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17 pages, 1145 KiB  
Article
Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
by Xiaodong Yang, Xiaotong Hua, Lun Cheng, Tao Wang and Yujing Su
Energies 2025, 18(15), 3926; https://doi.org/10.3390/en18153926 - 23 Jul 2025
Viewed by 53
Abstract
After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, [...] Read more.
After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems. Full article
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20 pages, 5656 KiB  
Article
A Quantitative Analysis Framework for Investigating the Impact of Variable Interactions on the Dynamic Characteristics of Complex Nonlinear Systems
by Yiming Tang, Chongru Liu and Chenbo Su
Electronics 2025, 14(14), 2902; https://doi.org/10.3390/electronics14142902 - 20 Jul 2025
Viewed by 144
Abstract
The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a [...] Read more.
The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a unified nonlinear modal analysis framework integrating second-order analytical solutions with novel nonlinear indices. Validated across diverse systems (DC microgrids and grid-connected PV), the framework yields significant findings: (1) second-order solutions outperform linearization in capturing critical oscillation/damping distortions under realistic disturbances, essential for fault analysis; (2) nonlinear effects induce modal dominance inversion and generate governing composite modes; (3) key interaction mechanisms are quantified, revealing distinct voltage regulation pathways in DC microgrids and multi-path dynamics driving DC voltage fluctuations. This approach provides a systematic foundation for dynamic characteristic assessment and directly informs control design for power electronics-dominated grids. Full article
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17 pages, 2066 KiB  
Article
A Mid-Term Scheduling Method for Cascade Hydropower Stations to Safeguard Against Continuous Extreme New Energy Fluctuations
by Huaying Su, Yupeng Li, Yan Zhang, Yujian Wang, Gang Li and Chuntian Cheng
Energies 2025, 18(14), 3745; https://doi.org/10.3390/en18143745 - 15 Jul 2025
Viewed by 136
Abstract
Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility [...] Read more.
Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article
(This article belongs to the Special Issue Optimal Schedule of Hydropower and New Energy Power Systems)
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25 pages, 9813 KiB  
Article
Digital Twin Approach for Fault Diagnosis in Photovoltaic Plant DC–DC Converters
by Pablo José Hueros-Barrios, Francisco Javier Rodríguez Sánchez, Pedro Martín Sánchez, Carlos Santos-Pérez, Ariya Sangwongwanich, Mateja Novak and Frede Blaabjerg
Sensors 2025, 25(14), 4323; https://doi.org/10.3390/s25144323 - 10 Jul 2025
Viewed by 275
Abstract
This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar [...] Read more.
This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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24 pages, 4035 KiB  
Article
Coordinated Optimization Scheduling Method for Frequency and Voltage in Islanded Microgrids Considering Active Support of Energy Storage
by Xubin Liu, Jianling Tang, Qingpeng Zhou, Jiayao Peng and Nanxing Huang
Processes 2025, 13(7), 2146; https://doi.org/10.3390/pr13072146 - 5 Jul 2025
Viewed by 313
Abstract
In islanded microgrids with high-proportion renewable energy, the disconnection from the main grid leads to the characteristics of low inertia, weak damping, and high impedance ratio, which exacerbate the safety risks of frequency and voltage. To balance the requirements of system operation economy [...] Read more.
In islanded microgrids with high-proportion renewable energy, the disconnection from the main grid leads to the characteristics of low inertia, weak damping, and high impedance ratio, which exacerbate the safety risks of frequency and voltage. To balance the requirements of system operation economy and frequency–voltage safety, a coordinated optimization scheduling method for frequency and voltage in islanded microgrids considering the active support of battery energy storage (BES) is proposed. First, to prevent the state of charge (SOC) of BES from exceeding the frequency regulation range due to rapid frequency adjustment, a BES frequency regulation strategy with an adaptive virtual droop control coefficient is adopted. The frequency regulation capability of BES is evaluated based on the capacity constraints of grid-connected converters, and a joint frequency and voltage regulation strategy for BES is proposed. Second, an average system frequency model and an alternating current power flow model for islanded microgrids are established. The influence of steady-state voltage fluctuations on active power frequency regulation is analyzed, and dynamic frequency safety constraints and node voltage safety constraints are constructed and incorporated into the optimization scheduling model. An optimization scheduling method for islanded microgrids that balances system operation costs and frequency–voltage safety is proposed. Finally, the IEEE 33-node system in islanded mode is used as a simulation case. Through comparative analysis of different optimization strategies, the effectiveness of the proposed method is verified. Full article
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35 pages, 5841 KiB  
Article
A Network Analysis of the Real Estate Fluctuation Propagation Effect in the United States
by Wenwen Xiao, Xuemei Pei, Wenhao Song and Lili Wang
Buildings 2025, 15(12), 2013; https://doi.org/10.3390/buildings15122013 - 11 Jun 2025
Viewed by 265
Abstract
Under the background of intensified global economic fluctuations, to prevent the systemic risk of real estate (e.g., the U.S. subprime crisis), this study constructs a linkage network of the real estate industry in the U.S. based on the complex network method, reveals the [...] Read more.
Under the background of intensified global economic fluctuations, to prevent the systemic risk of real estate (e.g., the U.S. subprime crisis), this study constructs a linkage network of the real estate industry in the U.S. based on the complex network method, reveals the fluctuation diffusion mechanism, identifies the key pivotal industries through the network characteristic indicators, and analyses the characteristics of the fluctuation conduction paths by applying the industrial fundamental association trees. The study found that (1) the U.S. real estate industry is a ‘supply hub’ industry, with first-order and second-order weighted degrees of mean 6.78, 3.98, and significant asymmetry in the supply structure of the industrial network; (2) industries like architectural, engineering, and related services (541300), nonresidential maintenance and repair (230301), and electric power generation, transmission, and distribution (221100) show high degree centrality and betweenness centrality. Their strong propagation and control capabilities form real estate fluctuations’ core transmission mechanisms; (3) foundational association trees reveal long, broad propagation paths where financial investment and energy-supply sectors act as “traffic hubs,” decisively influencing risk diffusion depth and breadth. Targeted policy recommendations address four dimensions: optimizing industrial chain structures, strengthening financial risk isolation, improving housing supply systems, and enhancing policy coordination. This aims to help China avoid U.S.-style real-estate-bubble risks and achieve coordinated real estate macroeconomy development. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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22 pages, 4567 KiB  
Article
Thermodynamic-Based Perceived Predictive Power Control for Renewable Energy Penetrated Resident Microgrids
by Wenhui Shi, Lifei Ma, Wenxin Li, Yankai Zhu, Dongliang Nan and Yinzhang Peng
Energies 2025, 18(12), 3027; https://doi.org/10.3390/en18123027 - 6 Jun 2025
Viewed by 437
Abstract
Heating, ventilation, and air conditioning (HVAC) systems and microgrids have garnered significant attention in recent research, with temperature control and renewable energy integration emerging as key focus areas in urban distribution power systems. This paper proposes a robust predictive temperature control (RPTC) method [...] Read more.
Heating, ventilation, and air conditioning (HVAC) systems and microgrids have garnered significant attention in recent research, with temperature control and renewable energy integration emerging as key focus areas in urban distribution power systems. This paper proposes a robust predictive temperature control (RPTC) method and a microgrid control strategy incorporating asymmetrical challenges, including uneven power load distribution and uncertainties in renewable outputs. The proposed method leverages a thermodynamics-based R-C model to achieve precise indoor temperature regulation under external disturbances, while a multisource disturbance compensation mechanism enhances system robustness. Additionally, an HVAC load control model is developed to enable real-time dynamic regulation of airflow, facilitating second-level load response and improved renewable energy accommodation. A symmetrical power tracking and voltage support secondary controller is also designed to accurately capture and manage the fluctuating power demands of HVAC systems for supporting operations of distribution power systems. The effectiveness of the proposed method is validated through power electronics simulations in the Matlab/Simulink/SimPowerSystems environment, demonstrating its practical applicability and superior performance. Full article
(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)
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24 pages, 1293 KiB  
Article
Singular Perturbation Decoupling and Composite Control Scheme for Hydraulically Driven Flexible Robotic Arms
by Jianliang Xu, Zhen Sui and Xiaohua Wei
Processes 2025, 13(6), 1805; https://doi.org/10.3390/pr13061805 - 6 Jun 2025
Viewed by 445
Abstract
Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical [...] Read more.
Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching 104 rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection. Full article
(This article belongs to the Special Issue Modelling and Optimizing Process in Industry 4.0)
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17 pages, 1564 KiB  
Review
Capsule Endoscopy: Current Trends, Technological Advancements, and Future Perspectives in Gastrointestinal Diagnostics
by Chang-Chao Su, Chu-Kuang Chou, Arvind Mukundan, Riya Karmakar, Binusha Fathima Sanbatcha, Chien-Wei Huang, Wei-Chun Weng and Hsiang-Chen Wang
Bioengineering 2025, 12(6), 613; https://doi.org/10.3390/bioengineering12060613 - 4 Jun 2025
Viewed by 3201
Abstract
Capsule endoscopy (CE) has revolutionized gastrointestinal (GI) diagnostics by providing a non-invasive, patient-centered approach to observing the digestive tract. Conceived in 2000 by Gavriel Iddan, CE employs a diminutive, ingestible capsule containing a high-resolution camera, LED lighting, and a power supply. It specializes [...] Read more.
Capsule endoscopy (CE) has revolutionized gastrointestinal (GI) diagnostics by providing a non-invasive, patient-centered approach to observing the digestive tract. Conceived in 2000 by Gavriel Iddan, CE employs a diminutive, ingestible capsule containing a high-resolution camera, LED lighting, and a power supply. It specializes in visualizing the small intestine, a region frequently unreachable by conventional endoscopy. CE helps detect and monitor disorders, such as unexplained gastrointestinal bleeding, Crohn’s disease, and cancer, while presenting a lower procedural risk than conventional endoscopy. Contrary to conventional techniques that necessitate anesthesia, CE reduces patient discomfort and complications. Nonetheless, its constraints, specifically the incapacity to conduct biopsies or therapeutic procedures, have spurred technical advancements. Five primary types of capsule endoscopes have emerged: steerable, magnetic, robotic, tethered, and hybrid. Their performance varies substantially. For example, the image sizes vary from 256 × 256 to 640 × 480 pixels, the fields of view (FOV) range from 140° to 360°, the battery life is between 8 and 15 h, and the frame rates fluctuate from 2 to 35 frames per second, contingent upon motion-adaptive capture. This study addresses a significant gap by methodically evaluating CE platforms, outlining their clinical preparedness, and examining the underexploited potential of artificial intelligence in improving diagnostic precision. Through the examination of technical requirements and clinical integration, we highlight the progress made in overcoming existing CE constraints and outline prospective developments for next-generation GI diagnostics. Full article
(This article belongs to the Special Issue Novel, Low Cost Technologies for Cancer Diagnostics and Therapeutics)
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15 pages, 2501 KiB  
Article
A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction
by Pinzhang Zhao, Lihui Wang, Jian Wei, Yifan Wang and Haifeng Wu
Sensors 2025, 25(11), 3478; https://doi.org/10.3390/s25113478 - 31 May 2025
Viewed by 335
Abstract
This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer [...] Read more.
This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer network, allowing for the prediction of future trends and the provision of early short-term warnings. First, we enhance the symplectic geometric mode decomposition (SGMD) algorithm and introduce wavelet packet decomposition reconstruction before recombination, successfully isolating the prominent harmonics of leakage current. Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. Finally, leveraging the prediction results from multiple adjacent columns mitigates the impact of power grid fluctuations. By integrating these data with the deterioration interval, we can issue timely warnings regarding the condition of lightning arresters across each column. Experimental results demonstrate that the proposed ISGMD-WP effectively decomposes leakage current, achieving a decomposition ability evaluation index (EIDC) 1.95 under intense noise. Furthermore, in long-term prediction, the I-Informer network yields mean absolute error (MAE) and root mean square error (RMSE) indices of 0.02538 and 0.03175, respectively, enabling the accurate prediction of the energy device’s fault. Full article
(This article belongs to the Section Electronic Sensors)
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21 pages, 1611 KiB  
Article
Coordinated Reactive Power–Voltage Control in Distribution Networks with High-Penetration Photovoltaic Systems Using Adaptive Feature Mode Decomposition
by Yutian Fan, Yiqiang Yang, Fan Wu, Han Qiu, Peng Ye, Wan Xu, Yu Zhong, Lingxiong Zhang and Yang Chen
Energies 2025, 18(11), 2866; https://doi.org/10.3390/en18112866 - 30 May 2025
Viewed by 507
Abstract
As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband [...] Read more.
As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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10 pages, 1391 KiB  
Article
Precise Temperature Measurement Through Wavelength Modulation Heterodyne Phase-Sensitive Dispersion Spectroscopy
by Guoquan Wang, Rende Wang and Weiqian Zhao
Photonics 2025, 12(6), 537; https://doi.org/10.3390/photonics12060537 - 26 May 2025
Viewed by 403
Abstract
This work proposes a precise temperature measurement method based on wavelength modulation heterodyne phase-sensitive dispersion spectroscopy (WM-HPSDS). Before the light intensity of the laser was modulated by an electro-optic modulator to generate a three-tone beam, the laser produced additional wavelength modulation by superimposing [...] Read more.
This work proposes a precise temperature measurement method based on wavelength modulation heterodyne phase-sensitive dispersion spectroscopy (WM-HPSDS). Before the light intensity of the laser was modulated by an electro-optic modulator to generate a three-tone beam, the laser produced additional wavelength modulation by superimposing a high-frequency sinusoidal waveform on a slow sawtooth wave. The second harmonic peak value of the H2O dispersion phase at 7185.59 cm−1 and 7182.94 cm−1 was used to extract temperature through two-line thermometry. The experiment was carried out on a water-based thermostat and an acoustically excited Bunsen burner. The extracted temperatures of the thermostat agreed well with the reference temperature, and the deviation was within 1.5 °C. The measurement stability of the Bunsen burner flame was approximately 10.4 dB higher than that of direct HPSDS. Furthermore, measuring the peak values under varying laser powers demonstrated that WM-HPSDS was immune to optical power fluctuations. Therefore, this method has potential for measuring temperature in harsh environments. Full article
(This article belongs to the Section Lasers, Light Sources and Sensors)
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33 pages, 4373 KiB  
Article
Nuclear–Thermal Power Generation: Multicriteria Optimization of the Economic Sustainability
by Stylianos A. Papazis
Sustainability 2025, 17(11), 4781; https://doi.org/10.3390/su17114781 - 22 May 2025
Viewed by 498
Abstract
As is well known, due to carbon dioxide emissions, the combustion of lignite in power plants creates environmental pollution. In contrast, nuclear fuels do not produce carbon dioxide emissions. This paper investigates the effects of replacing lignite thermal power plants with small modular [...] Read more.
As is well known, due to carbon dioxide emissions, the combustion of lignite in power plants creates environmental pollution. In contrast, nuclear fuels do not produce carbon dioxide emissions. This paper investigates the effects of replacing lignite thermal power plants with small modular nuclear reactors (SMRs) of equivalent rated power and related characteristics. In terms of the emissions criterion, nuclear fuels belong to the same category of clean sources as the sun and wind. A second criterion is the economic one and concerns the operating cost of the nuclear–thermal power plant. Based on the economic criterion, although nuclear reactors require a higher initial invested capital, they have lower fuel costs and lower operating costs than lignite plants, which is important due to their long service life. A third criterion is the effect of the operation mode of an SMR, constant or variable, on the cost of energy production. In terms of the operation mode criterion, two cycles were investigated: the production of a constant amount of energy and the production of a variable amount of energy related to fluctuations in the electric load demand or the operation load-following. Using multi-criteria managerial scenarios, the results of the research demonstrate that the final mean minimal cost of energy generated by hybrid thermal units with small nuclear reactors in constant power output operation is lower than the mean minimal cost of the energy generated in the load-following mode by 2.45%. At the same time, the carbon dioxide emissions in the constant power output operation are lower than those produced in the load-following mode by 2.14%. In conclusion, the constant power output operation of an SMR is more sustainable compared to the load-following operation and also is more sustainable compared to generation by lignite thermal power plants. Full article
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23 pages, 8487 KiB  
Article
An Artificial Intelligence Frequency Regulation Strategy for Renewable Energy Grids Based on Hybrid Energy Storage
by Qiang Zhang, Qi Jia, Tingqi Zhang, Hui Zeng, Chao Wang, Wansong Liu, Hanlin Li and Yihui Song
Energies 2025, 18(10), 2629; https://doi.org/10.3390/en18102629 - 20 May 2025
Viewed by 472
Abstract
To address the frequency regulation requirements of hybrid energy storage (HES) in renewable-dominated power grids, this paper proposes an asymmetric droop control strategy based on an improved backpropagation (BP) neural network. First, a simulation model of HES (comprising supercapacitors for the power support [...] Read more.
To address the frequency regulation requirements of hybrid energy storage (HES) in renewable-dominated power grids, this paper proposes an asymmetric droop control strategy based on an improved backpropagation (BP) neural network. First, a simulation model of HES (comprising supercapacitors for the power support and batteries for the energy balance) participating in primary frequency regulation is established, with a stepwise frequency regulation dead zone designed to optimize multi-device coordination. Second, an enhanced Sigmoid activation function (with controllable parameters a, b, m, and n) is introduced to dynamically adjust the power regulation coefficients of energy storage units, achieving co-optimization of frequency stability and State of Charge (SOC). Simulation results demonstrate that under a step load disturbance (0.05 p.u.), the proposed strategy reduces the maximum frequency deviation by 79.47% compared to scenarios without energy storage (from 1.7587 × 10−3 to 0.0555 × 10−3) and outperforms fixed-droop strategies by 44.33%. During 6-min continuous random disturbances, the root mean square (RMS) of system frequency deviations decreases by 4.91% compared to conventional methods, while SOC fluctuations of supercapacitors and batteries are reduced by 49.28% and 45.49%, respectively. The parameterized asymmetric regulation mechanism significantly extends the lifespan of energy storage devices, offering a novel solution for real-time frequency control in high-renewable penetration grids. Full article
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22 pages, 4858 KiB  
Article
Research on the Double Frequency Suppression Strategy of DC Bus Voltage on the Rectification Side of a Power Unit in a New Type of Same Phase Power Supply System
by Jinghua Zhou and Yuchen Li
Electronics 2025, 14(10), 2047; https://doi.org/10.3390/electronics14102047 - 17 May 2025
Viewed by 303
Abstract
This work provides a new solution for high-power quality traction power systems. The rapid development of electrified railways not only promotes economic development, but also seriously restricts the improvement of electric locomotive operation performance due to power quality problems, such as second harmonic [...] Read more.
This work provides a new solution for high-power quality traction power systems. The rapid development of electrified railways not only promotes economic development, but also seriously restricts the improvement of electric locomotive operation performance due to power quality problems, such as second harmonic distortion and negative sequence in the power supply system. In view of the shortcomings of the traditional in-phase power supply system in DC bus voltage stability control, a new in-phase power supply topology based on a back-to-back H-bridge power supply unit is proposed in this study. By establishing the iterative analysis model of the rectifier side double closed-loop control system, the internal correlation mechanism between the DC bus voltage second harmonic fluctuation and the grid side current harmonic is deeply revealed. On this basis, a rectifier-side disturbance compensation control strategy with a second harmonic suppression function is designed. Through real-time detection and compensation of second harmonic components, the active stability control of DC bus voltage is realized. The simulation model of the new cophase power supply system based on the experimental platform shows that the strategy can reduce the ripple coefficient of the DC bus voltage and the total harmonic distortion of the grid side current, which effectively verifies the superiority of the second harmonic suppression strategy in improving the power quality of the cophase power supply system. This work provides a new solution for a high-power quality traction power system. Full article
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