Energies

0 pages, 2763 KiB

Open AccessArticle

Model–Data Hybrid-Driven Real-Time Optimal Power Flow: A Physics-Informed Reinforcement Learning Approach

by Ximing Zhang, Xiyuan Ma, Yun Yu, Duotong Yang, Zhida Lin, Changcheng Zhou, Huan Xu and Zhuohuan Li

Energies 2025, 18(13), 3483; https://doi.org/10.3390/en18133483 (registering DOI) - 1 Jul 2025

Abstract

With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory behavior policies and experience-based data exploitation [...] Read more.

With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory behavior policies and experience-based data exploitation in practical applications, compounded by (b) users’ distrust from the opacity of model decision mechanics. To address these, a model–data hybrid-driven physics-informed reinforcement learning (PIRL) algorithm is proposed in this paper. Specifically, the proposed methodology uses the proximal policy optimization (PPO) algorithm as the agent’s foundational framework and constructs a PI-actor network embedded with prior model knowledge derived from power flow sensitivity into the agent’s actor network via the PINN method, which achieves dual optimization objectives: (a) enhanced environmental perceptibility to improve experience utilization efficiency via gradient-awareness from model knowledge during actor network updates, and (b) improved user trustworthiness through mathematically constrained action gradient information derived from explicit model knowledge, ensuring actor updates adhere to safety boundaries. The simulation and validation results show that the PIRL algorithm outperforms the baseline PPO algorithm in terms of training stability, exploration efficiency, economy, and security. Full article

(This article belongs to the Topic Game Theory and Artificial Intelligence Methods in Sustainable and Renewable Energy Power Systems)

0 pages, 4190 KiB

Open AccessArticle

Ruderal Habitats: A Source for Biomass and Biogas

by Maciej Murawski, Wojciech Czekała, Leszek Majchrzak, Bogusława Waliszewska, Alicja Lerczak, Magdalena Janyszek-Sołtysiak, Magdalena Zborowska, Marta Cieślik, Agnieszka Sieradzka and Jacek Dach

Energies 2025, 18(13), 3482; https://doi.org/10.3390/en18133482 - 1 Jul 2025

Abstract

The aim of the study was to evaluate the chemical composition and biogas potential of selected ruderal and expansive plant species: Heracleum sosnowskyi, Aegopodium podagraria, Chaerophyllum bulbosum, Acer negundo, and Urtica dioica. Plant material was collected from a [...] Read more.

The aim of the study was to evaluate the chemical composition and biogas potential of selected ruderal and expansive plant species: Heracleum sosnowskyi, Aegopodium podagraria, Chaerophyllum bulbosum, Acer negundo, and Urtica dioica. Plant material was collected from a 19th-century park in the village of Niegolewo (Greater Poland Voivodship) and analyzed for cellulose, lignin, hemicellulose, extractives, and ash content before and after methane fermentation. Fermentation followed DIN 38 414-S8, and chemical analyses used standardized methods (TAPPI, Seifert, and DIN). Statistical analyses included ANOVA, CVA, and hierarchical clustering. The highest biogas yield was obtained from A. podagraria, which is associated with low lignin and high hemicellulose degradation. The results confirm the potential of ruderal biomass as a diverse source for biogas production. Full article

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0 pages, 3348 KiB

Open AccessArticle

An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties

by Tolulope David Makanju, Ali N. Hasan, Oluwole John Famoriji and Thokozani Shongwe

Energies 2025, 18(13), 3481; https://doi.org/10.3390/en18133481 - 1 Jul 2025

Abstract

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of [...] Read more.

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. 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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0 pages, 4306 KiB

Open AccessArticle

A Dynamic Investigation of a Solar Absorption Plant with Nanofluids for Air-Conditioning of an Office Building in a Mild Climate Zone

by Luca Cirillo, Sabrina Gargiulo, Adriana Greco, Claudia Masselli, Sergio Nardini, Vincenzo Orabona and Lucrezia Verneau

Energies 2025, 18(13), 3480; https://doi.org/10.3390/en18133480 - 1 Jul 2025

Abstract

This study explores the impact of using water-Al₂O₃ nanofluids, at different nanoparticle concentrations, in solar thermal collectors for solar cooling applications. Improving the seasonal energy performance of solar cooling systems is a current research priority, and this work investigates whether [...] Read more.

This study explores the impact of using water-Al₂O₃ nanofluids, at different nanoparticle concentrations, in solar thermal collectors for solar cooling applications. Improving the seasonal energy performance of solar cooling systems is a current research priority, and this work investigates whether nanofluids can significantly enhance system efficiency compared to traditional heat transfer fluids. A transient simulation was carried out using a dynamic model developed in TRNSYS (TRANsient SYstem Simulation), evaluating the system performance throughout the cooling season. The results show that in July, under low volumetric flow conditions and with nanoparticle concentrations of 0.6% and 0.3%, the solar fraction reaches a maximum value of 1. Using a nanofluid at 0.6% concentration leads to significantly higher fractional energy savings compared to pure water. Despite increased pumping energy, the overall energy savings—which include the contribution from an auxiliary boiler—exceed 80% when nanofluids are used. This study goes beyond previous work by providing a dynamic, system-level simulation of nanofluid-enhanced solar cooling performance under realistic operating conditions. The findings demonstrate the practical potential of nanofluids as a valid and more energy-efficient alternative in solar thermal applications. Full article

(This article belongs to the Special Issue Advanced Thermal Simulation of Energy Systems: 2nd Edition)

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0 pages, 2478 KiB

Open AccessArticle

On the Influence of PV Cell and Diode Configurations on the Performance of a CPVT Collector: A Comparative Analysis

by João Gomes, Juan Pablo Santana, Damu Murali, George Pius and Iván P. Acosta-Pazmiño

Energies 2025, 18(13), 3479; https://doi.org/10.3390/en18133479 - 1 Jul 2025

Abstract

Concentrating photovoltaic-thermal (CPVT) collectors use reflective surfaces to focus sunlight onto a smaller receiver area, increasing thermal energy output while maintaining annual energy efficiency. Ray-tracing simulations are employed in this study using Tonatiuh to optimise the characteristics of the Double MaReCo (DM) collector, [...] Read more.

Concentrating photovoltaic-thermal (CPVT) collectors use reflective surfaces to focus sunlight onto a smaller receiver area, increasing thermal energy output while maintaining annual energy efficiency. Ray-tracing simulations are employed in this study using Tonatiuh to optimise the characteristics of the Double MaReCo (DM) collector, which is an improved version of the commercially available Solarus Power Collector (PC). Focused on enhancing electrical performance, the photovoltaic (PV) cell configurations are varied on the bottom side of the receiver, while the top-side PV cells remain constant. The study also analyses the influence of diodes and transparent gables on the annual solar irradiance received by the PV cells. From the analysis, it is observed that the specific annual irradiance received by the PV cells in the DM collector with transparent gables is nearly 64% more compared to that of the PC counterpart. It is also observed that the transparency of gables becomes significant only when the whole area of the receiver is covered by PV cells. With the goal of improving performance while lowering the cost and complexity of the DM collector, the study investigates various collector design characteristics that may shed more light on optimising the current model. Full article

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

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0 pages, 1078 KiB

Open AccessSystematic Review

Application of TOPSIS for Multi-Criteria Decision Analysis (MCDA) in Power Systems: A Systematic Literature Review

by Jack Mathebula and Nhlanhla Mbuli

Energies 2025, 18(13), 3478; https://doi.org/10.3390/en18133478 - 1 Jul 2025

Abstract

In this study, the authors present the results of a systematic literature review on applications of the technique for order of preference by similarity to ideal solution (TOPSIS) in power systems. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was [...] Read more.

In this study, the authors present the results of a systematic literature review on applications of the technique for order of preference by similarity to ideal solution (TOPSIS) in power systems. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was used in the identification of publications used in this research. The SCOPUS database was utilized to locate the publication, and a total of 78 articles published between 2014 and 2024 were included in the review. A bibliometric analysis was performed, and reports were given on the annual number of publications and the top 10 cited journals. The main themes emerging from the content review of the publications were types of TOPSIS approaches, calculation of weights in multi-criteria decision-making (MCDM) problems, energy markets applications, renewable energy technologies assessment, heating and cooling systems combined with power systems, power system operation strategies, power system stability assessment, power system operations planning, and other power systems applications. Research trends and developments in the area were analyzed to identify the existing gaps. Proposed future research areas were identified based on trends and gaps presented. Full article

31 pages, 2872 KiB

Open AccessArticle

Intelligent Management of Renewable Energy Communities: An MLaaS Framework with RL-Based Decision Making

by Rafael Gonçalves, Diogo Gomes and Mário Antunes

Energies 2025, 18(13), 3477; https://doi.org/10.3390/en18133477 - 1 Jul 2025

Abstract

Given the increasing energy demand and the environmental consequences of fossil fuel consumption, the shift toward sustainable energy sources has become a global priority. Renewable Energy Communities (RECs)—comprising citizens, businesses, and legal entities—are emerging to democratise access to renewable energy. These communities allow [...] Read more.

Given the increasing energy demand and the environmental consequences of fossil fuel consumption, the shift toward sustainable energy sources has become a global priority. Renewable Energy Communities (RECs)—comprising citizens, businesses, and legal entities—are emerging to democratise access to renewable energy. These communities allow members to produce their own energy, sharing or selling any surplus, thus promoting sustainability and generating economic value. However, scaling RECs while ensuring profitability is challenging due to renewable energy intermittency, price volatility, and heterogeneous consumption patterns. To address these issues, this paper presents a Machine Learning as a Service (MLaaS) framework, where each REC microgrid has a customised Reinforcement Learning (RL) agent and electricity price forecasts are included to support decision-making. All the conducted experiments, using the open-source simulator Pymgrid, demonstrate that the proposed agents reduced operational costs by up to 96.41% compared to a robust baseline heuristic. Moreover, this study also introduces two cost-saving features: Peer-to-Peer (P2P) energy trading between communities and internal energy pools, allowing microgrids to draw local energy before using the main grid. Combined with the best-performing agents, these features achieved trading cost reductions of up to 45.58%. Finally, in terms of deployment, the system relies on an MLOps-compliant infrastructure that enables parallel training pipelines and an autoscalable inference service. Overall, this work provides significant contributions to energy management, fostering the development of more sustainable, efficient, and cost-effective solutions. Full article

(This article belongs to the Special Issue Artificial Intelligence in Energy Sector)

23 pages, 952 KiB

Open AccessArticle

Electromechanical Model and Transient Behaviors of Power Systems

by Xueting Cheng and Deqiang Gan

Energies 2025, 18(13), 3476; https://doi.org/10.3390/en18133476 - 1 Jul 2025

Abstract

A large-scale power system is often represented by a set of differential-algebraic equations, so-called electromechanical models. In this paper, it is shown that these models can have several analytical properties, which can be exploited to help the study of power system dynamics. These [...] Read more.

A large-scale power system is often represented by a set of differential-algebraic equations, so-called electromechanical models. In this paper, it is shown that these models can have several analytical properties, which can be exploited to help the study of power system dynamics. These analytical properties, associated with the complete power system model and subsystem models, are described in detail. The implications of such analytical properties are also examined. Full article

26 pages, 2757 KiB

Open AccessArticle

Comparative Life Cycle Analysis for Duct Air Conditioning Systems Based on Evaporative and Vapor Compression Technologies

by Andrzej Marcinkowski and Dmytro Levchenko

Energies 2025, 18(13), 3475; https://doi.org/10.3390/en18133475 - 1 Jul 2025

Abstract

The environmental impact of innovative indirect regenerative evaporative cooling (IREC) technology is analyzed using the life cycle assessment. This study compared typical equipment using this technology from Innovative Ideas LLC with available-on-the-market traditional vapor compression ducted air conditioning systems as the closest analogous [...] Read more.

The environmental impact of innovative indirect regenerative evaporative cooling (IREC) technology is analyzed using the life cycle assessment. This study compared typical equipment using this technology from Innovative Ideas LLC with available-on-the-market traditional vapor compression ducted air conditioning systems as the closest analogous representatives of the vapor compression technology. For comparison, units with the same cooling capacity (5 kW) were selected. The endpoint indicators demonstrated that the air conditioning systems using IREC technology had lower environmental load compared to the vapor compression system by 29–70%, depending on the scenario and damage category. This advantage resulted from the significantly higher coefficient of performance of the IREC system. The amounts of cooling energy generated and electricity consumption were determined based on temperature and relative humidity data recorded at hourly intervals in the summer seasons of 2023 and 2024. The operation turned out to be a life cycle stage with dominating environmental load. The uncertainty analysis carried out with Monte Carlo simulations indicated significant deviation, particularly for the ecosystem category. The sensitivity analysis showed that the assumed electricity mix did not significantly affect the general conclusions. Full article

(This article belongs to the Special Issue Planning and Management of Buildings’ Energy and Environmental Efficiency in Urban Environment)

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27 pages, 2593 KiB

Open AccessArticle

Increasing Efficiency of Energy Conversion Systems from Renewable Sources Using Voltage Source Inverters with Soft Switching of Transistors

by Witold Mazgaj and Zbigniew Szular

Energies 2025, 18(13), 3474; https://doi.org/10.3390/en18133474 - 1 Jul 2025

Abstract

This article presents proposals to increase the efficiencies of energy conversion systems from renewable sources using a soft-switching technique in three-phase voltage source inverters. The first part of this article briefly presents basic systems for generating energy from renewable sources. Special attention is [...] Read more.

This article presents proposals to increase the efficiencies of energy conversion systems from renewable sources using a soft-switching technique in three-phase voltage source inverters. The first part of this article briefly presents basic systems for generating energy from renewable sources. Special attention is paid to both photovoltaic and wind power plants. The next section describes the voltage source inverter with the soft-switching system of transistors, which is resistant to disturbances in the control systems of inverters. Laboratory tests on cooperation between the voltage source inverter and the AC grid are carried out for two cases, when energy is transmitted from the DC circuit to the AC grid and vice versa. In the final part, the efficiencies of energy conversion systems operating under the voltage source inverter with the soft-switching technique are compared with those of an inverter using hard switching of transistors. A comparison is made for energy conversion systems with a rated power of 100 kW and 1 MW. Full article

37 pages, 1850 KiB

Open AccessArticle

Evaluation of the Reliability of Thermogravimetric Indices for Predicting Coal Performance in Utility Systems

by Krzysztof M. Czajka

Energies 2025, 18(13), 3473; https://doi.org/10.3390/en18133473 - 1 Jul 2025

Abstract

A thorough understanding of fuel behaviour is essential for designing and operating thermochemical systems. Thermogravimetric analysis (TGA) is among the most widely used fuel characterization methods, offering parameters like reactivity and ignition temperature, and enabling comprehensive fuel behaviour assessment through combined indices. This [...] Read more.

A thorough understanding of fuel behaviour is essential for designing and operating thermochemical systems. Thermogravimetric analysis (TGA) is among the most widely used fuel characterization methods, offering parameters like reactivity and ignition temperature, and enabling comprehensive fuel behaviour assessment through combined indices. This study critically examines the applicability of TGA-based indices for predicting coal performance in industrial processes such as gasification and combustion, where devolatilization, ignition, and burnout stages are key. TGA-derived data are compared with results from established methods, including drop tube furnace (DTF), pulse ignition (PI), and entrained flow reactor (EFR) tests. Findings indicate that the Volatile Matter Release Index (D₂) effectively predicts DTF behaviour (R² = 0.938, max residuals: 4.1 pp), proving useful for fast devolatilization analysis. The Flammability Index (C₁) and Ignition Index (C₃) correlate well with PI results (R² = 0.927 and 0.931, max residuals: 53.3a °C), making them reliable ignition indicators. While TGA tools showed limited accuracy in burnout prediction, the proposed Modified Burnout Characteristic Index (B_1′) achieved reasonable performance (R² = 0.734, max residuals: 0.062%∙°C⁻¹). Overall, selected TGA-based indices offer strong predictive potential for key thermochemical conversion stages. Full article

(This article belongs to the Special Issue Towards Cleaner and More Efficient Combustion)

17 pages, 1567 KiB

Open AccessArticle

Long-Term Forecast of Peak Power Demand for Poland—Construction and Use of Simplified Forecasting Models

by Tomasz Popławski

Energies 2025, 18(13), 3472; https://doi.org/10.3390/en18133472 - 1 Jul 2025

Abstract

This article presents a simplified method for forecasting Poland’s long-term peak electricity demand using a modified Prigogine logistic equation. While complex models like the WEM or PRIMES offer high precision, their complexity and data requirements can be limiting. The proposed model offers a [...] Read more.

This article presents a simplified method for forecasting Poland’s long-term peak electricity demand using a modified Prigogine logistic equation. While complex models like the WEM or PRIMES offer high precision, their complexity and data requirements can be limiting. The proposed model offers a quicker and more accessible alternative, using the average annual load factor (

A L F

) as a key indicator. Based on historical data (1985–2024), the model was validated and optimized (MAPE < 2%), then applied to forecast the demand through 2040 under three scenarios: coal-based energy, nuclear energy and energy from RESs (renewables). Depending on the scenario, the peak demand is expected to rise from 28.7 GW in 2024 to 34–40 GW in 2040. The model’s strength lies in its ability to capture dynamic system behavior, including chaos and bifurcations, making it suitable for rapid assessments and strategic planning. Despite its limitations—such as a lower level of detail and an inability to integrate sectoral policies—the Prigogine-based approach offers a transparent, cost-effective forecasting tool, especially when complemented by the use of advanced simulation models. Full article

(This article belongs to the Special Issue Improvements of the Electricity Power System: 3rd Edition)

21 pages, 5776 KiB

Open AccessArticle

Thermal Effects on Fines Migration: Insights from Sand Pack Experiments

by Fernando Rengifo Barbosa, Rahman Miri, Mahmood Salimi and Alireza Nouri

Energies 2025, 18(13), 3471; https://doi.org/10.3390/en18133471 - 1 Jul 2025

Abstract

Mobilisation of in situ fine particles within oil sands reservoirs plays a critical role in permeability reduction and pore throat blockage, ultimately impairing reservoir performance and diminishing well productivity during thermal recovery operations. Variations in reservoir fluid conditions, such as changes in salinity [...] Read more.

Mobilisation of in situ fine particles within oil sands reservoirs plays a critical role in permeability reduction and pore throat blockage, ultimately impairing reservoir performance and diminishing well productivity during thermal recovery operations. Variations in reservoir fluid conditions, such as changes in salinity and temperature, trigger the detachment, transport, and redeposition of fines within porous media. This study introduces a novel high-pressure high-temperature (HP-HT) sand retention testing (SRT) facility designed for evaluating formation damage by fines migration in SAGD producer wells, under salinity change and elevated temperature conditions. Such an integrated approach accounting for conditions closer to near-wellbore SAGD producers has not been explored in previous SRT methodologies. Laboratory tests were conducted on synthetic sand mixtures replicating the particle size distribution (PSD) and sand composition of the McMurray Formation, packed over a slotted liner coupon as a common sand control device used in SAGD producer wells. Produced fines concentration analysis, permeability measurements, and post-mortem retention profile analysis were employed to explain the fines transport mechanisms. The results highlighted the influence of repulsive electrostatic forces in mobilising, transport mechanisms and retention of fine particles at elevated temperature and low salinity conditions. The findings of this paper provide a deeper understanding of fines migration in SAGD reservoirs, delivering insights for optimising field strategies to mitigate fines-related flow restrictions and enhance bitumen recovery efficiency. Full article

(This article belongs to the Section H: Geo-Energy)

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15 pages, 10531 KiB

Open AccessArticle

Sensorless Dual TSEP (V_th, R_dson) Implementation for Junction Temperature Measurement in Parallelized SiC MOSFETs

by Louis Alauzet, Patrick Tounsi and Jean-Pierre Fradin

Energies 2025, 18(13), 3470; https://doi.org/10.3390/en18133470 - 1 Jul 2025

Abstract

This article presents a method for detecting the temperature distribution of two parallelized Silicon Carbide (SiC) MOSFETs. Two thermally sensitive electrical parameters (TSEPs), namely the on-state resistance (

R_{d s o n}

) and the threshold voltage (

V_{t h}

), [...] Read more.

This article presents a method for detecting the temperature distribution of two parallelized Silicon Carbide (SiC) MOSFETs. Two thermally sensitive electrical parameters (TSEPs), namely the on-state resistance (

R_{d s o n}

) and the threshold voltage (

V_{t h}

), are introduced. A comparison of the temperatures interpolated by

V_{t h}

and

R_{d s o n}

shows disparity, enabling the detection of individual junction temperatures.

V_{t h}

instability and its measurement are discussed for SiC devices. Experimental results show that, depending on the instability of the

V_{t h}

and the sensitivity of the two TSEPs at certain temperatures, a combination of different TSEPs could be a solution for extracting the maximum junction temperature of parallelized devices. Full article

(This article belongs to the Special Issue Advances in Thermal Management and Reliability of Electronic Systems)

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6 pages, 426 KiB

Open AccessEditorial

The Tyrrhenian Link: A Next-Generation Electrical Infrastructure for the Mediterranean Grid

by Antony Vasile, Davide Astolfi, Marco Pasetti, Rossano Musca, Gaetano Zizzo and Salvatore Favuzza

Energies 2025, 18(13), 3469; https://doi.org/10.3390/en18133469 - 1 Jul 2025

Abstract

The evolution of transmission systems towards decarbonized and renewable energy-based generation has brought HVDC (High Voltage Direct Current) systems to the attention of stakeholders involved in infrastructure development, planning, and component manufacturing [...] Full article

(This article belongs to the Special Issue Technological Horizons in High Voltage Engineering: Towards the Power Systems of the Future)

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27 pages, 3476 KiB

Open AccessReview

Production of Compacted Biofuels in Terms of Their Quality—Current State of Research

by Jakub Styks, Marek Wróbel and Sławomir Francik

Energies 2025, 18(13), 3468; https://doi.org/10.3390/en18133468 - 1 Jul 2025

Abstract

This paper aims to fill a gap in relation to herbaceous biomass, which is the second most important source of biomass used for the production of solid biofuels in the form of pellets. Our research focuses on the final stage of compacted fuel [...] Read more.

This paper aims to fill a gap in relation to herbaceous biomass, which is the second most important source of biomass used for the production of solid biofuels in the form of pellets. Our research focuses on the final stage of compacted fuel production, specifically on the pressure densification of biomass into pellets. This review was conducted using bibliometric techniques. This analysis included searching indexed publications in the Web of Science—Core Collection and Scopus databases. The time range of the searched data was 1991–2024. First, bibliometric data were collected and quantitative analyses were performed. The next stage consisted of analyzing keywords from the authors’ publications using the VOSviewer program. The generated term maps allowed for the analysis of the frequency of keywords in different years and the most frequently cited ones, which enabled the identification of current research topics and the evolution of trends. Based on the collected data, applications, process parameters and raw materials used in the biomass densification process were determined. Following the bibliometric analysis, detailed analyses were carried out on the factors influencing the pressure compaction process. Full article

(This article belongs to the Special Issue Biomass Conversion Technologies: 4th Edition)

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27 pages, 5923 KiB

Open AccessArticle

Assessment of Climate Change Impacts on Renewable Energy Resources in Western North America

by Hsiang-He Lee, Robert S. Arthur, Jean-Christophe Golaz, Thomas A. Edmunds, Jessica L. Wert, Matthew V. Signorotti and Jean-Paul Watson

Energies 2025, 18(13), 3467; https://doi.org/10.3390/en18133467 - 1 Jul 2025

Abstract

We examine a 25 km resolution climate model dataset to evaluate how regional climate change impacts solar and wind energy under a high-emission scenario. Our study considers the Western Electricity Coordinating Council (WECC) region, which covers the western United States and southwestern Canada, [...] Read more.

We examine a 25 km resolution climate model dataset to evaluate how regional climate change impacts solar and wind energy under a high-emission scenario. Our study considers the Western Electricity Coordinating Council (WECC) region, which covers the western United States and southwestern Canada, focusing specifically on locations with existing solar and wind infrastructure. First, we conduct a historical model comparison of solar and wind energy capacity factors to highlight model uncertainties across the study area. Using future climate projections, we then assess the seasonal patterns of solar and wind capacity factors for three timeframes: historical, mid-century, and end of century. Additionally, we estimate the frequency of solar and wind resource droughts during these periods for the entire WECC and its five operational subregions, finding that certain subregions are more susceptible to energy droughts due to limited renewable resources. Finally, we present day-ahead capacity factor forecasts to support energy storage planning and provide estimates of offshore wind energy capacity within the WECC. Our results indicate that offshore wind capacity factors are nearly twice as high as onshore values, with less seasonal variation, which suggests that offshore wind could offer a more consistent renewable energy supply in the future. Full article

(This article belongs to the Special Issue The Application of Weather and Climate Research in the Energy Sector)

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14 pages, 1851 KiB

Open AccessArticle

Effects of Ethanol–Gasoline Blends on the Performance and Emissions of a Vehicle Spark-Ignition Engine

by Maciej Gajewski, Szymon Wyrąbkiewicz and Jerzy Kaszkowiak

Energies 2025, 18(13), 3466; https://doi.org/10.3390/en18133466 - 1 Jul 2025

Abstract

This article presents experimental results related to the influence of bioethanol content in fuel blends on the performance and emissions of a spark-ignition engine. Tests were conducted for six ethanol–gasoline mixtures (ranging from 0% to 100% ethanol) under three engine control strategies: factory [...] Read more.

This article presents experimental results related to the influence of bioethanol content in fuel blends on the performance and emissions of a spark-ignition engine. Tests were conducted for six ethanol–gasoline mixtures (ranging from 0% to 100% ethanol) under three engine control strategies: factory settings, a fuel dose increased by 10%, and a fuel dose increased by 20%—both with an ignition timing adjustment of +3°. Measurements included engine power and torque, as well as emissions of CO, CO₂, HC, O₂, and particulate matter, all performed under a full engine load. The results revealed the strong dependence of engine behavior on ethanol content. Increasing the ethanol concentration significantly reduced CO and HC emissions, as well as markedly lowering particulate emissions—particularly at 30% ethanol. Conversely, pure ethanol led to substantial reductions in power (up to 28%) and torque (up to 32%) compared to conventional gasoline. Adjustments to the fuel dose and ignition timing partially mitigated these losses. Emissions of CO₂ and oxygen content in exhaust gases varied depending on the blend, highlighting the complex nature of the combustion process. The findings contribute to the understanding of renewable fuel behavior in SI engines and underscore the influence of both fuel composition and control strategies on performance and emission characteristics. Full article

(This article belongs to the Topic Advanced Engines Technologies)

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23 pages, 1742 KiB

Open AccessArticle

Regional Disparities, Spatial Effects, and the Dynamic Evolution of Distorted Energy Prices in China

by Zhiyuan Gao, Ziying Jia and Yu Hao

Energies 2025, 18(13), 3465; https://doi.org/10.3390/en18133465 - 1 Jul 2025

Abstract

The distortion of energy prices has become an important obstacle to the high-quality development of China’s economy. Moreover, energy price distortions are not merely a domestic issue. They may trigger carbon leakage by diverting emissions-intensive production to countries with cheaper energy. Although the [...] Read more.

The distortion of energy prices has become an important obstacle to the high-quality development of China’s economy. Moreover, energy price distortions are not merely a domestic issue. They may trigger carbon leakage by diverting emissions-intensive production to countries with cheaper energy. Although the existing literature has extensively examined the effects of energy price distortions, two significant research gaps remain. First, most studies treat energy price distortions merely as an influencing factor, lacking a systematic analysis that places it at the core. Second, the spatial correlation characteristics of energy price distortions are often overlooked. This study measures the degree of energy price distortions across Chinese provinces from 2000 to 2022 and employs methods such as the Global Moran’s I, Local Moran’s I, and kernel density estimation to systematically analyze the spatial correlation, spatial distribution of coordination indices, and dynamic evolution patterns of these distortions. The results reveal that: (1) the overall degree of energy price distortions in China exhibited a trend of rising first and then declining, with significant regional disparities; (2) the regional gap followed an “expansion-contraction” trajectory; (3) there is notable spatial autocorrelation, with high-distortion areas concentrated in Northeast China, the middle reaches of the Yellow River, and Northwest China; and (4) the dynamic evolution suggests that distortion levels in high- and medium-value regions may continue to decline, while those in low-value regions may increase. This study fills a critical gap in the systematic spatial analysis of energy price distortions and provides new empirical evidence and policy insights for advancing market-oriented reforms in energy markets. Full article

(This article belongs to the Special Issue Environmental Sustainability and Energy Economy)

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