Energies

25 pages, 6560 KB

Open AccessArticle

R-SATNet: Robust Self-Attention Transformer Network for Multi-Step Building Load Forecasting in Smart Energy Systems

by Amel Ksibi, Manel Ayadi, Jawaher Alyami and Ghadah Aldehim

Energies 2026, 19(9), 2248; https://doi.org/10.3390/en19092248 - 6 May 2026

Cited by 1 | Viewed by 559

Accurate multi-step building load forecasting is critical for optimizing energy management in smart grids and reducing operational costs. However, existing forecasting methods struggle with complex temporal dependencies, seasonal variations, and robust performance under noisy conditions. This paper proposes R-SATNet (Robust Self-Attention Transformer Network), [...] Read more.

Accurate multi-step building load forecasting is critical for optimizing energy management in smart grids and reducing operational costs. However, existing forecasting methods struggle with complex temporal dependencies, seasonal variations, and robust performance under noisy conditions. This paper proposes R-SATNet (Robust Self-Attention Transformer Network), a novel deep learning architecture that integrates multi-head self-attention mechanisms with robust optimization techniques for enhanced building load prediction. The proposed framework incorporates temporal feature extraction modules, adaptive noise suppression layers, and multi-scale attention blocks to capture both short-term fluctuations and long-term seasonal patterns. Extensive experiments on real-world building load datasets demonstrate that R-SATNet achieves superior forecasting accuracy with 15.7% lower RMSE and 12.3% improved MAPE compared to state-of-the-art methods. The model maintains robust performance under various noise conditions and provides reliable multi-step predictions up to 24 h ahead, making it highly suitable for practical smart energy system deployments. The proposed framework is validated across six diverse building datasets spanning commercial, residential, industrial, campus, mixed-use, and healthcare facilities, confirming its generalizability and practical applicability in heterogeneous smart energy environments. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, 2nd Edition)

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20 pages, 6572 KB

Open AccessArticle

A Complex-Valued Neural Network Approach to Time Series Forecasting in Smart Grid Energy Systems

by Igor Aizenberg, Lorenzo Becchi, Marco Bindi, Matteo Intravaia and Antonio Luchetta

Energies 2026, 19(9), 2247; https://doi.org/10.3390/en19092247 - 6 May 2026

Viewed by 526

Abstract

This work is devoted to the application of complex-valued neural networks based on the multilayer neural network with multi-valued neurons (MLMVN) for short-term electrical load forecasting in smart grid energy systems. Accurate forecasting is a critical component of energy management systems, as it [...] Read more.

This work is devoted to the application of complex-valued neural networks based on the multilayer neural network with multi-valued neurons (MLMVN) for short-term electrical load forecasting in smart grid energy systems. Accurate forecasting is a critical component of energy management systems, as it directly impacts the efficiency of control and optimization strategies in increasingly distributed and stochastic environments. The proposed approach leverages the intrinsic properties of complex numbers to model periodicity and nonlinear relationships typical of load time series. A compact feedforward architecture with two hidden layers is adopted and combined with multiple preprocessing strategies, including unit circle encoding, Fourier transform representations, and hybrid feature mappings incorporating temporal information such as the day of the week. The performance of the proposed models is evaluated on real-world prosumer data and compared against two benchmarks: a seasonal persistence model and a Long Short-Term Memory network. Results show that MLMVN-based approaches achieve comparable or improved performance in terms of RMSE and error reduction capability, despite their lower architectural complexity. Fourier-based preprocessing methods demonstrate strong effectiveness in capturing underlying temporal patterns. These findings suggest that complex-valued representations provide a promising alternative to traditional deep learning approaches, offering a favorable balance between accuracy, interpretability, and computational efficiency in Smart Grid forecasting applications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Modern Power and Energy Systems)

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14 pages, 29597 KB

Open AccessArticle

Backstepping Super-Twisting Sliding Mode Control for MMC-HVDC in Passive Networks

by Zerong Wang, Xinhong Wu, Hao Dong, Hao Huang and Yongxi Zhao

Energies 2026, 19(9), 2246; https://doi.org/10.3390/en19092246 - 6 May 2026

Viewed by 407

Abstract

Due to their superior harmonic profiles and minimal switching energy losses, modular multilevel converters (MMCs) have emerged as the primary topology for high voltage direct current (HVDC) applications. However, traditional Proportional–Integral (PI) control exhibits inferior dynamic performance using MMC-HVDC supplying power in the [...] Read more.

Due to their superior harmonic profiles and minimal switching energy losses, modular multilevel converters (MMCs) have emerged as the primary topology for high voltage direct current (HVDC) applications. However, traditional Proportional–Integral (PI) control exhibits inferior dynamic performance using MMC-HVDC supplying power in the passive networks. This study proposes a backstepping super-twisting sliding mode control strategy, which significantly improves the dynamic performance of the MMC-HVDC system and mitigates fluctuations in the DC side voltage. First, a mathematical model is established based on the topology of the modular multilevel HVDC transmission system. Then, utilizing the backstepping method, a virtual control law for the current inner loop is designed according to the mathematical model. Subsequently, the super-twisting sliding mode algorithm is introduced based on the backstepping method to form the backstepping super-twisting sliding mode control law. Finally, a comprehensive model is established within the Matlab/Simulink environment, and extensive simulation studies are carried out to evaluate the effectiveness the effectiveness and advantages of the proposed backstepping super-twisting sliding mode control under stable operation, grid voltage sag, and single-phase grounding fault conditions. Comparative evaluations verify that the introduced strategy effectively lowers the total harmonic distortion (THD) of the current and suppresses DC voltage ripples. Moreover, compared to the conventional PI method, the new approach provides enhanced transient robustness with noticeably reduced overshoot with considerably lower overshoot compared to traditional PI control, thereby providing a highly reliable and stable solution for MMC-HVDC systems supplying passive networks. Full article

(This article belongs to the Special Issue Modular Multilevel Converters: Technologies, Control and Applications)

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19 pages, 4312 KB

Open AccessArticle

State-Dependent Switching Control with Dwell Time Regulation for Three-Phase VSCs Based on 4D Switching Model

by Xin Guo, Hongyi Qi, Hongbo Cao, Celso Grebogi and Shangbin Jiao

Energies 2026, 19(9), 2245; https://doi.org/10.3390/en19092245 - 6 May 2026

Viewed by 517

Abstract

This paper proposes a novel modeling and control strategy for three-phase voltage source converters (VSCs) based on a switched system framework. A four-dimensional (4D) switched model and state-dependent switching control strategy with dwell time regulation are proposed. The key contributions of this work [...] Read more.

This paper proposes a novel modeling and control strategy for three-phase voltage source converters (VSCs) based on a switched system framework. A four-dimensional (4D) switched model and state-dependent switching control strategy with dwell time regulation are proposed. The key contributions of this work are: (1) The proposed switching model accurately represents both the continuous and discrete dynamics of the AC current and DC voltage in three-phase VSCs without relying on linearization or approximation techniques. (2) The proposed method enables the simultaneous control of three-phase AC currents and DC voltage within a single loop under the switching control framework. Complex phase-locked loops (PLLs), pulse width modulation (PWM), and the control parameter tuning process are avoided. (3) The steady-state and transient performance of the system was enhanced through the adaptive adjustment of the dwell time of the switching signal. The simulation and experimental results confirm the effectiveness and advantages of the proposed method. Full article

(This article belongs to the Special Issue Advanced Control Methods for Power Electronics, Energy Storage, Photovoltaics, and Microgrids)

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20 pages, 4934 KB

Open AccessArticle

Integrating Molecular Dynamics Simulations with Machine Learning to Predict Shale Oil Spontaneous Imbibition Efficiency

by Yun Liang, Abubakar Mustafa Zubeir, Xueliang Liu, Xuecheng Gong, Jing Liu, Leng Tian and Juhua Li

Energies 2026, 19(9), 2244; https://doi.org/10.3390/en19092244 - 6 May 2026

Viewed by 528

Abstract

Low porosity and ultra-low permeability are common characteristics of shale reservoirs. Traditional imbibition theory is unable to adequately describe fluid transport behavior in nanopores or capture microscopic mechanisms. In this study, imbibition efficiency was defined as the proportion of oil molecules displaced outside [...] Read more.

Low porosity and ultra-low permeability are common characteristics of shale reservoirs. Traditional imbibition theory is unable to adequately describe fluid transport behavior in nanopores or capture microscopic mechanisms. In this study, imbibition efficiency was defined as the proportion of oil molecules displaced outside the initial oil phase region relative to the initial oil quantity. This study investigates shale oil spontaneous imbibition mechanisms by integrating molecular dynamics (MD) simulations with machine learning (ML) approaches. MD simulations were performed under baseline conditions of 353 K and 10 MPa, with additional simulations at temperatures ranging from 323 to 393 K, across quartz, calcite and dolomite, and at surfactant concentrations of 0.1% to 0.4% to analyze the influencing factors. Wettability differences among minerals were assessed indirectly through analysis of water density distributions, hydrogen bonding, and water–surface interaction energies, which consistently indicated that dolomite exhibits the strongest hydrophilic character, followed by calcite, with quartz showing the weakest water affinity. Results show that increased temperature, enhanced mineral hydrophilicity, and an optimal surfactant concentration of 0.3% significantly improve imbibition efficiency. Using four algorithms—Support Vector Regression trained, Gradient Boosting Regression Tree, XGBoost, and Random Forest—on the 36 MD-derived datasets, we built an ML model as a proof of concept. The Random Forest model performed the best after cross-validation and hyperparameter adjustment, with a validation R² of 0.81. The novelty of this study therefore is a proof of concept demonstrating the feasibility of MD with ML integration for imbibition prediction, while clearly identifying limitations and directions for future improvement. This provides theoretical foundations for optimizing shale reservoir development and field-scale recovery enhancement. Full article

(This article belongs to the Topic Advances and Application in Intelligent Oil and Gas Field Development Technology)

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24 pages, 4459 KB

Open AccessArticle

A Complete CFD Methodology Based on Iterative Model Adjustment to Improve Wind Simulation Accuracy in Highly Dense Forest Area

by Edouard Leonard, Ru Li, Eric Tromeur, Marianne Dupont, Aurélien Gaussorgues, Gaetan Martellozzo, Stavros Koutsioumpas and Mustafa Akcakaya

Energies 2026, 19(9), 2243; https://doi.org/10.3390/en19092243 - 6 May 2026

Viewed by 899

Abstract

Wind resource assessment (WRA) in densely forested and complex terrain remains challenging due to strong canopy-induced turbulence and enhanced wind shear, which significantly affect wind flow characteristics and increase modeling uncertainties. Methods relying on Plant Area Density (PAD) or Leaf Area Density (LAD) [...] Read more.

Wind resource assessment (WRA) in densely forested and complex terrain remains challenging due to strong canopy-induced turbulence and enhanced wind shear, which significantly affect wind flow characteristics and increase modeling uncertainties. Methods relying on Plant Area Density (PAD) or Leaf Area Density (LAD) estimation require costly airborne surveys and site-specific calibration, limiting their industrial applicability. Based on a scientific collaboration between Meteodyn and EDF Power, this study proposes a complete and reproducible Computational Fluid Dynamics (CFD) methodology built around an Iterative Model Adjustment (IMA) procedure implemented in Meteodyn WT™ to improve wind resource assessment accuracy in highly forested areas using standard industrial inputs. The IMA procedure iteratively calibrates the canopy drag coefficient and forest model parameters using wind speed profile measurements from a single reference mast until the simulated wind shear matches observations. The methodology was evaluated at three sites located in Finland, France, and Scotland, yielding six calibration and cross-prediction cases under heterogeneous forest and complex terrain conditions. Cross-prediction uncertainties were reduced significantly, with horizontal mean speed errors decreasing from the range [1.0–9.5%] to [0.5–2.2%] and a global mean absolute error of approximately 1.1%. The study provides new physical insight into the sensitivity of the canopy drag force term within RANS-based forest models, showing that both drag coefficient and canopy height have a comparable and jointly necessary influence on wind shear simulation. These findings demonstrate that robust and accurate wind resource assessment can be achieved in complex terrain and forested areas without relying on remote-sensing-derived canopy density datasets, providing a pragmatic and industrially scalable alternative. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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26 pages, 4406 KB

Open AccessArticle

Variables for Planning Hydrogen Refueling Infrastructure

by Agustín Álvarez Coomonte, Zacarías Grande Andrade and Rocío Porras Soriano

Energies 2026, 19(9), 2242; https://doi.org/10.3390/en19092242 - 6 May 2026

Viewed by 359

Abstract

Hydrogen-based zero-emission transport technologies have reached a level of technical maturity that enables their operational deployment; however, their large-scale uptake remains constrained by the limited availability of refuelling infrastructure. This gap between technological readiness and infrastructure provision represents one of the main bottlenecks [...] Read more.

Hydrogen-based zero-emission transport technologies have reached a level of technical maturity that enables their operational deployment; however, their large-scale uptake remains constrained by the limited availability of refuelling infrastructure. This gap between technological readiness and infrastructure provision represents one of the main bottlenecks for the transition towards hydrogen-powered mobility systems. In this context, stakeholders evaluate hydrogen deployment through a set of key quality objectives, primarily related to emissions, economic performance, and efficiency. The achievement of these objectives is inherently conditioned by the configuration of the hydrogen value chain and, in particular, by the spatial dimension of infrastructure deployment. Despite its relevance, the combined effect of value chain variables and location-specific factors on quality outcomes remains insufficiently characterised in the literature. To address this gap, this study proposes a probabilistic modelling framework based on Bayesian Networks to capture the relationships between value chain variables, location-dependent conditions, and resulting quality indicators. This approach enables the explicit representation and propagation of uncertainty across the system, providing a robust analytical basis for evaluating alternative infrastructure deployment strategies. By integrating technical, economic, and spatial dimensions within a unified modelling structure, the proposed framework supports informed decision-making in the planning and optimisation of hydrogen refuelling infrastructure. Full article

(This article belongs to the Special Issue Hydrogen Storage and Transportation: Materials, Technologies, and Infrastructure Development)

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26 pages, 6698 KB

Open AccessArticle

An Integrated Model of Microgrid Energy Storage Planning and Operation Considering Multi-Scenario Source–Load Timing Correlation

by Xinyuan Zhang, Xing Liu and Zhenbo Wei

Energies 2026, 19(9), 2241; https://doi.org/10.3390/en19092241 - 6 May 2026

Cited by 1 | Viewed by 597

Abstract

Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the [...] Read more.

Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the generated scenarios and lead to suboptimal planning outcomes. To address this issue, this paper proposes an integrated model for microgrid energy storage planning and operation that explicitly considers the joint distribution of source–load scenarios. First, a comprehensive similarity metric is developed by combining dynamic time warping (DTW) distance, slope distance, and source–load correlation distance. An improved K-medoids clustering algorithm is then employed to cluster the joint source–load time series, generating a set of typical scenarios that effectively preserve the coupling characteristics between photovoltaic generation and load demand. Subsequently, a bi-level optimization model is formulated, with energy storage capacity as the primary decision variable. The upper-level planning problem aims to maximize the return on investment (ROI) under energy storage investment constraints, determining the optimal capacity configuration. The lower-level operational problem maximizes the daily net revenue by optimizing the charging and discharging strategies of the energy storage system. Through iterative interaction between the two levels, the model achieves optimal coordination between investment decisions and economic dispatch. Case studies on a campus microgrid demonstrate that the proposed joint scenario generation method effectively captures the temporal correlation between source and load, enhancing both the credibility of the scenarios and the economic rationality of the integrated planning and operation framework. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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29 pages, 1864 KB

Open AccessArticle

Matrix Analysis of Structural Convergence of Energy-Relevant and Policy-Relevant AI Research: Implications for Energy Policy

by Walery Okulicz-Kozaryn, Artem Artyukhov and Nadiia Artyukhova

Energies 2026, 19(9), 2240; https://doi.org/10.3390/en19092240 - 6 May 2026

Cited by 1 | Viewed by 487

Abstract

The rapid expansion of artificial intelligence (AI) research does not automatically imply its structural integration into industry governance systems. In the energy sector, this raises the question of whether a policy-relevant AI regime has already emerged or whether a structural gap persists between [...] Read more.

The rapid expansion of artificial intelligence (AI) research does not automatically imply its structural integration into industry governance systems. In the energy sector, this raises the question of whether a policy-relevant AI regime has already emerged or whether a structural gap persists between technological development and institutional integration. This study is based on a dataset of 792,417 publications indexed in Scopus (1981–2025). Using the AI-Assisted Research Methodology, a piecewise linear phase segmentation of the AI corpus publications was applied. A matrix model was developed to analyze the distribution of energy relevance (Y) and policy relevance (X) in X–Y coordinates. The results indicate that AI research entered a phase of unstable growth after 2017 and a phase of methodological acceleration after 2021. Despite the growth of both indicators (X, Y), the structural concentration of research related to energy and policy remains moderate (zone 2). The adoption of AI in policy is significantly faster than its integration into energy, suggesting an institutional lag. This study introduces the concept of a “synchronization zone” as an indicator of structural convergence and proposes a framework for assessing the degree of AI integration in energy governance. The findings shift the analytical focus from the growth of publications to the structural configuration and contribute to the development of more coordinated strategies in digital and energy policy. Full article

(This article belongs to the Special Issue Management and Economics in the Energy Policy: Theoretical Approaches and Practical Solutions)

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35 pages, 4670 KB

Open AccessArticle

Grid-Forming Energy Storage Optimization and Adaptive Voltage Control for Rural Networks with High-Penetration Photovoltaic

by Tongzhang Wang, Ye Tian, Hui Li, Shang Chen and Haoran Chen

Energies 2026, 19(9), 2239; https://doi.org/10.3390/en19092239 - 6 May 2026

Viewed by 688

Abstract

To address the voltage over-limit issue in rural distribution networks caused by high-penetration distributed photovoltaic (DPV) integration, as well as the frequent voltage disturbances resulting from frequent load switching and variable operating conditions in agricultural grid systems, this paper proposes a dual-layer optimized [...] Read more.

To address the voltage over-limit issue in rural distribution networks caused by high-penetration distributed photovoltaic (DPV) integration, as well as the frequent voltage disturbances resulting from frequent load switching and variable operating conditions in agricultural grid systems, this paper proposes a dual-layer optimized configuration and adaptive voltage control method for grid-forming energy storage systems. First, an outer-layer siting and sizing model is established with constraints including voltage stability, deviation, network losses, and economic factors. The configuration scheme is solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Then, an inner-layer rolling optimized dispatch model is constructed with Model Predictive Control (MPC) as the core, and an adaptive reactive power–voltage control method based on Virtual Synchronous Generator (VSG) control is proposed to enhance transient voltage support under disturbance conditions. Simulation analysis based on an actual 10 kV rural distribution line verifies that the proposed method can effectively mitigate overvoltage issues and alleviate reverse power flow during typical daily operation, while significantly improving node voltage recovery speed and reactive power support capability under voltage disturbances. Full article

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36 pages, 9578 KB

Open AccessArticle

Electric Vehicle Charging and Discharging Scheduling Method Based on Clustering and Deep Reinforcement Learning

by Chunqi He and Jiang Li

Energies 2026, 19(9), 2238; https://doi.org/10.3390/en19092238 - 6 May 2026

Cited by 2 | Viewed by 603

Abstract

With the large-scale integration of electric vehicles (EVs) into the power grid, uncoordinated charging behavior has aggravated load fluctuations in the power system. Deep reinforcement learning can optimize EV charging and discharging strategies through dynamic decision-making, thereby alleviating the operational pressure imposed on [...] Read more.

With the large-scale integration of electric vehicles (EVs) into the power grid, uncoordinated charging behavior has aggravated load fluctuations in the power system. Deep reinforcement learning can optimize EV charging and discharging strategies through dynamic decision-making, thereby alleviating the operational pressure imposed on the grid by load variations. However, under large-scale EV integration scenarios, challenges still remain, including the excessively high dimensionality of the state space and the resulting decline in training efficiency. In addition, the coupling between existing clustering methods and dynamic scheduling mechanisms is still insufficiently tight. To address these issues, this study proposes a cluster-based deep reinforcement learning method for EV charging and discharging scheduling, referred to as CDRL. First, a probabilistic behavioral model is constructed based on EV charging transaction data to characterize the stochasticity of user charging behavior. A Density–Centroid Hybrid Clustering (DCHC) method is then adopted to cluster the charging behavior characteristics of EVs. Subsequently, at the cluster level, a day-ahead base load forecasting model is introduced, and the forecasting results are fed into a mixed-integer linear programming (MILP) model to generate the charging and discharging power allocation tasks for each cluster. At the individual level, the EV charging and discharging process is formulated as a Markov decision process (MDP), and a deep Q-network (DQN) is employed for policy learning, thereby achieving the decomposition of cluster-level tasks into individual scheduling decisions. The simulation results demonstrate that the proposed method can effectively reduce charging costs and smooth system load fluctuations while improving training convergence speed and policy stability. Full article

(This article belongs to the Section E: Electric Vehicles)

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36 pages, 1680 KB

Open AccessReview

Energy Optimization in Fuel Depots: A System-of-Systems Review of Cyber–Physical–Human–Institutional Integration

by David Onwong’a, Moses Barasa Kabeyi, Kenneth Njoroge and Oludolapo Olanrewaju

Energies 2026, 19(9), 2237; https://doi.org/10.3390/en19092237 - 6 May 2026

Cited by 1 | Viewed by 709

Abstract

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in [...] Read more.

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots. Full article

(This article belongs to the Topic Oil and Gas Pipeline Network for Industrial Applications)

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32 pages, 9956 KB

Open AccessArticle

Study on Natural Stratified Cooling Release Characteristics of Micro-Encapsulated Phase Change Material Suspension

by Minghao Yu, Xun Zhou, Haibo Hong, Gangxin Lyu, Zack Lueng and Jiali Pei

Energies 2026, 19(9), 2236; https://doi.org/10.3390/en19092236 - 6 May 2026

Cited by 1 | Viewed by 499

Abstract

To enhance the energy efficiency of data center cooling systems, this study introduces Micro-encapsulated Phase Change Material Suspension (MPCMS) into a naturally stratified cold storage system. Leveraging its superior properties, including high latent heat, high specific heat, and excellent fluidity, a three-dimensional transient [...] Read more.

To enhance the energy efficiency of data center cooling systems, this study introduces Micro-encapsulated Phase Change Material Suspension (MPCMS) into a naturally stratified cold storage system. Leveraging its superior properties, including high latent heat, high specific heat, and excellent fluidity, a three-dimensional transient numerical model was developed to investigate the thermal stratification characteristics during the discharging process. The analysis focuses on the impacts of operational conditions (flow rate and mass fraction) alongside key tank structural parameters (height-to-diameter ratio, uniform flow plate perforation rate, installation position, and aperture). The results indicate that the thermal stratification performance of MPCMS is significantly superior to that of water. Specifically, during the middle discharge stage (t* = 0.4) at a high flow rate of 12.56 m³/h, the thermocline thickness of MPCMS-10 wt% is restricted to only 245 mm, representing a 93.82% reduction compared to 3964 mm for water. Furthermore, at the initial discharge stage (t* = 0.05), the thermocline thickness decreases significantly with increasing MPCMS mass fraction; as the mass fraction rises from 10 wt% to 30 wt%, the thickness sharply drops from 421 mm to 120 mm (a 71.44% reduction), and the stratification number (Str) reaches an optimal 1.00. In terms of macroscopic structural optimization, a height-to-diameter (H/D) ratio between 2 and 4 provides the best balance of stratification stability and cold storage efficiency. Mechanistically, integrating a uniform flow plate effectively suppresses thermal jet disturbances. During the initial discharge stage, a plate with a 10% perforation ratio reduces the thermocline thickness by 69.12% (from 421 mm to 130 mm) relative to the no-plate baseline. The optimal flow plate configuration was identified as a 10% perforation rate, a 20 mm aperture, and an installation spacing of 1.25% of the tank height. Ultimately, this study validates the substantial potential of MPCMS through robust quantitative data, providing a solid theoretical foundation and precise design guidelines for high-efficiency cold storage systems. Full article

(This article belongs to the Special Issue Phase Change Materials for Thermal Energy Storage: Advances and Applications)

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21 pages, 3411 KB

Open AccessArticle

Evaluation of Impacts of Historical and Future Climates on Designing Residential Buildings—Case Study of GCC Region

by Aysha Ramadhan, Joe Huang and Moncef Krarti

Energies 2026, 19(9), 2235; https://doi.org/10.3390/en19092235 - 5 May 2026

Viewed by 651

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section B1: Energy and Climate Change)

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26 pages, 3481 KB

Open AccessArticle

Multi-Objective Optimal Dispatch of Integrated Energy Systems Under Tiered Carbon Pricing: From Economic Arbitrage to Carbon Buffering

by Qi Han, Jingyuan Bian, Xiaojing Bai, Jingxin Wei and Shuang Tian

Energies 2026, 19(9), 2234; https://doi.org/10.3390/en19092234 - 5 May 2026

Viewed by 682

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

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18 pages, 4848 KB

Open AccessArticle

Static Synchronous Stability Analysis of Synchronous Condensers Based on the Simplified Heffron–Phillips Model

by Yong Meng, Yuanfei Lin, Xingwei Xu, Yugang Bao and Yibo Zhou

Energies 2026, 19(9), 2233; https://doi.org/10.3390/en19092233 - 5 May 2026

Viewed by 444

Abstract

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 [...] Read more.

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. Full article

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44 pages, 10656 KB

Open AccessArticle

A Detailed Analysis of Long-Term Modelling Method of Power-to-Gas Hydrogen Generation Using Curtailed Wind Energy

by Abdussalam A. Aburziza, Mobin Naderi and Daniel T. Gladwin

Energies 2026, 19(9), 2232; https://doi.org/10.3390/en19092232 - 5 May 2026

Viewed by 640

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue The Future of Renewable Energy—3rd Edition)

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21 pages, 4372 KB

Open AccessFeature PaperArticle

Physics-Informed Domain Adaptation for Stator Inter-Turn Short Circuit Diagnosis in Synchronous Machines Using Excitation Current Signatures

by Jarosław Kozik

Energies 2026, 19(9), 2231; https://doi.org/10.3390/en19092231 - 5 May 2026

Viewed by 575

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Artificial Neural Network Applications in the Simulation Modeling, Control, and Fault Diagnosis of Energy Systems)

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21 pages, 1311 KB

Open AccessArticle

Interpretable Multi-Sensor Fusion for Short-Term Energy Consumption Forecasting

by Rakibul Hasan, Majdi Mansouri, Jura Arkhangelski and Mahamadou Abdou Tankari

Energies 2026, 19(9), 2230; https://doi.org/10.3390/en19092230 - 5 May 2026

Cited by 2 | Viewed by 648

Abstract

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 [...] Read more.

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

R^{2}

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. Full article

(This article belongs to the Special Issue Intelligent, Data-Driven, and Trustworthy Methods for Energy System Optimization, Monitoring, and Fault Diagnosis)

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19 pages, 954 KB

Open AccessArticle

Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach

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

Energies 2026, 19(9), 2229; https://doi.org/10.3390/en19092229 - 5 May 2026

Viewed by 675

Abstract

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 [...] Read more.

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. Full article

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27 pages, 5866 KB

Open AccessArticle

Power System Risk Specified Operational Scenario Generation Based on Conditional Generative Adversarial Networks

by Bo Zhou, Yunyang Xu, Xinwei Sun, Congkai Huang and Yikui Liu

Energies 2026, 19(9), 2228; https://doi.org/10.3390/en19092228 - 5 May 2026

Cited by 1 | Viewed by 499

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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24 pages, 5067 KB

Open AccessArticle

Online Measured Impedance-Assisted State-of-Charge Estimation for Lithium-Ion Batteries Under Low Excitation Conditions via Fractional-Order Modeling

by Zheng Chen, Yanlong Li, Chaohou Liu, Yuying Wu, Lei Wang, Yousu Yao and Jian Li

Energies 2026, 19(9), 2227; https://doi.org/10.3390/en19092227 - 5 May 2026

Viewed by 644

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Modeling and State Estimation Technologies for Next-Generation Battery Management)

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25 pages, 1850 KB

Open AccessArticle

Performance Analysis of E-, F- and H-Class Gas Turbines with Pressure-Gain Combustion in Simple- and Combined-Cycle Operation

by Antonio Giuffrida and Paolo Chiesa

Energies 2026, 19(9), 2226; https://doi.org/10.3390/en19092226 - 4 May 2026

Cited by 1 | Viewed by 1006

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section B: Energy and Environment)

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15 pages, 277 KB

Open AccessArticle

Assessing the Key Mediating and Moderating Factors in the Renewable Energy Generation and Financial Institution Development Nexus Among African Economies

by Lumengo Bonga-Bonga and Frederich Kirsten

Energies 2026, 19(9), 2225; https://doi.org/10.3390/en19092225 - 4 May 2026

Cited by 1 | Viewed by 685

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section A: Sustainable Energy)

18 pages, 12795 KB

Open AccessArticle

Quantitative Contribution Effect Analysis of Working Fluid Viscosity on COP of High-Temperature Heat Pump Systems

by Hanchi Xu and Na Deng

Energies 2026, 19(9), 2224; https://doi.org/10.3390/en19092224 - 4 May 2026

Viewed by 458

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section A: Sustainable Energy)

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43 pages, 4968 KB

Open AccessArticle

Nonlinear Dynamics and Spatial Correlation Pattern of the Digital Economy on Energy Efficiency: Evidence from Ensemble Learning and Spatio-Temporal Graph Neural Network

by Rui Cao, Chenjun Zhang, Xiangyang Zhao and Yanan Deng

Energies 2026, 19(9), 2223; https://doi.org/10.3390/en19092223 - 4 May 2026

Viewed by 505

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Economic and Technological Advances Shaping the Energy Transition)

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15 pages, 3303 KB

Open AccessArticle

Study on the Electroacoustic Pulse Method for Space Charge Recovery Algorithm Considering Temperature Gradient Aging

by Jia Chu, Yanqing Li, Heng Yang and Tao Han

Energies 2026, 19(9), 2222; https://doi.org/10.3390/en19092222 - 4 May 2026

Viewed by 577

Abstract

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. [...] Read more.

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. Full article

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27 pages, 3967 KB

Open AccessArticle

A Nonlinear Strong-Contraction-Criterion-Based Voltage Stability Analysis for Renewable Energy Bases with Coupled Reactive-Power Resources

by Pengyu Wu, Da Xie and Yanchi Zhang

Energies 2026, 19(9), 2221; https://doi.org/10.3390/en19092221 - 4 May 2026

Viewed by 422

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

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28 pages, 4741 KB

Open AccessArticle

A Decision-Support Framework for Techno-Economic and Environmental Assessment of Hybrid Rooftop PV and Dome-Integrated BIPV Under Harsh Climatic Conditions

by Mohammed A. AlAqil

Energies 2026, 19(9), 2220; https://doi.org/10.3390/en19092220 - 4 May 2026

Cited by 1 | Viewed by 842

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Simulation and Optimisation for Operational Decision-Making in Renewable Energy Supply Chains and Systems)

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40 pages, 13673 KB

Open AccessReview

Advances in Tunnel Kiln Technology for Sustainable Ceramic Manufacturing: Heat Transfer, Energy Efficiency, and Digital Optimization

by Hassanein A. Refaey and Bandar Awadh Almohammadi

Energies 2026, 19(9), 2219; https://doi.org/10.3390/en19092219 - 3 May 2026

Viewed by 1056

Abstract

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. [...] Read more.

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. Full article

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