Energies

17 pages, 1685 KB

Open AccessArticle

Sensitivity Factors of Thermally Regenerative Electrochemical Cycle Systems Using Fuel Cell’s Waste Heat

by Ákos Bereczky and Emese Lévai

Energies 2025, 18(20), 5422; https://doi.org/10.3390/en18205422 (registering DOI) - 14 Oct 2025

Recovering waste heat is widely seen as an effective way to improve energy efficiency. Because of its potential to lower both energy costs and greenhouse gas emissions, it has been used for many years in industries with high energy demand. While several technologies [...] Read more.

Recovering waste heat is widely seen as an effective way to improve energy efficiency. Because of its potential to lower both energy costs and greenhouse gas emissions, it has been used for many years in industries with high energy demand. While several technologies are already available for this purpose, most of them require relatively high temperatures to achieve high performance. One approach that can make use of lower temperature heat sources is the thermally regenerative electrochemical cycle (TREC). Systems based on this principle can be a cost-effective option for capturing heat from sources such as fuel cells, although their efficiency depends on several factors. This study applies parameter sensitivity analysis to support more efficient system design. The results show that chemical properties, especially the thermal coefficients of redox pairs, have the strongest effect on performance. Geometric aspects, particularly the size of the active membrane area, also play an important role. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy IV)

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35 pages, 12982 KB

Open AccessArticle

A Data-Driven Decision-Making Tool for Prioritizing Resilience Strategies in Cold-Climate Urban Neighborhoods

by Ahmed Nouby Mohamed Hassan and Caroline Hachem-Vermette

Energies 2025, 18(20), 5421; https://doi.org/10.3390/en18205421 (registering DOI) - 14 Oct 2025

Abstract

Cold-climate urban neighborhoods face mounting energy and thermal risks from extreme weather and power outages, creating trade-offs between different resilience capacities and objectives. This study develops a scalable, data-driven decision-making tool to support early-stage prioritization of resilience strategies at both the building component [...] Read more.

Cold-climate urban neighborhoods face mounting energy and thermal risks from extreme weather and power outages, creating trade-offs between different resilience capacities and objectives. This study develops a scalable, data-driven decision-making tool to support early-stage prioritization of resilience strategies at both the building component and neighborhood levels. A database of 48 active and passive strategies was systematically linked to 14 resilience objectives, reflecting energy- and thermally oriented capacities. Each strategy–objective pair was qualitatively assessed through a literature review and translated into probability distributions. Monte Carlo simulations (10,000 iterations) were performed to generate possible outcomes and several scores were calculated. Comparative scenario analysis—spanning holistic, short-term, long-term, energy-oriented, and thermally oriented perspectives—highlighted distinct adoption patterns. Active energy strategies, such as ESS, decentralized RES, microgrids, and CHP, consistently achieved the highest adoption (A) scores across levels and scenarios. Several passive measures, including green roofs, natural ventilation with passive heat recovery, and responsive glazing, also demonstrated strong multi-objective performance and outage resilience. A case study application integrated stakeholder-specific objective weightings, revealing convergent strategies suitable for immediate adoption and divergent ones requiring negotiation. This tool provides an adaptable probabilistic foundation for evaluating resilience strategies under uncertainty. Full article

(This article belongs to the Special Issue Climate Change in Power and Energy Systems: Challenges, Innovations, and Solutions)

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37 pages, 2918 KB

Open AccessSystematic Review

Machine Learning Applications in Energy Consumption Forecasting and Management for Electric Vehicles: A Systematic Review

by Emilia M. Szumska, Łukasz Pawlik, Damian Frej and Jacek Łukasz Wilk-Jakubowski

Energies 2025, 18(20), 5420; https://doi.org/10.3390/en18205420 (registering DOI) - 14 Oct 2025

Abstract

This literature review addresses a major research gap in electromobility by providing a comprehensive synthesis of machine learning (ML) and deep learning (DL) applications for forecasting energy consumption, managing battery state of charge (SoC), and integrating electric vehicles (EVs) with charging infrastructure and [...] Read more.

This literature review addresses a major research gap in electromobility by providing a comprehensive synthesis of machine learning (ML) and deep learning (DL) applications for forecasting energy consumption, managing battery state of charge (SoC), and integrating electric vehicles (EVs) with charging infrastructure and smart grids, including vehicle-to-grid (V2G) systems. Despite the rapid increase in publications between 2016 and 2025, few comparative studies systematically evaluate ML/DL approaches, their effectiveness in specific applications, and their limitations under real-world conditions. To bridge this gap, this review analyzes 95 publications, covering methods from ensemble learners (e.g., Random Forest, XGBoost) to advanced hybrids (e.g., LSTM + MPC). Key influencing factors such as driving style, topography, and weather are considered. This review identifies persistent challenges, including the lack of standardized datasets, limited model generalization, and high computational demands. It also outlines research directions, such as adaptive online learning and integration with V2X technologies. By consolidating current knowledge, this review supports engineers, EV system designers, and policymakers in planning effective energy management and charging strategies, thereby contributing to the sustainable development of electromobility. Full article

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

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19 pages, 8474 KB

Open AccessArticle

Study on Ultrasonic Phased Array Inspection Method of Crack Defects in Butt Joints of Multi-Layered Steel Vessel for High-Pressure Hydrogen Storage

by Bo Deng, Zilong Wu, Rui Yan and Chilou Zhou

Energies 2025, 18(20), 5419; https://doi.org/10.3390/en18205419 (registering DOI) - 14 Oct 2025

Abstract

The full multilayer high-pressure hydrogen storage vessel plays an important role in hydrogen refueling stations. However, these vessels may fail after a certain period due to crack formation, necessitating periodic inspections. Among the various parts, the butt joints connecting the thick-walled nozzles and [...] Read more.

The full multilayer high-pressure hydrogen storage vessel plays an important role in hydrogen refueling stations. However, these vessels may fail after a certain period due to crack formation, necessitating periodic inspections. Among the various parts, the butt joints connecting the thick-walled nozzles and hemispherical heads represent critical and challenging areas for inspection. In this study, a one-shot multi-receiver defect detection and localization method is developed based on the ultrasonic phased array method. In order to verify the feasibility of the method, the interaction between the ultrasonic wave and the crack defects at the key position of the butt joint is analyzed based on finite element, enabling the accurate localization of crack tips; an experimental specimen was designed and fabricated, and a corresponding phased array detection test was conducted to validate the method. Full article

(This article belongs to the Special Issue Safety of Hydrogen Energy: Technologies and Applications)

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31 pages, 1516 KB

Open AccessArticle

Federated Quantum Machine Learning for Distributed Cybersecurity in Multi-Agent Energy Systems

by Kwabena Addo, Musasa Kabeya and Evans Eshiemogie Ojo

Energies 2025, 18(20), 5418; https://doi.org/10.3390/en18205418 (registering DOI) - 14 Oct 2025

Abstract

The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. [...] Read more.

The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. By integrating parameterized quantum circuits (PQCs) at the local agent level with secure federated learning protocols, the framework enhances detection accuracy while preserving data privacy. A trimmed-mean aggregation scheme and differential privacy mechanisms are embedded to defend against Byzantine behaviors and data-poisoning attacks. The problem is formally modeled as a constrained optimization task, accounting for quantum circuit depth, communication latency, and adversarial resilience. Experimental validation on synthetic smart grid datasets demonstrates that FQML achieves high detection accuracy (≥96.3%), maintains robustness under adversarial perturbations, and reduces communication overhead by 28.6% compared to classical federated baselines. These results substantiate the viability of quantum-enhanced federated learning as a practical, hardware-conscious approach to distributed cybersecurity in next-generation energy infrastructures. Full article

(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)

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Graphical abstract

20 pages, 3336 KB

Open AccessArticle

Adaptive Risk-Driven Control Strategy for Enhancing Highway Renewable Energy System Resilience Against Extreme Weather

by Peiqiang Cui, Hongde Li, Wenwu Zhao, Xiaowu Tian, Jin Liu, Weijie Qin, Liya Hai and Fan Wu

Energies 2025, 18(20), 5417; https://doi.org/10.3390/en18205417 (registering DOI) - 14 Oct 2025

Abstract

Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic [...] Read more.

Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic risks or the interdependencies between transportation and power systems. This study proposes an adaptive, risk-driven control framework that holistically coordinates power generation infrastructures, microgrids, demand-side loads, energy storage systems, and transport dynamics through continuous risk assessment. This enables the system to dynamically shift operational priorities—from cost-efficiency in stable periods to robustness during emergencies. A multi-objective optimization model is established, integrating infrastructure resilience, operational costs, and traffic impacts. It is solved using an enhanced evolutionary algorithm that combines the non-dominated sorting genetic algorithm II with differential evolution (NSGA-II-DE). Extensive simulations under extreme weather scenarios validate the framework’s ability to autonomously reconfigure operations, achieving 92.5% renewable energy utilization under low-risk conditions while elevating critical load assurance to 98.8% under high-risk scenarios. This strategy provides both theoretical and technical guarantees for securing highway renewable energy system operations. Full article

(This article belongs to the Special Issue Recent Advances in Renewable Energy and Hydrogen Technologies)

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16 pages, 1224 KB

Open AccessArticle

A Cost-Optimization Model for EV Charging Stations Utilizing Solar Energy and Variable Pricing

by An Nguyen, Hung Pham and Cuong Do

Energies 2025, 18(20), 5416; https://doi.org/10.3390/en18205416 (registering DOI) - 14 Oct 2025

Abstract

Managing electric vehicle (EV) charging at stations with on-site solar (PV) generation is a complex task, made difficult by volatile electricity prices and the need to guarantee services for drivers. This paper proposes a robust optimization (RO) framework to schedule EV charging, minimizing [...] Read more.

Managing electric vehicle (EV) charging at stations with on-site solar (PV) generation is a complex task, made difficult by volatile electricity prices and the need to guarantee services for drivers. This paper proposes a robust optimization (RO) framework to schedule EV charging, minimizing electricity costs while explicitly hedging against price uncertainty. The model is formulated as a tractable linear program (LP) using the Bertsimas–Sim reformulation and is implemented in an online, adaptive manner through a model predictive control (MPC) scheme. Evaluated on extensive real-world charging data, the proposed controller demonstrates significant cost reductions, outperforming a PV-aware Greedy heuristic by 17.5% and a deep reinforcement learning (DRL) agent by 12.2%. Furthermore, the framework exhibits lower cost volatility and is proven to be computationally efficient, with solving times under five seconds even during peak loads, confirming its feasibility for real-time deployment. The results validate our framework as a practical, reliable, and economically superior solution for the operational management of modern EV charging infrastructure. Full article

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

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20 pages, 298 KB

Open AccessArticle

Bridging Borders and Brains: ESG Sustainability, Integration, Education and Energy Choices in Developed Economies

by Abrahem Anbea, Kolawole Iyiola and Ahmad Alzubi

Energies 2025, 18(20), 5415; https://doi.org/10.3390/en18205415 (registering DOI) - 14 Oct 2025

Abstract

As ESG sustainability uncertainty intensifies and globalisation deepens, the energy trilemma—security, equity, and sustainability—emerges as the defining calculus of modern energy policy. Therefore, this investigation explores the influence of ESG sustainability uncertainty intensification and globalisation on the energy trilemma, while controlling education, urbanization [...] Read more.

As ESG sustainability uncertainty intensifies and globalisation deepens, the energy trilemma—security, equity, and sustainability—emerges as the defining calculus of modern energy policy. Therefore, this investigation explores the influence of ESG sustainability uncertainty intensification and globalisation on the energy trilemma, while controlling education, urbanization and economic growth, using data from 2001 to 2022. The energy trilemma offers an all-inclusive gauge for understanding the effect of ESG sustainability uncertainty on energy trilemma. The study employed Lewbel’s Two Stage Least Squares method to examine the connection. The results disclose that ESG sustainability uncertainty is negatively associated with all three trilemma pillars. Globalisation displays a nonlinear influence: its squared terms are negative and statistically significant, implying diminishing marginal benefits at high levels of openness. This paper’s significance lies in evidence that ESG sustainability uncertainty erodes all three pillars of the energy trilemma, while globalization’s benefits taper at high openness—strengthening the mandate for a clean, just, secure, and sustainable transition. Full article

(This article belongs to the Special Issue Economic Approaches to Energy, Environment and Sustainability)

14 pages, 5404 KB

Open AccessArticle

Emission Characteristics During the Co-Firing of Fine Coal and Refuse-Derived Fuel from Municipal Waste

by Zbigniew Jelonek and Przemysław Rompalski

Energies 2025, 18(20), 5414; https://doi.org/10.3390/en18205414 (registering DOI) - 14 Oct 2025

Abstract

The co-firing of coal and refuse-derived fuel (RDF) from municipal solid waste recycling is gaining support in countries in which energy production is based on solid fuels. It is the result of the rising priority given to renewable energy sources, the circular economy, [...] Read more.

The co-firing of coal and refuse-derived fuel (RDF) from municipal solid waste recycling is gaining support in countries in which energy production is based on solid fuels. It is the result of the rising priority given to renewable energy sources, the circular economy, and effective waste management through sorting, recycling, and thermal conversion. Despite the increasing efficiency of recycling and the ever-lower quantities of waste delivered to waste dumps, the problem of the residual fraction remains unsolved. The portion of mixed municipal waste that cannot be recycled exhibits a high energy value. For this reason, it should be neither stored nor burnt in household boiler rooms, as doing so would constitute an environmental hazard. However, the waste can be used as an additive to fine coal in power boilers, provided that they are equipped with flue gas monitoring and purification systems. Tests involving proportionally prepared compositions of fine coal and refuse-derived fuel burnt in a laboratory boiler revealed a major variability in the flue gas parameters (physicochemical), depending on the applied proportions of the individual components. For instance, when burning a composition of 50% fine coal and 50% refuse-derived fuel, a reduction in CO₂ emissions by about 12% was noted compared with that when burning fine coal exclusively. Furthermore, when burning refuse-derived fuel, an addition of 20% fine coal is enough to produce a 2.8% reduction in CO emission. Meanwhile, a composition of 80% fine coal and 20% refuse-derived fuel would reduce the emissions by 393 ppm. During the measurements, it was also noted that most of the measured parameters indicated a decrease in individual gas contents relative to the emissions obtained when burning fine coal or refuse-derived fuel exclusively. These relationships can be applied to prepare fuel compositions based on refuse-derived fuel and fine coal, depending on the power and flue gas purification capabilities of individual cogeneration systems. Full article

(This article belongs to the Special Issue Advanced Clean Coal Technology)

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38 pages, 3075 KB

Open AccessArticle

Analysis of the Profitability of Heating a Retrofitted Building with an Air Heat Pump in Polish Climatic Conditions

by Aleksander Iwaszczuk, Jarosław Baran and Natalia Iwaszczuk

Energies 2025, 18(20), 5413; https://doi.org/10.3390/en18205413 (registering DOI) - 14 Oct 2025

Abstract

The transformation of energy systems towards low emission is one of the key assumptions of the climate and energy policy of the European Union and many countries around the world. These changes include not only the power and transport sectors but also the [...] Read more.

The transformation of energy systems towards low emission is one of the key assumptions of the climate and energy policy of the European Union and many countries around the world. These changes include not only the power and transport sectors but also the heating of residential buildings, which consume significant amounts of energy and emit large amounts of greenhouse gases. This article presents a detailed comparative analysis of the costs of heating using an air-to-water heat pump and a condensing gas boiler. The study concerned a retrofitted single-family building from the 1990s, located in southern Poland. The calculations were made taking into account daily meteorological data for two full heating seasons: 2022/2023 and 2023/2024. This approach made it possible to more precisely reproduce real operating conditions. The study was conducted for various configurations of the central heating system: surface and radiator. The following parameters were also taken into account: (1) variable heat pump parameters, such as supply temperature LWT and coefficient of performance COP; (2) current tariffs for electricity and natural gas; and (3) forecasted tariffs for electricity and natural gas in the conditions of market liberalization and phasing out of protective mechanisms. A comparison of the two heating seasons revealed lower costs with a heat pump. In some cases, the cost of heat generated by a gas boiler was over 100% higher than with a heat pump. This applies to both heating seasons. Under the current tariffs, the calculated gas cost for the first season was PLN 6856 (EUR 1605) (1 EUR = 4.27 PLN) compared to heat pump heating costs ranging from PLN 3191 to PLN 4576 (EUR 747 to 1072). For future gas and electricity tariffs, the costs were PLN 8227 (EUR 1926) for gas and PLN 3841 to PLN 5304 (EUR 899 to 1242) for a heat pump. Similarly, for the second heating season, these values were PLN 6055 (EUR 1418) for gas heating and PLN 2741–3917 (EUR 642–917) for a heat pump under the current tariffs, and PLN 7267 (EUR 1702) and PLN 3307–4540 (EUR 774–1064) under future tariffs. This means percentage savings of between approximately 33% and 55%, depending on the heating type and tariff. Therefore, the obtained results indicate the higher profitability of using an air heat pump compared to a gas boiler. This advantage was maintained in all the discussed scenarios, and its scale depended on the type of installation, supply temperature, and the selected electricity tariff. The highest economic profitability was noted for low-temperature systems. These results can provide a basis for making rational investment and design decisions in the context of the energy transformation of single-family housing. Full article

(This article belongs to the Special Issue From Consumer Behavior to Business Innovation: Paths to a Sustainable Energy Future)

27 pages, 3717 KB

Open AccessArticle

The Impact of Fixed-Tilt PV Arrays on Vegetation Growth Through Ground Sunlight Distribution at a Solar Farm in Aotearoa New Zealand

by Matlotlo Magasa Dhlamini and Alan Colin Brent

Energies 2025, 18(20), 5412; https://doi.org/10.3390/en18205412 (registering DOI) - 14 Oct 2025

Abstract

The land demands of ground-mounted PV systems raise concerns about competition with agriculture, particularly in regions with limited productive farmland. Agrivoltaics, which integrates solar energy generation with agricultural use, offers a potential solution. While agrivoltaics has been extensively studied, less is known about [...] Read more.

The land demands of ground-mounted PV systems raise concerns about competition with agriculture, particularly in regions with limited productive farmland. Agrivoltaics, which integrates solar energy generation with agricultural use, offers a potential solution. While agrivoltaics has been extensively studied, less is known about its feasibility and impacts in complex temperate maritime climates such as Aotearoa New Zealand, in particular, the effects of PV-induced shading on ground-level light availability and vegetation. This study modelled the spatial and seasonal distribution of ground-level irradiation and Photosynthetic Photon Flux Density (PPFD) beneath fixed-tilt PV arrays at the Tauhei solar farm in the Waikato region. It quantifies and maps PPFD to evaluate light conditions and its implications for vegetation growth. The results reveal significant spatial and temporal variation over a year. The under-panel ground irradiance is lower than open-field GHI by 18% (summer), 22% (spring), 16% (autumn), and 3% (winter), and this seasonal reduction translates into PPFD gradients. This variation supports a precision agrivoltaic strategy that zones land based on irradiance levels. By aligning crop types and planting schedules with seasonal light profiles, land productivity and ecological value can be improved. These findings are highly applicable in Aotearoa New Zealand’s pasture-based systems and show that effective light management is critical for agrivoltaic success in temperate maritime climates. This is, to our knowledge, the first spatial PPFD zoning analysis for fixed-tilt agrivoltaics, linking year-round ground-light maps to crop/pasture suitability. Full article

(This article belongs to the Special Issue Solar Energy, Governance and CO₂ Emissions)

22 pages, 1165 KB

Open AccessArticle

Technical, Technological, Environmental and Energetical Aspects in Livestock Building Construction Using Structural Timber

by Jan Barwicki, Witold Jan Wardal, Kamila Ewelina Mazur and Mikhail Tseyko

Energies 2025, 18(20), 5411; https://doi.org/10.3390/en18205411 (registering DOI) - 14 Oct 2025

Abstract

The demand for energy-efficient construction in agriculture calls for a reassessment of materials used in livestock buildings. This study evaluated the use of timber as an alternative to traditional materials, with a focus on embodied energy (EE) and carbon footprint (CFP) Eight EU [...] Read more.

The demand for energy-efficient construction in agriculture calls for a reassessment of materials used in livestock buildings. This study evaluated the use of timber as an alternative to traditional materials, with a focus on embodied energy (EE) and carbon footprint (CFP) Eight EU countries (Germany, Poland, Spain, Italy, Denmark, France, Sweden, and Finland), were analyzed considering both forest resources and livestock populations. The forest area varied from more than 310,000 km² in Sweden to just 6464 km² in Denmark. Meanwhile, livestock populations varied significantly, with Germany reporting over 8.2 million LSU (livestock unit, 500 kg) in cattle alone. The number of livestock buildings was estimated assuming 100 LSU per building, allowing for a comparison between timber and conventional designs. Timber-based cowsheds were found to lower embodied carbon by up to 10,433 kg CO₂e per barn compared with 17,450 kg CO₂e for conventional structures. Embodied energy for a single wooden cowshed was around 151 GJ versus more than 246 GJ for a traditional counterpart. Scaled up to the national level, this represents a 35–40% reduction in total embodied energy. In addition to environmental outcomes, the analysis considered economic, technical, and regulatory aspects influencing adoption. The results suggest that substituting conventional materials with timber can contribute to emission reductions in agricultural construction, while further research is needed on fire safety, prefabrication, and policy harmonizations. Full article

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

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42 pages, 2304 KB

Open AccessArticle

Sustainable Component-Level Prioritization of PV Panels, Batteries, and Converters for Solar Technologies in Hybrid Renewable Energy Systems Using Objective-Weighted MCDM Models

by Swapandeep Kaur, Raman Kumar and Kanwardeep Singh

Energies 2025, 18(20), 5410; https://doi.org/10.3390/en18205410 (registering DOI) - 14 Oct 2025

Abstract

Data-driven prioritization of photovoltaic (PV), battery, and converter technologies is crucial for achieving sustainability, efficiency, and cost-effectiveness in the increasingly complex domain of hybrid renewable energy systems (HRES). Conducting an in-depth and systematic ranking of these components for solar-based HRESs necessitates a comprehensive [...] Read more.

Data-driven prioritization of photovoltaic (PV), battery, and converter technologies is crucial for achieving sustainability, efficiency, and cost-effectiveness in the increasingly complex domain of hybrid renewable energy systems (HRES). Conducting an in-depth and systematic ranking of these components for solar-based HRESs necessitates a comprehensive multi-criteria decision-making (MCDM) framework. This study develops as the most recent and integrated approach available in the literature. To ensure balanced and objective weighting, five quantitative weighting techniques, Entropy, Standard Deviation, CRITIC, MEREC, and CILOS, were aggregated through the Bonferroni operator, thereby minimizing subjective bias while preserving robustness. The final ranking was executed using the measurement of alternatives and ranking according to compromise solution method (MARCOS). Subsequently, comparative validation was conducted across eight additional MCDM methods, supplemented by correlation and sensitivity analysis to evaluate the consistency and reliability of the obtained results. The results revealed that thin-film PV modules (0.7108), hybrid supercapacitor batteries (0.6990), and modular converters (1.1812) emerged as the top-performing technologies, reflecting optimal trade-offs among technical, economic, and environmental performance criteria. Correlation analysis (ρ > 0.9 across nine MCDM methods) confirmed the stability of the rankings. The results establish a reproducible decision-support framework for designing sustainable hybrid systems. These technologies demonstrated superior thermal stability, cycling endurance, and system scalability, respectively, thus laying a foundation for more sustainable and resilient hybrid energy system deployments. The proposed framework provides a reproducible, transparent, and resilient decision-support tool designed to assist engineers, researchers, and policy-makers in developing reliable low-carbon components for the realization of future carbon-neutral energy infrastructures. Full article

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

27 pages, 9304 KB

Open AccessArticle

A Graphical Tool for Predicting Class EF Inverter Behavior Including Non-Ideal Load Conditions

by Baptiste Daire, Christian Martin, Fabien Sixdenier, Charles Joubert and Loris Pace

Energies 2025, 18(20), 5409; https://doi.org/10.3390/en18205409 (registering DOI) - 14 Oct 2025

Abstract

This paper presents a novel analytical framework for the design and understanding of class EF inverters under both optimal and non-optimal load conditions. Unlike conventional approaches that rely heavily on numerical simulations, the proposed method provides a fast, visual, and intuitive tool for [...] Read more.

This paper presents a novel analytical framework for the design and understanding of class EF inverters under both optimal and non-optimal load conditions. Unlike conventional approaches that rely heavily on numerical simulations, the proposed method provides a fast, visual, and intuitive tool for analyzing inverter operation. Its effectiveness is demonstrated experimentally on a 15 MHz class EF inverter across three distinct load conditions, showing good agreement with theoretical predictions. To highlight the robustness and broad applicability of the approach, a class

Φ_{2}

inverter—a lumped-element analog of the class EF inverter—is also implemented and successfully analyzed. By combining theoretical insight, experimental validation, and generalization to alternative topologies, the proposed framework offers an efficient, accessible, and versatile tool for high-frequency resonant inverter design. Full article

(This article belongs to the Special Issue New Power System Planning and Scheduling)

21 pages, 2034 KB

Open AccessArticle

Explainable Machine Learning Prediction of Vehicle CO₂ Emissions for Sustainable Energy and Transport

by Dong Yuan, Long Tang, Xueyuan Yang, Fanqin Xu and Kailong Liu

Energies 2025, 18(20), 5408; https://doi.org/10.3390/en18205408 (registering DOI) - 14 Oct 2025

Abstract

Transport is a major contributor to anthropogenic greenhouse gases, making accurate assessment of vehicle emissions essential for climate change mitigation. This study develops a comparative machine learning framework to predict CO₂ emissions from internal combustion engines (ICEs) and hybrid electric vehicles (HEVs), [...] Read more.

Transport is a major contributor to anthropogenic greenhouse gases, making accurate assessment of vehicle emissions essential for climate change mitigation. This study develops a comparative machine learning framework to predict CO₂ emissions from internal combustion engines (ICEs) and hybrid electric vehicles (HEVs), using data from the UK Vehicle Certification Agency. In addition to standard technical variables, the study considers noise level, a factor seldom integrated into emission modeling, reflecting potential interactions between acoustic conditions and vehicular emission patterns. Explainable machine learning techniques, including accumulated local effects, are employed to clarify how engine capacity, fuel consumption and pollutant indicators influence CO₂ outputs under different driving conditions. Results show that medium- and high-speed driving dominate ICE emissions, whereas HEVs maintain lower emissions except under high power demand. By combining predictive modeling with interpretability, the study advances environmental informatics and provides actionable insights for low-carbon vehicle design, emission standards and sustainable transportation policies aligned with global climate goals. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

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24 pages, 4688 KB

Open AccessArticle

Evaluation of Steam Channeling Severity Between Cyclic Steam Simulation Wells in Offshore Heavy Oil Reservoirs Based on Cloud Model and Improved AHP-CRITIC Method

by Yigang Liu, Jianhua Bai, Qiuxia Wang, Yongbin Zhao, Zhiyuan Wang, Jia Wen and Xiaofei Sun

Energies 2025, 18(20), 5407; https://doi.org/10.3390/en18205407 (registering DOI) - 14 Oct 2025

Abstract

Steam channeling significantly affects the production performance of cyclic steam stimulation (CSS) wells in offshore heavy oil reservoirs. However, there remains a lack of effective methods for evaluating the steam channeling severity between CSS wells in offshore heavy oil reservoirs. This study develops [...] Read more.

Steam channeling significantly affects the production performance of cyclic steam stimulation (CSS) wells in offshore heavy oil reservoirs. However, there remains a lack of effective methods for evaluating the steam channeling severity between CSS wells in offshore heavy oil reservoirs. This study develops a novel evaluation model to quantitatively evaluate the steam channeling severity between CSS wells in offshore heavy oil reservoirs via the improved AHP-CRITIC (IAHP-CRITIC) method and the cloud model. The results indicated that, compared with the reservoir survey results for the three typical reservoirs, the accuracies of the results obtained by the AHP, CRITIC, AHP-CRITIC, and IAHP-CRITIC methods were 88%, 52%, 92%, and 100%, respectively. Therefore, the IAHP-CRITIC method was more reliable than the other methods in terms of calculating the indicator weights and evaluating the steam channeling severity between the CSS wells. The Lw7 and Lw12 in the L reservoir and Rw2, Rw3, and Rw6 in the R reservoir exhibited strong steam channeling. It is necessary to control the steam channeling of these CSS wells. This is the first study to report the evaluation of steam channeling severity between CSS wells in offshore heavy oil reservoirs. This study provides an effective model to quantitatively evaluate the steam channeling severity between CSS wells and offers valuable insights for the selection of effective strategies to control the steam channeling between CSS wells and enhance offshore heavy oil recovery. Full article

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

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37 pages, 1915 KB

Open AccessArticle

A Multicriteria Approach to the Study of the Energy Transition Results for EU Countries

by Alla Polyanska, Dariusz Sala, Vladyslav Psyuk and Yuliya Pazynich

Energies 2025, 18(20), 5406; https://doi.org/10.3390/en18205406 (registering DOI) - 14 Oct 2025

Abstract

The article presents a multicriterial approach to evaluating the efficiency of the energy transition in EU countries, emphasizing the relationship between resource efficiency and the results of transition. The study uses a data analysis methodology (DEA) to evaluate how effectively countries use resources [...] Read more.

The article presents a multicriterial approach to evaluating the efficiency of the energy transition in EU countries, emphasizing the relationship between resource efficiency and the results of transition. The study uses a data analysis methodology (DEA) to evaluate how effectively countries use resources (inputs), such as energy consumption, investment and innovative development, to achieve the desired results (outputs), including the renewable energy sources, reduction of CO₂ and labour trends. The use of DEA with Python 3.10 software made it possible to obtain objective performance and compare them with the energy transition index (ETI). The DEA and ETI based efficiency matrix has identified four clusters of countries: high efficiency and high transition readiness; high efficiency and low transition readiness; low efficiency and high transition readiness; low efficiency and transition readiness. Validation by means of a solution (DS) confirmed the reliability of the results. The conclusions emphasize that the higher efficiency of resource use does not automatically meet the higher transition indicators, which indicates the need to improve management, innovation spread and investment distribution. The study helps to develop evidence policy by offering a system for monitoring and comparative analysis of the efficiency of the energy transition in EU countries. Full article

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

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20 pages, 4070 KB

Open AccessArticle

Study on Meta-Learning-Improved Operational Characteristic Model of Central Air-Conditioning Systems

by Shuai Guo, Guiping Peng, Shiheng Chai, Jiwei Jia, Zhenhui Deng and Zhenqian Chen

Energies 2025, 18(20), 5405; https://doi.org/10.3390/en18205405 (registering DOI) - 14 Oct 2025

Abstract

Establishing accurate models for central air-conditioning systems is an indispensable part of energy-saving optimization research. This paper focuses on large commercial buildings and conducts research on improving the energy efficiency model of chillers in central air-conditioning systems based on meta-learning. Taking the Model-Agnostic [...] Read more.

Establishing accurate models for central air-conditioning systems is an indispensable part of energy-saving optimization research. This paper focuses on large commercial buildings and conducts research on improving the energy efficiency model of chillers in central air-conditioning systems based on meta-learning. Taking the Model-Agnostic Meta-Learning (MAML) framework as the core, the study systematically addresses the energy efficiency prediction problem of chillers under different operating conditions and across different equipment. It constructs a comprehensive research process including data preparation, meta-model training, fine-tuning and evaluation, cross-device transfer, and energy efficiency analysis. Through its bi-level optimization mechanism, MAML significantly enhances the model’s rapid adaptability to new tasks. Experimental validation demonstrates that: under varying operating conditions on the same device, only 5 data points are required to achieve a relative error (RE) within 3%; under similar operating conditions across different devices, 4 data points achieve a RE within 5%. This represents a reduction in data requirements by 50% and 73%, respectively, compared to standard Multi-Layer Perceptron (MLP) models. This method effectively addresses modeling challenges in complex operating scenarios and offers an efficient solution for intelligent management. It significantly enhances the model’s rapid adaptation capability to new tasks, particularly its generalization performance in data-scarce scenarios. Full article

(This article belongs to the Special Issue Heat Transfer and Thermodynamics Technologies for Supercritical/Phase-Change CO₂ Energy Cycles)

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23 pages, 9577 KB

Open AccessArticle

Polarity-Dependent DC Dielectric Behavior of Virgin XLPO, XLPE, and PVC Cable Insulations

by Khomsan Ruangwong, Norasage Pattanadech and Pittaya Pannil

Energies 2025, 18(20), 5404; https://doi.org/10.3390/en18205404 (registering DOI) - 14 Oct 2025

Abstract

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin [...] Read more.

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article

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