Search Results (770)

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

To address the issue of significant perturbations caused by the limited flexibility of clean energy grid integration, along with the combined effects of electric vehicle charging demand and the uncertainty of high-penetration intermittent energy in the integrated energy system (IES), a capacity optimization method for the IES subsystem of a hydrogen-containing chemical park, accounting for strong perturbations, is proposed in the context of the park’s energy usage. Firstly, a typical scenario involving source-load disturbances is characterized using Latin hypercube sampling and Euclidean distance reduction techniques. An energy management strategy for subsystem coordination is then developed. Building on this, a capacity optimization model is established, with the objective of minimizing daily integrated costs, carbon emissions, and system load variance. The Pareto optimal solution set is derived using a non-dominated genetic algorithm, and the optimal allocation case is selected through a combination of ideal solution similarity ranking and a subjective–objective weighting method. The results demonstrate that the proposed approach effectively balances economic efficiency, carbon reduction, and system stability while managing strong perturbations. When compared to relying solely on external hydrogen procurement, the integration of hydrogen storage in chemical production can offset high investment costs and deliver substantial environmental benefits. Full article

Green H₂ production using electrolyzer technology is an emerging method in the current mandate, using renewable-based power sources integrated with electrolyzer technology. Prior research has been extensively studied to understand the effects of intermittent power sources on the hydrogen production output. However, in this context, the characteristics of the working electrolyzer behave differently under system-level operation. In this paper, we investigated a 25 kW alkaline electrolyzer for its stack performance in terms of stack efficiency, the stack current vs. stack voltage, and the relationship between the H₂ flow rate and stack current. It was found that the current of 52 A produces the best system efficiency of 64% under full load operation for 1 h. The H₂ flow rate behaves in an exponential asymptotic pattern, and it is also found that the ramp-up time for hydrogen generation by the electrolyzer is significantly low, thus marking it as an efficient option for producing green hydrogen with the input of a hybrid grid and renewable PV-based power sources. Hydrogen production techno-economic analysis has been conducted, and the LCOH is found to be on the higher side for the current electrolyzer under investigation. Full article

The surrounding rocks of roadways are typically subjected to cyclic loading–unloading stress states in underground engineering. In addition, cyclic loads are discontinuous under real working conditions, usually while loading rock mass in a cycle–intermission–cycle manner. Based on the XTDIC 3D (XTOP Three-dimensional Digital Image Correlation) full-field strain measurement system and AE (Acoustic Emission) system, the work performed uniaxial cyclic loading–unloading tests with constant-pressure durations of 0, 0.5, 2, and 6 h. The purpose was to investigate the damage degradation mechanism of sandstone under multi-stage equal-amplitude intermittent cyclic loading and unloading. The results are as follows. (1) As the constant-pressure duration increased, the uniaxial compressive strength of sandstone samples decreased, along with a decline in elastic modulus and a deterioration in stiffness and deformation recovery capacity. (2) The evolution of deformation localization zones became more intense in sandstone samples during cyclic loading and unloading with the increased constant-pressure duration. The maximum principal strain field became more active at failure. Sandstone samples exhibited shear failure accompanied by spalling failure and an increased failure degree. (3) As the constant-pressure duration increased, the damage variable of sandstone samples increased, indicating that the constant-pressure stage promoted the damage degradation of sandstone samples. The above results reveal the damage degradation mechanism of sandstone under multi-stage equal-amplitude intermittent cyclic loading and unloading, which is of significant importance for maintaining the safety of underground engineering. Full article

The On-Load Tap Changer (OLTC), as a critical component of transformers, undergoes frequent switching operations that can lead to faults such as contact wear and arc discharge, which are often difficult to detect at an early stage using traditional monitoring methods. In particular, dissolved gas analysis (DGA) in OLTC oil is challenged by the unique oil gas decomposition mechanisms and the presence of background noise, making conventional DGA criteria less effective. Moreover, OLTC oil monitoring data are typically obtained through intermittent sampling, resulting in sparse time series with low resolution that complicate fault prediction. To address these challenges, this paper proposes an integrated framework combining LGOD-based anomaly detection, Locally Weighted Regression (LWR) for data repair, and the ETSformer temporal prediction model. This approach effectively identifies and corrects anomalies, restores the dynamic variation trends of gas concentrations, and enhances prediction accuracy through deep temporal modeling, thereby providing more reliable data support for OLTC state assessment and fault diagnosis. Experimental results demonstrate that the proposed method significantly improves prediction accuracy, enhances sensitivity to gas concentration evolution, and exhibits robust adaptability under both normal and fault scenarios. Furthermore, ablation experiments confirm that the observed performance gains are attributable to the complementary contributions of LGOD, LWR, and ETSformer, rather than any single component alone, highlighting the effectiveness of the integrated approach. Full article

This article proposes an Energy Management System (EMS) for smart microgrids with a decentralized multi-agent system (MAS) based on a bio-inspired T-Cell optimization algorithm. The proposed system allows real-time control and dynamic balancing of loads while addressing the challenges of intermittent renewable energy sources like solar and wind. The system operates within the tertiary control layer; the optimal set points are computed by the T-Cell algorithm across energy sources and storage units. The set points are implemented and validated in real-time by the OPAL-RT simulation platform. The system contains a real-time feedback loop, which continuously monitors voltage levels and system performance, allowing the system to readjust in case of anomalies or power imbalances. Contrary to classical methods like Model Predictive Control (MPC) or Particle Swarm Optimization (PSO), the T-Cell algorithm demonstrates greater robustness to uncertainty and better adaptability to dynamic operating conditions. The MAS is implemented over the JADE platform, enabling decentralized coordination, autonomous response to disturbances, and continuous system optimization to ensure stability and reduce reliance on the main grid. The results demonstrate the system’s effectiveness in maintaining the voltages within acceptable limits of regulation (±5%), reducing reliance on the main grid, and optimizing the integration of renewable sources. The real-time closed-loop solution provides a scalable and reliable microgrid energy management solution under real-world constraints. Full article

The rapid growth of low-power Internet of Things (IoT) applications has created an urgent demand for compact, battery-free power solutions. However, most existing RF energy harvesters rely on active rectifiers, multi-phase topologies, or complex tuning networks, which increase circuit complexity and static power overhead while struggling to maintain high efficiency under microwatt-level inputs. To address this challenge, this work proposes a harmonic-recycling, passive, RF-energy-harvesting system with integrated power management (HR-P-RFEH). The system adopts a planar microstrip architecture compatible with MEMS fabrication, integrating a dual-stage voltage multiplier rectifier (VMR) and a stub-based harmonic suppression–recycling network. The design was verified through combined electromagnetic/circuit co-simulations, PCB prototyping, and experimental measurements. Operating at 915 MHz under a 0 dBm input and a 2 k$\Omega$ load, the HR-P-RFEH achieves a stable 1.4 V DC output and a peak rectification efficiency of 70.7%. Compared with a conventional single-stage rectifier, it improves the output voltage by 22.5% and the efficiency by 16.4%. The rectified power is further regulated by a BQ25570-based unit to provide a stable 3.3 V supply buffered by a 47 mF supercapacitor, ensuring continuous operation under intermittent RF input. In comparison with the state of the art, the proposed fully passive, harmonic-recycling design achieves competitive efficiency without active bias or adaptive tuning while remaining MEMS- and LTCC-ready. These results highlight HR-P-RFEH as a scalable and fabrication-friendly building block for next-generation energy-autonomous IoT and MEMS systems. Full article

Access to renewable energy is vital for rural development and climate change mitigation. The intermittency of renewable sources necessitates efficient energy storage, especially in off-grid applications. This study evaluates the technical, economic, and environmental performance of an off-grid hybrid system for the rural settlement of Soma, Turkey. Using HOMER Pro 3.14.2 software, a system consisting of solar, wind, battery, and hydrogen components was modeled under four scenarios with Cyclic Charging (CC) and Load Following (LF) control strategies for optimization. Life cycle assessment (LCA) and hydrogen leakage impacts were calculated separately through MATLAB R2019b analysis in accordance with ISO 14040 and ISO 14044 standards. Scenario 1 (PV + wind + battery + H₂) offered the most balanced solution with a net present cost (NPC) of USD 297,419, with a cost of electricity (COE) of USD 0.340/kWh. Scenario 2 without batteries increased hydrogen consumption despite a similar COE. Scenario 3 with wind only achieved the lowest hydrogen consumption and the highest efficiency. In Scenario 4, hydrogen consumption decreased with battery reintegration, but COE increased. Specific CO₂ emissions ranged between 36–45 gCO₂-eq/kWh across scenarios. Results indicate that the control strategy and component selection strongly influence performance and that hydrogen-based hybrid systems offer a sustainable solution in rural areas. Full article

The smart home concept, transforming traditional homes into smart homes thanks to technological advancements, is widespread around the world. In addition, energy consumers are also becoming energy producers by adding renewable energy sources, namely solar, wind, etc., to their homes along with traditional energy sources. However, intermittent weather conditions impact the power generation of renewable sources. Hence, there is a need to understand the correlation between several weather parameters and power generation. Traditional statistical methods such as Pearson, and Spearman, Kendall’s Tau, and Phi correlation coefficients are available but are limited to only two variables. Instead, multiple linear regression (MLR) offers multivariate analysis. Thus, this paper employs MLR to analyze the correlation between weather conditions such as temperature, apparent temperature, visibility, humidity, pressure, wind speed, dew point, precipitation, and power generation in kW. All the weather conditions are independent variables, and the generated power is a dependent variable. The key objective is to investigate the significant predictors and their impact on power generation. To implement this, a recent smart home dataset titled “Smart Home Dataset with Weather Information” that provides the required information was downloaded from Kaggle. This dataset contains 32 variables and 503,910 observations. The whole dataset with the considered variables (1 dependent variable and 11 independent variables) is utilized to implement the proposed correlation analysis. A regression model is developed to find the correlation between the parameters mentioned above in the dataset, and the multicollinearity among the independent variables is presented using the variance inflation factor (VIF). If the VIF value is more than 10, it represents high multicollinearity. The results showcase that those variables, such as temperature, humidity, apparent_temperature, and dew_point, produce VIF values of 296.67, 37.35, 126.29, and 152.15, respectively, and are thereby considered critical weather parameters that significantly influence solar energy generation. This aids in better generation and load management planning in smart homes. Full article

Objetives: This study aimed to (1) describe and compare the external physical requirements of international cerebral palsy (CP) football players during training sessions and official matches at the 2024 IFCPF World Cup, and (2) analyze the relationships between standardized field-based physical performance tests and the physical requirements recorded in both contexts. Methods: Twelve international outfield players from the Spanish national CP football team were monitored throughout the tournament. Physical performance was evaluated two weeks prior using 5-m and 30-m sprints, a Modified Agility Test (MAT), a dribbling test, and the 30–15 Intermittent Fitness Test (vIFT). Match and training physical requirements were assessed using inertial devices, including total and relative distances, velocity metrics, and acceleration/deceleration outputs. Results: Matches imposed significantly greater demands than training sessions in terms of peak velocity, total distance per minute, and distance at moderate (>12–18 km/h) and high (>18 km/h) intensities (t = 2.79 to 8.06; p = 0.01; ES(d) = 0.50 to 1.45). Training sessions exhibited greater variability in load while match requirements were consistent across games. Performance in the MAT and dribbling tests correlated with several physical indicators in both training and competition. In contrast, vIFT and sprint tests showed limited associations, especially with match variables. Conclusions: Match play elicits higher and more stable physical requirements than training. The MAT and dribbling tests appear to be ecologically valid tools for assessing functional readiness in CP football. These findings support the integration of specific physical tests and tailored training designs to better replicate the competitive requirements of international CP football. Full article

With the increasing penetration of renewable energy, the stochastic and intermittent nature of its generation increases operational uncertainty and vulnerability, posing significant challenges for grid stability. However, traditional algorithms typically identify critical nodes by focusing solely on the network topology or power flow, or by combining the two, which leads to the inaccurate and incomplete identification of essential nodes. To address this, we propose the Three-Dimensional Value-Based Gravity Model (3V-GM), which integrates structural and electrical–physical attributes across three layers. In the plane layer, we combine each node’s global topological position with its real-time supply–demand voltage state. In the line layer, we introduce an electrical coupling distance to quantify the strength of electromagnetic interactions between nodes. In the point layer, we apply eigenvector centrality to detect latent hub nodes whose influence is not immediately apparent. The performance of our proposed method was evaluated by examining the change in the load loss rate as nodes were sequentially removed. To assess the effectiveness of the 3V-GM approach, simulations were conducted on the IEEE 39 system, as well as six other benchmark networks. The simulations were performed using Python scripts, with operational parameters such as bus voltages, active and reactive power flows, and branch impedances obtained from standard test cases provided by MATPOWER v7.1. The results consistently show that removing the same number of nodes identified by 3V-GM leads to a greater load loss compared to the six baseline methods. This demonstrates the superior accuracy and stability of our approach. Additionally, an ablation experiment, which decomposed and recombined the three layers, further highlights the unique contribution of each component to the overall performance. Full article

This article presents an optimized approach to battery sizing and economic dispatch in wind-powered microgrids. The primary focus is on integrating battery depth of discharge (DoD) constraints to prolong battery life and ensure cost-effective energy storage management. Because of the intermittent nature of wind energy, wind-powered microgrids require sophisticated energy storage systems to ensure stable operation. This study develops a metaheuristic optimization method that balances power supply, battery lifespan, and economic dispatch in a microgrid. The proposed method optimizes both battery size and dispatch strategy while considering wind energy variability and the impact of DoD on battery lifespan. Case studies conducted on a wind-powered microgrid under varying load conditions show that the developed approach achieves a 40 to 50% reduction in operating costs and cost of electricity (CoE) compared to other approaches. The results also reveal that the inclusion of DoD constraints enhances battery lifespan. The proposed method offers a practical solution for improving the economic and operational efficiency of wind-powered microgrids, providing valuable understanding for energy planners and grid operators in renewable energy systems. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article