a low-carbon transformation of power systems has become a national priority. To clarify this objective, China issued the Blue Book on the Development of New Power System, which comprehensively defines the guiding concepts and characteristic features of a new power system. In this study, natural language processing-based keyword extraction techniques were applied to the document, employing both the TF-IDF and TextRank algorithms to identify its high-frequency terms as characteristic keywords. These keywords were then used as topic queries in the Web of Science Core Collection, yielding 1568 relevant publications. CiteSpace was employed to perform a bibliometric analysis of these records, extracting research hotspots in the new power system domain and tracing their evolutionary trajectories. The analysis revealed that “renewable energy” appeared 247 times as the core high-frequency term, while “energy storage” exhibited both high frequency and high centrality, acting as a bridge across multiple subfields. This pattern suggests that research in the new power system field has evolved from a foundation in renewable energy and storage toward smart grids, market mechanisms, carbon capture, and artificial intelligence applications. Taken together, these results indicate that early research was primarily grounded in renewable energy and storage technologies, which provided the technical basis for subsequent exploration of smart grids and market mechanisms. In the more recent stage, under the dual-carbon policy and digital intelligence imperatives, research hotspots have further expanded toward carbon capture, utilization, and storage (CCUS) and artificial intelligence applications. Looking ahead, interdisciplinary studies focusing on intelligent dispatch and low-carbon transition are poised to emerge as the next major research frontier. Full article

Driven by the rapid advancement of smart grid frameworks, the volume of visual data collected from power system diagnostic equipment has surged exponentially. A substantial portion of these images (30–40%) are redundant or highly similar, primarily due to periodic monitoring and repeated acquisitions from multiple angles. Traditional redundancy removal methods based on manual screening or single-feature matching are often inefficient and lack adaptability. In this paper, we propose a two-stage redundancy removal paradigm for power inspection imagery, which integrates abstract semantic priors with fine-grained perceptual details. The first stage combines an improved discrete cosine transform hash (DCT Hash) with the multi-scale structural similarity index (MS-SSIM) to efficiently filter redundant candidates. In the second stage, a Vision Transformer network enhanced with a hierarchical sparse attention mechanism precisely determines redundancy via cosine similarity between feature vectors. Experimental results demonstrate that the proposed method achieves an algorithm sensitivity of 0.9243, surpassing ResNet and VGG by 5.86 and 8.10 percentage points, respectively, highlighting its robustness and effectiveness in large-scale power grid redundancy detection. These results underscore the paradigm’s capability to balance efficiency and precision in complex visual inspection scenarios. Full article

The complex electricity consumption situation on the customer side and large-scale wind and solar power generation have gradually shifted the traditional “source-follow-load” model in the power system towards the “source-load interaction” model. At present, the voltage regulation methods require excessive computing resources to accurately predict the fluctuating load under the new energy structure. However, with the development of artificial intelligence and cloud computing, more methods for processing big data have emerged. This paper proposes a new method for electricity consumption analysis that combines traditional mathematical statistics with machine learning to overcome the limitations of non-intrusive load detection methods and develop a distributed optimization of cloud–edge–device distribution networks based on electricity consumption. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation prediction, it is proposed to use the long short-term memory method to process time series data, and an improved algorithm is developed in combination with error feedback correction. The R² value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4, respectively. Power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the regulation strategy, stability can be achieved after 25 iterations, and the optimal regulation is obtained. Finally, the cloud–edge–device distributed coevolution model of the power grid is obtained to achieve the economy of power grid voltage control. Full article

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article

Cyber-physical power systems (CPPSs) are increasingly vital to the reliable and resilient operation of modern electricity infrastructure. Within these systems, both physical components—such as power substations and lines—and cyber components—such as communication links, mobile base stations, and controllers—are interdependent, making the identification of critical elements essential for improving system robustness. While prior research has largely focused on node-level analysis, this study addresses the underexplored challenge of identifying critical edges using tools from complex network theory. We evaluate edge importance through edge betweenness centrality (EBC) and edge removal analysis (ERA) across a real-world CPPS located in Northeastern Italy. Three network scenarios are analyzed: a directed power network, an undirected power network, and an undirected cyber network. Nearly 10 percent of the important edges, based on the EBC and ERA methods, are discussed. A Pearson correlation is considered to find the correlation between the results of the two methods. The findings can support distribution system operators in prioritizing infrastructure hardening and enhancing resilience against both physical failures and cyber threats. Full article

To effectively address challenges posed by high-penetration renewable energy to power system operation and reserves, this paper proposes a novel research framework. The framework considers transmission–distribution coordinated dispatch and optimizes reserve capacity. First, the framework addresses the volatility and uncertainty of wind and solar power output. It constructs a three-dimensional objective function incorporating generation cost, spinning reserve cost, and linear wind/solar curtailment penalties as core components. The study uses the IEEE 30-bus system as the transmission network and the IEEE 33-bus system as the distribution network to build a transmission–distribution coordinated optimization model. Power dynamic mutual support across voltage levels is achieved through tie transformers. Second, the framework designs three typical scenarios for comparative analysis. These include separate dispatch of transmission and distribution networks, coordinated dispatch of transmission and distribution networks, and a fixed reserve ratio mode. The approach breaks through the limitations of traditional fixed reserve allocation. It optimizes the coordinated mechanism between reserve capacity spatiotemporal allocation and renewable energy accommodation. Case study results demonstrate that the proposed coordinated optimization scheme reduces total system operating costs and wind/solar curtailment rates. This is achieved by exploiting the potential of regulation resources on both the transmission and distribution sides. The results verify the significant advantages of transmission–distribution coordination in improving reserve resource allocation efficiency and promoting renewable energy accommodation. The approach helps enhance power grid operational economics and reliability. Full article

To assess multi-energy complementarity and commercial development status in thermodynamic energy storage systems, this review systematically examines compressed air energy storage (CAES), compressed CO₂ energy storage (CCES), and Carnot battery (CB), focusing on principles, engineering demonstrations, and thermal integration. Their ability to integrate external heat, conduct combined cooling, heating and power (CCHP), or achieve high round-trip efficiency (RTE) through different pathway positions them as critical enablers for achieving net-zero emissions. Over 240 research articles retrieved from Web of Science and other databases, supplemented by publicly available reports published between 2020 and 2025, were systematically analyzed and synthesized. Current technologies demonstrate evolution from single-function storage to multi-energy hubs, with RTEs reaching 75% (CAES/CCES) and 64% (CB). Thermal integration significantly enhances RTEs. The CCES features a 100 MW/1000 MWh demonstration facility, concurrently necessitating accelerated distributed applications with high efficiency (>70%) and energy density (>50 kWh/m³). All three enable grid flexibility (China’s CAES network), industrial decarbonization (CCES carbon–energy depositories), and thermal integration (CB-based CCHP). These systems require >600 °C compressors and AI-optimized thermal management (CAES), high-pressure turbines and carbon–energy coupling (CCES), as well as scenario-specific selection and equipment reliability validation (CB) to achieve the targets of the Paris Agreement. Full article