Long-term Power Research Articles (Page 2)

Energy Storage System (ESS) integration is essential to improving the sustainability, dependability, and effectiveness of contemporary power systems. The goal of this study is to create an extensive structure for the long-term modelling and optimisation of power systems that are connected with ESS. The study tackles important issues such controlling varying demand, preserving system stability, and managing transitory Renewable Energy (RE) production. Key parameters, including storage capacity, charge/discharge cycles, and operational constraints, are incorporated into the framework to assess the technical and economic impacts of ESS integration. Advanced optimization techniques are applied to identify optimal strategies for minimizing operational costs, maximizing RE utilization, and improving overall system performance. The results underline the potential of optimized ESS to enhance energy efficiency, reduce costs, and support the transition to sustainable and resilient power systems, ensuring long-term reliability and environmental benefits.

This study will evaluate the transition from conventional combat methods to a technology-driven battlefield. In particular, the Lebanon-Israel conflict serves a crucial case study to analyze how digital tools and AI reshape battlefield strategies, operational efficiency, and psychological warfare, triggering a broader evolution in modern military doctrine. This study will tackle a longitudinal comparison of digital strategy evolution in a single dyadic conflict zone. It adds to fields of security studies, cyberwarfare strategy, and AI-driven conflict analysis by analyzing how asymmetric technological adoption constructs long-term power dynamics. Through a theoretical lens of realism and a complementary military geopolitical framework, this research will analyze the impact of cyber warfare, AI-driven decision-making, intelligence, and precision missile systems on military strategies, political decision-making, and regional security.

Flexible design and operation of off-grid green ammonia systems with gravity energy storage under long-term renewable power uncertainty

This study presents a predictive modeling approach to evaluate the solar energy potential in Nigeria’s Federal Capital Territory (FCT), Abuja, and Nasarawa State over a ten-year period using statistical methods. The research integrates descriptive statistics and solar radiation modeling to assess both the electricity generation potential and atmospheric clarity of the two regions. Results show that both FCT and Nasarawa consistently experience average daily solar radiation levels exceeding 17.5 MJ/m²/day, which surpasses the global threshold for high solar energy potential. Specifically, solar radiation in FCT ranges from 11.4 to 22.0 MJ/m²/day, with an annual total of 211.7 MJ/m²/day, while Nasarawa State records 11.8 to 22.2 MJ/m²/day, totaling 209.6 MJ/m²/day. Reliability indices during off-rainy seasons are 0.62 for FCT and 0.56 for Nasarawa, indicating favorable conditions for solar power generation. A photovoltaic (PV) performance analysis using a 550W panel predicts annual energy outputs of 6.206 × 10⁷ MWh for FCT and 2.27 × 10⁸ MWh for Nasarawa. The clearness index (Kt) varies from 0.414 to 0.665 in FCT and 0.432 to 0.662 in Nasarawa, with slightly clearer atmospheric conditions observed in FCT. Skewness values of –0.379 (FCT) and –0.220 (Nasarawa) suggest left-tailed distributions, while kurtosis values of 1.322 and 1.168 indicate platykurtic behaviour. These findings validate the use of statistical models in predicting solar energy potential and highlight the suitability of both regions for long-term solar power deployment.

Due to the prevalent “small data”, “seasonal”, and “periodicity” characteristics in China’s renewable energy power generation data, there are certain difficulties in long-term power generation prediction. For this reason, this paper uses the data preprocessing method of periodical aggregation to enhance the “quasi-exponentiality” characteristics of original data, eliminate “seasonality” and “periodicity”, use the DGM (1,1) model to predict aggregated data, and then use the periodical component factor to reduce the DGM (1,1)-predicted data. A seasonal discrete grey prediction model based on periodical aggregation is constructed. The proposed methodology employs streamlined data preprocessing coupled with conventional grey prediction modeling to enable the precise forecasting of nonlinear periodic sequences. This approach demonstrates an enhanced operational efficiency by mitigating the structural complexity and implementation barriers inherent in classical seasonal grey prediction frameworks. Validation experiments conducted on China’s photovoltaic (PV) and wind power generation datasets through comparative multi-model analysis confirm the model’s superior predictive accuracy, with performance metrics significantly outperforming benchmark methods across both training and validation cohorts.

ABSTRACT With the rapid development of the renewable energy sector, accurate forecasting of photovoltaic (PV) power generation is becoming increasingly important. However, existing PV forecasting techniques often fail to capture the intricate relationship between PV power generation and auxiliary factors such as meteorological conditions. In addition, these techniques have difficulty accurately modelling the short-term and long-term dependencies of time-series data. To address these challenges, we propose the Mixer-Pvformer model, which combines Graph Convolutional Networks (GCNs) with a transformer architecture. Extensive experiments on datasets from different PV plants show that Mixer-Pvformer achieves higher prediction accuracy and generalisation capabilities compared to state-of-the-art methods.

Accurate long-term power load forecasting in the grid is crucial for supply–demand balance analysis in new power systems. It helps to identify potential power market risks and uncertainties in advance, thereby enhancing the stability and efficiency of power systems. Given the temporal and nonlinear features of power load, this paper proposes a hybrid load-forecasting model using attention mechanisms, CNN, and BiLSTM. Historical load data are processed via CEEMDAN, K-means clustering, and VMD for significant regularity and uncertainty feature extraction. The CNN layer extracts features from climate and date inputs, while BiLSTM captures short- and long-term dependencies from both forward and backward directions. Attention mechanisms enhance key information. This approach is applied for seasonal load forecasting. Several comparative experiments show the proposed model’s high accuracy, with MAPE values of 1.41%, 1.25%, 1.08% and 1.67% for the four seasons. It outperforms other methods, with improvements of 0.25–2.53 GWh2 in MSE, 0.15–0.1 GWh in RMSE, 0.1–0.74 GWh in MAE and 0.22–1.40% in MAPE. Furthermore, the effectiveness of the data processing method and the impact of training data volume on forecasting accuracy are analyzed. The results indicate that decomposing and clustering historical load data, along with large-scale data training, can both boost forecasting accuracy.

Purpose: This study aims to analyze the cultural differences between Brazil, Colombia, and Ecuador through Hierarchical Cluster Analysis (HCPC) and validating the Cultural Values Scale (CVSCALE) in South America. Method: Employing a multi-stage methodology, the research integrates a comprehensive literature review with scale adaptation and validation procedures to ensure linguistic accuracy and sociocultural relevance. Exploratory and confirmatory factor analyses were performed on samples from all three nations, followed by HCPC analysis using Ward's minimum variance criterion and K-means clustering to identifiy regional cultural profiles. Results: In this study, three cultural clusters emerged: Brazil demonstrated heightened uncertainty avoidance and power distance, Colombia exhibited a combination of long-term orientation and power distance moderated by feminine cultural values, while Ecuador displayed high values in masculinity paired with strong long-term orientation. The findings validate CVSCALE's reliability for cross-cultural research in South America. Originality: This work presents the first culturally validated measurement tool specifically adapted for South American contexts, offering a reliable instrument for intercultural research in the region. The integration of HCPC analysis provides an innovative methodological approach to identifying and comparing cultural configurations between countries. Theoretical Contributions: By validating CVSCALE’s applicability in South America, this study enriches the understanding of cultural dimensions in the region. It also demonstrates how HCPC techniques can segment cultural profiles, offering empirical evidence of regional cultural characteristics while confirming measurement invariance across countries. Managerial Implications: The validated scale and identified cultural profiles provide professionals with a reliable tool to develop culturally adapted strategies and interventions in South American markets. This tool can be applied in various fields, both social and commercial, by identifying psychographic segments related to behavior in each country.

Wind power generation(WPG) plays a crucial role in China's "double carbon" strategic goal, boasting broad development prospects in the country. Guangdong Province, in particular, is endowed with rich WPG resources and great potential. Thus, forecasting long - term WPG in Guangdong is of great significance. This paper commences by introducing diverse machine - learning algorithms in the context of the "two - carbon" target and long - term wind power prediction. Subsequently, historical data on WPG in Guangdong Province from 2009 to 2023, along with its potential influencing factors, are collected. After screening these influencing factors, appropriate independent variables related to energy factors are identified as the feature input for machine - learning algorithms.. According to the fitting effect of different algorithms, those suitable for long-term wind power generation in Guangdong Province are selected, including RR, Enet, Sufit, Stepwise and SVM (linear kernel function). Finally, based on the historical data of energy factors, the paper models and predicts the WPG in the future of Guangdong Province under the five algorithms. The predicted values of WPG in Guangdong for 2030, 2035, 2050, and 2060 are obtained by taking the average of the results from these five algorithms.

Aimed at the large disturbance of a power system caused by frequent new energy clusters going off-grid, we propose a cooperative optimization strategy of variable-speed and constant-speed pumped-storage units to address power oscillation due to significant power shortages following the clusters going off-grid. From a multi-time-scale perspective, we first investigate the fast power support control strategy of variable-speed pumped-storage (VSPS) units during new energy cluster off-grid scenarios. Using a consensus algorithm, the VSPS acts as the primary unit, while the constant-speed unit provides long-term power support. We present a rapid power control method for VSPS to prioritize frequency stability in mainland grids with high new energy penetration. This ensures stable power support for large-scale new energy clusters under large disturbances across multiple time scales. Simulation analysis on a high proportion of new energy power networks with new energy clusters confirms the effectiveness of our proposed method.

This paper solves the challenge of precise dual-frequency laser control in Airborne Coherent Doppler LiDAR systems by implementing an innovative laser driver architecture, which integrates compact hardware design with cascade Proportional-Integral-Derivative (PID) control and a frequency-temperature compensation mechanism. The experimental results demonstrate eminent performance with long-term temperature fluctuation below 0.007 °C, temperature stabilizing time under 4 s and long-term power fluctuation of the linear constant current source being <1%. The system enables wide-range temperature-frequency adjustment for individual lasers and dynamically adjusts the dual-laser beat frequencies between -1 GHz and +2 GHz, achieving the frequency difference fluctuation within 3 MHz. These achievements greatly enhance LiDAR performance and create possibilities for broader applications in dynamic environmental sensing, atmospheric monitoring, deep-space exploration, and autonomous systems.

ABSTRACT Purpose While most longitudinal research on pre-reading predictors has focused on the transition from kindergarten to Grades 1–2, less is known about their long-term predictive power as children progress from phonological decoding to proficient word recognition. This 5-year study investigated kindergarten language-related predictors of word-reading accuracy and fluency trajectories from initial to advanced stages, examining developmental changes in their relative importance within Hebrew’s unique characteristics, including its dual writing versions – pointed-transparent and unpointed-deep – and its rich Semitic morphology. Method A total of 515 Hebrew-speaking children (55.3% girls) from northern Israel was assessed on phonological awareness (PA), rapid automatized naming (RAN), letter knowledge (LK), and morphological awareness (MA) in kindergarten (mean age=5.9 years). Word-reading accuracy and fluency were measured in Grade 1 using pointed script, and in Grade 4 using both pointed and unpointed versions. Results Structural equation modeling revealed that early PA predicted word-reading abilities only in initial stages, while early MA specifically contributed to advanced stages, particularly in unpointed script. Early LK and RAN predicted word-reading abilities in both grades, with sustained direct effects on Grade 4 word-reading fluency across both versions and accuracy in unpointed script. Conclusion These findings demonstrate that kindergarten language-related skills, despite their early origin, not only establish fundamental reading abilities but also directly support advanced reading proficiency, underscoring the importance of comprehensive kindergarten programs that foster both early and long-term reading success. The distinct predictive patterns observed across reading stages, demands, and orthographic contexts enhance theoretical understanding of general reading development and script-specific frameworks.

Piezo-pyroelectric coupled nanogenerators (PPCNGs) capable of collecting vibration energy and thermal energy in complex environments are expected to provide a long-term power supply for multifunctional electronic devices. However, the piezoceramics as the core of the PPCNGs are limited in their coupled power generation capabilities due to low thermal conductivity and high internal resistance. In this work, it is proposed to construct Na0.5Bi0.5TiO3-K0.5Bi0.5TiO3/Ag (NBT-KBT/Ag) composite ceramics with a suspended network structure by introducing a low-melting-point metal Ag second phase. The network structure that can serve as a transmission path effectively reduces the scattering of phonons and carriers in the ceramics, improves the transport efficiency, and achieves the dual effects of increasing thermal conductivity and reducing internal resistance in the composite ceramics. And the output power density of PPCNG composed of the optimal components is 736.4 nW/cm3, which is 4.7 times that of the unoptimized virgin components. Furthermore, the optimal PPCNG possesses the capability to recognize object information through pressure and temperature sensing. This work reinforces the output characteristics of PPCNGs by constructing a suspended network structure. More importantly, the simple and efficient design strategy of constructing a suspended network structure is expected to be extended to material modification for the application of multifunctional smart electronic devices.

This research paper proposes a novel hybrid model for short-term and long-term wind power forecasting. The model integrates the strengths of Recurrent Neural Networks (RNNs), primarily focusing on Long Short-Term Memory (LSTM) networks due to their superior ability to handle long-range dependencies in time-series data [1], [2], [3], with Particle Swarm Optimization (PSO) [4], [5], [6] and Harmony Search (HS) [7], [5], [8] algorithms. PSO and HS, both meta-heuristic optimization techniques, are employed to optimize the hyperparameters of the LSTM network, enhancing its accuracy and generalization capabilities. The proposed hybrid model aims to overcome the limitations of individual techniques, such as premature convergence in PSO and local optima entrapment in HS [5], [9], [10], while leveraging the temporal dependency capturing abilities of LSTMs for improved wind power forecasting. The performance of the proposed model will be evaluated using real-world wind farm data, and compared with existing state-of-the-art methods, demonstrating its efficacy and potential for practical applications in renewable energy systems. The model's robustness and accuracy will be assessed through various metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) [11], [1], [2], considering various forecasting horizons.

Abstract We investigate the underlying stochastic processes driving the multiwavelength variability of the blazar BL Lacertae over the past two decades. Observations from Weihai Observatory over 224 nights reveal that the power spectral slopes of intra-night variability follow a Gaussian distribution, ranging from approximately 0.4–2.6, with an average trend consistent with the long-term variability power spectrum. Using power spectral analysis methods, such as the classical periodogram and the Lomb–Scargle periodogram, in combination with modeling techniques such as the power spectral response method, the multiple fragment variance function, and the continuous time autoregressive moving average, we study the multiband power spectral characteristics across a wide range of timescales (∼7 dex). The results demonstrate that, at lower frequencies, the power spectral density across different bands shows remarkable consistency, suggesting that these variations may be driven by a common stochastic process related to the accretion disk. However, significant discrepancies arise at higher frequencies, indicating the presence of multiple stochastic processes. We propose that the variability is governed by at least two distinct processes: one related to disk stochastic processes, dominating the long-term variability, and another associated with jet stochastic processes, which may result from turbulence, particle acceleration, and shock interactions within the jet, and drive the short-term, high-frequency variations. These findings provide unique insights into the complex mechanisms underlying blazar variability and suggest an intrinsic connection between the accretion disk and jet dynamics.

The increasing integration of renewable energy sources, particularly photovoltaic (PV) systems, into the power grid has introduced challenges related to energy variability, grid stability, and energy management. Supercapacitor-battery hybrid storage systems (SBHSS) have emerged as an effective solution to address these issues by combining the high energy density of batteries with the high-power density and fast response of supercapacitors. This paper explores the potential of SBHSS in grid-tied PV setups to enhance energy management and system stability. Batteries provide long-term energy storage and support sustained power delivery, while supercapacitors handle rapid fluctuations and transient spikes, thereby reducing the strain on batteries and extending their lifespan. A hybrid energy management strategy is proposed, which integrates real-time monitoring, adaptive power control, and dynamic load balancing to optimize energy flow between the PV array, supercapacitor, and battery. The proposed system minimizes power losses, improves grid frequency and voltage regulation, and enhances the reliability of power delivery during sudden load changes or cloud cover events. Simulation and experimental results demonstrate that the SBHSS can effectively smooth PV power output, reduce battery cycling stress, and improve overall system efficiency. The hybrid system also ensures faster response to grid disturbances, thereby improving grid resilience and reducing reliance on conventional backup sources. The combination of supercapacitors and batteries enables a more balanced and stable power supply, ensuring that renewable energy sources are efficiently integrated into the grid. This research highlights the practical advantages of SBHSS in grid-tied PV applications and provides insights into system design, control strategies, and performance improvements, paving the way for more sustainable and resilient power systems.

Asymmetric micro-supercapacitors (AMSCs) with a small size and high energy density can be compatible with portable and wearable electronic devices and are capable of providing stable, long-term power supply, attracting great research interest in recent years. Here, we present a simple and rapid preparation method for AMSCs' fabrication. By regulating the hydrophilicity and hydrophobicity of coplanar laser-induced graphene (LIG) through the adjustment of the laser parameters, two electrode materials with distinct hydrophilic-hydrophobic properties were selectively deposited by sequentially dip-coating. The LIGs serve as current collectors, with activated carbon and poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate) as active materials. After coating the electrolytes and folding the two electrodes, a high-performance AMSC was achieved. The device exhibits a high areal capacitance of 85.88 mF cm-2 at a current density of 0.4 mA cm-2, along with an impressive energy density of 11.93 µWh cm-2 and a good rate performance. Moreover, it is demonstrated to be highly stable in 500,000 cycles. Two AMSCs in series can supply power to an electronic clock and birthday card. The method of preparing asymmetric electrodes in the same plane greatly facilitates the large-area preparation of AMSCs and series-parallel connection, providing an excellent idea for developing high-performance miniature energy storage devices.

Medium- to long-term wind power output scenarios are crucial for power system planning and operational simulations. This paper proposes a two-stage hidden Markov model-based approach for modeling the time series output of multiple wind farms. First, based on the key features of the wind power output sequence, the daily typical patterns of wind power output are extracted. Then, the process of simulating the wind power output time-series is modeled as a two-layer temporal model. The upper layer uses a discrete hidden Markov model to describe the day-to-day transition process of wind power output patterns and the lower layer uses a Gaussian mixture hidden Markov model to describe the fluctuation process of wind power output values within each output pattern. Finally, the upper models corresponding to each quarter and the lower models corresponding to each pattern are trained respectively and the time-series scenarios of wind power output for multiple wind farms are generated quarter-by-quarter and day-by-day through Monte Carlo sampling. Validation using real-world wind power data demonstrates that the proposed method can effectively generate medium- to long-term output scenarios for multiple wind farms. Compared to traditional methods, the proposed method shows improvements in terms of accuracy, statistical characteristics, temporal correlation, and mutual correlation.

Over-the-Air Federated Learning in Cell-Free MIMO With Long-Term Power Constraint

Thermoelectric textiles have garnered significant attention in energy harvesting and temperature sensing due to their comfort and reliable long-term power generation capabilities. However, existing thermoelectric textiles rarely realize antibacterial, high output performance, and sensing capabilities simultaneously. Here, we present a facile and scalable method for fabricating n-type silver selenide (Ag2Se) cotton threads with exceptional antibacterial, high power output, and advanced sensing capabilities. The Ag-Ag2Se segmented structures are prepared using the segmented selenization method. Subsequently, a thermoelectric textile consisting of 50 pairs of p-n legs is fabricated, which can generate a power density of 500 μW m-2 at a temperature difference of 30 K, and it can provide an output voltage of 24.7 mV when worn on the arm at room temperature. The textile-based sensor exhibits temperature detection (0.7 K) and a response time (2.49 s). Integrating Ag2Se cotton threads onto textiles enables the utilization of multipixel touchpads for writing and communication. Additionally, these sensors can be incorporated into gloves to accurately detect the surrounding objects' temperatures. This thermoelectric cotton thread not only facilitates energy harvesting but also establishes a solid foundation for widespread application in multifunctional textile electronics.