Energies doi: 10.3390/en19092235

Authors: Aysha Ramadhan Joe Huang Moncef Krarti

This paper explores the impact of various historical and future climate periods on the energy performance of residential buildings across the GCC. Specifically, five representative climate periods, Historic-1 (1991–2005), Historic-2 (2006–2018), Present (2010–2024), and Future-1 (2040–2050) and Future-2 (2080–2090), are considered to assess the energy performance for four design configurations of residential buildings in six GCC representative cities. The four building configurations encompass (i) baseline design defined by common traditional construction practices in most GCC countries using uninsulated walls and roofs with minimal air conditioning system efficiencies; (ii) code-compliant design using each GCC country’s current energy efficiency code requirements; (iii) optimized life cycle cost design using proven and cost-effective energy efficiency technologies; and (iv) net-zero energy design integrating the optimal set of energy efficiency strategies with rooftop PV systems. The analysis results have indicated that the energy performance of various designs depends closely on the climate periods, with the annual energy use of a today code-compliant typical residential building expected to increase by 20% in 2050 and 25% 2090. Moreover, larger PV systems by up to 25% need to be deployed for GCC homes designed with the present climatic conditions to continue achieving net-zero energy performance beyond 2050.

Energies doi: 10.3390/en19092234

Authors: Qi Han Jingyuan Bian Xiaojing Bai Jingxin Wei Shuang Tian

Traditional fixed or linear carbon prices often fail to reflect the nonlinear incentives of real carbon markets. To address this, we propose a multi-objective optimal dispatch framework for integrated energy systems (IESs) incorporating a tiered carbon trading mechanism. The system—comprising photovoltaics, wind power, a gas turbine, energy storage (ESS), power-to-gas (P2G), and grid interaction—aims to minimize operating and carbon trading costs while maximizing renewable utilization. This is solved using an improved multi-objective particle swarm optimization (IMOPSO) algorithm. Simulations across five configurations reveal that tiered pricing nonlinearly penalizes high emissions, reshaping the Pareto front toward low-carbon outcomes. Consequently, the ESS evolves from a simple economic arbitrageur into a proactive “carbon buffer”, absorbing midday photovoltaic surpluses and substituting gas turbine output during evening peaks. Compared to a grid-only baseline, the optimized multi-energy configuration (gas turbine + ESS + P2G) reduced operating costs by 13.1% and carbon emissions by 9.9%, while increasing renewable utilization by 8.5%. Ultimately, this study demonstrates that a well-designed nonlinear carbon pricing mechanism is decisive for guiding the IES to achieve coordinated economic and low-carbon operation.

Energies doi: 10.3390/en19092231

Authors: Jarosław Kozik

Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical industrial scenarios, make it difficult to collect sufficiently rich labeled datasets for data-driven and deep-learning-based diagnostic methods. Training diagnostic models purely on simulated signals often results in a severe domain shift between the digital twin and the physical machine due to nonlinearities, mechanical noise, and measurement imperfections, causing a significant degradation of performance when the model is deployed in practice. This paper proposes a hybrid diagnostic framework that combines a nonlinear physics-based digital twin of a synchronous machine, formulated using an extended Park’s transformation model with a dedicated fault loop, with a Domain-Adversarial Neural Network (DANN) driven by a minimal physics-guided feature vector composed of the 100 Hz and 200 Hz harmonic amplitudes of the excitation current. Simulated data from the digital twin are used as a labeled source domain, whereas test-bench measurements of the excitation current form an unlabeled target domain, enabling unsupervised sim-to-real transfer of the stator fault resistance. The proposed architecture achieves accurate regression of the stator fault-loop resistance on a laboratory machine without any labeled measurements of real faults. Experimental results demonstrate Mean Absolute Error (MAE) below 3% across the investigated fault severity range, significantly outperforming baseline approaches that lack domain adaptation. The industrial significance of this approach lies in its potential to facilitate a transition from reactive to predictive maintenance. By enabling early-stage detection, the framework allows power plant operators to avoid catastrophic failures and significantly reduce exceptionally high costs associated with unplanned outages and cascading grid disturbances.

Energies doi: 10.3390/en19092233

Authors: Yong Meng Yuanfei Lin Xingwei Xu Yugang Bao Yibo Zhou

To address insufficient dynamic reactive power support during large-scale new energy grid connection, synchronous condensers are widely used in centralized new energy delivery. As a special rotating electrical machine, their operational stability is critical to new energy power stations’ safe operation. Targeting practical application scenarios (synchronous condenser power delivery and new energy grid-connected systems with synchronous condensers), this paper establishes a simplified Heffron–Phillips model for their static stability analysis by integrating their actual operating characteristics into the traditional model. Specific electromagnetic torque component expressions are derived to reflect static stability. Mechanistically, it reveals the correlation between excitation system gain and torque, their impact on static synchronous stability, and obtains critical gain parameters for positive damping. Influencing factors are determined, and essential differences in additional torque characteristics between synchronous condensers and generators are clarified. Simulation models of the two systems verify the conclusions, providing theoretical support for engineering applications and a reference for practical parameter setting.

Energies doi: 10.3390/en19092232

Authors: Abdussalam A. Aburziza Mobin Naderi Daniel T. Gladwin

Wind curtailment in Great Britain (GB) is increasing, leading to underutilisation of low-carbon energy and higher system costs. This paper develops a data-driven techno-economic framework for a hydrogen generation and storage system that converts curtailed wind energy into hydrogen. By modelling curtailment time series and electricity prices, and considering a proton exchange membrane (PEM) electrolyser-based power-to-gas system, The framework explicitly represents the operation and interaction of the PEM electrolyser, hydrogen compression, and high-pressure storage under time-varying curtailment and electricity price conditions using reconstructed GB curtailment time series. The levelised cost of hydrogen (LCOH), net present value (NPV), and delivered hydrogen volumes are evaluated. A new sizing metric, curtailment utilisation, is introduced to link curtailment availability with electrolyser and storage productivity. Using a GB curtailment dataset, two key relationships are identified. First, increasing access to low-cost curtailed energy reduces the LCOH until electrolyser utilisation saturates, beyond which additional energy purchases provide diminishing benefits. Second, hydrogen storage exhibits an economic optimum: Undersized tanks increase costs due to ramping and venting losses, whereas oversized tanks raise capital investment requirements and increase the LCOH. For the best-performing configuration, corresponding to 70.2 MWh of curtailed energy, a 2.3 MW electrolyser, and a 94 m3 high-pressure tank, the system achieves an LCOH of £3.51/kg H2 (excluding downstream delivery) and an NPV of £2.17 M and meets 98.01% of the hydrogen demand. These results indicate that optimal system design requires not only appropriate component sizing but also explicit consideration of curtailment profiles and pricing structures. The proposed framework provides decision-grade guidance for developers and policymakers evaluating hydrogen production from wind curtailment. Future work will extend the model to hybridise with other energy storage system technologies, enable revenue stacking across multiple markets, address real-gas storage modelling, examine the sensitivity of stack degradation, and incorporate transport and delivery costs. These findings show that viable hydrogen production from curtailed wind depends on both low-cost electricity and coordinated electrolyser storage sizing under realistic curtailment conditions. The framework provides practical guidance for developers and policymakers.

Energies doi: 10.3390/en19092230

Authors: Rakibul Hasan Majdi Mansouri Jura Arkhangelski Mahamadou Abdou Abdou Tankari

Accurate forecasting of energy consumption in sensor-rich environments remains challenging due to strong inter-sensor dependencies, temporal variability, and heterogeneous sensor behavior. This paper proposes a lightweight and interpretable multi-sensor fusion framework for short-term energy consumption forecasting. The heterogeneous sensor dataset is first preprocessed to handle missing values, outliers, and temporal misalignment, followed by synchronization of the multivariate signals on a common timeline to enable consistent learning. The proposed framework systematically investigates multiple strategies for exploiting information from synchronized multi-sensor data without performing explicit feature elimination or time-lag engineering. In particular, three fusion paradigms are considered: (i) Early Fusion, where all sensor measurements are jointly used as input features for a multivariate regression model; (ii) Late Fusion, where individual sensor predictors are trained independently and their outputs are combined using reliability-based weighting; and (iii) an attention-inspired fusion strategy, in which adaptive weights are assigned to sensor-level predictions based on their predictive reliability estimated from training errors and normalized via a softmax function. In addition, classical machine learning models including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB) are evaluated under the same experimental conditions to provide a consistent benchmark. Experimental results on a real-world building energy monitoring dataset consisting of nine heterogeneous sensors demonstrate that multi-sensor fusion approaches consistently improve forecasting performance compared to single-model baselines. Among the evaluated strategies, Late Fusion provides stable performance across strongly correlated loads, while the attention-inspired fusion strategy exhibits improved robustness when handling sensors with varying predictive reliability. To ensure robustness and reproducibility, results are reported using multiple chronological validation splits, with performance evaluated in terms of RMSE, MAE, and R2 along with statistical measures including standard deviation and confidence intervals. The proposed framework provides a practical balance between predictive accuracy, interpretability, and computational efficiency, making it suitable for smart building energy management and real-world deployment scenarios.

Energies doi: 10.3390/en19092229

Authors: Lucas Camaz Ferreira Felipe Leite Coelho da Silva Josiane da Silva Cordeiro Javier Linkolk López-Gonzales Esteban Tocto-Cano Lennin Centurion

The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities.

Energies doi: 10.3390/en19092228

Authors: Bo Zhou Yunyang Xu Xinwei Sun Congkai Huang Yikui Liu

The rapid growth of wind and solar energy poses new challenges to safe and reliable system operation. Effectively characterizing and generating high-risk wind and photovoltaic (PV) power output scenarios is therefore essential for system risk assessment and preventive dispatch and control. However, existing scenario generation methods either rely on predefined probability distributions or focus narrowly on extreme output levels, failing to comprehensively reflect system-level operational risk induced by renewable energy. To this end, a power system optimal dispatch model and a flexibility indicator system mainly incorporating system ramping and transmission margins are established. Thereafter, analytic hierarchy process (AHP) and the entropy weight method (EWM) are used to fuse indicators into a quantitative operational risk index. Historical wind and PV scenarios are evaluated through the dispatch model to generate risk-labeled samples, based on which a conditional generative adversarial network (cGAN) is trained to produce wind and PV power output scenarios with specified risk levels. Case studies verify that the risk labels constructed can effectively guide the subsequent conditional generation model and scenarios corresponding to a given risk level can be effectively generated by the model.

Energies doi: 10.3390/en19092227

Authors: Zheng Chen Yanlong Li Chaohou Liu Yuying Wu Lei Wang Yousu Yao Jian Li

Accurate online parameter identification and state-of-charge (SOC) estimation are essential for lithium-ion battery management systems. However, under constant or quasi-constant current operating conditions, the system excitation is inherently weak, leading to poor parameter identifiability when conventional model-based estimation methods are used. This issue is particularly critical in grid-connected battery energy storage systems, where current dynamics are limited. To address this problem, this paper proposes an online measured impedance-assisted SOC estimation framework that integrates online electrochemical impedance measurements with a fractional-order battery model and an extended Kalman filter. Online impedance data are utilized to update the model parameters in real time through a geometric-based fitting algorithm, thereby enhancing model adaptability under low excitation conditions. Experimental results obtained from lithium-ion cells with different aging states demonstrate that the proposed method enables stable and accurate online parameter identification and SOC estimation under the tested low-excitation conditions, where conventional time-domain approaches tend to degrade or diverge. Robustness under highly dynamic operating conditions remains to be further validated.

Energies doi: 10.3390/en19092226

Authors: Antonio Giuffrida Paolo Chiesa

Efficiency improvements in gas turbines have been realized in recent decades by raising the turbine inlet temperature. This work devotes attention to pressure-gain combustion (PGC), which is a technology capable of yielding the same time-averaged combustor outlet temperature as conventional Brayton–Joule cycles but at a higher pressure. Here, PGC is implemented in a thermodynamic cycle wherein the compression system operates at a lower pressure ratio compared to the reference Brayton–Joule cycle. Focusing on E-, F- and H-class gas turbines, representative of three different technologies, the possible PGC advantages in both simple- and combined-cycle modes are investigated by means of in-house simulation code. Specifically, this work includes the energy penalty related to the PGC system cooling in the cycle analysis. In detail, the effects of different coolant amounts on the PGC system, as well as the lower efficiency at the first expansion stage compared to conventional gas turbine systems, are analyzed. Among the three classes of gas turbines, E is the one wherein the advantages are more significant, with ultimate efficiency values in simple-cycle mode calculated in the range of 38% to 41%. The higher the gas turbine technology and power class, the lower the benefit, and current H-class gas turbines already start from a higher efficiency level. Anyway, focusing on the latter, performance improvements for the PGC combined cycle seem to be possible, with efficiency greater than 65%, exceeding the current state-of-the-art systems.

Energies doi: 10.3390/en19092225

Authors: Lumengo Bonga-Bonga Frederich Kirsten

This paper investigates the role of financial institution development in promoting renewable energy generation in African economies. The paper is motivated by the increasing global emphasis on clean energy transition and the need to achieve the Sustainable Development Goals, particularly those related to affordable and clean energy and climate action. It focuses on identifying the mechanisms through which financial development influences renewable energy outcomes. Grounded in the Schumpeterian theory of finance, the paper argues that financial institutions facilitate innovation and structural transformation by allocating resources toward productive investments, including renewable energy projects. The analysis examines whether credit to the private sector serves as a mediating channel in this relationship. It also evaluates the moderating roles of institutional quality and natural resource rents. Using a Panel Autoregressive Distributed Lag (PARDL) model within a dynamic fixed-effects error correction framework, the findings reveal a nonlinear relationship. Financial institution development initially promotes renewable energy generation, but its positive effect weakens beyond a threshold of resource dependence. Institutional quality strengthens the effectiveness of financial development, while credit to the private sector fully transmits its impact on renewable energy generation. The results highlight the importance of strengthening financial systems, improving governance, and enhancing private sector credit allocation to support sustainable energy development in Africa.

Energies doi: 10.3390/en19092224

Authors: Hanchi Xu Na Deng

Irreversible loss caused by viscosity-dominated viscous dissipation is an important factor affecting high-temperature heat pump (HTHP) performance. To quantify the effect of viscosity on the coefficient of performance (COP) of HTHP systems, this study developed a contribution analysis model based on data samples from multiple working conditions, working fluids, and device types. Factor analysis and Varimax orthogonal rotation were employed to achieve multi-factor dimensionality reduction and mapping, quantitatively analyze viscosity factors, and compare the weight contribution distributions of other influencing factors with and without viscosity parameters. Results show that, in the global sample, viscosity corresponding to condensation temperature ranks among the top three negatively correlated factors, with a contribution of 7.40%. The sum of the absolute contributions of viscosity corresponding to condensation temperature and evaporation temperature reaches 9.86%, second only to temperature lift (16.10%). In the three local temperature ranges, the contributions of viscosity corresponding to condensation temperature are 6.31%, 6.75%, and 7.11%, respectively. The total contribution of irreversible loss parameters increases from 46.21% to 49.39%, and the increase reaches 12.02% in the high-temperature range. These results provide a theoretical basis for HTHP system design, working fluid selection, and performance improvement under high-temperature operating conditions.

Energies doi: 10.3390/en19092223

Authors: Rui Cao Chenjun Zhang Xiangyang Zhao Yanan Deng

Achieving synergy between the digital economy and energy efficiency is pivotal for realizing high-quality development under the “Dual Carbon” targets. However, traditional econometric methods struggle to capture the complex nonlinear and spatio-temporal dependencies inherent in this relationship. To address this issue, this study develops a two-stage framework using Chinese provincial panel data. It combines LightGBM/CatBoost and SHAP for critical factor identification, and employs STGNN for capturing nonlinear and spatial correlation patterns, to systematically decode the driving mechanisms of the digital economy on energy efficiency. The results reveal three key findings: (1) Complex Nonlinearity: The impact manifests in distinct U-shaped, inverted U-shaped, and weak correlation patterns, accompanied by significant spatial clustering. (2) Structural Heterogeneity: The dimensions of the digital economy show differential associations with energy efficiency. Industrial digitization and infrastructure are associated with more direct improvements in efficiency, whereas digital industrialization functions primarily through indirect technological supply. (3) Spatial Correlation Pattern: Higher levels of digital development correspond to higher local energy efficiency and are linked to positive predicted adjustments in neighboring regions, with notable regional heterogeneity. Combining machine learning-based feature selection with deep learning-based spatiotemporal modeling provides a scientific basis for formulating location-specific digital economy strategies and coordinated energy-saving policies.

Energies doi: 10.3390/en19092222

Authors: Jia Chu Yanqing Li Heng Yang Tao Han

This study addresses the impact of temperature gradient-induced non-uniform aging on the accuracy of space charge measurements in cross-linked polyethylene (XLPE) insulation for high-voltage direct-current cables. Existing pulse-echo acoustic (PEA) recovery algorithms neglect the evolution of material acoustic and dielectric properties during aging. To overcome this limitation, the systematic degradation of sound velocity, attenuation dispersion, and dielectric constant subjected to temperature gradient aging was experimentally investigated. Specimens were aged at temperatures ranging from 40 to 100 °C for durations up to 49 days. Then, quantitative models describing the dependence of acoustic and dielectric properties on aging severity were established. A space charge signal correction algorithm was then developed, incorporating nonlinear adjustments for sound velocity, attenuation, and permittivity according to the through-thickness aging profile. The algorithm’s accuracy was validated by comparing recovered charge waveforms and electric field distributions under 5 kV/mm for samples aged under different temperature gradients. The application of the method under high-voltage DC conditions revealed that aging induces non-monotonic changes in sound velocity, increased attenuation coefficients, and elevated low-frequency dielectric constants. Temperature gradient aging promotes heteropolar charge accumulation. This work provides a theoretical and methodological basis for improving the accuracy of the insulation condition assessment in long-term service HVDC cables.

Energies doi: 10.3390/en19092221

Authors: Pengyu Wu Da Xie Yanchi Zhang

Large-scale renewable energy bases increasingly employ automatic voltage control (AVC) to coordinate heterogeneous reactive-power resources. The resulting voltage regulation process inherently involves sampling, communication delay, and nonlinear device characteristics, which may induce nontraditional voltage oscillations and stability degradation that cannot be adequately captured by conventional continuous-time or small-signal analysis. This paper proposes a discrete-time nonlinear voltage stability analysis framework for renewable energy bases with multi-reactive-power-resource coupling under AVC-based coordinated control. The voltage regulation dynamics are formulated as a discrete-time nonlinear closed-loop system by incorporating sampled AVC actions, delayed voltage feedback, and nonlinear voltage–reactive-power coupling. An incremental system representation is constructed, and a strong-contraction-based stability criterion is derived using sector-bounded nonlinearity descriptions and linear matrix inequalities, providing a sufficient condition for global voltage convergence without local linearization. Extensive numerical studies are conducted on a representative renewable energy base with parallel and series coupling topologies. A total of 2916 randomized configurations are evaluated. The proposed criterion achieves consistency rates exceeding 96% for the parallel topology and 99% for the series topology when compared with time-domain simulations, while the probability of dangerous misjudgment remains below 1%. Scenario-based simulations further demonstrate that coupling topology plays a critical role in shaping voltage stability behaviors, and state-space analysis further supports the observed stability behaviors. These results indicate that nonlinear strong contraction offers an effective and practical stability notion for AVC-based voltage regulation in renewable energy bases.

Energies doi: 10.3390/en19092220

Authors: Mohammed A. AlAqil

The increasing integration of distributed photovoltaic (PV) systems in urban environments requires planning frameworks that simultaneously address economic viability, environmental sustainability, and power system performance. This study develops a simulation-based techno-economic and environmental assessment framework for evaluating hybrid rooftop photovoltaic (PV) and building-integrated photovoltaic (BIPV) deployment under harsh climatic conditions. Detailed system modelling using PVsyst and ETAP is conducted to analyse energy production, economic performance, environmental impact, and grid interaction characteristics, including voltage deviation and harmonic distortion. To support deployment planning and operational decision-making, the simulation outputs are incorporated into a multi-objective optimisation framework that evaluates trade-offs among levelized cost of energy (LCOE), net present value (NPV), carbon emission reduction, and power quality indicators. Three deployment configurations including rooftop PV only, BIPV only, and a hybrid PV–BIPV system are assessed using structured trade-off analysis and Pareto optimality principles. Results indicate that the hybrid configuration provides the most balanced performance across technical, economic, and environmental objectives. The system achieves an average performance ratio of 77.36% and generates approximately 2075 MWh of annual energy while maintaining grid voltages within acceptable limits and harmonic distortion well below IEEE 519 thresholds. Economic analysis shows strong financial feasibility with an LCOE of approximately 0.05 USD/kWh, a payback period of 8.1 years, a net present value of about 2.88 million USD, and a return on investment exceeding 145%. Loss analysis further identifies temperature effects and dust accumulation as the dominant performance constraints under harsh environmental conditions. Moreover, Pareto-based evaluation confirms the hybrid PV–BIPV configuration as the preferred deployment strategy among the evaluated alternatives. The proposed framework demonstrates how integrated simulation and multi-objective optimization can serve as a practical decision-support tool for planners and policymakers seeking to optimise distributed renewable energy deployment under climatic and operational uncertainties.

Energies doi: 10.3390/en19092219

Authors: Hassanein A. Refaey Bandar Awadh Almohammadi

Tunnel kilns are widely used in ceramic manufacturing due to their continuous operation, stable performance, and relatively high thermal efficiency. However, the firing stage remains highly energy-intensive and is a major source of environmental impact, necessitating advanced strategies for performance optimization and sustainability. This study presents a comprehensive and critical review of recent developments in tunnel kiln technology, focusing on heat transfer mechanisms, thermal modeling, process optimization, airflow management, energy recovery, computational fluid dynamics (CFD), and environmental sustainability. The literature shows that kiln performance is governed by strongly coupled interactions among fluid flow, heat transfer, combustion, and material transformations. Although significant progress has been achieved through analytical modeling, experimental studies, and numerical simulations, many approaches rely on simplified assumptions or isolated subsystem analyses, limiting their applicability to real industrial conditions. Key findings emphasize the importance of optimizing airflow distribution, kiln geometry, and product arrangement to enhance convective heat transfer and temperature uniformity. Energy optimization strategies—including waste heat recovery, combustion control, and reduction in kiln car thermal mass—demonstrate considerable potential, but their effectiveness depends on integrated, system-level implementation. Environmental analyses identify the firing stage as the primary source of greenhouse gas emissions, highlighting the need for coordinated energy and emission reduction strategies. In this context, Digital Twin and Industry 4.0 technologies offer promising capabilities for real-time monitoring, predictive control, and data-driven optimization. Generally, this review underscores the need to transition from isolated optimization approaches to integrated, multi-scale frameworks that combine advanced modeling, experimental validation, and intelligent digital systems to achieve sustainable and energy-efficient ceramic manufacturing.

Energies doi: 10.3390/en19092218

Authors: Jin-Hyeok Park Tai Duc Le Moo-Yeon Lee

This study proposes a multi-objective optimization framework for a cooling plate-based indirect liquid cooling system applied to a 6S2P lithium-ion battery module during 3C fast charging. A three-dimensional computational fluid dynamics (CFD) model coupled with the multi-scale multi-domain (MSMD)–Newman–Tiedemann–Gu–Kim (NTGK) battery heat generation model was developed to investigate the system thermal–hydraulic behavior. The numerical model was experimentally validated through single-cell charging tests, with temperature deviations below 5%, confirming its reliability. A systematic parametric analysis was conducted to evaluate the effects of coolant channel number, channel width, channel spacing, and coolant mass flow rate on maximum temperature (Tmax), temperature difference (ΔT), and pressure drop (ΔP). The results indicated that increasing the coolant flow rate significantly enhanced thermal performance but caused a substantial increase in hydraulic losses, whereas geometric parameters had comparatively smaller effects. To improve optimization efficiency, 30 design samples were generated using Latin hypercube sampling and used to train ANN surrogate models, which demonstrated high predictive accuracy with test R2 values of 0.9931, 0.9960, and 0.9842 for Tmax, ΔT, and pumping power (Ppump), respectively. Subsequently, NSGA-II combined with TOPSIS identified the optimal design with a channel width of 6.22 mm, channel spacing of 4.84 mm, and coolant flow rate of 2.55 LPM. Under these conditions, the optimized system achieved a Tmax of 30.47 °C, a ΔT of 4.50 °C, and a Ppump of 0.05879 W. The relative deviations between ANN predictions and CFD results were all below 1%, demonstrating the robustness of the proposed optimization framework. These findings provide an effective design methodology for enhancing heat transfer while minimizing pumping power in advanced battery thermal management systems.

Energies doi: 10.3390/en19092216

Authors: Zhao Li Wuqiang Long Hua Tian

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge.

Energies doi: 10.3390/en19092217

Authors: Viktor Alexiev Boris Marinov Vasil Shterev Rad Stanev Bozhidar Bozhilov

Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily rising. Accurate forecasting methods are a crucial enabler for the operational existence of power systems that rely on renewable sources. And while in the pursuit of increased accuracy of predictions, many recent research works rely on artificial intelligence and machine learning techniques, this study proposes and adopts a more conventional approach with standardized mathematical models to address the problem of hydropower production forecasting. The model predicts the runoff–power relationship. It starts with the normalization of different rain phenomena as a part of the statistical characterization of runoff events. The system transforms rain occurrence to runoff events via the USDA SCS CN model and then feature vectors are composed, which are used to generate kernel coefficients via interpolation. Contrary to models based on artificial intelligence, the proposed approach has several practical advantages requiring a minimal set of input parameters, which significantly reduces data preprocessing demands and allows for a straightforward integration into existing systems, thereby lowering the cost and the implementation and deployment time. Furthermore, the simplicity and universality of the model make it so that it can be adapted across a wide range of hydropower plants of varying scales and with diverse hydrological and meteorological conditions. The model’s performance and prediction accuracy are evaluated using empirical data records of time series over a five-year period for the meteorological parameters and production of an existing real-life hydropower plant in Bulgaria. The performance of the newly proposed model is assessed using widely accepted statistical error metrics, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the Nash–Sutcliffe Efficiency (NSE) coefficient, and the Pearson correlation coefficient (R). These metrics provide a comprehensive assessment of the forecasts’ precision and effectiveness. The results show that the proposed model offers admissible accuracy with low computational effort. Thus, it can be successfully implemented in practice in a number of hydropower plant production forecasting applications.

Energies doi: 10.3390/en19092215

Authors: Meruyet Zhanabayeva Peyman Pourafshary

The capacitance resistance model (CRM) is an analytical tool widely used to forecast reservoir performance in enhanced oil recovery (EOR) methods. By representing flow dynamics and the connectivity between injection and production wells through the parameter of interwell connectivity, CRM offers fast computational processing and minimal input data requirements. These advantages make CRM a practical alternative for rapid reservoir analysis, especially when full-scale numerical simulations are infeasible due to time and budget constraints. CRM, rooted in material balance and productivity equations, uses injection/production rates and bottom-hole pressure data to construct reservoir models through optimization techniques, which can then be combined with oil fractional flow models for predictive purposes. Initially designed for waterflooding operations, CRM has seen limited but promising applications in gas injection projects, where research remains incomplete. This study presents a new formulation of CRM tailored for immiscible gas injection, incorporating the pseudo-pressure concept coupled with a saturation profile. The pseudo-pressure concept is a mathematical transformation that linearizes gas flow equations by accounting for variations in gas compressibility and viscosity with pressure. The proposed CRM was evaluated using a PUNQ-S3 benchmark reservoir model in the CMG IMEX black oil simulator, involving two injectors and four producers. History- matching results for fluid production rates showed that the newly developed CRM achieved the lowest NRMSE, outperforming other CRM models across a wide range of reservoir properties. Sensitivity analyses were conducted to examine the effects of gas and oil PVT properties on the model’s performance. The newly developed CRM, incorporating the pseudo-pressure concept and saturation profiles, demonstrates superior performance in predicting fluid production rates, achieving an average NRMSE of 15.3% in a base case scenario, compared to other tested CRM models. Additionally, the sensitivity analysis on the effect of fluid properties shows that higher gas viscosity, lower gas formation volume factor, and increasing oil API gravity improve the CRM model’s performance, and under all tested conditions the newly developed CRM provides the most accurate production history match. This study not only establishes the new CRM as a robust and accurate predictive tool for immiscible gas injection but also provides a comprehensive discussion on reservoir parameter ranges and model limitations, advancing the applicability of CRM in EOR processes.

Energies doi: 10.3390/en19092214

Authors: Andrés Espin Alexander Aguila Téllez

Power factor correction in transmission networks with nonlinear loads cannot be addressed solely from the viewpoint of reactive compensation because harmonic distortion and resonance may compromise the expected technical benefits. In this context, this study proposes a resonance-aware and decision-oriented methodology that integrates nonlinear-load screening, weighted bus prioritization based on power factor degradation and harmonic severity, and tuned passive-filter design validated through impedance-frequency analysis and IEEE 519 compliance criteria. The methodology was implemented in DIgSILENT PowerFactory using the IEEE 14-bus test system, where nonlinear loads were allocated at buses 9 and 14 to emulate converter-dominated operating conditions. Under this scenario, the power factor decreased to 0.78271 and 0.85875, while total harmonic distortion increased to 22.01% and 20.07%, respectively. After the implementation of tuned passive filters, the power factor improved to 0.83023 at bus 9 and 0.90414 at bus 14, whereas total harmonic distortion was reduced to 4.61% and 5.22%, respectively, thus restoring compliance with IEEE 519. In addition, load currents decreased by approximately 16–19%. These results demonstrate that the proposed framework provides a technically consistent procedure for identifying critical buses, mitigating dominant harmonics, improving power factor, and avoiding adverse resonance conditions within a unified compensation workflow.

Energies doi: 10.3390/en19092213

Authors: Aron Rexhausen Benno Rothstein Charles Yousif

This study investigates the feasibility of viticultural photovoltaics (Viti-PV) in Malta—a small European island state in the Mediterranean—through a mixed-methods approach, combining a standardised questionnaire (n= 13 viticulturists) with expert interviews involving stakeholders from viticulture, energy and policy. Results show that while Viti-PV offers tangible benefits such as shading, reduced irrigation needs and income diversification to this sunny, warm and relatively dry island, adoption is constrained by high investment costs, regulatory prohibitions and concerns over landscape impacts. For policy and practice, the findings highlight the necessity of tailored financing models, regulatory adaptation and participatory pilot projects to build evidence and stakeholder confidence. Viti-PV can contribute simultaneously to renewable energy targets and viticultural climate resilience, but its implementation depends on coordinated support across technical, economic and institutional dimensions.

Energies doi: 10.3390/en19092212

Authors: Ming Chen Shui Liu Li Jiang Hangge Guo

Single-phase current source (SCS) converters typically necessitate a bulky inductor to mitigate DC current fluctuations. Furthermore, open circuit conditions must be avoided in these systems. Active power decoupling (APD) offers an effective solution by eliminating double-line frequency ripple power without a bulky inductor. However, it introduces additional power losses due to the extra devices. To address this issue, this paper proposes an efficient and reliable bridgeless power factor correction (PFC) APD converter. A diode is incorporated into the converter to construct new current paths. The new paths decrease the number of semiconductor devices to lower conduction losses and provide an inherent freewheeling path for DC current to enhance system reliability. Through a specific modulation strategy, the utilization of new paths is maximized, and switching losses are reduced. This paper begins by describing the operating modes of the proposed converter. It then introduces a specific modulation strategy. A detailed analysis of power losses is presented. Finally, a 360 W prototype is constructed, and experimental results demonstrate an efficiency improvement of 2.5%.

Energies doi: 10.3390/en19092204

Authors: Şeyma Songül Özdilli Işık Çadırcı Dinçer Gökcen

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation.

Energies doi: 10.3390/en19092211

Authors: Mohamed Adel Gabry Amr Ramadan Mohamed Y. Soliman

This study presents a wavelet-based framework for mapping inter-well connectivity (IWC) between multiple injectors and producers to support waterflood optimization. The method applies Cross-Wavelet Transform Coherence (CrWTC) with a complex Morlet wavelet to injection and production rate data, enabling the time-localized and frequency-dependent identification of dynamic injector–producer communication. The novelty of this work lies in continuous coherence mapping, the use of the complex Morlet wavelet for improved sensitivity to nonstationary responses, continuous updating as new data become available, and benchmarking on both the Volve and COSTA datasets. Validation using reservoir simulation and field data showed strong qualitative agreement with expected connectivity behavior and demonstrated clearer tracking of connectivity evolution and waterfront movement than the Capacitance Resistance Method (CRM). The proposed approach improves the reliability and interpretability of IWC assessment and offers a practical tool for reservoir surveillance and waterflood management.

Energies doi: 10.3390/en19092207

Authors: Jiayang Zhao Yingnan Gao Zhenzhen Jin

Electric vehicles are an important carrier for achieving energy savings and emission reductions in the transportation sector. As the decision-making core of the powertrain, the energy management strategy is responsible for power allocation and energy scheduling and directly determines vehicle economy, power-source lifetime, and overall performance. Model predictive control can handle multiple constraints and objectives within a prediction horizon and realize online closed-loop decision-making via receding-horizon optimization and has become an important research direction for energy management of electric vehicles. This paper presents the basic principles and typical modeling framework of model predictive control and reviews its research progress in hybrid electric vehicle energy management. The related studies are categorized and comparatively analyzed from three perspectives—prediction methods, solution strategies, and optimization objectives—and the characteristics of different approaches are summarized. The review shows that model predictive control has advantages in multi-objective trade-offs and adaptation to time-varying operating conditions. However, practical implementation still faces significant barriers, including prediction uncertainty and computational complexity. Finally, the challenges and future directions of model-predictive-control-based energy management strategies are discussed.

Energies doi: 10.3390/en19092210

Authors: Xiaobo Lin Chang Liu

Conventional coal mine reserve estimation methods are misaligned with modern production. This study develops a lifecycle, production-oriented framework for dynamic reserve accounting, including a strip inversion algorithm for mined faces and an improved GIS grid method for unmined faces, and a classification-coding system for coal pillar resources in remaining spaces. A case study on the Huainan Zhujidong Mine verifies the framework: the inverse calculations for the mined-faces yield of extracted coal were 9.20–14.30% lower than those of traditional methods, aligning better with reality; unmined-face predictions achieved relative errors of 1.91% (reserves) and 2.99% (extraction); remaining-space pillar resources were inventoried into five categories and 13 blocks (8.873 Mt). The framework supports accurate reserve accounting and refined decision-making and can be applied to similar coal mines.

Energies doi: 10.3390/en19092209

Authors: Przemysław Ptak Tadeusz Lorkowski Krzysztof Górecki

The article describes the results of research on the power supply quality of selected fluorescent lamps and solid-state light sources powered by voltage with different waveforms and supply voltage values. The power factor, total harmonic distortion (THD) factor and values of individual harmonics were measured and their compliance with international standards was assessed. The measurement set-up used and the measurement results obtained with it are described. The results of the experimental research showed that the light sources under consideration did not meet the criteria specified in international standards for the THD factor and the values of individual harmonics, regardless of the shape of the supply voltage waveform. Current total harmonic distortion always exceeded 44%, exceeding the upper limit of 23% specified in the IEC 61000-3-2:2018 standard. The third harmonic values far exceeded the 21.6% of the first harmonics, which is the limit specified in this standard as well. However, it was shown that supplying some light sources with a triangular voltage waveform can increase the illuminance value by up to 28%. On the other hand, the use of a rectangular voltage waveform leads to an increase in the power factor and a decrease in reactive power.

Energies doi: 10.3390/en19092208

Authors: Federico Di Prospero Marco Di Bartolomeo Davide Di Battista Roberto Cipollone

The transportation sector, a major source of urban air pollution and CO2 emissions, is the focus of extensive research aimed at developing cleaner and more efficient technologies. In this context, hydrogen–methane blends (HCNG) represent a promising alternative fuel, combining the zero-carbon combustion potential of hydrogen with the availability and cleaner profile of methane. This solution can be implemented in existing internal combustion engines, enabling a technically and economically feasible transition toward more sustainable mobility. This work investigates the use of an HCNG blend in a bus originally powered by natural gas, focusing on pollutant emissions under real driving conditions representative of typical urban operation. Measurements were performed using a Portable Emission Measurement System installed on-board. Two test campaigns were carried out: the first using methane, and the second using an HCNG blend (15% H2, 85% CH4 by volume), over identical urban and extra-urban routes with varying drivers and traffic conditions. Results show a reduction in CO2 emissions with HCNG, along with a more significant decrease in CO, HC, and PN emissions, while NOx exhibited a slight increase due to unchanged engine calibration. The analysis also includes the RPA index, which is related to fuel energy release characteristics, indicating improved vehicle responsiveness and torque delivery with HCNG.

Energies doi: 10.3390/en19092206

Authors: Ting Wei Lijuan Wang Honglei Ren Fei Lin

For heat transfer outside borehole heat exchanger (BHE) arrays in aquifers, existing analytical models mostly adopt Neumann or Robin boundary conditions, whereas constant-temperature (Dirichlet) boundaries are more practical and convenient for monitoring in engineering applications. Considering the coupled effects of heat advection and conduction induced by groundwater seepage, and based on the engineering reality that vertical heat flow is much smaller than horizontal heat flow, this study idealized the BHE array as a constant-temperature boundary and established a one-dimensional simplified model. The advection term of the governing equation was removed through the exponential transformation of the dependent variable, and an analytical solution was derived using Fourier transformation. A three-dimensional coupled hydro-thermal numerical model was established in FEFLOW for validation. The results indicate that relative errors between analytical and numerical solutions remain below 3% outside the BHE array; however, the analytical method is inapplicable inside the array due to significant thermal interference, and independent field validation is precluded by prior thermal disturbances. The proposed solution features fast computation and clear physical interpretation, providing a simple and efficient tool for rapid estimation of temperature variations during preliminary feasibility studies of ground-source heat-pump projects.

Energies doi: 10.3390/en19092205

Authors: Ye Yang Yetong Luo Jingrui Zhang

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems.

Energies doi: 10.3390/en19092202

Authors: Asrar Mahboob Muhammad Rashad Ahmed Bilal Awan Ghulam Abbas

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed.

Energies doi: 10.3390/en19092203

Authors: Xin Wang Zewei Li Zhenglong Sun

To address the frequency stability challenges arising from the high penetration of renewable energy, this study proposes a coordinated frequency regulation strategy for wind-power–hydrogen coupled systems, considering the Equivalent State of Charge (ESOC). While wind-power–hydrogen integration offers significant regulation potential, frequent ESOC excursions toward operational limits may lead to power interruptions and increased durability-related stress on hydrogen units. To resolve this, a refined mathematical model comprising wind turbines, electrolyzers, and fuel cells is first established to characterize system dynamics. The proposed method adopts an ESOC-based partitioning control logic: within normal ESOC ranges, the hydrogen storage system provides rapid frequency support via virtual inertia control; when ESOC reaches operational thresholds, the hydrogen unit seamlessly transitions out of service to prolong its lifespan, while the wind turbine dynamically compensates for the power deficit through adaptive droop control. Compared with other methods, the strategy proposed in this paper, implemented via DIgSILENT/PowerFactory simulations, improves the frequency nadir by 0.02 Hz during load increases and reduces the frequency peak by 0.04 Hz during load shedding. Under stochastic disturbances, the absolute steady-state frequency error is maintained below 0.02 Hz, while frequency deviations are strictly confined within ±0.5 Hz. These improvements significantly enhance both grid resilience and the operational safety of hydrogen units.

Energies doi: 10.3390/en19092201

Authors: Karina Standal Ayushi Saharan Solveig Aamodt Bhavya Batra

With a modest estimate of 11 million workers and high greenhouse gas emissions, the Indian brick sector is a relevant study for understanding how low-carbon energy transition impacts justice for the society, environment, and livelihoods. This empirical article provides an analysis of the ongoing policy-driven energy efficiency transition and justice trade-offs and benefits in the brick production sector in the state of Bihar. The transition is explored in a larger framework of power relations and vulnerability to determine whether the policies enable or challenge transformative justice for the labour force, nature and future generations. Present policies focus on regulations and financial incentives relevant for entrepreneurs with pre-existing skills, network and financial resources. Further, present policy narratives lack attention to mechanisms that reproduce the socio-economic inequality of the brick labour force, and implications for balancing different livelihood and environmental objectives. We conclude that the findings emphasise the need for integrating a wider variety of social dimensions and relevant support schemes to overcome inequality barriers and safeguard the environment for future generations.

Energies doi: 10.3390/en19092200

Authors: Magdalena Janyszek-Sołtysiak Leszek Majchrzak Maciej Krzysztof Murawski Magdalena Zborowska Bogusława Waliszewska

The increasing demand for energy, the finite nature of fossil fuel resources, and the necessity to reduce greenhouse gas emissions have intensified research on renewable energy sources of plant origin. Among potential energy feedstocks, herbaceous biomass has attracted growing interest due to its high productivity, rapid growth, and widespread occurrence. The aim of this study was to evaluate the energy potential of select sedge species (Carex spp.) commonly occurring in Poland as an alternative to fossil fuels. Aboveground biomass of eight sedge species was collected from natural habitats located in the Warta River valley. Cellulose, lignin, holocellulose, hemicellulose, and ash content in the biomass was determined. In addition, key energy parameters, namely net calorific value and gross calorific value, were analyzed. Differences among species were assessed using one-way analysis of variance, while similarities were explored using hierarchical clustering methods. The results revealed significant interspecific variation in both chemical composition and energy properties. Most analyzed sedge species had favorable lignocellulosic composition and energy parameters comparable to those of woody biomass, particularly willow and poplar. In contrast, Carex riparia was distinguished by a high ash content and lower calorific values, limiting its suitability for energy applications. Overall, the findings indicate that select Carex species may represent a valuable renewable feedstock for energy production, especially in the context of local and decentralized biomass-based energy systems.

Energies doi: 10.3390/en19092199

Authors: Wei Liu Shuheng Qiu Jinhua Chen Peisen Lu Xindong Shu Rong Li Chi Zhang

This paper proposes a novel arch-shaped-magnet variable-flux memory machine (ASM-VFMM). The proposed machine adopts a dual-layer permanent magnet (PM) rotor structure. In the first layer, an arch-shaped magnet arrangement is utilized to increase the volume of low-coercive-force (LCF) magnets, which contributes to improved magnetic flux adjustment (MFA) performance. The second layer incorporates an asymmetric PM (APM) layout to create a parallel magnetic circuit, enabling further suppression of air-gap flux density at the weakened-flux state. The topological development of the proposed machine is first described, covering the conventional series magnetic circuit (SMC) structure, the intermediary APM structure, and the proposed ASM structure. A theoretical modeling analysis is then conducted for the three machines. This confirms the superiority of the proposed design regarding its MFA capability. A comprehensive electromagnetic performance evaluation is carried out for the proposed machine, alongside comparative assessments of the other two machines. The results show that the proposed design outperforms the other two machines in terms of magnetization performance, MFA range, and on-load magnetization stabilization capability. Notably, the proposed machine exhibits excellent overall efficiency characteristics, especially under high-speed operating conditions.

Energies doi: 10.3390/en19092198

Authors: Enpu Lei Ping Lu Kama Huang

The design of stretchable energy harvesting systems entails complex multiphysics coupling between electromagnetic and mechanical domains, typically requiring engineers to proficiently use disparate simulation tools and optimization algorithms. This steep learning curve, combined with the absence of integrated workflows, poses a substantial obstacle to efficient design. To overcome these challenges, we present StretchCopilot, a multi-agent collaborative framework driven by Large Language Models (LLMs) for the generative design of stretchable radio frequency (RF) energy harvesters operating in the 2.45 GHz band. In contrast to conventional approaches dependent on manual iteration or isolated algorithmic methods, our framework utilizes a graph-based state machine architecture (LangGraph) to coordinate specialized agents. It interprets high-level user instructions, such as “design a robust energy harvester capable of withstanding 15% strain”, and autonomously manages domain-specific solvers, including inverse design networks and rectifier circuit synthesis tools, through a unified interface. Experimental evaluations indicate that the framework effectively streamlines the design workflow, allowing users to produce desired rectenna (rectifying antenna) systems via natural language interactions. Case studies confirm that, once the underlying surrogate models are fully trained, the proposed approach compresses the marginal design time from several hours to within minutes, while ensuring consistent energy harvesting performance under mechanical deformation.

Energies doi: 10.3390/en19092196

Authors: Pranta Barua Kannoorpatti Krishnan Naveen Kumar Elumalai

The ambient stability of ambient-processed lead-free perovskite absorbers remains a critical challenge toward scalable, eco-friendly photovoltaics. Herein, we systematically investigate the time-dependent structural and morphological evolution of drop-cast ambient-processed Cs3Sb2I9 thin films, being a potential non-toxic and stable solar absorber candidate (energy bandgap ~2 eV) for solar cells, stored under uncontrolled ambient condition (~60% Relative humidity) for 28 days. Sequential X-ray diffraction (XRD) and surface morphology analyses using scanning electron microscope (SEM) reveal that the films preserve their trigonal P3̅m1 phase throughout aging, confirming phase stability. Moderate moisture exposure may induce partial recrystallization and subtle structural reorganization, possibly including minor c-axis realignment, leading to reduced lattice strain and improved crystallite coherence. Even after prolonged aging, no secondary phases or micro-cracks are detected, underscoring the slow degradation kinetics and robust Sb–I bonding that stabilize the layered [Sb2I9]3− dimers. The late-stage increase in diffraction intensity and partial recovery of crystallographic parameters could indicate transient structural reorganization, potentially associated with moisture-mediated reordering within an overall degradation pathway. These observations suggest some degree of morphological persistence and structural tolerance of Cs3Sb2I9 under ambient conditions, rather than complete stability. This behavior offers useful insights into ambient processing and the long-term reliability of lead-free perovskite photovoltaics.

Energies doi: 10.3390/en19092197

Authors: Bagryan Malamin Denitza Zgureva Mina Daskalova-Karakasheva Kalin Filipov

As a member state of the European Union, Bulgaria is committed to decarbonisation and the achievement of sustainable development goals. The country has a well-established energy sector and is a net exporter of electricity produced from diverse sources. Electricity generation relies mainly on two key pillars: lignite-fired Thermal Power Plants (TPPs) and the Nuclear Power Plant (NPP) in Kozloduy. This study examines the status of Bulgarian state-owned energy companies (SOEC) and their capacity to respond to the challenges of a sustainable transition towards low- or zero-emission electricity production. The study contributes to the existing literature by providing insights from a comparative analysis of state-owned thermal and nuclear power generation in Bulgaria, examined through the lens of sustainable development. From a practical standpoint it contributes by outlining possible pathways for the sustainable transformation of carbon-intensive TPPs. The analy-sis is based on key sustainability indicators covering the three pillars of sustainable development—economic, social and environmental performance. It includes not only an assessment of the financial performance of state-owned thermal power plants and the nuclear power plant over the past five years but also selected social and environmental indicators. The findings suggest that nuclear energy production in Bulgaria is largely consistent with the core principles of sustainability, while coal-based thermal power plants face increasing economic pressures and contribute to significant environmental impacts. The results highlight the need to transform the coal-based electricity sector into a more economically viable and socially responsible alternative, such as low-carbon generation technologies including nuclear energy.

Energies doi: 10.3390/en19092195

Authors: Gürcan Gülen Jani Das Michael H. Young

Societies need a practical way to assess total costs of future energy mixes in complex power systems. To demonstrate such an approach, we assess the cost of electricity in the ERCOT system across two distinct generation mix scenarios, varying mostly by wind, solar and gas-fired generation, between 2023 and 2050. We use commercial software, also used by system operators and power plant developers, to ensure that evolving generation mixes in both scenarios can meet electricity demands at all times at all nodes. Such jurisdiction-specific, hourly nodal dispatch modeling is recognized as necessary for more accurate representation of costs to maintain reliable operations in complex electricity systems. We capture generation and some system costs, which we call consumer cost of electricity, CCOE, given that end-users pay these costs under different line items in their electricity bills. CCOEs are insightful cost estimates for system planning and policy discussions for a given power system and must be recalculated for different scenarios as technologies, market designs, policies, and more, change. This can be done as part of routine annual or bespoke analyses conducted by system operators.

Energies doi: 10.3390/en19092193

Authors: Mussad Alzahrani Taher Maatallah Ghazi Alotaibi Saud Alsehrani Ibrahim Alyousefi Abdullah Alazeb Muhammad I. Masud Sajid Ali

Improving the efficiency of electric water heating systems is essential for reducing energy use and supporting sustainable energy utilization. This study presents the development and optimization of an ultra-efficient electric water heater incorporating graphene-enhanced thermal elements to improve heat-transfer performance and overall system efficiency. The proposed design utilizes graphene-based extended surfaces to enhance heat spreading and increase the effective heat-transfer area of the heating element. A combined numerical analysis and three-dimensional transient simulation approach was employed to evaluate the thermal behavior of the system and quantify the performance improvements achieved through design optimization. The results demonstrate significant enhancement in heating performance compared with conventional heater configuration. Under identical operating conditions, the optimized heater achieved up to a 68.4% reduction in the modeled heater-side thermal load, while maintaining the required heating performance. Additionally, the effective heat-transfer rate increased by approximately 108%, primarily due to a 102% increase in effective heat-transfer area resulting from geometric refinement of the heating surfaces. The incorporation of graphene improved heat distribution within the heating element and facilitated more efficient heat transfer to the surrounding water. These improvements lead to enhanced thermal utilization, reduced peak heating demand, and improved compatibility with renewable energy systems, highlighting the strong potential of graphene-based thermal enhancements for next-generation high-efficiency electric water heating technologies.

Energies doi: 10.3390/en19092194

Authors: Zhihao Jin Dongfei Fu

This paper presents a LiDAR-informed adaptive-cost nonlinear model predictive control (NMPC) strategy for wind turbine pitch regulation. The proposed method uses a reinforcement learning (RL) agent as a supervisory cost-shaping module that adjusts the weights in the NMPC cost function. The pitch command is obtained from the constrained NMPC optimizer, which preserves the physical prediction model, actuator limits, and receding-horizon solution structure. LiDAR-derived preview wind-speed information is used as an estimate of the incoming disturbance and is introduced into both the prediction model and the agent state. This design helps the controller account for wind-field variation over the prediction horizon and adapt the relative emphasis on power regulation, load mitigation, and pitch-action smoothness. Compared with feedforward PID (FF-PID) and fixed-weight feedforward NMPC (FF-NMPC) controllers, the proposed controller shows stronger adaptability under abrupt and stochastic wind variations in OpenFAST-MATLAB/Simulink co-simulations.

Energies doi: 10.3390/en19092192

Authors: Jinling Wang Mingyang Liu Yinkun Gao Shuyun Guan Yongming Zhu Xudong Li

High-nickel layered oxide cathodes, exemplified by LiNi0.9Co0.05Mn0.05O2 (NCM90), exhibit high specific capacity but suffer from severe interfacial degradation and structural instability during electrochemical cycling. Herein, we present a phosphate-based in situ modification approach that forms a durable, self-established cathode–electrolyte interphase (CEI), thereby resolving these key challenges from the root. We employ a controlled (NH4)2HPO4 coating and optimized thermal treatment to fabricate a thin, dense layer of crystalline lithium phosphate on the NCM90 surface. This coherent layer serves as an artificial CEI precursor, which electrochemically evolves into a highly stable and ionically conductive interfacial shield during operation. It effectively suppresses parasitic reactions, mitigates transition metal dissolution, and alleviates mechanical strain induced by phase transitions. Comprehensive optimization of calcination temperature and coating content identifies 760 °C and 1 wt% as the optimal conditions, yielding a well-preserved layered structure and effectively suppressed Li+/Ni2+ mixing compared with the pristine NCM90. When tested at 0.1 C in the potential range of 2.75–4.3 V, the coated electrode delivers a high initial discharge specific capacity of 204.08 mAh g−1. After 100 charge–discharge cycles at 1 C, it retains 89.24% of its capacity, and its rate capability is also significantly improved. Collectively, these findings verify that forming a customized CEI via precursor coating successfully suppresses interfacial degradation and improves structural integrity, thus representing a viable, scalable pathway toward advanced lithium-ion batteries with exceptionally stable cathodes.

Energies doi: 10.3390/en19092190

Authors: Vitalijs Komasilovs Aleksejs Zacepins Jurijs Meitalovs Liga Paura Mihails Stetjuha Andrejs Varfolomejevs Vladimirs Salajevs Irina Arhipova

This paper presents an optimisation model for scheduling in-house energy consumption to improve efficiency and sustainability. Focus is on the integration of advanced scheduling techniques to improve the overall performance of the house appliances and energy storage system. The proposed model applies constraint programming and satisfiability (CP-SAT) techniques to analyse complex schedules. A sensitivity analysis was conducted by perturbing key input parameters, including electricity price variations and demand profiles, while tracking output metrics such as total cost, load distribution, and computational performance. The model incorporates real-world constraints, including fluctuating electricity prices and renewable energy availability, to improve efficiency and reduce operational costs. The optimisation of the scheduling task was set for a 36 h time period with time resolutions of 15 min, equal to the electricity price time step. The proposed approach is evaluated through simulation using representative household consumption profiles and real day-ahead electricity prices data. The performance of the proposed CP-SAT model was evaluated, and the model’s response to the input parameter change has been analysed. The computational performance and cost outcomes of the proposed CP-SAT approach are comparable to those reported for established HEMS optimisation methods.

Energies doi: 10.3390/en19092191

Authors: Barbara Siuta-Tokarska Jerzy Duda Agnieszka Thier Beata Basiura

Sustainable development is a core objective of European Union (EU) policy, making energy transition performance a key analytical and policy issue. This article examines the role of energy and the energy sector in Poland’s sustainable development through an indicator-based empirical assessment for 2010–2024 in a comparative EU context. In addition to the national analysis, Poland’s trajectories are positioned against the EU-27 average and selected Central and Eastern European (CEE) economies in order to assess whether the observed changes represent relative progress, convergence, or persistent lagging. The study combines a harmonised indicator framework with formal trend tests, structural-break analysis, a simplified additive LMDI decomposition, and exploratory cross-indicator analysis. The results show strong improvement in macro-level energy efficiency, continued growth in the renewable-energy share, and a reduction in the emissions intensity of energy use, while also revealing an asymmetric transition pattern in which transport remains less stable and energy self-reliance declines. From a comparative perspective, Poland performs relatively strongly in economy-wide, energy-efficiency improvement, but less favourably in renewable-energy deployment than the EU-27 average and the selected CEE comparators. Overall, the findings point to measurable progress in efficiency and decarbonisation alongside persistent tensions between sectoral adjustment, renewable energy expansion, and energy security.

Energies doi: 10.3390/en19092187

Authors: Fei Xiao Xiaoya Shang Qinxue Li Yiyi Zhan Rui Li Qian Ai Yi Zhang

A novel method based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) combined with a modified multiple branch convolutional neural network (MMBCNN) is designed to detect fault events in distribution networks and to classify various faults in a distribution system. Given the presence of noise components in transient voltage signals, a moving time window technique integrated with the NA-MEMD method is employed to process high-frequency sampling and long-term series signals. This method is also utilized to reliably identify noise components in modal components through permutation entropy. On this basis, the Clarke transform is employed to convert transient voltage signals into the d–q axis, and three-phase voltage waveforms are transformed into a ring image. Moreover, an MMBCNN is developed to accurately detect and classify distribution network faults, and a modified pooling function is introduced to improve feature extraction ability and model convergence performance. Finally, the accuracy and effectiveness of the proposed algorithm are estimated and analyzed using measurement and fault simulation data from distribution networks.

Energies doi: 10.3390/en19092188

Authors: Spyros Giannelos Danny Pudjianto

Grid-scale Battery Energy Storage Systems (BESSs) are becoming essential components of modern power grids undergoing rapid decarbonisation. This review examines how nine jurisdictions—Great Britain, Germany, Spain, Italy, France, California (USA), Australia, Singapore, and China—are enabling the growth of BESSs, focusing on market access and revenue streams, investment risks and mitigation strategies, support mechanisms, and regulatory conditions. A central finding is that batteries typically become investable only when they can stack revenue from multiple sources, including energy arbitrage, ancillary services, and capacity markets. Regulation proves as important as technology: frameworks that fail to recognise storage as a distinct asset class expose projects to double charging, unclear licensing, and limited market access. Grid connection delays, declining revenues in saturating ancillary service markets, and safety compliance represent significant practical barriers. International experience indicates that BESSs scale fastest when decarbonisation policy is credible and market rules enable diversified, financeable revenue streams.

Energies doi: 10.3390/en19092189

Authors: Gipyo Kweon Minseok Kim Daebeom Lee Chaea Kim Dongwon Lee Beomju Kim Jeonghoo Park

The large-scale integration of renewable energy into modern power grids has led to a critical reduction in both system-wide rotational inertia and localized system strength. While previous studies have often analyzed these two factors in isolation, this paper observes their interdependent relationship. Specifically, this paper demonstrates the impact of short-circuit capacity (SCC) on system reserve requirements by quantifying the Frequency Regulating Reserve (FRR) volume necessary to satisfy frequency nadir criteria under the most severe contingency. This quantification is based on a comparative analysis of regional renewable energy deployment in the Korean power system. The simulation results reveal that the required FRR increases significantly when renewables are deployed in areas with low SCC. These findings highlight the potential hidden costs of interconnecting renewables in a weak grid area, particularly from the perspective of system inertia, underscoring the necessity of considering location-aware markets in future power systems.

Energies doi: 10.3390/en19092172

Authors: Haozhe Xiong Bingyang Feng Fangbin Yan Yiqun Kang Yuxuan Hu Qiangsheng Li Qinyue Tan

This paper studies capacity planning for a wind–hydrogen integrated energy system under scenario-based uncertainty in wind generation, hydrogen demand, and electricity prices. The model is formulated as a two-stage stochastic program in which first-stage investment decisions are selected before uncertainty is realized and second-stage hourly operation is optimized for each representative scenario. The main methodological difficulty is that part of the first-stage hydrogen-storage investment cost may be available only through a non-analytical evaluator, such as supplier quotation logic, simulation software, or a data-driven estimator, while the operational recourse model remains linear. To address this setting, a hybrid heuristic–Benders framework, denoted as GSOA-Benders, is developed by coupling the General-Soldiers Optimization Algorithm for derivative-free first-stage search with Benders cuts generated from linear programming subproblems. The framework is not presented as a replacement for commercial solvers on explicit convex or mixed-integer models; rather, it is intended for cases where exact algebraic reformulation of the first-stage cost is unreliable or unavailable. In the black-box case study with 500 scenarios, the method converges in 35.86 s and obtains an investment plan expressed as x=[1,0.53,23.23,0], corresponding to wind-farm construction, a 0.53 MW electrolyzer, a 23.23 MWh hydrogen tank, and no fuel-cell investment. Additional discussion is provided on stability-gap interpretation, benchmark limitations, component lifetime assumptions, hydrogen losses, and environmental extensions.

Energies doi: 10.3390/en19092186

Authors: Wenhao Zhu Yankun Liao Gang Wu Fei Lei

Manufacturing tolerances inevitably induce cell-to-cell inconsistencies. These inconsistent cells are connected in series and parallel to form battery packs, which will affect the safety and reliability of the battery system. This study presents a novel optimization framework integrating the multi-level physical model with machine learning to improve battery consistency from the manufacturing perspective. The multi-level physical modeling approach is applied to establish the link between the parameter deviations of the calendering process and the battery inconsistency performance. Based on the multi-level physical model, the Monte Carlo method is used to describe parameter deviations and generate datasets of electrochemical properties. The coefficients of variations in battery capacity and resistance are calculated as the consistency evaluation index based on these datasets. The proposed optimization approach applies machine learning to reduce the computational cost of the multi-level physical simulations due to lots of Monte Carlo simulations. Combined with the multi-level physical model and neural network model, the multi-objective particle swarm optimization algorithm is adopted to provide the optimal calendering process parameter deviations by achieving the trade-off between battery consistency performance and manufacturing cost. Results indicate that the battery consistency performance is improved by controlling the precision of the calendering process and manufacturing cost. This approach can effectively give feedback and guidance to the inverse design of the manufacturing process.

Energies doi: 10.3390/en19092185

Authors: Minghao Shao Yongkai Jiang Yujia Han Chun Wei

With the increasing integration of wind power into the grid, power systems are exhibiting characteristics of low inertia and low short-circuit ratio. Virtual synchronous generator (VSG) control technology, which emulates the operational characteristics of synchronous generators, can effectively provide voltage and inertia support to the grid. However, its application in grid-connected permanent magnet synchronous generator (PMSG)-based wind turbines is prone to low-frequency oscillation issues. To address this, this paper first establishes a damping torque model for the grid-forming PMSG. The damping torque method is employed to quantify the damping characteristics of the system in the low-frequency band, while analyzing the influence of various torque components on the system’s damping composition and low-frequency oscillations. Based on this, a machine-side current loop controller incorporating a novel exponential sliding-mode control (NESMC) and a high gain disturbance observer (HGDO) is proposed. This controller aims to reduce the machine-side negative damping effect, thereby effectively suppressing low-frequency oscillations in the system. Finally, a simulation model is built in MATLAB/Simulink to verify the correctness of the damping torque analysis and the effectiveness of the proposed control method.

Energies doi: 10.3390/en19092181

Authors: Andrea Colletto Mirko Baratta Daniela Anna Misul

In the current environmental and political scenario, hybrid vehicles play crucial roles in the transition to sustainable mobility. The role of internal combustion engines (ICEs) is also of utmost importance to comply with the even more stringent emissions regulations. To that end, also considering the need for increased power density in ICEs, turbocharging allows for improved performance and reduced emissions. Within this context, the present paper introduces the novelties of a patented turbo compound layout with supercharging capabilities, i.e., the Turbo Generator Electric Multistage Supercharger (TGEMS) system. The analysis also allowed for providing evidence of a “backpressure supercompensation effect” associated with rising exhaust backpressure in the ICE. TGEMS introduces a novel compressor group decoupled from the turbine. The analyses were carried out on a 2.0 L turbocharged gasoline direct injection engine. The “supercompensation” phenomenon was isolated using a stepwise procedure in which TGEMS was initially applied to the baseline engine to be exploited on a modified configuration featuring a downscaled turbine. The results were analyzed from the perspectives of specific fuel consumption reduction and total power output as well as operating flexibility increase. The results indicate that, in a context like TGEMS, the assumption that rising exhaust backpressure is always penalizing is no longer valid. Under higher backpressure conditions, TGEMS alone achieved −4.92% in specific fuel consumption at 5000 rpm, with +8.75% in maximum power output. Moreover, with the configuration with a downscaled turbine and the possibility to control the engine operating line, specific fuel consumption reductions of −7.93% at 5000 rpm and −6.83% at 3000 rpm were achieved. The maximum power output increment was +11.04%. These outcomes could open up to new downsizing perspectives and a new generation of “super-backpressured engines”.

Energies doi: 10.3390/en19092184

Authors: Haoran Li Jiale Long Yuan Lu Zihan Lin Mingjue Zhou

Solid oxide electrolysis cells (SOECs) are regarded as a promising technology for sustainable hydrogen production because of their high energy conversion efficiency. In this study, a multiphysics numerical model combined with a random particle packing framework was used to evaluate the influence of anode microstructural parameters on the electrochemical performance of a button-type SOEC. The effects of anode porosity, particle size, and electrode thickness on current density were systematically analyzed. Increasing porosity from 0.3 to 0.5 reduced the current density because of the decreased fraction of electrochemically active material. Increasing the anode particle size from 50 to 300 nm significantly shortened the triple-phase boundary (TPB) length, leading to a decrease in current density from 6289 to 5502 A m−2. The effect of anode thickness reflects a trade-off between electrochemical activity and gas transport, with the current density increasing from 5502 to 5940 A m−2 as the thickness increased from 10 to 20 μm. Overall, the results highlight the coupled roles of reaction-site availability and oxygen transport in determining SOEC performance. This study provides a parametric assessment of how anode microstructure affects SOEC performance and may support the structural optimization of SOEC oxygen electrodes.

Energies doi: 10.3390/en19092183

Authors: Julian Hanusrichter Frank Jenau

High-voltage direct current (HVDC) transmission systems are increasingly used for long-distance power transmission and the integration of renewable energy sources. In such systems, outdoor insulators are exposed to combined electrical stresses, including steady DC voltage, transient overvoltages, and environmental contamination, which can significantly influence leakage current behaviour and insulation performance. This work presents an experimental and numerical investigation of leakage currents on artificially contaminated polymer insulators under two application-relevant HVDC operating scenarios. The first scenario considers superimposed HVDC voltage with switching impulses and very slow front overvoltages, which may occur during fault conditions in converter-based HVDC systems. The second scenario investigates electromagnetic coupling effects in a hybrid HVDC/HVAC transmission line configuration, where AC and DC conductors are installed on the same tower. Artificial contamination layers with different morphologies and conductivities are applied to the insulator surface to reproduce realistic pollution conditions. Leakage currents are measured using a high-resolution acquisition system, and the results are supported with numerical simulations based on finite-element modelling. The results show that transient overvoltages significantly increase leakage current amplitude and duration, leading to increased electrical stress on contaminated insulators. In the hybrid transmission configuration, electromagnetic coupling between AC and DC paths induces additional current components in the DC leakage current. The presented results contribute to a better understanding of leakage current behaviour under realistic HVDC operating conditions and provide useful information for insulation assessment and condition monitoring of outdoor insulators in modern HVDC transmission systems.

Energies doi: 10.3390/en19092180

Authors: Eugenio Navarro-Zeballos Petr Musilek

Wildfires increasingly threaten the reliable operation of electric power systems due to climate-driven factors and expanding infrastructure. However, existing research remains fragmented, limiting the development of integrated resilience strategies. The objective of this study is to systematically review the literature on power system resilience under wildfire events, focusing on modeling approaches, operational strategies, and learning-based methods. This review was conducted in accordance with PRISMA 2020 guidelines. A structured search was performed in the Scopus database (May 2025; updated January 2026). Studies published between 2016 and 2025 were screened in two stages using predefined eligibility criteria. Studies addressing power system operation under wildfire disturbances with optimization or learning-based methods were included, whereas purely ecological studies were excluded. Thirty studies were included. Data extraction and qualitative thematic synthesis were conducted across four analytical layers. Risk of bias was not formally assessed, and no meta-analysis was performed. Results show increasing research activity and a shift toward stochastic and data-driven methods. Optimization remains dominant, while reinforcement learning is emerging. Hybrid approaches that integrate optimization and learning-based methods are emerging as particularly promising solutions. However, the evidence is limited by methodological heterogeneity and lack of standardized validation.

Energies doi: 10.3390/en19092182

Authors: Xiang Wang Jianfeng Zhao

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

Energies doi: 10.3390/en19092177

Authors: Yang Liu Qishan Xu Feng Zhang Weijun Teng Jinggang Wang

This study develops a lifecycle economic comparison framework for shared energy storage, in which multiple users share a common storage asset through capacity leasing. A multi-service revenue structure, including capacity leasing, spot-market arbitrage, auxiliary frequency regulation, peak shaving, and capacity compensation, is established for comparative evaluation. Case studies are conducted for lithium iron phosphate (LFP) and vanadium redox flow (VRF) batteries across six representative Chinese electricity markets and six standardized revenue-combination scenarios. The results show that, among the scenarios that more closely reflect current operating practices, P3 (capacity compensation + spot market + auxiliary frequency regulation) delivers the highest net present value (NPV). P6 combines all five revenue streams without explicitly modeling service-coupling dispatch constraints, and is therefore treated as a theoretical benchmark rather than an immediately deployable operating mode. Under this benchmark assumption, its calculated NPV is 21.1% and 41.7% higher than that of P3 for the two battery types, respectively. The study also shows that power-related services are more sensitive to rated power, while spot-market and peak-shaving revenues are more dependent on rated capacity.

Energies doi: 10.3390/en19092178

Authors: Xiping Chen Qinghua Jiang Tiexiong Huang

Precise control of the diesel oxidation catalyst (DOC) outlet temperature is critical for reliable diesel particulate filter (DPF) regenerations. This paper proposes a novel and composite control strategy for the DOC outlet temperature control based on a representative identified transfer function model, which requires only a nominal value of the input gain parameter. By integrating a PID-type sliding variable with a non-singular terminal sliding mode (TSM) manifold through the second-order sliding mode technique, the strategy provides a continuous and chattering-free control signal. A linear extended state observer (LESO) is designed for real-time estimation and compensation of the lumped total disturbances. Feedforward compensation (FFC) is also integrated to proactively counteract the effects of exhaust flow and inlet temperature variations, thereby reducing the burden on the LESO. The disturbance rejection control scheme is designed by combining the LESO, the chattering-free terminal sliding mode (CTSM), and the FFC. Its stability is proved by using the Lyapunov method. Comprehensive co-simulations conducted in a high-fidelity GT-Power/MATLAB environment demonstrated that the proposed control scheme achieves superior performance with respect to set-point tracking and disturbance rejection. This work provides an effective solution for robust temperature control in DPF regeneration processes. It can also be applied to other types of robust process control systems attributed to its ease of implementation.

Energies doi: 10.3390/en19092179

Authors: Jiahui Chen Qian Peng Fangqiang Wang Jie Feng Hao Liu Hongjian Hou Jianmin Zhang

The radial heat transfer mechanism of compacted conductors in overhead insulated cables is unclear, and the insulation layer complicates the thermal boundary conditions, limiting the direct applicability of existing ampacity calculation methods. Based on the Morgan model framework, this paper proposes an ampacity calculation method that accounts for the “plastic-then-elastic” deformation characteristics of compacted conductors. Material plastic flow and elastic deformation of the substrate are incorporated to refine the formulations for interlayer thermal contact conductance and thin-layer air gap thickness, while the equivalent distance of air voids is corrected using the fill factor. An iterative convergence procedure for the insulation outer surface temperature is established to accurately evaluate conductor Joule losses. Validated by wind tunnel tests on JKLGYJ 240/30 cables, the proposed method yields a radial temperature difference of 2.41 °C, closely matching the measured 2.6 °C, with an error of 7.4% compared to 13.5% for the conventional Morgan model. Parametric analysis reveals that equivalent radial thermal conductivity is independent of external environmental factors. Conductor stress has a negligible effect on the ampacity (variation < 0.1%). Under low wind speeds (0–5 m/s), the ampacity increases substantially with wind speed.

Energies doi: 10.3390/en19092175

Authors: Muhammad Abdul Rauf Munira Batool Imtiaz Madni

Medium-voltage (MV) networks are increasingly relying on grid-forming inverter-based resources (IBRs) due to the worldwide transition towards renewable energy sources. This transformation poses considerable challenges for traditional protection schemes that were initially developed for systems powered by inertia-based generation. Key challenges include the low and controlled contributions of fault current, two-way power flows, diminished system inertia, and swiftly changing transient behaviors. These elements weaken the effectiveness of standard protection methods such as overcurrent, distance, and differential protection schemes. A critical review of recent advancements in adaptive protection schemes, impedance-based techniques, virtual synchronous machines, and enhancements in inverter control is provided. However, despite these advancements, current solutions frequently lack validation in real-world scenarios, encounter difficulties in detecting high-impedance faults, and face scalability issues. There remains a demand for protection strategies that are resilient, coordinated, and specifically designed to address the distinct dynamics of MV systems dominated by grid-forming inverters.

Energies doi: 10.3390/en19092176

Authors: Fei Li Senquan Yang Shaojun Ren Nan An Xi Li Fengqi Si

Maintaining safe and consistent performance in industrial energy networks necessitates the dependable detection of asynchronous motor failures. However, in practical scenarios, diagnostic models often suffer from poor generalization and high false alarm rates when faced with incomplete datasets and limited high-quality samples. Aiming to overcome the aforementioned constraints, a PCA-KPLS integrated multi-fidelity scheme is presented in this work. The method utilizes low-fidelity data to construct a Principal Component Analysis (PCA) model for extracting basic features, and then integrates a small amount of high-fidelity target data via Kernel Partial Least Squares (KPLS) to establish a cross-domain feature mapping, enabling knowledge transfer between data of different fidelities. Validation through mathematical simulation and an engineering case study on a primary air fan demonstrates that the proposed method achieves higher prediction accuracy and lower root-mean-square error compared to models using only low-fidelity or high-fidelity data, significantly reduces false alarms, and enhances the accuracy of fault diagnosis and model generalization capability when training samples are insufficient.

Energies doi: 10.3390/en19092166

Authors: Denis Grün Ulrich Misz

Due to the high sensitivity of proton exchange membrane fuel cells (PEMFCs) to feed gas contamination through balance of plant (BOP) materials, in situ qualification plays a crucial role to secure performance, durability, and economic viability. To be able to deliver verified and accurate qualification results it is necessary to analyze the test method in detail and to perform repetitions on certain measurements. This work focuses on validation of an in situ material qualification test method in terms of measuring precision on a previously developed test bench and statistical significance of collected data. As a statistical approach t-test was used to calculate confidence intervals based on a sample size of 15 reference measurements with the same parameters and setup but variable membrane electrode assemblies (MEAs). The results show substantial reduction in confidence intervals with growing measurement’s sample size clearly quantifying accuracy of the analyzed methodology. The precision of the test method, as indicated by the calculated confidence intervals of irreversible voltage loss is approximately 1.21 mV, corresponding to a relative deviation of about 0.17% with respect to the calculated mean value across all steady-state phases (SSPs). This approach also provides an insight into the natural degradation behavior of the tested MEAs. The calculated effects can serve as a basis for design of experiments (DOE) in future test series.

Energies doi: 10.3390/en19092174

Authors: Xiuyu Yang Yi Wang Gangui Yan Hongda Dong Chenggang Li

With the continuous decline in the cost of renewable energy such as wind power and photovoltaic power generation, the economic competitiveness in the power supply structure is increasing, and traditional thermal power units are gradually being replaced, resulting in a profound adjustment of the power supply structure. However, the unclear alternative between units may lead to system redundancy configuration or power supply shortage. At the same time, the volatility of the new energy output and the flexible allocation of resources and other factors work together, resulting in the cost of the power system showing complex evolution characteristics. Therefore, it is of great significance to study the evolution of system cost in the process of thermal power substitution. This paper first analyzes the internal mechanism of the cost change of the new power system. Second, the cost accounting model of the power system is constructed to reveal the relationship between ‘thermal power substitution mode-system cost’ in the process of thermal power installed capacity substitution. Finally, the Garver-6 system is taken as an example to carry out simulation analysis, solve the optimal thermal power substitution mode under different renewable energy penetration rates, and explore the evolution law of system cost. The results of the example show that with the increase of renewable energy penetration, the total cost of the system first decreases and then increases, and the optimal substitution method is ‘unit thermal power to replace more renewable energy’.

Energies doi: 10.3390/en19092173

Authors: Ruiming Gu Congwei Tang Haochen Huang Ge Deng Cheng Tan Jianhang Hu Jinlong Du Hua Wang

The efficient and optimized operation of copper smelting processes is comprehensively governed by process flow characteristics and energy efficiency. This study establishes a game theory–based comprehensive evaluation framework and proposes a hybrid weighting-TOPSIS method. The optimal combined weights are determined by integrating subjective and objective approaches, including the analytic hierarchy process (AHP), entropy weight method (EWM), and CRITIC method. The proposed model is used to quantitatively evaluate the effects of flue gas and steam flow rates, and temperature differences, on overall system energy efficiency. Compared with single-weighting methods, the presented strategy demonstrates greater rationality and comprehensiveness. Furthermore, exergy analysis is conducted to investigate energy and exergy efficiencies and exergy destruction of key components. Results show that the energy and exergy efficiencies of waste heat boilers are both approximately 30%, with a maximum exergy destruction of 3646 kW, indicating significant potential for improvement. This finding is consistent with the comprehensive evaluation results obtained from the game theory-based weighting method, providing a reliable basis for energy efficiency optimization of copper smelting waste heat recovery systems.

Energies doi: 10.3390/en19092171

Authors: Dengchao Li Xingqian Mao Xingkang Huang Haiqiao Wei Jiaying Pan

Plasma-assisted ammonia (NH3) decomposition is a promising strategy for hydrogen production. However, reactor geometry remains a key factor limiting its hydrogen yield per energy input (YH2). This study systematically investigates H2 production in outer-dielectric (OD), inner-dielectric (ID), and double-dielectric (DD) coaxial DBD reactors. The results show that the ammonia decomposition performance of OD- and ID-coaxial DBDs is significantly higher than that of the DD-coaxial DBD. OD- and ID-coaxial DBDs generate abundant micro-discharge pulses, enabling effective discharge energy deposition at lower peak voltages. Consequently, the reduced electric fields E/N are maintained within the optimal kinetic window for NH3 dissociation and H2 production. Moreover, by balancing residence time and energy density, the 8 cm length electrode achieves a peak YH2 of 1.22–1.24 gH2/kWh in the OD-coaxial DBD. For the ID-coaxial DBD, a 1 mm dielectric thickness yields a maximum capacitance of 86 pF, achieving a peak YH2 of ~1.35 gH2/kWh at the optimum E/N. In contrast, the DD-coaxial DBD exhibits the lowest YH2 (≤0.82 gH2/kWh) with minimal temperature rise. This is caused by the reduced current pulse numbers and the deviation of E/N from the optimal range with elevated operating voltages. This work provides guidance for the optimization of DBD reactors in plasma-assisted NH3 decomposition for efficient H2 production.

Energies doi: 10.3390/en19092167

Authors: Kalizhan Shakenov Seitkhan Azat Kydyr Askaruly Aigul Ashimova Assemgul Bektassova Jechan Lee

The demand for high-performance energy storage systems with enhanced energy and power density is growing alongside the renewable energy, mobile devices, and electric vehicle sectors. MXenes, a class of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides, have emerged as promising electrode materials for next-generation energy storage systems owing to their high electrical conductivity, hydrophilicity, and tunable surface chemistry. This review provides a comprehensive analysis of recent progress in MXene-based energy storage systems, focusing on MXene synthesis routes, their performance in energy storage applications, associated challenges, and future research directions. It discusses the advantages and disadvantages of various MXene synthesis routes and MXene-based composites, defect engineering, and MXene oxidation, which are crucial for energy storage applications, including rechargeable batteries and supercapacitors. The review also explores the challenges and prospects of scaling up MXenes and their composites for energy storage applications and the existing obstacles to integrating these materials into energy storage systems, with the aim of developing next-generation energy storage systems.

Energies doi: 10.3390/en19092170

Authors: Suhaib Sajid Bin Li Bing Qi Feng Liang Yang Lei Ali Muqtadir

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes n=70 peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization.

Energies doi: 10.3390/en19092169

Authors: Tomasz Neumann Paweł Wierzbicki

The decarbonization of multimodal transport systems requires assessment approaches that simultaneously address environmental impacts and economic performance at dynamic operational conditions. Conventional Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), including Total Cost of Ownership (TCO), are widely used for this purpose; however, they often rely on static assumptions and averaged data, limiting their ability to capture real-world variability. This study proposes an AI-enhanced LCA–LCC/TCO framework for the integrated evaluation of decarbonised multimodal Door-to-Port transport systems. Artificial intelligence is embedded directly into the life cycle inventory and cost inventory stages to generate scenario-specific estimates of energy consumption, greenhouse gas emissions, and operational costs. The framework is demonstrated through a case study of a multimodal Door-to-Port transport chain comprising road pre-haulage, rail line-haul, and port terminal operations. Three scenarios are analysed: conventional, partially decarbonised, and fully decarbonised configurations. The results indicate that partial decarbonization reduces greenhouse gas emissions by more than 60% compared to the baseline while achieving the lowest total cost of ownership. Full decarbonization achieves emission reductions exceeding 95% but is associated with slightly higher costs under current assumptions. Sensitivity analysis verifies the robustness of the relative scenario ranking under different energy prices, carbon pricing, and electricity carbon intensity. The proposed framework provides a structured decision-support framework for logistics operators, port authorities, and policymakers seeking cost-effective pathways to low-emission multimodal transport systems.

Energies doi: 10.3390/en19092168

Authors: Kailing Bai Huiyu Zhou

Scientific assessment of energy conservation, emissions reduction, public health externalities, and economic costs is crucial for the sustainable development of new energy vehicles (NEVs). Despite minimal emissions during the operational phase of NEVs, the production process of energy, such as electricity and hydrogen, contributes to pollution across the full supply chain, shifting environmental and health burdens to upstream sectors and raising concerns about the overall societal benefits. To address this, we apply a full-chain life cycle assessment (FC-LCA) framework that integrates emissions from vehicle production, energy supply, and end-of-life stages, while simultaneously quantifying health-related mortality attributable to key pollutants. By incorporating upstream energy production structure and downstream industry emissions, this approach captures the complete energy supply chain and enables a systematic comparison between NEVs and conventional vehicles. We further employed and compared ARIMA, LSTM, and Bi-LSTM models to forecast future vehicle demand and defined different forecasting scenarios for China’s passenger vehicle sector. Results provide policy-relevant insights for decision-makers to make informed policy choices concerning the widespread implementation of NEVs in a sustainable manner.

Energies doi: 10.3390/en19092165

Authors: Zhongxi Ou Lihong Qian Sui Peng Weijie Wu Liang Zhang Mingqian Feng Chuyuan Hong Haoran Shen Wei Dai

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources.

Energies doi: 10.3390/en19092164

Authors: Eugenija Farida Dzenajavičienė Egidijus Lemanas Nerijus Pedišius

The black carbon (BC) emission resulting from human activity comes mainly from fossil fuels and solid biomass burning, as well as transport fuels due to incomplete combustion. The biggest sources of BC pollution are currently diesel transport and domestic heating appliances burning solid fossil fuels or biomass. Firewood and pellet fuels were used for this BC research. The study used four domestic heating appliances using wood and agricultural waste pellets, as well as several types of firewood. The tests used a gravimetric particulate analysis method to determine the total amount of particulate matter. In further physical and chemical analyses, the emissions are broken down into components, i.e., substances of known composition that can be separated from the sample and weighed. In our study, the BC emissions varied from 0 to 120 mg/MJ depending on the type of boiler (automatic or manual), the combustion mode (based on oxygen supply), and the type of fuel. Emissions varied from 0–8 mg/MJ in a modern pellet-fired and automatically-controlled boiler, and from 1–25 mg/MJ in a wood-fired water heating boiler, with the highest emissions found for softwood (spruce). In the pellet stove with automatic feeding and control, BC emissions varied between 1 and 120 mg/MJ, with the highest emissions detected for wood pellets, and in the wood-burning fireplace, the emissions varied between 6 and 80 mg/MJ, with the highest emissions detected for birch firewood.

Energies doi: 10.3390/en19092163

Authors: Bignon Stéphanie Nounagnon Yrébégnan Moussa Soro Wiomou Joévin Bonzi Sebastian Romuli Klaus Meissner Joachim Müller

This study evaluates the techno-economic performance of solar photovoltaic (PV) systems for powering a 7 t/day shea butter processing plant to address electricity constraints limiting rural processing and local value capture. Annual electricity demand is modeled under three operational scenarios: (i) a typical processing season from November to February; (ii) an extended season until mid-May; and (iii) near year-round operation with eleven months of processing. Using detailed load modeling and techno-economic simulations in HOMER Pro, off-grid PV/battery systems and grid-connected PV hybrids are compared using the levelized cost of electricity (LCOE). In scenario 1, the national grid remains the most cost-effective solution. Scenario 2 reveals that integrating 35% solar PV into the grid becomes economically attractive, offering a recoverable value of 263.33 thousand USD within 7.73 years. In scenario 3, the grid/PV/battery configuration emerges as the optimal solution, providing the lowest cost of electricity at 0.246 USD/kWh compared to 0.319 USD/kWh for a grid-only supply and delivering an internal rate of return (IRR) of 20.7%. Under the same scenario, the standalone PV/battery system also demonstrates strong economic viability, with a cost of 0.292 USD/kWh and an IRR of 9.2%, lower than average tariffs from PV mini-grid developers in sub-Saharan Africa. These results demonstrate the profitability and viability of PV-based systems in powering food processing facilities in off-grid regions.

Energies doi: 10.3390/en19092162

Authors: Yulong Qin Yifan Hou Han Zhang Ding Liu

Accurate and rapid fault diagnosis is critical for active distribution networks characterized by growing structural complexity and diverse load profiles. This paper proposes a two-stage fault diagnosis framework that synergistically combines colored Petri nets (CPN) and fuzzy colored Petri nets (FCPN). In the first stage, a CPN fault zone search model employing a breadth-first search (BFS) strategy is developed to identify suspected faulty components by processing circuit breaker operation information and grid topology. In the second stage, an FCPN diagnosis model is constructed by extending hierarchical fuzzy Petri nets through color assignment to confidence tokens. A key feature of this model is a dedicated initial confidence assessment module that dynamically evaluates the reliability of protection and circuit breaker actions by synthesizing device self-check alarms and operational timing information, thereby overcoming the limitation of empirical, static confidence assignment in existing methods. The resulting initial confidence values are then propagated through a hierarchical confidence inference module to determine the fault likelihood of each suspected component. Comparative simulations across four fault scenarios demonstrate that the proposed method achieves higher diagnostic accuracy and stronger fault tolerance than state-of-the-art approaches, correctly identifying all faulty components even under degraded alarm conditions.

Energies doi: 10.3390/en19092161

Authors: Yuxiang Zhang Zhenglin Cao Yong Hu Haijun Yan Jianlin Guo Chunyan Jiao Mingqiu Li Yu Luo Peng Yu Nan Qin

Addressing the problem of limited methane (CH4) recovery degree under different production conditions in a target low-permeability carbonate gas reservoir, this study intends to further investigate the effect of carbon dioxide (CO2) injection on enhanced gas recovery (EGR). A group of long-core physical simulation experiments of CO2 injection for EGR was adopted. Field injection–production parameters were converted to laboratory conditions through similarity criteria to simulate the actual production process of gas wells. Systematic experiments on CH4 depletion and CO2 displacement were carried out under different irreducible water saturation, gas injection timing pressure and injection rates. The influence laws of each key parameter on the CO2 breakthrough time and CH4 recovery degree were analyzed emphatically, and the optimal injection–production scheme was obtained. For the target low-permeability carbonate gas reservoir (permeability < 1 mD), the optimal CO2 injection scheme is as follows: for layers with medium to high irreducible water saturation (≥40%), CO2 injection at a rate of 36,000 m3/d per well after the end of stable production (formation pressure > 7.38 MPa) can increase the CH4 recovery degree by 3–5%. This study provides experimental support for the optimization of CO2 injection schemes for enhanced recovery in gas reservoirs and the adjustment of gas reservoir development strategies under different irreducible water saturation conditions.

Energies doi: 10.3390/en19092160

Authors: Yasser Bin Salamah

This paper introduces a policy-search-based reinforcement learning algorithm aimed at generating optimal set-points of wind turbines in wind farms. The proposed approach addresses the problem of multivariable optimization in systems where the objective function is unknown or difficult to model. The algorithm is a model-free framework and relies solely on measured performance of the system. Namely, it does not require gradient information of the objective function or an explicit model of the aerodynamic interaction between wind turbines. The proposed scheme utilizes stochastic policy perturbations to explore the search space and update the policy parameters directly based on the observed reward signal. In this way, the algorithm progressively drives the control variables toward optimal operating conditions. The proposed policy-search reinforcement learning framework is analyzed to establish its connection with gradient-free optimization methods. The proposed method is applied to wind farm power optimization, where multiple turbine control variables must be adjusted in the presence of wake interactions cooperatively. The performance of the proposed approach is evaluated through extensive simulations under both steady-state and time-varying wind conditions. The proposed algorithm is compared with an extremum-seeking control method that was previously suggested for the same problem. The results demonstrate that the proposed approach is able to effectively maximize power production in wind farms while maintaining a simple and model-free optimization structure.

Energies doi: 10.3390/en19092157

Authors: Fredy Alexis Dulce Jackeline Murillo-Hoyos Eduardo Francisco Caicedo-Bravo

This study analyzes the economic viability of electric vehicle charging stations (EVCSs) in medium-sized cities with low electric vehicle (EV) adoption. Based on EVCS usage patterns from both Europe and the USA, and validating EV energy consumption with a microscopic model of roads and traffic through the Eclipse SUMO simulator, the analysis provides a comprehensive assessment. Also, level 2 and level 3 (DC fast) charging stations are considered with installation and operation costs. Finally, a photovoltaic (PV) system and governmental subsidies are considered as support. The Pasto city, Colombia, is the case study due to its medium-sized city characteristics in an emerging economy country, where EV penetration is concentrated in the capital and large cities, with a national EV penetration rate of less than 1%. Scenarios are developed with varying annual EV penetration rates and different financial discount rates. The results suggest that, without significant increases in EV adoption, government subsidies, and PV generation, EVCSs are not economically viable in most of the analyzed scenarios. The study concludes that the financial sustainability of these projects is heavily reliant on supportive public policies that incentivize infrastructure deployment, particularly in medium-sized cities.

Energies doi: 10.3390/en19092159

Authors: Noori Park Chang Hwan Choi

The global energy transition has fundamentally reshaped the conditions under which green trade generates sustainable productivity gains. This study investigates whether renewable energy adoption mediates the relationship between green trade export (GTE) and green total factor productivity (GTFP) across 37 OECD economies over 2003–2023. Employing two-way fixed-effects panel regression, dynamic System-GMM estimation, and Hansen’s panel threshold regression with 500 bootstrap iterations, we identify a nonlinear, inverted-N-shaped relationship between GTE and GTFP. Sequential threshold testing reveals a statistically significant double threshold structure: a first clean energy threshold at approximately 8.72% of total final energy consumption and a second threshold at approximately 24.63%, yielding three distinct productivity regimes. Below the first threshold, green trade suppresses GTFP through pollution displacement and insufficient absorptive capacity; between thresholds, green trade exerts a significant positive productivity effect driven by clean technology diffusion and innovation spillovers; above the second threshold, the positive effect moderates, consistent with diminishing returns to green technology absorption. Heterogeneity analysis reveals that early-stage energy transitioners bear disproportionately larger productivity penalties, while advanced transitioners capture stronger above-threshold gains. These findings underscore that trade liberalization alone is insufficient—sustainable productivity growth requires concurrent and targeted investment in renewable energy infrastructure under the post-Paris Agreement framework. Policy implications are presented as evidence-consistent hypotheses, acknowledging that the observational panel framework precludes definitive causal claims pending corroboration from quasi-experimental designs.

Energies doi: 10.3390/en19092158

Authors: Jintian Wu Zixuan Fang Lifen Wang

Limited ionic conductivity and unstable interfaces, primarily caused by poor solid–solid contact, pose significant challenges to the stable cycling of solid-state batteries. In this study, an interfacial in situ polymerization strategy is proposed to construct a poly(1,3-dioxolane) (PDOL) gel electrolyte layer between a poly(vinylidene fluoride) (PVDF)-based solid polymer electrolyte and the electrodes. This approach aims to address interfacial compatibility issues in solid-state lithium metal batteries. By precisely tuning the composition of the gel precursor and employing characterization techniques such as FTIR and NMR, the efficient ring-opening polymerization of 1,3-dioxolane (DOL) was confirmed, achieving a high conversion rate of 90%. The precursor was drop-cast onto the PVDF-based electrolyte/electrode interfaces before cell assembly. Electrochemical evaluations revealed that the in situ formed solidified interlayer significantly enhanced interfacial compatibility and ion transport, yielding a high Li+ transference number (0.341), an exceptional critical current density (1.4 mA cm−2), and remarkable cycling stability exceeding 1600 h in Li||Li symmetric cells. Furthermore, full cells incorporating LiFePO4 cathodes demonstrated excellent rate capability and long-term cyclability, retaining 98.7% of their capacity after 1000 cycles. These results collectively underscore the effectiveness of this in situ solidification strategy in optimizing the interface structure and improving the overall performance of PVDF-based solid-state batteries.

Energies doi: 10.3390/en19092156

Authors: Camilla Nayara Santos Mota Reginaldo José da Silva Mara Lúcia Martins Lopes

Accurate short-term load forecasting at disaggregated levels is critical for energy management in microgrids and institutional environments, yet it remains a challenge due to high consumption variability and limited contextual information. This paper proposes a hybrid model that combines Fuzzy ARTMAP neural networks with K-means clustering to improve hourly load forecasting using real data from a university microgrid. The methodology includes key preprocessing steps such as filtering low-load records, removing holidays, interpolating missing values, and applying cyclic encoding to standardize the data into 96 time intervals per day (15-min resolution). For each prediction, the average load profile of the five most recent weekdays is computed and compared to cluster centroids to identify the most similar group, which is then used to train the neural network. Results demonstrate consistent improvements in MAPE, RMSE, and MAE compared to the non-clustered baseline. The model showed robustness to non-stationary behavior and atypical patterns, even when relying solely on timestamp and load data. The proposed strategy outperformed conventional approaches and proved suitable for complex, data-limited environments.

Energies doi: 10.3390/en19092155

Authors: Daniela Summa Stefano Raimondi Valerio Mangeruga Matteo Giacopini Elena Tamburini Alberto Amaretti

Emissions from transportation are rapidly increasing, representing the second-largest source within the energy sector. Switching to biofuels is a promising strategy to mitigate these environmental impacts. The main aim of this study is to evaluate and compare the environmental performance of fossil gasoline and bioethanol blends in a high-performance Formula SAE race car using a comprehensive well-to-wheel (WTW) life cycle assessment (LCA) approach. The vehicle was tested under three fuel scenarios: (i) 100% fossil gasoline, (ii) a blend of 85% first-generation bioethanol (1G-pure bioethanol) derived from corn and 15% fossil gasoline (E85-1G), and (iii) a blend of 85% second-generation bioethanol (2G-pure bioethanol) derived from grape pomace, a winemaking waste product, and 15% fossil gasoline (E85-2G). The novelty of this work lies in the combined experimental and LCA-based comparison of crop-based and waste-derived bioethanol under identical high-performance operating conditions, enabling a direct assessment of feedstock influence on environmental impacts. The well-to-tank (WTT) results show that 2G bioethanol achieves the lowest environmental burdens across all impact categories, while 1G-pure bioethanol is significantly affected by emissions from corn cultivation. Fossil gasoline exhibits the highest impacts in terms of global warming potential (GWP) and Abiotic Resource Depletion (ARD). The tank-to-wheel (TTW) analysis confirms the superior environmental performance of the E85-2G blend. Despite requiring 6–16% more fuel to complete the race, E85-2G maintains its environmental advantage, and both biofuel blends produce lower air emissions than conventional gasoline.

Energies doi: 10.3390/en19092153

Authors: Yan Li Qinhan Guo Qianchang Li Wenke Zhang Tishi Huang Ping Cui

Ground-source heat pump (GSHP) systems are widely used because of their energy-saving and environmentally friendly characteristics. However, the long-term operation of a standalone GSHP system leads to heat accumulation in the soil for cooling load-dominated buildings, which results in a decline in system performance. To address this issue, in this study, a high-speed railway station in Jinan was considered as the research object, and a hybrid system scheme in which a GSHP is coupled with an air-source heat pump (ASHP) was developed. The system uses the outdoor dry-bulb temperature as the control parameter and establishes a multi-unit operation control strategy. A dynamic simulation model of the hybrid system was constructed using TRNSYS software, and then the energy consumption, soil thermal balance, economics and environmental benefits of the system under various schemes and operating conditions were simulated and analyzed. Through a comparative analysis of the operating strategies, the optimal strategy that achieved the best performance was determined. Under the optimal strategy, the soil thermal imbalance rate after 10 years of operation was only 1%, the total energy consumption was significantly lower than that of a standalone ASHP system, and the initial investment was clearly lower than that of a standalone GSHP system. The results demonstrate that the hybrid system ensures soil thermal balance and high-efficiency operation while providing significant energy savings (a 28% primary energy savings rate compared to a standalone ASHP) and environmental benefits (reducing annual CO2, SO2, NOx, and dust emissions by 56.5 t, 384.2 kg, 361.6 kg, and 339 kg, respectively). Therefore, the emission of atmospheric pollutants such as CO2, SO2, NOx, and dust can be effectively reduced, thus providing an important reference for the development of building energy-saving technologies under the “dual carbon” goals.

Energies doi: 10.3390/en19092154

Authors: Tolulope David Makanju Ali N. Hasan Thokozani Shongwe

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control.

Energies doi: 10.3390/en19092149

Authors: Tao Yu Zhiguo Lei Yubo Xiao Xuesheng Shen

Driven by increasingly stringent requirements for energy saving and emission reduction in non-road machinery, hybrid wheel loaders have attracted growing attention as a practical pathway toward cleaner construction equipment. However, conventional energy management strategies often show limited adaptability to highly transient operating cycles and struggle to balance fuel economy, real-time applicability, and battery charge sustainability. To address these issues, this study proposes an improved sparrow-search-algorithm-based equivalent consumption minimization strategy (ISSA-ECMS) for a series hybrid wheel loader. A quasi-static powertrain model was established, while ISSA was used to optimize both the hyperparameters of a Convolutional Neural Network-Long Short-Term Memory (CNN–LSTM) stage-recognition model and the stage-dependent ECMS parameters. A hidden Markov model (HMM)-based post-processing framework was further introduced to improve temporal consistency in operating-stage recognition. The results show that the optimized ISSA-CNN–LSTM achieved 93.22% accuracy, 93.08% Macro-F1, and 93.21% Weighted-F1, while HMM refinement further improved recognition accuracy from 94.02% to 97.92%. In energy management simulations, ISSA-ECMS maintained the terminal state of charge (SOC) at 50.0069%, reduced fuel consumption by 2.1% and 1.4% compared with conventional ECMS and A-ECMS, respectively, and increased the proportion of engine operating points in the economical region to 77.549%. Compared with dynamic programming, its fuel-consumption increase was only 0.28%, while retaining online applicability. These results demonstrate that the proposed method provides an effective and practical solution for real-time energy management of series hybrid wheel loaders.

Energies doi: 10.3390/en19092152

Authors: Yulin Luo Zihao Zhang Deshuai Sun Facheng Wang Qi Zhang Dafang Wang

To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles.

Energies doi: 10.3390/en19092151

Authors: Matej Đuranović Marija Živić Ivan Samardžić Siniša Bikić

This study investigates the tariff-oriented operation of residential air-to-water heat pump systems integrated with thermal energy storage under long-term real climatic conditions. In contrast to studies based on short-term simulations or advanced predictive control, this work evaluates a simple rule-based control strategy with a focus on practical applicability. The analysis is based on hourly simulations using measured meteorological data over an eight-year period for multiple locations characterized by continental climatic conditions. Two system configurations were considered: a reference system without thermal energy storage and a storage-integrated system operating under a dual-tariff electricity pricing scheme. The results show that thermal energy storage enables effective load shifting toward lower tariff periods, resulting in consistent electricity cost reductions of 19–23% across all analyzed years and locations. These savings are achieved without significant changes in seasonal performance. However, the economic analysis indicates that the payback period remains relatively long (20 years), exceeding typical thresholds for residential investments under current conditions. Overall, the findings highlight the importance of operational flexibility and demonstrate that simple control strategies can improve the economic performance of residential heat pump systems.

Energies doi: 10.3390/en19092150

Authors: Yanling Chen Zhongyun Tian Mingzhi Kong Lei Sun Lizhi Zhou Wujun Wang Mengyao Liu

The rapid increase in memory power density has made memory thermal management a critical challenge in high-density servers, where extremely limited DIMM spacing significantly reduces the effectiveness of air cooling. Compared with CPUs and GPUs, memory-level liquid cooling has received less systematic study, particularly regarding the influence of cold plate structural design on thermal and hydraulic performance under realistic server conditions. In this paper, three engineering-feasible memory liquid cooling solutions (water-flowing cold plate, clamp-type cold plate and heat-pipe-based cold plate) are experimentally compared on a high-density server system. Experiments are conducted at coolant inlet temperatures of 37–50 °C with a fixed flow rate of 0.8–1.5 L/min. Memory, CPU, and voltage regulator temperatures, as well as system pressure drop, are measured. Results show that memory temperature increases with coolant inlet temperature for all configurations, while their relative performance remains unchanged. Memory temperatures range from 62.04 to 71.13 °C, 57.65 to 66.98 °C, and 66.22 to 76.07 °C, with corresponding pressure drops of 24.19–26.69 kPa, 32.73–35.98 kPa, and 27.00–29.96 kPa. These results provide insight into the role of coolant distribution and flow-path topology in memory thermal performance.

Energies doi: 10.3390/en19092148

Authors: Wenbo Gu Xutao Li Hongqiang Li Lei Zhou Wenchao Zhang Minghui Huang

This study proposes a coordinated allocation model for grid-forming energy storage and synchronous condensers considering transient stability constraints, with the following key aims: mitigate the continuous degradation of power systems’ capability to withstand inertia and the severe threats to dynamic rotor angle stability and frequency, while integrating renewable energy-centered frameworks using wind and photovoltaic power, and guarantee the secure and stable operation of transmitting power grids containing such bases. First, based on a virtual synchronous inertia quantification model of grid-forming energy storage and grid-forming wind and PV equipment, the inertia support capability of the renewable energy base is investigated. Subsequently, the impact of grid-forming equipment integration on transient rotor angle stability and frequency is studied, and a model of rotor angle stability and frequency constraints for the renewable energy base is established. Considering conditions such as investment cost constraints, transmission power constraints, and rotor angle stability and frequency constraints, a coordinated allocation model of grid-forming energy storage and synchronous condensers is formulated and solved to minimize the overall cost. Finally, the simulation verification results show that, compared with the configuration models that consider only the synchronous condenser or only the grid-forming energy storage, the proposed model reduces the comprehensive cost of the renewable energy base by 11.9% and 8.74%, respectively, reduces the minimized value of the power angle stability index by 80.95% and 78.95%, respectively, and meets the synchronous inertia demand of the renewable energy base throughout the period.

Energies doi: 10.3390/en19092147

Authors: Zerui Chen Zhukui Tan Xin Wu Huan Li Houpeng Hu

In order to maximize the use of low-temperature heat sources for refrigeration, a Li-Br absorption chiller combined with single-effect absorption refrigeration cycle and two-stage absorption refrigeration cycle (STAC) was developed. Experimental research on STAC was conducted on a prototype with a refrigeration capacity of 500 KW. A numerical model validated by experimental data was used to study the refrigeration performance of STAC under variable operating conditions. Compared to single-effect units and two-stage units, STAC demonstrates remarkable heat source conservation capability and adaptability to a broad spectrum of heat source temperatures. This advantage renders the STAC unit more adaptable to new energy or waste heat scenarios characterized by unstable heat sources. As the inlet temperature of the hot water increases, the temperature difference between the inlet and outlet of the hot water also increases. When the inlet temperature of the hot water is 70 °C, 90 °C and 120°C, the temperature difference between the inlet and outlet of the hot water is 10 °C, 30°C and 70 °C, respectively. Both increasing the inlet temperature of hot water and decreasing the temperature of cooling water will enhance the cooling capacity and coefficient of performance (COP) of STAC. As the flow rate of chilled water increases, the refrigeration capacity of STAC will also increase, but the COP will first increase and then decreases

Energies doi: 10.3390/en19092146

Authors: Jacek Stelmach

With rising energy prices, increasing the efficiency of the mechanical mixing process is becoming an important design issue. It is expected that with propeller impellers, efficiency can be increased by changing the shape and/or pitch of the blades. Three-blade propeller mixers with five different blade shapes and four strokes (from pb/D = 0.5 to pb/D = 2) were tested. The mixing power (by measuring the torque on the shaft), the pumping capacity (by measuring the axial velocity using the PIV method) and the pressure on the bottom of the stirred tank were determined. Based on the results of the research, it was found that the shape, surface and pitch of the blades affect the pumping efficiency. However, for a specific blade stroke, the effect of blade shape on mixing efficiency is small. Impellers with a small stroke show the best efficiency, and increasing the blade stroke reduces the efficiency of the process. However, the small pitch of the blades means that the liquid stream pumped by the impeller may turn out to be too small for the proper conduct of the process, e.g., obtaining slurries. Therefore, the most commonly used pb/D = 1 pitch turns out to be a good solution. Determining the relationship between pumping efficiency and bottom pressure allows you to determine the liquid stream pumped by the impeller based on the bottom pressure measurement. It has been confirmed that increasing pumping efficiency causes an increase in power demand according to pump theory because propeller impellers show similarities to the rotors of pumps and ship propellers. The theory of ship propellers is much better developed than the theory of mixing. Therefore, the possibility of using it to describe the mixing process could facilitate further research of propeller impellers. It was found that not all dependencies for ship propellers can be used to describe the mixing process.

Energies doi: 10.3390/en19092144

Authors: Su Wang Liye Xiao

With the increasing power fluctuations and growing pressure on grid stability resulting from the high penetration of renewable energy, the demand for exploring various energy storage technologies with large-scale, long-duration, and low-cost features has become increasingly urgent. This paper proposes a novel single-track gravity energy storage generation system. This system utilizes non-standardized masses (such as natural rocks) operating stably on an inclined track, and combines coordinated feedforward–feedback electromagnetic torque control, multi-station loading scheduling, and synchronous loading/unloading strategies to effectively smooth the power fluctuations of renewable energy sources such as wind power. The core innovations of this system lie in: (1) utilizing non-standardized mass units to achieve gravity energy storage, thereby expanding the application scenarios and design flexibility of solid gravity energy storage systems; and (2) introducing intelligent scheduling strategies and multi-station loading coordination to effectively smooth the power output fluctuations caused by load randomness, rendering the system insensitive to load variations. Simulation results verify that, for power smoothing in a 10 MW-level wind farm, the system can accurately track the target power and maintain a stable output over a long duration. The power fluctuations are controlled to under 0.2%, even when the total load varies by 10% and the instantaneous load fluctuates by 5%. This system demonstrates the theoretical feasibility and scalability of utilizing natural rock resources in mountainous terrains for long-duration energy storage, providing a novel solution for long-duration power smoothing in renewable energy systems.

Energies doi: 10.3390/en19092143

Authors: Ying Chen Haicheng Wu Lili Wen Yue Xiao Jinchao Zhang Qian Zhang Xiaofei Wu Huanyu Zhang

Core physics multigroup cross-section libraries provide essential cross-section and burnup data for reactor neutron physics calculations, serving as a fundamental prerequisite for reactor physics analysis. The China Nuclear Data Center has developed the TPEX multigroup cross-section library for pressurized water reactors (PWRs) based on the Chinese Evaluated Nuclear Data Library CENDL-3.2. A systematic critical benchmark validation of the newly developed TPEX library has been performed. To verify its applicability and accuracy, the validation has been conducted against 131 critical benchmark experiments from the International Criticality Safety Benchmark Evaluation Project (ICSBEP 2006) and the WIMS-D library update project. The calculated effective multiplication factors (keff) are compared with the experimental values, results from equivalent multigroup libraries, and reference solutions from Monte Carlo code. The results indicate that the absolute average deviations between the calculated keff values using the TPEX library and the experimental measurements are 280 pcm for the uranium solution experiments, 410 pcm for the plutonium solution experiments, 10 pcm for the uranium metal lattice experiments, 20 pcm for the uranium dioxide lattice experiments, 22 pcm for the MOX fuel lattice experiments, and 150 pcm for the LCT001 uranium oxide assembly experiments. Accordingly, the TPEX library demonstrates excellent performance in reactivity predictions for PWRs.

Energies doi: 10.3390/en19092145

Authors: Guoyan Chen Min Zhu Haisheng Shu Jian Chi Wencong Chen

The normal operational characteristics of power cables are a crucial foundation for developing their protection devices. To analyze the current and voltage characteristics of parallel cable lines, the parameter matrix of the phase-aligned parallel cable lines is first established based on Carson’s formula. The metal sheath is treated as a general line, considering its self-inductance and mutual inductance with the core loop, and its sheath circulating current is calculated. Then, the relationship between line voltage and current is established, and the effect of the sheath circulating current is equivalently incorporated into the line’s phase impedance matrix. A π-type equivalent circuit of the cable line is established, from which the operational parameters of the phase-aligned parallel cables are calculated. Indicators measuring the operational characteristics of phase-aligned parallel operation—sheath circulating current, imbalance, carrying capacity, and voltage deviation—are introduced, and the optimal arrangement is determined using the analytic hierarchy process. This study integrates Carson’s formula for impedance modeling and fuzzy AHP for multi-criteria optimization, addressing gaps in single-indicator approaches. The proposed method identifies the three-phase vertical layout as optimal, improving ampacity by 10% and reducing sheath circulating current by 28%, offering direct guidance for 500 kV cable projects.

Energies doi: 10.3390/en19092142

Authors: Xiaobo Wang Chenguang Qiu Yu Cui Haiqiang Zhou Yutong Wang

Virtual synchronous generator control-based permanent magnetic synchronous generators (VSG-PMSGs) have been widely used for their stable operation in a weak grid and strong voltage and frequency support capacity. However, VSG-PMSGs have complex and time-varying dynamics due to control strategy switching, current limiters, and saturations. Additionally, they are prone to transient angle instability during voltage faults. A dynamic analysis method for VSG-PMSGs considering low-voltage ride-through (LVRT) control is proposed in this paper. First, an improved LVRT control strategy based on active power reference reduction and virtual electromagnetic force (EMF) reset is introduced to mitigate the instability risk of VSG-PMSGs. Then, the mechanisms by which initial power and fault voltages influence the dynamic responses are revealed. The dynamics of VSG-PMSGs under different conditions are classified into four types according to the current and EMF limiters’ state. To predict VSG-PMSG dynamics, we propose a method based on fault steady-state power flow for calculating the fault voltage. Using this approach, fault voltage dips in VSG-PMSGs within a wind farm are computed with an error of less than 0.002 p.u., and the dynamic behavior of each unit is accurately predicted within 10 s. To verify the validity of the proposed method, simulations were conducted across diverse scenarios. The results demonstrate that this method enables accurate and computationally efficient prediction of VSG-PMSG fault dynamics.

Energies doi: 10.3390/en19092141

Authors: Qingliang Ma Zhaoshui He Yangjian Li Junfeng Liu

Under the demand of electric vehicle DC fast-charging stations for medium-voltage DC–DC converters, the indirect series-connected hybrid clamped topology based on quasi-two-level (Q2L) modulation demonstrates significant application potential due to its balanced advantages in cost and performance, as well as its inherent voltage self-balancing capability. However, a unified analytical framework for the voltage self-balancing mechanism of the hybrid clamped topology under different Q2L modulation strategies has not yet been established, which limits further topology optimization and broader application expansion. To address this issue, this paper first investigates the key factors influencing voltage self-balancing and conducts a comprehensive analysis of the self-balancing behavior under different Q2L modulation strategies, thereby deriving the conditions required to maintain the self-balancing capability of the hybrid clamped topology. On this basis, an improved Q2L modulation method is proposed to ensure stable and reliable voltage self-balancing performance. Finally, both the simulation and experimental results verify the accuracy of the proposed analysis method and the effectiveness of the improved modulation strategy.

Energies doi: 10.3390/en19092140

Authors: Wei Chen Shiwei Zhang Zhiqiang Wang Xinmin Li Shuxin Xiao Zhezhun Xu

To address the current surge and speed fluctuation that occur when high-speed surface-mounted permanent magnet synchronous motors (HSPMSMs) switch from I-f open-loop control to sensorless closed-loop control, an adaptive switching method based on the cosine of the angle error is proposed. In this method, the angle error between the I-f open-loop reference angle and the angle estimated by the sensorless observer serves as the regulating variable, and its cosine is introduced to construct an adaptive attenuation factor, so that the rate of current reduction can vary continuously with the angle error. Specifically, a relatively large rate of current reduction is generated in the early stage of the switching process, when the angle error is large, to shorten the switching time. As the angle error decreases, the rate of current reduction is gradually lowered, allowing the current regulation process to better match the convergence process of the angle error and thereby improving switching stability. The proposed switching method is validated on a high-speed air compressor experimental platform. The experimental results show that the proposed method can shorten the switching time, reduce the current surge and speed fluctuation at switching, and exhibit good robustness under varying operating conditions.

Energies doi: 10.3390/en19092139

Authors: Zhuang Yang Zhixian Yin Fan Zhang Qiuhong Wang Changding Wang

In order to achieve more accurate and efficient oil-paper insulation aging assessment, and to improve the operation and maintenance level of oil-paper insulated power equipment, this paper proposes an aging evaluation method of oil-paper insulation based on Raman spectrum and frequency-domain spectroscopy. First, oil-paper insulation samples with different aging degrees were prepared by an accelerated thermal aging test in this experiment. Then, Raman spectroscopy and frequency-domain dielectric spectroscopy were used to examine the samples and analyze the aging characteristics of the samples by LightGBM R2019b. Finally, the gray neural network is used to establish a prediction model for the degree of polymerization of insulating paper based on frequency-domain dielectric features and Raman spectral features. The results of this study showed that there is a certain correlation between the Raman characteristics of insulating oil and the FDS characteristics of insulating paper. The average absolute error of the prediction of the R-F-PGNN model developed in this paper is 20.4. The research in this paper provides a strong support for the development of Raman spectroscopy diagnosis technology for oil-paper insulation aging in the power industry, which has certain academic value and engineering application significance.

Energies doi: 10.3390/en19092138

Authors: Long He Chen Cao Yongming Zhu Baojun Ma Huan Lei Yan Hu

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s2. Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s2. Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure.

Energies doi: 10.3390/en19092135

Authors: Dongxiao Ren Xinyu Zhong Zixiang Ye Xing-Liang Xu

For battery management systems, accurate remaining useful life (RUL) prediction is important, yet models trained offline may not remain well matched to individual cells during operation, because degradation trajectories differ across cells and evolve over aging stages. This study examines a lightweight online personalization strategy under a representative convolutional neural network–long short-term memory (CNN–LSTM) online-transfer setting while keeping the backbone architecture and fixed input length unchanged. The proposed method restricts online updates to a small adaptation path and adjusts the effective history span according to recent degradation behavior. Experiments on 22 test cells under unseen protocols show that the method improves average post-adaptation RUL performance relative to the representative baseline, reducing the root mean square error (RMSE) from 186.00 to 160.58. The number of trainable parameters involved in online updating is reduced from 74,880 to 2193, while the average update time per step decreases slightly from 2.54 s to 2.29 s. Cell-level analysis further shows that the benefit is not uniform across all cells, motivating more selective updating for safer deployment. Overall, the results indicate that lightweight online personalization can improve the accuracy–cost trade-off of deployment-oriented battery prognostics.

Energies doi: 10.3390/en19092137

Authors: Sara Zaoui Abdelmalek Kouadri Mohamed Faouzi Harkat Majdi Mansouri Lazhar Kheriji

The utilization of photovoltaic (PV) energy has witnessed significant recent growth, positioning it as the fastest-growing renewable energy technology. This rise in deployment underscores the importance of monitoring PV systems, which has become an increasingly critical area of research. However, the heightened focus on monitoring has also revealed a key challenge: various types of faults often remain undetected, potentially causing severe performance degradation. This study proposes a machine learning-based fault classification method that integrates Principal Component Analysis (PCA) for feature extraction with Kullback–Leibler (KL) divergence for distribution-based classification. The approach is applied to a publicly available dataset on a single benchmark of a 5 kW PV plant containing voltage, current, temperature, and irradiance measurements for five operating conditions: normal operation, short circuit, open circuit, partial shading, and degradation. A dedicated normalization strategy and optimal bin-width selection are employed to enhance the stability and accuracy of probability density function estimation. Experimental results demonstrate that the proposed PCA–KLD framework achieves superior accuracy compared to existing methods applied to the same dataset. These results confirm the effectiveness and robustness of the approach for fault classification in PV systems and highlight its potential for handling challenging fault scenarios.