Energies

18 pages, 2459 KB

Open AccessArticle

Effect of Moisture and Aging of Kraft Paper Immersed in Mineral Oil and Synthetic Ester on Bubbling Inception Temperature in Power Transformers

by Ghada Gmati, Issouf Fofana, Patrick Picher, Oscar Henry Arroyo-Fernàndez, Djamal Rebaine, Fethi Meghnefi, Youssouf Brahami and Kouba Marie Lucia Yapi

Energies 2025, 18(17), 4579; https://doi.org/10.3390/en18174579 (registering DOI) - 29 Aug 2025

Abstract

Bubbling Inception Temperature (BIT) is a critical metric that indicates the temperature at which gas bubbles form due to cellulose decomposition in a paper–oil insulation system. It serves as a key indicator of the thermal stability of transformer insulation, offering valuable insights into [...] Read more.

Bubbling Inception Temperature (BIT) is a critical metric that indicates the temperature at which gas bubbles form due to cellulose decomposition in a paper–oil insulation system. It serves as a key indicator of the thermal stability of transformer insulation, offering valuable insights into its performance under elevated temperatures. Building on findings from a companion study that examined the BIT of Kraft paper (KP), thermally upgraded Kraft paper (TUK), and aramid paper in mineral oil, this research expands the analysis to assess the impact of moisture, aging, and alternative dielectric fluids. Using the same customized experimental setup featuring precise dynamic load control, real-time bubble detection, and continuous monitoring of moisture and temperature, this study evaluates BIT across four distinct oil–paper aging stages: new (0 h) and 2 weeks, 4 weeks, and 6 weeks of accelerated thermal aging. This approach enables a comparative analysis of BIT in various paper–oil systems, focusing on both mineral oil and synthetic esters, as well as the influence of different moisture levels in the paper insulation. The results show that BIT decreases with aging, indicating reduced thermal stability. Furthermore, KP impregnated with synthetic ester exhibits a higher BIT than when impregnated with mineral oil, suggesting that synthetic esters may offer better resistance to bubble formation under thermal stress. Based on these results, empirical BIT models were developed as a function of degree of polymerization (DP) and water content in paper (WCP). This study further demonstrates how these models can be applied to quantify safety margins under emergency overloading conditions, providing a practical tool for operational decision-making in transformer thermal risk management. Full article

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20 pages, 1786 KB

Open AccessArticle

Characteristics of Domestic Hot Water Consumption Profiles in Multi-Family Buildings for Energy Modeling Purposes

by Agnieszka Chmielewska

Energies 2025, 18(17), 4578; https://doi.org/10.3390/en18174578 - 29 Aug 2025

Abstract

This paper presents a domestic hot water (DHW) consumption model for multi-family residential buildings that captures demand variability across monthly, daily, and hourly timescales. The model enables both the disaggregation of annual consumption for dynamic simulations and the generation of synthetic yet realistic [...] Read more.

This paper presents a domestic hot water (DHW) consumption model for multi-family residential buildings that captures demand variability across monthly, daily, and hourly timescales. The model enables both the disaggregation of annual consumption for dynamic simulations and the generation of synthetic yet realistic DHW load profiles when detailed measurements are unavailable. It is developed from a dataset of 42 buildings containing 1376 apartments. The analysis identifies seasonal, weekly, and hourly usage patterns, reflecting the influence of apartment layout, building size, and user behavior under Polish climatic and cultural conditions. The proposed model reproduces seasonal deviations of up to 23%, with average daily demand falling to 77% of the annual mean in August and rising above the yearly average during winter months. It also captures weekly variability, with weekend consumption exceeding weekday levels by more than 16%. On working days, the hourly profile exhibits a clear dual-peak structure, with approximately 18% of daily demand occurring in the morning and up to 45% in the evening, whereas weekends show a flatter distribution without pronounced peaks. These results provide a robust basis for more accurate demand representation in energy modeling, system design, and optimization under local conditions. Full article

(This article belongs to the Special Issue Smart and Energy Efficient HVAC Systems Towards Building Decarbonizations)

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19 pages, 7454 KB

Open AccessArticle

SOC Balancing Control Strategy for Multiple Storage Units Based on Battery Life Degradation Characteristics

by Guiquan Chen, Xiangyang Xia, Dan Lu, Ting Ouyang, Xiaoyue Zhao, Nanlan Wang, Naitong Liu, Xianliang Luo and Yichong Luo

Energies 2025, 18(17), 4577; https://doi.org/10.3390/en18174577 (registering DOI) - 29 Aug 2025

Abstract

To resolve the issue of state of charge (SOC) inconsistency among energy storage units under traditional equal-power allocation strategies, this paper proposes a multi-unit SOC balancing control strategy based on battery life degradation characteristics. Prior to system operation, the proposed strategy optimizes power [...] Read more.

To resolve the issue of state of charge (SOC) inconsistency among energy storage units under traditional equal-power allocation strategies, this paper proposes a multi-unit SOC balancing control strategy based on battery life degradation characteristics. Prior to system operation, the proposed strategy optimizes power distribution according to each unit’s state of health (SOH) and predefined depth of discharge (DOD), ensuring SOC balance at the end of each charge–discharge cycle. Simulation and experimental results demonstrate that, compared with traditional equal-power distribution control, the proposed strategy significantly improves capacity utilization and extends the overall system lifetime. For instance, in Simulation Scenario 1, the available capacity per cycle is increased by 8.14%, and the overall system lifetime is prolonged by 11.04%. Furthermore, the strategy eliminates the need for dynamic power redistribution, thus reducing communication overheads and effectively meeting engineering requirements for SOC balancing. This research provides valuable insights for the safe and economical operation of energy storage power stations. Full article

(This article belongs to the Section D: Energy Storage and Application)

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27 pages, 1853 KB

Open AccessArticle

DynaG Algorithm-Based Optimal Power Flow Design for Hybrid Wind–Solar–Storage Power Systems Considering Demand Response

by Xuan Ruan, Lingyun Zhang, Jie Zhou, Zhiwei Wang, Shaojun Zhong, Fuyou Zhao and Bo Yang

Energies 2025, 18(17), 4576; https://doi.org/10.3390/en18174576 - 28 Aug 2025

Abstract

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm [...] Read more.

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm (DynaG) for a multi-energy complementary distribution network incorporating wind power, photovoltaic, and energy storage systems. A multi-scenario OPF model is developed considering the time-varying characteristics of wind and solar penetration (low/medium/high), seasonal load variations, and demand response participation. The model aims to minimize both network loss and operational costs, while simultaneously optimizing power supply capability indicators such as power transfer rates and capacity-to-load ratios. Key enhancements to DynaG algorithm include the following: (1) an adaptive gravitational constant adjustment strategy to balance global exploration and local exploitation; (2) an inertial mass updating mechanism constrained to improve convergence for high-dimensional decision variables; and (3) integration of chaotic initialization and dynamic neighborhood search to enhance solution diversity under complex constraints. Validation using the IEEE 33-bus system demonstrates that under 30% penetration scenarios, the proposed DynaG algorithm reduces capacity ratio volatility by 3.37% and network losses by 1.91% compared to non-dominated sorting genetic algorithm III (NSGA-III), multi-objective particle swarm optimization (MOPSO), multi-objective atomic orbital search algorithm (MOAOS), and multi-objective gravitational search algorithm (MOGSA). These results show the algorithm’s robustness against renewable fluctuations and its potential for enhancing the resilience and operational efficiency of high-penetration renewable energy distribution networks. Full article

(This article belongs to the Special Issue Modern Technologies for Renewable Energy Development and Utilization: 5th Edition)

33 pages, 1580 KB

Open AccessArticle

Selection and Classification of Small Wind Turbines for Local Energy Systems: Balancing Efficiency, Climate Conditions, and User Comfort

by Waldemar Moska, Leszek Piechowski and Andrzej Łebkowski

Energies 2025, 18(17), 4575; https://doi.org/10.3390/en18174575 - 28 Aug 2025

Abstract

Micro and small wind turbines (MAWTs) are increasingly integrated into residential and prosumer hybrid energy systems. However, their real-world performance often falls short of catalog specifications due to mismatched wind resources, siting limitations, and insufficient attention to human comfort. This paper presents a [...] Read more.

Micro and small wind turbines (MAWTs) are increasingly integrated into residential and prosumer hybrid energy systems. However, their real-world performance often falls short of catalog specifications due to mismatched wind resources, siting limitations, and insufficient attention to human comfort. This paper presents a comprehensive decision-support framework for selecting the type and scale of MAWTs under actual local conditions. The energy assessment module combines aerodynamic performance scaling, wind speed-frequency modeling based on Weibull distributions, turbulence intensity adjustments, and component-level efficiency factors for both horizontal and vertical axis turbines. The framework addresses three key design objectives: efficiency—aligning turbine geometry and control strategies with local wind regimes to maximize energy yield; comfort—evaluating candidate designs for noise emissions, shadow flicker, and visual impact near buildings; and climate adaptation—linking turbine siting, hub height, and rotor type to terrain roughness, turbulence, and built environment characteristics. Case studies from low and moderate wind locations in Central Europe demonstrate how multi-criteria filtering avoids oversizing, improves the autonomy of hybrid PV–wind systems, and identifies configurations that may exceed permissible limits for noise or flicker. The proposed methodology enables evidence-based deployment of MAWTs in decentralized energy systems that balance technical performance, resilience, and occupant well-being. Full article

(This article belongs to the Special Issue Designing for Both Efficiency and Resilience: Harmonizing Energy, Comfort, and Climate Adaptation)

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20 pages, 5833 KB

Open AccessArticle

Power Factor Adaptive DPWM Control Strategy for T-Type Three-Level Inverters

by Jialiang Tian, Yingying Xu, Mingxia Xu, Zhenjiang Liu and Yuchi Zhou

Energies 2025, 18(17), 4574; https://doi.org/10.3390/en18174574 - 28 Aug 2025

Abstract

With the widespread application of multilevel inverters, device losses have become a critical area of research. A key limitation of conventional three-level discontinuous pulse width modulation (DPWM) strategies is their inability to maintain switching device clamping during the peak intervals of the load [...] Read more.

With the widespread application of multilevel inverters, device losses have become a critical area of research. A key limitation of conventional three-level discontinuous pulse width modulation (DPWM) strategies is their inability to maintain switching device clamping during the peak intervals of the load current, especially under varying load power factor conditions, thereby reducing switching losses. This paper proposes an improved three-level power factor adaptive DPWM (PFA-DPWM) strategy that minimizes switching losses by clamping the power devices during the one-third fundamental period of maximum load current. First, a unified mathematical model of DPWM strategies is established. Theoretical analysis demonstrates that phase disposition (PD) carrier modulation for three-level inverter exhibits superior line voltage harmonic characteristics. Based on this, a theoretical comparison of switching losses and harmonic distortion for various DPWM schemes is conducted. The proposed PFA-DPWM control strategy has the minimum switching loss without compromising harmonic performance. The efficacy and validity of the proposed strategy are confirmed by comprehensive simulation and experimental results. Full article

(This article belongs to the Special Issue Advanced Power Electronics Technology: 2nd Edition)

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17 pages, 1155 KB

Open AccessArticle

Modeling and Validation of High-Pressure Hydrogen Joule-Thomson Effect for Enhanced Hydrogen Energy System Safety

by Mu-Yao Zhou, Yi Fang, Qian-Hua Wang, Yi-Ming Dai, Zhan-Hao Liu, Ji-Qiang Li and Jeong-Tae Kwon

Energies 2025, 18(17), 4573; https://doi.org/10.3390/en18174573 - 28 Aug 2025

Abstract

With the rapid development of hydrogen fuel cell vehicles, the research on the throttling effect of high-pressure hydrogen is crucial to the safety of hydrogen circulation systems for fuel cells. This paper studies the Joule-Thomson coefficients (

μ_{J T}

) of ten [...] Read more.

With the rapid development of hydrogen fuel cell vehicles, the research on the throttling effect of high-pressure hydrogen is crucial to the safety of hydrogen circulation systems for fuel cells. This paper studies the Joule-Thomson coefficients (

μ_{J T}

) of ten gas state equations. The four equations, Van Der Waals (VDW), Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), and Beattie Bridgeman (BB), were selected for calculation. These were compared with the database of the National Institute of Standards and Technology (NIST), aiming to determine the optimal state equation under different temperature and pressure conditions. The empirical formula of the

μ_{J T}

pressure and temperature was compounded, and the temperature rise effect was further calculated using the empirical formula of compounding. The results show that the calculated value of

μ_{J T}

by using the VDW equation in the low-pressure range (0–2 MPa) is closer to the value in the NIST database with an error less than 0.056

K ∙ {M P a}^{- 1}

. The tendency of

μ_{J T}

described by the RK equation corresponds to the NIST database; meanwhile, the maximum error in the SRK equation is 0.143916

K ∙ {M P a}^{- 1}

. The BB equation is more applicable within the pressure range of 20 to 50 MPa with a maximum error of 0.042853

K ∙ {M P a}^{- 1}

. The fitting error of the empirical formula is within 9.52%, and the relative error of the calculated temperature rise is less than 4%. This research might provide several technical ideas for the study of the throttling effect of hydrogen refueling stations and the hydrogen circulation system of on-board hydrogen fuel cells. Full article

(This article belongs to the Special Issue Towards Sustainable Hydrogen Production: Integrating Technologies and Carbon Capture, Utilization and Storage for Industrial and Energy Applications)

18 pages, 3161 KB

Open AccessArticle

Experimental Validation of Manufacturable Edgewise Winding Solutions Considering Parallel Slot and Parallel Tooth Stator Structures

by Ellis George, Adam Walker, Fengyu Zhang, Gaurang Vakil and Chris Gerada

Energies 2025, 18(17), 4572; https://doi.org/10.3390/en18174572 - 28 Aug 2025

Abstract

High-power-density electric machines play a key role in decarbonising transportation technologies. A critical component of the movement towards high-performance machines is the structure and manufacture of the windings, as this is the dominant source of machine loss. Manufacturing time is important to the [...] Read more.

High-power-density electric machines play a key role in decarbonising transportation technologies. A critical component of the movement towards high-performance machines is the structure and manufacture of the windings, as this is the dominant source of machine loss. Manufacturing time is important to the effectiveness of the production line, with equivalent importance to the electromagnetic and thermal characteristics. Edgewise windings are increasingly considered to have high potential to be quickly and automatically manufactured. However, they are rarely studied considering all the aspects, these being electromagnetic, thermal, and manufacturing characteristics. This paper will experimentally assess the performance of edgewise machines compared to a stranded winding machine, covering all the aforementioned aspects. Two edgewise winding types are considered, parallel slot and parallel tooth. Firstly, a baseline 11 kW stranded winding machine will be introduced, then two edgewise type machines are proposed to be compared to the baseline machine. These comparisons will initially be made based on simulated torque and thermal performance, then the manufacturing time and quality are assessed for each of the coil structures, showing the achievable time reduction by using edgewise coil structures. Motorettes are used to validate thermal performance of the structures, which are used to calibrate simulation models and evaluate the performance of a full machine equivalent model. Under the thermal limit condition, it is shown that the edgewise parallel tooth windings can achieve a torque increase of 27.8% compared to stranded and 24% compared to edgewise parallel slot. Full article

(This article belongs to the Special Issue Designs and Control of Electrical Machines and Drives)

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38 pages, 12663 KB

Open AccessArticle

A Transformer-Based Hybrid Neural Network Integrating Multiresolution Turbulence Intensity and Independent Modeling of Multiple Meteorological Features for Wind Speed Forecasting

by Hongbin Liu, Ziyan Wang, Yizhuo Liu, Jie Zhou, Chen Chen, Haoyuan Ma, Xi Huang, Hongqing Wang and Xiaodong Ji

Energies 2025, 18(17), 4571; https://doi.org/10.3390/en18174571 - 28 Aug 2025

Abstract

Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), [...] Read more.

Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), the original wind speed series is decomposed into eight pairs of mean wind speeds and turbulence intensities at different time scales, which are then modeled and predicted in parallel using eight independent LSTM sub-models. Unlike traditional methods treating meteorological variables such as air pressure, temperature, and wind direction as static input features, WaveNet, LSTM, and TCN neural networks are innovatively adopted here to independently model and forecast these meteorological series, thoroughly capturing their dynamic influences on wind speed. Finally, a Transformer-based self-attention mechanism dynamically integrates multiple outputs from the four sub-models to generate final wind speed predictions. Experimental results averaged over three datasets demonstrate superior accuracy and robustness, with MAE, RMSE, MAPE, and

R^{2}

values around 0.65, 0.87, 23.24%, and 0.92, respectively, for a 6 h forecast horizon. Moreover, the proposed framework consistently outperforms all baselines across four categories of comparative experiments, showing strong potential for practical applications in wind power dispatching. Full article

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16 pages, 7984 KB

Open AccessArticle

Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer

by Wensheng Luo, Zejian Shu, Ruifang Zhang, Jose I. Leon, Abraham M. Alcaide and Leopoldo G. Franquelo

Energies 2025, 18(17), 4570; https://doi.org/10.3390/en18174570 - 28 Aug 2025

Abstract

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, [...] Read more.

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients. Full article

(This article belongs to the Special Issue Modeling, Scheduling, Optimising, and Control of Power Grids and Integrated Energy Systems)

28 pages, 6470 KB

Open AccessReview

A Review of Lithium-Ion Battery Thermal Management Based on Liquid Cooling and Its Evaluation Method

by Hongkai Liu, Chentong Shi, Chenghao Liu and Wei Chang

Energies 2025, 18(17), 4569; https://doi.org/10.3390/en18174569 - 28 Aug 2025

Abstract

Electric vehicles (EVs) provide a feasible solution for the electrification of the transportation sector. However, the large-scale deployment of EVs over wide working conditions is limited by the temperature sensitivity of lithium-ion batteries (LIBs). Therefore, an efficient and reliable battery thermal management system [...] Read more.

Electric vehicles (EVs) provide a feasible solution for the electrification of the transportation sector. However, the large-scale deployment of EVs over wide working conditions is limited by the temperature sensitivity of lithium-ion batteries (LIBs). Therefore, an efficient and reliable battery thermal management system (BTMS) becomes essential to achieve precise temperature control of batteries and prevent potential thermal runaway. Owing to their high heat-transfer efficiency and controllability, liquid-based cooling technologies have become a key research focus in the field of BTMS. In both design and operation, BTMSs are required to comprehensively consider thermal characteristics, energy consumption, economics, and environmental impact, which demands more scientific and rational evaluation criteria. This paper reviews the latest research progress on liquid-based cooling technologies, with a focus on indirect-contact and direct-contact cooling. In addition, existing evaluation methods are summarized. This work proposes insights for future research on liquid-cooled BTMS development in EVs. Full article

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22 pages, 2243 KB

Open AccessArticle

Selective Extraction and Hydrotreatment of Biocrude from Sewage Sludge: Toward High-Yield, Alkane-Rich, Low-Heteroatom Biofuels

by Muhammad Usman, Shuo Cheng, Sasipa Boonyubol, Muhammad Aziz and Jeffrey S. Cross

Energies 2025, 18(17), 4568; https://doi.org/10.3390/en18174568 (registering DOI) - 28 Aug 2025

Abstract

This study investigates the hydrothermal liquefaction (HTL) of sewage sludge across a temperature range of 250–375 °C, combined with selective solvent extraction and catalytic hydrotreatment to produce high-quality biocrude. Four solvents including dichloromethane (DCM), hexane, ethyl butyrate (EB), and ethyl acetate (EA), were [...] Read more.

This study investigates the hydrothermal liquefaction (HTL) of sewage sludge across a temperature range of 250–375 °C, combined with selective solvent extraction and catalytic hydrotreatment to produce high-quality biocrude. Four solvents including dichloromethane (DCM), hexane, ethyl butyrate (EB), and ethyl acetate (EA), were used to evaluate temperature-dependent extraction performance and product quality. Biocrude yields increased from 250 °C to a maximum at 350 °C for all solvents: hexane (9.3–18.1%), DCM (16.3–49.7%), EB (17.6–50.1%), and EA (9.6–23.5%). A yield decline was observed at 375 °C due to secondary cracking and gasification. Elemental analysis revealed that hexane and EB extracts had higher carbon (up to 61.6 wt%) and hydrogen contents, while DCM retained the most nitrogen (up to 3.96 wt%) due to its polarity. Sulfur remained below 0.5 wt% in all biocrudes. GC–MS analysis of 350 °C biocrudes showed fatty acids as dominant components (43–53%), especially palmitic acid, along with ketones, amides, and heterocyclic compounds. Hydrotreatment using Ni/SiO₂–Al₂O₃ significantly enhanced biocrude quality by increasing alkane content by 40–60% and reducing nitrogen levels by up to 75%, with higher heating values reaching 38–44 MJ/kg. These findings demonstrate the integrated potential of HTL process tuning, green solvent extraction, and catalytic upgrading for converting sewage sludge into cleaner, energy-dense biofuels. Full article

(This article belongs to the Special Issue New Trends in Biofuels and Bioenergy for Sustainable Development: 3rd Edition)

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20 pages, 2684 KB

Open AccessArticle

Food Waste Bioconversion Features Depending on the Regime of Anaerobic Digestion

by Marta Zofia Cieślik, Andrzej Jan Lewicki, Wojciech Czekała and Iryna Vaskina

Energies 2025, 18(17), 4567; https://doi.org/10.3390/en18174567 - 28 Aug 2025

Abstract

Approximately one-third of global food production is wasted annually, which contributes significantly to greenhouse gas emissions and economic costs. Anaerobic digestion (AD) is an effective method for converting food waste into biogas, but its efficiency depends on factors such as temperature and substrate [...] Read more.

Approximately one-third of global food production is wasted annually, which contributes significantly to greenhouse gas emissions and economic costs. Anaerobic digestion (AD) is an effective method for converting food waste into biogas, but its efficiency depends on factors such as temperature and substrate composition. This study compared mesophilic and thermophilic AD of selectively collected fruit and vegetable waste, quantifying process efficiency and identifying factors leading to collapse. Studies were performed in 1 dm³ reactors with gradually increasing organic loading rates until process collapse. Process dynamics, stability, and gas yields were assessed through daily biogas measurements and analyses of pH, FOS/TAC ratio, sCOD, ammonia, volatile fatty acids, alcohols, total and volatile solids, and C/N ratio. Research has shown that peak methane yields occurred at OLR = 0.5–1.0 kg VS·m⁻³·d⁻¹, with thermophilic systems producing 0.63–5.48% more methane during stable phases. Collapse occurred at OLR = 3.0 in thermophilic and 4.0 in mesophilic reactors, accompanied by sharp increases in methanol, acetic acid, butyric acid, propionic acid, and FOS/TAC. The pH dropped to 5.49 and 6.09. While thermophilic conditions offered higher methane yields, they were more susceptible to rapid process destabilization due to intermediate metabolite accumulation. Full article

(This article belongs to the Special Issue Biomass and Waste-to-Energy for Sustainable Energy Production)

23 pages, 7910 KB

Open AccessArticle

Coordination of Multiple BESS Units in a Low-Voltage Distribution Network Using Leader–Follower and Leaderless Control

by Margarita Kitso, Bagas Ihsan Priambodo, Joel Alpízar-Castillo, Laura Ramírez-Elizondo and Pavol Bauer

Energies 2025, 18(17), 4566; https://doi.org/10.3390/en18174566 - 28 Aug 2025

Abstract

High shares of photovoltaic energy in low-voltage distribution systems lead to voltage limit violations. Deploying energy storage systems in the network can compensate for the mismatch between the generation and the consumption; nevertheless, the mismatch is unevenly distributed throughout the network, suggesting aggregated [...] Read more.

High shares of photovoltaic energy in low-voltage distribution systems lead to voltage limit violations. Deploying energy storage systems in the network can compensate for the mismatch between the generation and the consumption; nevertheless, the mismatch is unevenly distributed throughout the network, suggesting aggregated control strategies as a solution. This paper proposes two coordination control strategies of batteries to address network overvoltage conditions caused by high penetration of photovoltaic systems. The leader–follower coordination strategy determines a battery’s utilization factor by using the node closest to a voltage violation as a reference. The leaderless control uses a shared utilization factor to avoid excessive usage of a particular agent in the network. We tested both approaches in the 18-node CIGRE network for scenarios when not all agents were available and when they had different starting states-of-charge. Our results demonstrate that both strategies are capable of voltage control; however, the leader–follower control leads to uneven storage usage, ultimately leading to short-time failure to comply with the voltage limits under extreme conditions where neighbouring agents must compensate for the unavailable one. Conversely, the leaderless approach presents more balanced use of the agents thanks to the distributed utilization factor, resulting in a more robust control strategy. Full article

(This article belongs to the Special Issue Development and Modeling of Energy Storage Systems for Future Power Grids)

25 pages, 2541 KB

Open AccessReview

Grid-Forming Converters for Renewable Generation: A Comprehensive Review

by Muhammad Waqas Qaisar and Jingyang Fang

Energies 2025, 18(17), 4565; https://doi.org/10.3390/en18174565 - 28 Aug 2025

Abstract

Grid-forming converters (GFMCs) play an increasingly vital role in integrating renewable energy sources into modern power systems. This article reviews GFMCs, emphasizing their importance in enabling reliable, stable, and resilient operation as power systems evolve toward low-inertia, inverter-dominated configurations. Various GFMC topologies are [...] Read more.

Grid-forming converters (GFMCs) play an increasingly vital role in integrating renewable energy sources into modern power systems. This article reviews GFMCs, emphasizing their importance in enabling reliable, stable, and resilient operation as power systems evolve toward low-inertia, inverter-dominated configurations. Various GFMC topologies are examined based on their suitability for grid-forming functions and performance across different voltage levels. Small-signal modeling approaches are presented to provide deeper insights into system dynamics and converter–grid interactions. The article reviews primary control strategies, including droop control, virtual synchronous machines, and virtual oscillator control, and discusses their impact on synchronization, stability, and power sharing. Finally, the article outlines GFMC applications and challenges, highlighting their impact on system stability. Full article

(This article belongs to the Special Issue Advances in Power Converters and Microgrids)

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19 pages, 2725 KB

Open AccessArticle

Enhancing Photovoltaic Energy Output Predictions Using ANN and DNN: A Hyperparameter Optimization Approach

by Atıl Emre Cosgun

Energies 2025, 18(17), 4564; https://doi.org/10.3390/en18174564 - 28 Aug 2025

Abstract

This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs [...] Read more.

This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs (PanelTemp, Irradiance, AmbientTemp, Humidity), together with physically motivated-derived features (ΔT, IrradianceEff, IrradianceSq, Irradiance × ΔT). Samples acquired under very low irradiance (<50 W m⁻²) were excluded. Predictors were standardized with training-set statistics (z-score), and the target variable was modeled in log space to stabilize variance. A shallow artificial neural network (ANN; single hidden layer, widths {4–32}) was compared with deeper multilayer perceptrons (DNN; stacks {16 8}, {32 16}, {64 32}, {128 64}, {128 64 32}). Hyperparameters were selected with a grid search using validation mean squared error in log space with early stopping; Bayesian optimization was additionally applied to the ANN. Final models were retrained and evaluated on a held-out test set after inverse transformation to watts. Test performance was obtained as MSE, RMSE, MAE, R², and MAPE for the ANN and DNN. Hence, superiority in absolute/squared error and explained variance was exhibited by the ANN, whereas lower relative error was achieved by the DNN with a marginal MAE advantage. Ablation studies showed that moderate depth can be beneficial (e.g., two-layer variants), and a simple bootstrap ensemble improved robustness. In summary, the ANN demonstrated superior performance in terms of absolute-error accuracy, whereas the DNN exhibited better consistency with relative-error accuracy. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

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29 pages, 7926 KB

Open AccessArticle

Application of Artificial Intelligence Methods in the Analysis of the Cyclic Durability of Superconducting Fault Current Limiters Used in Smart Power Systems

by Sylwia Hajdasz, Marek Wróblewski, Adam Kempski and Paweł Szcześniak

Energies 2025, 18(17), 4563; https://doi.org/10.3390/en18174563 - 28 Aug 2025

Abstract

This article presents a preliminary study on the potential application of artificial intelligence methods for assessing the durability of HTS tapes in superconducting fault current limiters (SFCLs). Despite their importance for the selectivity and reliability of power networks, these devices remain at the [...] Read more.

This article presents a preliminary study on the potential application of artificial intelligence methods for assessing the durability of HTS tapes in superconducting fault current limiters (SFCLs). Despite their importance for the selectivity and reliability of power networks, these devices remain at the prototype testing stage, and the phenomena occurring in HTS tapes during their operation—particularly the degradation of tapes due to cyclic transitions into the resistive state—are difficult to model owing to their highly non-linear and dynamic nature. A concept of an engineering decision support system (EDSS) has been proposed, which, based on macroscopically measurable parameters (dissipated energy and the number of transitions), aims to enable the prediction of tape parameter degradation. Within the scope of the study, five approaches were tested and compared: Gaussian process regression (GPR) with various kernel functions, k-nearest neighbours (k-NN) regression, the random forest (RF) algorithm, piecewise cubic hermite interpolating polynomial (PCHIP) interpolation, and polynomial approximation. All models were trained on a limited set of experimental data. Despite the quantitative limitations and simplicity of the adopted methods, the results indicate that even simple GPR models can support the detection of HTS tape degradation in scenarios where direct measurement of the critical current is not feasible. This work constitutes a first step towards the construction of a complete EDSS and outlines directions for further research, including the need to expand the dataset, improve validation, analyse uncertainty, and incorporate physical constraints into the models. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Smart Energy Systems)

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21 pages, 3922 KB

Open AccessArticle

Spatiotemporal Evolution and Influencing Factors of China’s Embodied Oil Flows: A Consumer-Side Perspective

by Chuanguo Zhang, Pengyan Wu and Sirui Zhou

Energies 2025, 18(17), 4562; https://doi.org/10.3390/en18174562 - 28 Aug 2025

Abstract

Oil consumption brings both energy security risks and environmental responsibilities. While traditional studies assign environmental responsibility primarily to oil producers, our research uncovers a geographical displacement of accountability: substantial oil volumes are embedded in traded goods and ultimately consumed in distant regions. Although [...] Read more.

Oil consumption brings both energy security risks and environmental responsibilities. While traditional studies assign environmental responsibility primarily to oil producers, our research uncovers a geographical displacement of accountability: substantial oil volumes are embedded in traded goods and ultimately consumed in distant regions. Although China’s “dual control” policy regulates fossil energy use, it fails to account for the complexities of embodied oil flows. This oversight leads to imbalanced interregional responsibility allocation and resource exploitation issues. Adopting the “consumer pays” principle, this study makes methodological advances by innovatively combining multi-regional input–output (MRIO) modeling with geographically and temporally weighted regression (GTWR) analysis. The integrated approach provides spatial–temporal resolution in tracking embodied oil flows and their drivers across China’s provinces. Key findings include (1) strong concentration of oil inflows in developed eastern and central provinces, alongside rapid growth in southwestern regions; (2) evolving temporal patterns where economic growth and distance remain persistent drivers, while green technology and urbanization emerge as growing mitigating factors; (3) spatially, northwestern regions rely heavily on external supplies due to economic growth and urbanization, southeastern areas face rising transport costs, while green technologies in coastal regions have yet to significantly curb inflows due to rebound effects. These insights provide a new analytical framework for energy policy, supporting region-specific solutions to balance development and sustainability from a consumption perspective. Full article

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21 pages, 807 KB

Open AccessArticle

Enhanced Renewable Energy Integration: A Comprehensive Framework for Grid Planning and Hybrid Power Plant Allocation

by Mahmoud Taheri, Abbas Rabiee and Innocent Kamwa

Energies 2025, 18(17), 4561; https://doi.org/10.3390/en18174561 - 28 Aug 2025

Abstract

Renewable energy sources play a crucial role in the urgent global pursuit of decarbonizing electricity systems. However, persistent grid congestion and lengthy planning approval processes remain the main barriers to the accelerated deployment of new green energy source capacities. Capitalizing on the synergies [...] Read more.

Renewable energy sources play a crucial role in the urgent global pursuit of decarbonizing electricity systems. However, persistent grid congestion and lengthy planning approval processes remain the main barriers to the accelerated deployment of new green energy source capacities. Capitalizing on the synergies afforded by co-locating hybrid power plants—particularly those that harness temporally anti-correlated renewable sources such as wind and solar—behind a unified connection point presents a compelling opportunity. To this end, this paper pioneers a comprehensive planning framework for hybrid configurations, integrating transmission grid and renewable energy assets planning to include energy storage systems, wind, and solar energy capacities within a long-term planning horizon. A mixed-integer linear programming model is developed that considers both the technical and economic aspects of combined grid planning and hybrid power plant allocation. Additionally, the proposed framework incorporates the N − 1 contingency criterion, ensuring system reliability in the face of potential transmission line outages, thereby adding a layer of versatility and resilience to the approach. The model minimizes the net present value of costs, encompassing both capital and operational expenditures as well as curtailment costs. The efficacy of the proposed model is demonstrated through its implementation on the benchmark IEEE 24-bus RTS system, with findings underscoring the pivotal role of hybrid power plants in enabling cost-effective and rapid sustainable energy integration. Full article

(This article belongs to the Special Issue Modern Technologies for Renewable Energy Development and Utilization: 5th Edition)

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