Energies

23 pages, 2289 KB

Open AccessArticle

Impact of Hydraulic Fracture Conductivity Modeling on Well Performance and Development in Unconventional Reservoirs

by Cecilia Gachanja, Zeinab Zargar, Seyed Mahdi Razavi and S. M. Farouq Ali

Energies 2026, 19(11), 2498; https://doi.org/10.3390/en19112498 (registering DOI) - 22 May 2026

Reservoir simulation of unconventional resources has become increasingly important due to advancing technologies and the growing demand for efficient hydrocarbon recovery. These reservoirs present distinct challenges related to characterization, multiphase flow behavior, and production forecasting. Reservoir simulation workflows integrate geological, petrophysical, and fluid-property [...] Read more.

Reservoir simulation of unconventional resources has become increasingly important due to advancing technologies and the growing demand for efficient hydrocarbon recovery. These reservoirs present distinct challenges related to characterization, multiphase flow behavior, and production forecasting. Reservoir simulation workflows integrate geological, petrophysical, and fluid-property data to better represent subsurface behavior. As hydraulic fractures provide the primary pathways for fluid flow in unconventional reservoirs, accurately modeling fracture geometry and conductivity is essential for reliable reserves estimation, production forecasting, and field development planning. Fracture conductivity is controlled by several coupled processes, including fracture propagation, proppant transport, proppant placement, and stress-dependent permeability, which makes its accurate estimation challenging. Although commercial hydraulic fracturing software incorporates geomechanical and particle-transport models to predict conductivity, their outputs are not always directly compatible with reservoir simulation models and often fail to reproduce observed production trends. This study implements various conductivity models in commercial software and evaluates their performance within reservoir simulation. A fracture model built from publicly available data is integrated into a mechanistic reservoir simulation framework to assess the impact of different conductivity models on pressure distribution, depletion behavior, and production profiles. The results provide practical insights into conductivity modeling approaches that balance realism with applicability in reservoir simulation workflows. Different conductivity models, including constant, expanded conductive zone, linear, and multiple exponential forms, were evaluated. It was found that conductivity variation directly impacts well profiles, ultimate recoveries and well spacing in unconventional reservoir development. Full article

(This article belongs to the Section H1: Petroleum Engineering)

19 pages, 3188 KB

Open AccessArticle

Investigation of Fatigue Failure and Electrical Insulation Properties of Glass Fiber-Reinforced Epoxy Resin (EPGF) Composites Under Different Temperatures

by Bowen Xu, Jinghan Wang, Chenglu Wang and Chen Cao

Energies 2026, 19(11), 2497; https://doi.org/10.3390/en19112497 (registering DOI) - 22 May 2026

Abstract

This study investigates the influence of temperature on the bending properties, fatigue life, and breakdown voltage of glass fiber/epoxy composites (EPGF). The three-point bending tests were conducted at room temperature (RT) and 60 °C, and the bending fatigue tests were carried out under [...] Read more.

This study investigates the influence of temperature on the bending properties, fatigue life, and breakdown voltage of glass fiber/epoxy composites (EPGF). The three-point bending tests were conducted at room temperature (RT) and 60 °C, and the bending fatigue tests were carried out under three displacement amplitudes (0.80, 0.75, 0.70). At the same time, fatigue life prediction was conducted using the Weibull distribution fitting, microscopic structure analysis by scanning electron microscopy (SEM), and breakdown voltage tests in accordance with the GB/T1408-2006 standard. The results show that at 60 °C, the ultimate bending strength and flexural modulus of EPGF decreased by 52.67% and 65.45%, respectively. At high displacement amplitudes (S = 0.80, 0.75), 60 °C leads to a sharp rise in data dispersion with the coefficient of variation (CV) surging by 1.56 and 2.32 times separately. S and temperature exert a significant synergistic degradation effect on fatigue life, and the two-parameter Weibull distribution (R² > 0.85) can well characterize the fatigue life of EPGF. In terms of dielectric properties, 60 °C reduces the initial breakdown voltage of EPGF by 4.23% (p < 0.05). Fatigue damage causes a continuous drop in breakdown voltage. At RT with 80% damage, the reduction rate increases from 16.28% to 26.95% as S rises, showing a synergistic characteristic between amplitude and fatigue damage. Moreover, 60 °C only affects the initial breakdown voltage and has no significant effect on the fatigue-induced decrease in breakdown voltage. SEM observations indicate that 60 °C induces matrix cracking, fiber curling and interfacial debonding in EPGF. This study provides key experimental data and theoretical support for the fatigue life prediction and insulation performance evaluation of EPGF under different temperature fatigue conditions. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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22 pages, 1239 KB

Open AccessArticle

Federated Learning-Based Distributed Solar Forecasting for Smart Buildings in Muscat, Oman Using GRU Networks

by Mazhar Baloch, Mohamed Shaik Honnurvali, Touqeer Ahmed, Abdul Manan Sheikh and Sohaib Tahir Chaudhary

Energies 2026, 19(11), 2496; https://doi.org/10.3390/en19112496 (registering DOI) - 22 May 2026

Abstract

The present paper suggests a federated learning-based distributed solar forecasting model based on gated recurrent unit (GRU) networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models [...] Read more.

The present paper suggests a federated learning-based distributed solar forecasting model based on gated recurrent unit (GRU) networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models able to manage geographically dispersed and statistically heterogeneous data. The suggested solution will include federated learning and GRU networks to train a global forecasting model across several smart buildings and avoid the exchange of raw energy data to overcome these challenges. The local GRU models are trained on local PV generation data and only parameters of the model are relayed to a central aggregation server. This provides privacy of data without compromising the effectiveness of collaborative learning. The proposed framework is tested in a variety of realistic scenarios such as scalability analysis, non-identically distributed (non-IID) data, client dropout, communication constraints, seasonal variability, and privacy saving noise injection. Simulation outcomes show that the proposed FL-GRU model presents a final RMSE of 0.129, MAE of 0.100 and forecasting accuracy of 97%. When increasing the number of clients involved in the process, 2 to 10, RMSE decreases to 0.129, which supports the high scalability advantages. In non-IID scenarios, RMSE ranges between 0.129 and 0.167, and even with half of the clients dropping, the system is robust with an RMSE of 0.172. The proposed FL-GRU is better than the benchmark models, Local GRU, centralized GRU, FL-LSTM, and FL-ANN with a maximum improvement of 22.29% in RMSE reduction. Also, the best predictive consistency is found with correlation analysis with R² = 0.957. On the whole, the suggested approach can offer an efficient, privacy-aware, and scalable solution to distributed solar energy prediction in smart cities. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Photovoltaic Energy Systems)

23 pages, 16803 KB

Open AccessArticle

Design and Implementation of a High-Power-Density DC Power Supply Based on a Novel Integration of Z-Source and Isolated Full-Bridge DC–DC Converter Topologies

by Mehmet Akif Ozdemir, Ali Shan, Emrullah Aydin, Bulent Dag, Bunyamin Tamyurek and Mehmet Timur Aydemir

Energies 2026, 19(11), 2494; https://doi.org/10.3390/en19112494 (registering DOI) - 22 May 2026

Abstract

High-voltage DC (HVDC) power supplies are essential for several advanced applications, including medical imaging, aerospace systems, and additive manufacturing. Traditional HVDC supplies often suffer from performance limitations due to high transformer turn ratios, which increased stray capacitance and degraded inverter performance. This paper [...] Read more.

High-voltage DC (HVDC) power supplies are essential for several advanced applications, including medical imaging, aerospace systems, and additive manufacturing. Traditional HVDC supplies often suffer from performance limitations due to high transformer turn ratios, which increased stray capacitance and degraded inverter performance. This paper proposes a novel two-stage HVDC power supply architecture that addresses these challenges by combining a Z-source converter with a full-bridge inverter, both enabled by high-performance Silicon Carbide (SiC) devices. The first stage boosts the rectified line voltage to 2 kV using a Z-source topology and inverts it at high frequency, while the second stage employs a high-voltage, high-frequency (HVHF) transformer and a voltage doubler to achieve a regulated 10 kV DC output. Simulation results using PLECS and experimental validation demonstrate the effectiveness of the proposed design in minimizing the reflected capacitance, enabling constant-frequency operation at the boundary of continuous conduction mode for improved efficiency, and providing high power density and compactness. This approach offers a promising solution for high-efficiency, high-voltage applications. Full article

(This article belongs to the Collection Advanced Control and Applications of Power Electronics and Power Converters)

30 pages, 1536 KB

Open AccessArticle

Behaviorally Aware Pricing of Energy Storage as a Service Platform: A Prospect Theory-Based Bi-Level Framework

by Seyed Shahin Parvar, Nima Amjady and Hamidreza Zareipour

Energies 2026, 19(11), 2493; https://doi.org/10.3390/en19112493 (registering DOI) - 22 May 2026

Abstract

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, [...] Read more.

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

(This article belongs to the Special Issue Modeling and Optimization of Energy Storage in Power Systems)

15 pages, 1555 KB

Open AccessArticle

Comparative Assessment of Kaolin Addition and Acid Washing for Fouling Mitigation in Alkali-Rich Kenaf Biomass

by Joo Chang Park, Tae-Jin Kang and Sang-Phil Yoon

Energies 2026, 19(11), 2491; https://doi.org/10.3390/en19112491 - 22 May 2026

Abstract

Herbaceous biomass is a promising renewable energy resource, but its use in thermochemical systems is often limited by severe fouling and ash agglomeration resulting from alkali-rich ash chemistry. This study directly compares two practical fouling mitigation strategies, kaolin addition and acid washing, for [...] Read more.

Herbaceous biomass is a promising renewable energy resource, but its use in thermochemical systems is often limited by severe fouling and ash agglomeration resulting from alkali-rich ash chemistry. This study directly compares two practical fouling mitigation strategies, kaolin addition and acid washing, for alkali-rich torrefied kenaf biomass under identical experimental conditions. The study quantitatively distinguishes aluminosilicate-based alkali stabilization from pretreatment-based alkali removal as two distinct pathways for controlling ash transformation. Kenaf exhibited severe ash agglomeration and contained high levels of K₂O (17.38 wt.%), CaO (31.52 wt.%), MgO (14.98 wt.%), SO₃ (9.43 wt.%), and P₂O₅ (6.90 wt.%). Kaolin addition progressively shifted the ash composition toward a SiO₂–Al₂O₃-rich system. From KA-10 to KA-30, SiO₂ increased from 22.86 to 33.58 wt.%, while Al₂O₃ increased from 7.65 to 15.43 wt.%. X-ray diffraction (XRD) analysis further showed that increasing kaolin addition suppressed alkali-salt phases and promoted the formation of aluminum-silicate phases. In contrast, acid washing directly reduced alkali species, decreasing K₂O to 5.66–7.83 wt.% and eliminating detectable Na₂O. The acid-washed samples were characterized by calcium-rich sulfate and silicate phases, indicating a distinct ash transformation pathway. Kaolin addition primarily reduced fouling by promoting aluminosilicate-based alkali stabilization, whereas acid washing reduced alkali–metal contents before thermal treatment. This distinction clarifies the different roles of additive-based and pretreatment-based strategies for fouling control in alkali-rich herbaceous biomass. Full article

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23 pages, 6291 KB

Open AccessArticle

Comprehensive Phase-Shift Control for Zero-Circulating Operation of Triple Active Bridge Converters in Dual-EV Charging

by Afraz Ahmad, Ilamparithi Thirumarai Chelvan and Waqas Hassan

Energies 2026, 19(11), 2490; https://doi.org/10.3390/en19112490 - 22 May 2026

Abstract

A triple active bridge (TAB) converter used for simultaneous fast charging of two dissimilar EVs can exhibit significant circulating power under asymmetric port voltages and power levels. This internal power exchange increases losses and current stress and limits the effectiveness of conventional magnetic [...] Read more.

A triple active bridge (TAB) converter used for simultaneous fast charging of two dissimilar EVs can exhibit significant circulating power under asymmetric port voltages and power levels. This internal power exchange increases losses and current stress and limits the effectiveness of conventional magnetic design optimization. This paper develops a generalized five-variable phase-shift model of the TAB and formulates explicit zero-circulating-power conditions that characterize non-circulating operating points in asymmetric dual-EV charging. Based on this formulation, a decoupled control law is synthesized that assigns the five phase-shift variables to suppress circulating power while independently regulating the power delivered to each EV port over a wide operating range, without requiring specialized transformer or leakage-inductance design. Results from representative dynamic dual-EV charging scenarios demonstrate 15% reduction in RMS current stress compared with conventional phase-shift control. Full article

(This article belongs to the Special Issue High-Efficiency Power Conversion and Power Quality in Future Grids)

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24 pages, 3332 KB

Open AccessArticle

Life-Cycle Techno-Economic Optimization of Complex-Terrain Wind Farms

by Xin Wang and Fashe Li

Energies 2026, 19(11), 2489; https://doi.org/10.3390/en19112489 - 22 May 2026

Abstract

To address the poor quality of early-stage wind measurement data and the limited representativeness of short-term observations for long-term climatic conditions in mountainous wind farms, this study takes a 150 MW wind power project in Guangxi, China, as a case study and proposes [...] Read more.

To address the poor quality of early-stage wind measurement data and the limited representativeness of short-term observations for long-term climatic conditions in mountainous wind farms, this study takes a 150 MW wind power project in Guangxi, China, as a case study and proposes an integrated framework of “stepwise data fusion-key parameter refinement-life-cycle techno-economic optimization”. For wind resource assessment, a two-stage fusion strategy combining same-mast correlation-based infilling and mesoscale data extrapolation was developed, effectively resolving the heterogeneous data quality among six meteorological masts and revealing significant spatial variations in the wind shear exponent (0.058–0.348). Based on a conservative criterion, the 50-year return-period maximum wind speed was determined to be 31.4 m/s. For turbine selection, the levelized cost of energy was adopted as the core evaluation metric to compare six turbine models rated at 6.0–6.25 MW. The results show that WTG5-200-6.25 is the optimal option, with a levelized cost of energy (LCOE) of 0.321 CNY/kWh, an annual grid-connected electricity generation of 269.915 GWh, and 1799 equivalent full-load hours. In addition, the project can save 82.9 thousand tons of standard coal annually and yield approximately CNY 311 million in carbon-trading revenue over 25 years. The proposed framework provides a useful reference for wind power projects in complex terrain. Full article

(This article belongs to the Special Issue Advances in Electric Power Systems and Renewable-Energy-Integrated Grids)

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Energies, Volume 19, Issue 11 (June-1 2026) – 8 articles

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