Energies

21 pages, 885 KB

Open AccessArticle

Analysis on the Insulation Improvements in Dutch Houses

by Joel Alpízar-Castillo and Laura Ramírez-Elizondo

Energies 2025, 18(20), 5467; https://doi.org/10.3390/en18205467 (registering DOI) - 17 Oct 2025

Thermal comfort accounts for significant residential energy consumption in high latitudes; however, quantitative information about insulation improvements is not widely available. First, we performed a study to quantify the effects of improving the insulation in walls, roofs, and windows of typical dwellings in [...] Read more.

Thermal comfort accounts for significant residential energy consumption in high latitudes; however, quantitative information about insulation improvements is not widely available. First, we performed a study to quantify the effects of improving the insulation in walls, roofs, and windows of typical dwellings in the Netherlands (a studio, an apartment, and a stand-alone house). Our results indicate that improving from single- to double-glazing is the most significant change, reducing gas consumption up to 50%, whereas the difference between double- and triple-glazing is less than 7%. Improving the roof insulation, filling cavity walls with insulation, or adding external wall insulation did not show attractive business cases, as the payback time was too high. Second, we evaluated upgrading the dwelling energy label by improving the insulation or adding a PV system and a heat pump. The results showed that, for energy labels C or above, the insulation reached a saturation point where it is not attractive to improve it before its end-of-life proactively. Instead, investing in the energy system by adding a PV system and a heat pump has better payback times. Our results allow policymakers and project developers to focus on the most relevant changes to accelerate the energy transition. Full article

(This article belongs to the Special Issue Thermal Behaviour, Energy Efficiency in Buildings and Sustainable Construction: 4th Edition)

► Show Figures

Figure 1

18 pages, 3398 KB

Open AccessArticle

PlugID: A Platform for Authenticated Energy Consumption to Enhance Accountability and Efficiency in Smart Buildings

by Raphael Machado, Leonardo Pinheiro, Victor Santos and Bruno Salgado

Energies 2025, 18(20), 5466; https://doi.org/10.3390/en18205466 (registering DOI) - 17 Oct 2025

Abstract

Energy efficiency in shared environments, such as offices and laboratories, is hindered by a lack of individual accountability. Traditional smart metering provides aggregated data but fails to attribute consumption to specific users, limiting the effectiveness of behavioral change initiatives. This paper introduces the [...] Read more.

Energy efficiency in shared environments, such as offices and laboratories, is hindered by a lack of individual accountability. Traditional smart metering provides aggregated data but fails to attribute consumption to specific users, limiting the effectiveness of behavioral change initiatives. This paper introduces the “authenticated energy consumption” paradigm, an innovative approach that directly links energy use to an identified user. We present PlugID, a low-cost, open-protocol IoT platform designed and built to implement this paradigm. The PlugID platform comprises a custom smart plug with RFID-based authentication and a secure, cloud-based data analytics backend. The device utilizes an ESP8266 microcontroller, Tasmota firmware, and the MQTT protocol over TLS for secure communication. Seven PlugID units were deployed in a small office environment to demonstrate the system’s feasibility. The main contribution of this work is the design, implementation, and validation of a complete, end-to-end system for authenticated energy monitoring. We argue that by making energy consumption an auditable and attributable event, the PlugID platform provides a powerful new tool to enforce energy policies, foster user awareness, and promote genuine efficiency. Full article

(This article belongs to the Special Issue Energy Efficiency of the Buildings: 4th Edition)

► Show Figures

Figure 1

36 pages, 7238 KB

Open AccessArticle

Physics-Aware Reinforcement Learning for Flexibility Management in PV-Based Multi-Energy Microgrids Under Integrated Operational Constraints

by Shimeng Dong, Weifeng Yao, Zenghui Li, Haiji Zhao, Yan Zhang and Zhongfu Tan

Energies 2025, 18(20), 5465; https://doi.org/10.3390/en18205465 (registering DOI) - 16 Oct 2025

Abstract

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven [...] Read more.

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven reinforcement learning approaches risk violating physical feasibility constraints, leading to unsafe or economically inefficient operation. To address this challenge, this paper develops a Physics-Informed Reinforcement Learning (PIRL) framework that embeds first-order physical models and a structured feasibility projection mechanism directly into the training process of a Soft Actor–Critic (SAC) algorithm. Unlike traditional deep reinforcement learning, which explores the state–action space without physical safeguards, PIRL restricts learning trajectories to a physically admissible manifold, thereby preventing battery over-discharge, thermal discomfort, and infeasible hydrogen operation. Furthermore, differentiable penalty functions are employed to capture equipment degradation, user comfort, and cross-domain coupling, ensuring that the learned policy remains interpretable, safe, and aligned with engineering practice. The proposed approach is validated on a modified IEEE 33-bus distribution system coupled with 14 thermal zones and hydrogen facilities, representing a realistic and complex multi-energy microgrid environment. Simulation results demonstrate that PIRL reduces constraint violations by 75–90% and lowers operating costs by 25–30% compared with rule-based and DRL baselines while also achieving faster convergence and higher sample efficiency. Importantly, the trained policy generalizes effectively to out-of-distribution weather conditions without requiring retraining, highlighting the value of incorporating physical inductive biases for resilient control. Overall, this work establishes a transparent and reproducible reinforcement learning paradigm that bridges the gap between physical feasibility and data-driven adaptability, providing a scalable solution for safe, efficient, and cost-effective operation of renewable-rich multi-energy microgrids. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

► Show Figures

Figure 1

23 pages, 6932 KB

Open AccessArticle

Design of a Boomerang-Type Rotor for Achieving IE4 Efficiency in a 37 kW LS-SynRM

by Choung-Seo Kim, Chan-Bae Park, Jae-Bum Lee, Seong-Hwi Kim and Hyung-Woo Lee

Energies 2025, 18(20), 5464; https://doi.org/10.3390/en18205464 (registering DOI) - 16 Oct 2025

Abstract

Motors are among the most energy-consuming devices worldwide. With growing interest in eco-friendly solutions, minimum efficiency regulations for industrial motors are being enforced. In response to continuously rising minimum efficiency requirements, research on improving the efficiency of motors is actively underway. In the [...] Read more.

Motors are among the most energy-consuming devices worldwide. With growing interest in eco-friendly solutions, minimum efficiency regulations for industrial motors are being enforced. In response to continuously rising minimum efficiency requirements, research on improving the efficiency of motors is actively underway. In the case of induction motors, which are the most widely used industrial electric motors, rotor ohmic loss occurs due to their operating characteristics. In contrast, line-start synchronous reluctance motors (LS-SynRMs) have a significant advantage in efficiency because once they reach synchronous speed, no eddy currents are generated by the fundamental current waveform. This leads to a sharp decrease in rotor ohmic losses, greatly enhancing efficiency. In this paper, a rotor design is carried out to improve the efficiency of LS-SynRMs. To support the rotor design, the torque characteristics of LS-SynRMs were analyzed under both asynchronous and synchronous state operations, and improvement directions for enhancing efficiency were identified. For rotor type selection, two bar-type rotors with linear flux barriers and two boomerang-type rotors with curved flux barriers were designed. The electromagnetic characteristics of these designs were compared using finite element analysis. Among them, the boomerang-type rotor that exhibited the best electromagnetic performance was selected as the final rotor type. Its final geometry was derived through detailed design, considering the mechanical safety of the rotor. Finally, experimental validation was conducted to verify the effectiveness of the proposed rotor design. Full article

(This article belongs to the Special Issue Electrical Machine Systems with High Efficiency, Reliability and Integration)

33 pages, 6175 KB

Open AccessArticle

Fluorocarbon Interfacial Modifier: Wettability Alteration in Reservoir Rocks for Enhanced Oil Recovery and Field Application

by Ruiyang Liu, Huabin Li, Zhe Li, Xudong Yu, Lide He, Xutong Guo, Feng Zhao, Huaqiang Shi and Wenzhao Sun

Energies 2025, 18(20), 5463; https://doi.org/10.3390/en18205463 (registering DOI) - 16 Oct 2025

Abstract

The peripheral reservoirs of the Daqing Oilfield exhibit low permeability and partial heterogeneity, resulting in a rapid injection pressure increase, limited sweep efficiency, and significant residual oil retention. To enhance recovery, this study synthesized a fluorocarbon siloxane (FHB) via free radical addition for [...] Read more.

The peripheral reservoirs of the Daqing Oilfield exhibit low permeability and partial heterogeneity, resulting in a rapid injection pressure increase, limited sweep efficiency, and significant residual oil retention. To enhance recovery, this study synthesized a fluorocarbon siloxane (FHB) via free radical addition for rock surface wettability modification. At a concentration of 0.1 wt%, FHB increased water and oil contact angles to 136° and 117°, respectively, at 60 °C. Fourier transform infrared spectroscopy, thermogravimetric analysis, and aging tests confirmed stable hydrophobic/oleophobic properties through chemical bonding to the rock. Furthermore, the low surface energy FHB significantly reduced adhesion work and decreased oil-water interfacial tension from 27 mN/m to 0.55 mN/m, thereby improving fluid transport in pore throats and promoting residual oil mobilization. Core flooding experiments resulted in an increase in total recovery by 11%, with low-field NMR analysis confirming reduced oil saturation across various pore sizes. A field trial in a production well in Daqing Oilfield successfully increased output from 3.1 t/d to 4.9 t/d, validating the efficacy of this strategy under real reservoir conditions—representing the first successful field application of a fluorocarbon-based modifier for wettability alteration and oil production enhancement in China. This study provides valuable experimental data and a practical framework for implementing chemical-enhanced recovery. Full article

(This article belongs to the Section I1: Fuel)

► Show Figures

Figure 1

37 pages, 3273 KB

Open AccessArticle

Model Predictive Control of a Hybrid Li-Ion Energy Storage System with Integrated Converter Loss Modeling

by Paula Arias, Marc Farrés, Alejandro Clemente and Lluís Trilla

Energies 2025, 18(20), 5462; https://doi.org/10.3390/en18205462 (registering DOI) - 16 Oct 2025

Abstract

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while [...] Read more.

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article

(This article belongs to the Special Issue Fuel Cell-Based and Hybrid Power Generation Systems Modeling, 3rd Edition)

22 pages, 950 KB

Open AccessArticle

Adaptive Maximum Power Capture Control for Wind Power Systems with VRB Storage Using SVR-Based Sensorless Estimation and FPNN-IPSO Optimization

by Kai-Hung Lu, Chih-Ming Hong and Fu-Sheng Cheng

Energies 2025, 18(20), 5461; https://doi.org/10.3390/en18205461 (registering DOI) - 16 Oct 2025

Abstract

This study proposes a novel sensorless maximum power capture control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to [...] Read more.

This study proposes a novel sensorless maximum power capture control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to enable adaptive learning capabilities. Additionally, support vector regression (SVR) is employed to estimate wind speed without the use of mechanical sensors, thereby enhancing system reliability and reducing maintenance requirements. A vanadium redox battery (VRB) is integrated to enhance power stability under fluctuating wind conditions. Simulation results demonstrate that the proposed FPNN-IPSO-based controller achieves superior performance compared to conventional Takagi–Sugeno–Kang (TSK) fuzzy and proportional–integral (PI) controllers. Specifically, the FPNN-IPSO controller exhibits notable improvements in average power output, tracking accuracy, and overall system efficiency. The proposed method increases power output by 9.71% over the PI controller and supports Plug-and-Play operation, making it suitable for intelligent microgrid integration. This work demonstrates an effective approach for intelligent, sensorless MPC control in hybrid wind–battery microgrids. Full article

(This article belongs to the Special Issue Advanced Control Technology of Integrated Wind and Wave Energy Conversion Systems: 2nd Edition)

32 pages, 1963 KB

Open AccessArticle

Assessing the Feasibility of Enzymatic Biodiesel Production Using the 5W2H Framework: A Brazilian Case Study with Distiller’s Corn Oil

by Victor Hugo Souza de Abreu, Mariane Gonzalez da Costa, Tássia Faria de Assis, Márcio de Almeida D’Agosto, Rejane Silva Rocha, Luís Otávio Días de Paula and Arsénio Massautso Simoco Laissone

Energies 2025, 18(20), 5460; https://doi.org/10.3390/en18205460 (registering DOI) - 16 Oct 2025

Abstract

This study adopts the 5W2H management tool to investigate the opportunities and challenges of enzymatic biodiesel production from residual oils. The methodological approach enables a structured evaluation of technical, economic, environmental, and governance aspects, clarifying critical conditions for feasibility and scalability. To illustrate [...] Read more.

This study adopts the 5W2H management tool to investigate the opportunities and challenges of enzymatic biodiesel production from residual oils. The methodological approach enables a structured evaluation of technical, economic, environmental, and governance aspects, clarifying critical conditions for feasibility and scalability. To illustrate this framework, the research applies it to the Brazilian biofuel sector, focusing on the valorization of Distillers Corn Oil (DCO), a by-product of corn ethanol production not intended for human consumption. Results show that enzymatic conversion of DCO can reduce feedstock costs by more than 50% and energy demand by up to 86.8% compared with conventional chemical processes. Nevertheless, the scalability of this technology faces critical barriers, such as enzyme costs, reaction times, and regulatory uncertainties. The study concluded that public–private partnerships and targeted policies, such as those under Brazil’s National Biodiesel Program (PNPB), are essential to overcoming these challenges and bridging the “valley of death” toward commercialization. By combining technical, economic, and governance perspectives, the study demonstrates that DCO-based enzymatic biodiesel has the potential to reinforce Brazil’s role in the global biofuels market while promoting a circular and sustainable energy transition. Full article

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

► Show Figures

Figure 1

19 pages, 1609 KB

Open AccessArticle

Trajectory Optimization for Airborne Wind Energy Systems Based on a Multi-Strategy Improved Salp Swarm Algorithm

by Yanjun Lv, Yan Pang, Zifeng Sun, Chenghao Zou and Yupeng Yang

Energies 2025, 18(20), 5459; https://doi.org/10.3390/en18205459 (registering DOI) - 16 Oct 2025

Abstract

Airborne Wind Energy (AWE) systems offer benefits such as high altitude access to stronger and more stable winds, reduced environmental impact, and cost effective infrastructure. However, these systems face several challenges including complex flight trajectory optimization, limited control robustness, and unstable power generation. [...] Read more.

Airborne Wind Energy (AWE) systems offer benefits such as high altitude access to stronger and more stable winds, reduced environmental impact, and cost effective infrastructure. However, these systems face several challenges including complex flight trajectory optimization, limited control robustness, and unstable power generation. This paper focuses on optimizing the flight trajectory of a tethered rigid wing AWE system to maximize power generation. A mathematical model of the system is constructed, and a constrained trajectory optimization problem is formulated. The multiple shooting method is employed for discretization, and a Multi-Strategy Improved Salp Swarm Algorithm (MISSA) is proposed to solve the optimization problem. Simulation results indicate that MISSA can generate a closed optimal trajectory, significantly enhance power output, and demonstrate superior performance in addressing complex trajectory optimization challenges. Full article

► Show Figures

Figure 1

27 pages, 2859 KB

Open AccessArticle

Evaluating the Energy Conservation Effects of Implementing Automatic Voltage Regulator: A Case Study of Department Stores

by Montree Utakrue, Nuttapon Chaiduangsri, Narongkorn Uthathip and Nattawoot Suwannata

Energies 2025, 18(20), 5458; https://doi.org/10.3390/en18205458 (registering DOI) - 16 Oct 2025

Abstract

Commercial buildings and shopping malls face rising electricity costs and increasing pressure to adopt sustainable practices. This paper presents the first long-term, multi-site empirical validation of Automatic Voltage Regulator (AVR) deployment in Thai retail facilities, providing robust evidence for tropical, motor-heavy load contexts. [...] Read more.

Commercial buildings and shopping malls face rising electricity costs and increasing pressure to adopt sustainable practices. This paper presents the first long-term, multi-site empirical validation of Automatic Voltage Regulator (AVR) deployment in Thai retail facilities, providing robust evidence for tropical, motor-heavy load contexts. The study evaluates the engineering, economic, and environmental performance of an AVR with an autotransformer core under real operating conditions. High-resolution measurements were collected before and after AVR installation, using Class 0.2s analyzers and a Building Energy Management System (BEMS) across multiple branches during a four-month monitoring campaign (February–May). Results indicate that a modest voltage reduction of 8.06% yielded a 12.02% decrease in active power demand, a 6.22% current reduction, and a 2.26% improvement in power factor. The greatest savings occurred in HVAC (8.19%) and refrigeration loads (8.20%), while lighting loads remained nearly unchanged. Economically, the system delivered ~177 kWh/day savings, equivalent to 262,212 THB/year, with a simple payback of 2.67 years and an ROI of 37.5%. Environmentally, the AVR reduced 36.6 tCO₂/year (±5%), aligning with Thailand’s Energy Efficiency Plan (EEP) 2018–2037 and Carbon Neutrality Roadmap and offering additional potential for T-VER monetization. These findings confirm AVR technology as a scalable, standards-compliant, and high-return retrofit solution for commercial facilities in tropical climates. Full article

► Show Figures

Figure 1

32 pages, 2224 KB

Open AccessArticle

Fuel Cell–Battery Hybrid Trains for Non-Electrified Lines: A Dynamic Simulation Approach

by Giuliano Agati, Domenico Borello, Alessandro Ruvio and Paolo Venturini

Energies 2025, 18(20), 5457; https://doi.org/10.3390/en18205457 (registering DOI) - 16 Oct 2025

Abstract

Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based [...] Read more.

Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based tool for the dynamic simulation of fuel cell–battery hybrid powertrains. The model integrates train dynamics, rule-based energy management, system efficiencies, and component degradation, enabling both energy and cost analyses over the vehicle’s lifetime. The objective is to assess the techno-economic performance of different powertrain configurations. Sensitivity analyses were carried out by varying two sizing parameters: the nominal power of the fuel cell (parameter m) and the total battery capacity (parameter n), across multiple real-world railway routes. Results show a slight reduction in lifecycle costs as m increases (5.1 €/km for m = 0.50) mainly due to a lower FC degradation. Conversely, increasing battery capacity (n) lowers costs by reducing cycling stress for both battery and FC, from 5.3 €/km (n = 0.10) to 4.5 €/km (n = 0.20). In general, lowest values of m and n provide unviable solutions as the battery discharges completely before the end of the journey. The study highlights the critical impact of the operational profile: for a fixed powertrain configuration (m = 0.45, n = 0.20), the specific cost dramatically increases from 4.44 €/km on a long, flat route to 15.8 €/km on a hilly line and up to 76.7 €/km on a mountainous route, primarily due to severe fuel cell degradation under transient loads. These findings demonstrate that an “all-purpose” train sizing approach is inadequate, confirming the necessity of route-specific powertrain optimization to balance techno-economic performance. Full article

(This article belongs to the Special Issue Future of Transport: Hybrid Electrification and Hydrogen Integration in Vehicles and Infrastructure)

16 pages, 3008 KB

Open AccessArticle

Lithium-Ion Battery State of Health Estimation Based on Multi-Dimensional Health Characteristics and GAPSO-BiGRU

by Lv Zhou, Yu Zhang, Kuiting Pan and Xiongfan Cheng

Energies 2025, 18(20), 5456; https://doi.org/10.3390/en18205456 (registering DOI) - 16 Oct 2025

Abstract

The state of health (SOH) of lithium-ion batteries (LIBs) is a key parameter that is crucial for delaying their lifespan degradation and ensuring safe use. To further explore the potential of charge curves in SOH estimation for LIBs, this paper proposes a method [...] Read more.

The state of health (SOH) of lithium-ion batteries (LIBs) is a key parameter that is crucial for delaying their lifespan degradation and ensuring safe use. To further explore the potential of charge curves in SOH estimation for LIBs, this paper proposes a method based on multi-dimensional health features and a genetic algorithm–particle swarm optimization (GAPSO)–bidirectional gated recurrent unit (BiGRU) neural network for SOH estimation. First, we extracted differential thermal voltammetry curves from the charging curve and defined the peak, valley, and their positions. Then, based on the charging temperature curve, we defined the time at which the maximum charging temperature occurs and the average charging temperature. Subsequently, we validated the correlation between the aforementioned six health features and SOH using the Pearson correlation coefficient. Finally, we used the multi-dimensional health features as model inputs to construct the BiGRU estimation model and employed the GAPSO hybrid strategy to achieve global adaptive optimization of the model’s hyperparameters. Experimental results on different LIBs show that the proposed method has relatively high accuracy, with an average absolute error and root mean square error of no more than 0.2771%. The comparison results with various methods further verify the superiority of the proposed method. Full article

(This article belongs to the Special Issue Advances in Battery Management Systems for Lithium-Ion Batteries)

► Show Figures

Figure 1

22 pages, 10515 KB

Open AccessArticle

Experimental Investigations of the Melting/Solidification of Coconut Oil Using Ultrasound-Based and Image Processing Approaches

by Rafał Andrzejczyk, Radosław Drelich and Michał Pakuła

Energies 2025, 18(20), 5455; https://doi.org/10.3390/en18205455 (registering DOI) - 16 Oct 2025

Abstract

The present study aims to compare the feasibility of using ultrasound techniques and image processing to obtain comprehensive experimental results on the dynamics of solid–liquid fraction changes during the melting and solidification of coconut oil as a phase change material (PCM). The discussion [...] Read more.

The present study aims to compare the feasibility of using ultrasound techniques and image processing to obtain comprehensive experimental results on the dynamics of solid–liquid fraction changes during the melting and solidification of coconut oil as a phase change material (PCM). The discussion will focus on the advantages and limitations of various ultrasonic techniques and image data analysis for inspecting materials during phase transitions. Ultrasound enables the detection of phase changes in materials by analysing variations in their acoustic properties, such as wave velocity and amplitude, during transitions. This method is not only cost-effective compared to traditional non-destructive techniques, such as X-ray tomography, but also offers the potential for real-time monitoring in thermal energy storage systems. Furthermore, it can provide valuable information about internal mechanical parameters and the material’s structure. A detailed analysis of the melting and solidification dynamics has been conducted, confirming the feasibility of using ultrasound parameters to assess the reconstruction of material structures during phase changes. This study paves the way for more efficient and cost-effective monitoring of phase change materials in various applications. Full article

► Show Figures

Figure 1

18 pages, 2728 KB

Open AccessArticle

Monthly Power Outage Maintenance Scheduling for Power Grids Based on Interpretable Reinforcement Learning

by Wei Tang, Xun Mao, Kai Lv, Zhichen Cai and Zhenhuan Ding

Energies 2025, 18(20), 5454; https://doi.org/10.3390/en18205454 (registering DOI) - 16 Oct 2025

Abstract

This paper proposes an interpretable optimization method for power grid outage scheduling based on reinforcement learning. An outage scheduling optimization model is proposed, considering the convergence of power flow calculation, voltage violations, and operational economic behavior as objectives, while considering constraints such as [...] Read more.

This paper proposes an interpretable optimization method for power grid outage scheduling based on reinforcement learning. An outage scheduling optimization model is proposed, considering the convergence of power flow calculation, voltage violations, and operational economic behavior as objectives, while considering constraints such as simultaneous outage constraints, mutually exclusive constraints, and maintenance windows. Key features of the outage schedule are selected based on Shapley values to construct a Markov optimization model for outage scheduling. A deep reinforcement learning agent is established to optimize the outage schedule. The proposed method is applied to the IEEE-39 and IEEE-118 bus system for validation. Experimental results show that the proposed method outperforms existing algorithms in terms of voltage violation, total power losses, and computational time. The proposed method eliminates all voltage violations and reduces active power losses up to 5.7% and computation time by 6.8 h compared to conventional heuristic algorithms. Full article

► Show Figures

Figure 1

16 pages, 2485 KB

Open AccessArticle

Experimental Methods and Equivalence Research on Inter-Turn Short Circuits in Power Transformers

by Xuelong Li, Chun Yang, Yuanming Shuai, Dongyang Wu, Zhengyang Zhang and Lanjun Yang

Energies 2025, 18(20), 5453; https://doi.org/10.3390/en18205453 (registering DOI) - 16 Oct 2025

Abstract

Inter-turn short-circuit faults in power transformers generate enormous short-circuit currents within the affected turns, making full-scale experimental investigations impractical. To address this issue, this study proposes an experimental method utilizing a third external short-circuit winding to simulate inter-turn faults through structural improvements in [...] Read more.

Inter-turn short-circuit faults in power transformers generate enormous short-circuit currents within the affected turns, making full-scale experimental investigations impractical. To address this issue, this study proposes an experimental method utilizing a third external short-circuit winding to simulate inter-turn faults through structural improvements in winding configuration and conductor current-carrying capacity. A simulation calculation model for transformer inter-turn short circuits was first established to investigate the equivalence between the proposed equivalent fault model and actual fault conditions under varying short-circuit positions and proportions. Simulation results demonstrate that both models exhibit consistent primary/secondary winding currents, short-circuit turn currents, and spatial radial leakage magnetic field distributions post-fault, with average errors less than 5%. Subsequently, an experimental platform for inter-turn short-circuit fault simulation was constructed. Current and leakage magnetic field measurements under different fault positions and proportions were validated against simulation data, confirming the proposed method’s equivalence. This approach provides an effective pathway for investigating fault characteristics and monitoring methodologies of transformer inter-turn short circuits. Full article

(This article belongs to the Special Issue Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers)

► Show Figures

Figure 1

21 pages, 6551 KB

Open AccessArticle

Mapping Solar–Wind Complementarity with BARRA

by Abhnil Prasad and Merlinde Kay

Energies 2025, 18(20), 5452; https://doi.org/10.3390/en18205452 (registering DOI) - 16 Oct 2025

Abstract

Australia’s renewable energy transition will be dominated by solar and wind power, yet their contrasting variability necessitates hybrid integration with storage to ensure reliability. This study uses Australian reanalysis data, BARRA (Bureau of Meteorology Atmospheric High-Resolution Regional Reanalysis for Australia), to quantify solar [...] Read more.

Australia’s renewable energy transition will be dominated by solar and wind power, yet their contrasting variability necessitates hybrid integration with storage to ensure reliability. This study uses Australian reanalysis data, BARRA (Bureau of Meteorology Atmospheric High-Resolution Regional Reanalysis for Australia), to quantify solar (global horizontal irradiance, GHI) and wind (wind power density, WPD) resources by examining their availability, variability, synergy, episode length, and lulls. The novelty of this work is the use of rarely examined metrics such as variability, availability, episode length, and extended lull events (Dunkelflaute) with a high-resolution and 29-year duration reanalysis dataset. The results show that solar is the more reliable resource, with high daytime availability and relatively short lulls. Wind, despite being abundant in coastal regions, is highly intermittent, characterized by a skewed distribution, low availability, and extended periods of lulls. Synergy metrics demonstrate significant complementarity, with combined solar–wind synergy reducing deficits in single resources, while joint non-synergy events define critical system vulnerabilities. Importantly, hybrid systems limit maximum joint lulls, which are far shorter than wind-only extremes, thereby reducing the scale of long-duration storage required. These findings underscore that, while solar provides a stable baseline supply and wind contributes spatial diversity, hybrid systems supported by batteries offer a resilient pathway. Synergy and non-synergy statistics provide essential parameters for optimally sizing storage to withstand rare but severe shortfalls, ensuring a reliable, utility-scale renewable future for Australia. Full article

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

► Show Figures

Figure 1

14 pages, 4678 KB

Open AccessArticle

Design and Development of High-Power, High-Efficiency, and Low-Noise Microwave Sources for Wireless Power Transmission

by Kaviya Aranganadin and Ming-Chieh Lin

Energies 2025, 18(20), 5451; https://doi.org/10.3390/en18205451 (registering DOI) - 16 Oct 2025

Abstract

This study investigates the feasibility and possible improvement of microwave power transmission (MPT) in a space-based solar power system (SSPS). SSPS, a concept proposed by Dr. Peter Glaser in 1968, aims to harness solar energy in space, free from atmospheric constraints, and transmit [...] Read more.

This study investigates the feasibility and possible improvement of microwave power transmission (MPT) in a space-based solar power system (SSPS). SSPS, a concept proposed by Dr. Peter Glaser in 1968, aims to harness solar energy in space, free from atmospheric constraints, and transmit it to Earth using microwaves or lasers. Our focus is on enhancing the efficiency and cost-effectiveness of MPT, which accounts for a significant portion of SSPS expenses. In this work, we propose the use of a novel field emission-based rising-sun magnetron (RM) in the MPT subsystem, aiming to extend system longevity and simplify construction, while dramatically reducing the implementation cost. It is demonstrated that the optimization of the RM design at 2.45 GHz can achieve a high output power of >100 kW with a high efficiency of >85%, based on the well-established conformal finite-difference time-domain particle-in-cell simulations. This research provides valuable insights into improving SSPS, making it a more viable and sustainable renewable energy solution. Full article

► Show Figures

Figure 1

16 pages, 1675 KB

Open AccessArticle

Probabilistic State of Health Prediction for Lithium-Ion Batteries Based on Incremental Capacity and Differential Voltage Curves

by Qingbin Wang, Hangang Yan, Yuxi Wang, Yun Yang, Xiaoguang Liu, Zhuoqi Zhu, Gancai Huang and Zheng Huang

Energies 2025, 18(20), 5450; https://doi.org/10.3390/en18205450 (registering DOI) - 16 Oct 2025

Abstract

The rapid proliferation of lithium-ion batteries in electric vehicles and grid-scale energy storage systems has underscored the critical need for advanced battery management systems, particularly for accurate state of health (SOH) monitoring. In this study, a hybrid data-driven framework incorporating the whale optimization [...] Read more.

The rapid proliferation of lithium-ion batteries in electric vehicles and grid-scale energy storage systems has underscored the critical need for advanced battery management systems, particularly for accurate state of health (SOH) monitoring. In this study, a hybrid data-driven framework incorporating the whale optimization algorithm (WOA) for Bidirectional Long Short-Term Memory (BiLSTM) networks is introduced. The framework extracts battery aging-related features based on incremental capacity (IC) and differential voltage (DV), which are used as inputs to the SOH prediction model. Then, the BiLSTM network is optimized by WOA to improve convergence performance and model generalization. To further quantify the prediction uncertainty, the Bootstrap approach was used to construct SOH prediction intervals for various confidence levels. Experimental results based on the Oxford dataset show that the proposed WOA-BiLSTM model outperforms the baseline methods including standard LSTM, BiLSTM, and BiGRU. Model performance is evaluated using the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In addition, the integration of Bootstrap enables flexible and reliable interval prediction. The results show that PICP reaches 1 at the 90% confidence level and exceeds 0.85 at the 80% confidence level, with PINAW and CWC metrics validating the interval quality. The proposed method provides accurate point prediction and robust uncertainty quantification, offering a promising tool for smart battery health management. Full article

► Show Figures

Figure 1

28 pages, 3160 KB

Open AccessReview

Plugging into Onshore Power Supply System Innovation: A Review from Standards and Patents to Port Deployment

by Daniel Jesus, Tiago Oliveira, Marina Perdigão and André Mendes

Energies 2025, 18(20), 5449; https://doi.org/10.3390/en18205449 - 16 Oct 2025

Abstract

Shore power systems, also known as cold ironing or shore-to-ship (STS) connections, are increasingly recognized as a viable solution to reduce emissions and noise from ships during berthing operations. This paper provides a comprehensive overview of shore power technology, with a focus on [...] Read more.

Shore power systems, also known as cold ironing or shore-to-ship (STS) connections, are increasingly recognized as a viable solution to reduce emissions and noise from ships during berthing operations. This paper provides a comprehensive overview of shore power technology, with a focus on typical onboard energy consumption profiles across different types of ship, the main electrical architectures used in shore-side systems, and the compatibility challenges related to frequency, voltage, and control integration. The paper reviews international standards, particularly the ISO/IEC/IEEE 80005 series, that define technical requirements for interoperability and safety. A detailed analysis of recent patents highlights technological innovations in mobility, conversion topologies, and high-voltage integration. In addition, commercially available shore power solutions from major manufacturers are surveyed, with comparative data on power ratings, voltage levels, and converter topologies. Finally, the study discusses current limitations and outlines development directions for Onshore Power Supply systems, including regulatory developments, digital integration, and grid support functionalities. The insights presented aim to support the design, standardization, and deployment of efficient and scalable STS systems in line with global maritime decarbonization goals. Full article

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

► Show Figures

Figure 1

Journal Description

Energies

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI