Energies

27 pages, 910 KiB

Open AccessEditor’s ChoiceArticle

QES Model Aggregating Quality, Environmental Impact, and Social Responsibility: Designing Product Dedicated to Renewable Energy Source

by Dominika Siwiec and Andrzej Pacana

Energies 2025, 18(15), 4029; https://doi.org/10.3390/en18154029 - 29 Jul 2025

Viewed by 236

Abstract

The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting [...] Read more.

The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting the decision-making process of RES product development based on meeting the criteria of quality, environmental impact, and social responsibility (QES). The model was developed in four main stages, implementing multi-criteria decision support methods such as DEMATEL (decision-making trial and evaluation laboratory) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), as well as criteria for social responsibility and environmental impact from the ISO 26000 standard. The model was tested and illustrated using the example of photovoltaic panels (PVs): (i) five prototypes were developed, (ii) 30 PV criteria were identified from the qualitative, environmental, and social groups, (iii) the criteria were reduced to 13 key (strongly intercorrelated) criteria according to DEMATEL, (iv) the PV prototypes were assessed taking into account the importance and fulfilment of their key criteria according to TOPSIS, and (v) a PV ranking was created, where the fifth prototype turned out to be the most advantageous (QES = 0.79). The main advantage of the model is its simple form and transparency of application through a systematic analysis and evaluation of many different criteria, after which a ranking of design solutions is obtained. QES ensures precise decision-making in terms of sustainability of new or already available products on the market, also those belonging to RES. Therefore, QES will find application in various companies, especially those looking for low-cost decision-making support techniques at early stages of product development (design and conceptualization). Full article

(This article belongs to the Special Issue Advances in Electrical Engineering: Intelligent Systems, Modern Algorithms and Advanced Technologies)

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34 pages, 6236 KiB

Open AccessEditor’s ChoiceArticle

Factors Impacting Projected Annual Energy Production from Offshore Wind Farms on the US East and West Coasts

by Rebecca J. Barthelmie, Kelsey B. Thompson and Sara C. Pryor

Energies 2025, 18(15), 4037; https://doi.org/10.3390/en18154037 - 29 Jul 2025

Viewed by 236

Abstract

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences [...] Read more.

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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29 pages, 697 KiB

Open AccessEditor’s ChoiceArticle

Economic Performance of the Producers of Biomass for Energy Generation in the Context of National and European Policies—A Case Study of Poland

by Aneta Bełdycka-Bórawska, Rafał Wyszomierski, Piotr Bórawski and Paulina Trębska

Energies 2025, 18(15), 4042; https://doi.org/10.3390/en18154042 - 29 Jul 2025

Viewed by 417

Abstract

Solid biomass (agro-residue) is the most important source of renewable energy. The accelerating impacts of climate change and global population growth contribute to air pollution through the use of fossil fuels. These processes increase the demand for energy. The European Union has adopted [...] Read more.

Solid biomass (agro-residue) is the most important source of renewable energy. The accelerating impacts of climate change and global population growth contribute to air pollution through the use of fossil fuels. These processes increase the demand for energy. The European Union has adopted a climate action plan to address the above challenges. The main aim of this study was to assess the economic performance of the producers of biomass for energy generation in Poland. The detailed objectives were to determine land resources in the studied agricultural farms and to determine the value of fixed and current assets in the analyzed farms. We used questionnaires as the main method to collect data. Purposive sampling was used to choose the farms. We conducted various tests to analyze the revenues from biomass sales and their normality, such as the Dornik–Hansen test, the Shapiro–Wilk test, the Liliefors test, and the Jargue–Berra statistical test. Moreover, we conducted regression analysis to find factors that are the basis for the economic performance (incomes) of farms that sell biomass. Results: This study demonstrated that biomass sales had a minor impact on the performance of agricultural farms, but they enabled farmers to maintain their position on the market. The economic analysis was carried out on a representative group of Polish agricultural farms, taking into account fixed and current assets, land use, production structure, and employment. The findings indicate that a higher income from biomass sales was generally associated with better economic results per farm and per employee, although not always per hectare of land. This suggests that capital intensity and strategic resource management play a crucial role in the profitability of bioenergy-oriented agricultural production. Conclusions: We concluded that biomass sales had a negligible influence on farm income. But a small income from biomass sales could affect a farm’s economic viability. Full article

(This article belongs to the Section A4: Bio-Energy)

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35 pages, 3995 KiB

Open AccessEditor’s ChoiceReview

Recent Advancements in Latent Thermal Energy Storage and Their Applications for HVAC Systems in Commercial and Residential Buildings in Europe—Analysis of Different EU Countries’ Scenarios

by Belayneh Semahegn Ayalew and Rafał Andrzejczyk

Energies 2025, 18(15), 4000; https://doi.org/10.3390/en18154000 - 27 Jul 2025

Viewed by 695

Abstract

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) [...] Read more.

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article

(This article belongs to the Special Issue Development of Distributed Energy Systems Based on Renewable Energy Sources—Current Status and Development Prospects)

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31 pages, 3300 KiB

Open AccessEditor’s ChoiceArticle

Economic Growth and Energy Consumption in Thailand: Evidence from the Energy Kuznets Curve Using Provincial-Level Data

by Thanakhom Srisaringkarn and Kentaka Aruga

Energies 2025, 18(15), 3980; https://doi.org/10.3390/en18153980 - 25 Jul 2025

Viewed by 433

Abstract

This study investigates the relationship between economic growth and energy consumption using the Energy Kuznets Curve (EKC) framework. Spatial econometric models, including the Spatial Panel Lag Model and the Spatial Dynamic Panel Lag IV Model, are employed to capture both spatial and dynamic [...] Read more.

This study investigates the relationship between economic growth and energy consumption using the Energy Kuznets Curve (EKC) framework. Spatial econometric models, including the Spatial Panel Lag Model and the Spatial Dynamic Panel Lag IV Model, are employed to capture both spatial and dynamic effects. The results indicate that energy consumption in Thailand is spatially clustered, with energy use tending to spill over into neighboring provinces and concentrating in specific regions. Key factors that positively influence energy consumption include gross provincial product (GPP) per capita, population density, and road density. Regions characterized by favorable climates, sufficient infrastructure, and high levels of economic activity exhibit higher per capita energy consumption. The EKC analysis reveals a U-shape relationship between GPP per capita and energy consumption in the BKK&VIC, CE, EA, WE, and NE regions. As many regions continue to experience rising energy consumption, the findings underscore the importance of Thailand adopting more efficient energy usage strategies in tandem with its economic development. Full article

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

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29 pages, 1682 KiB

Open AccessEditor’s ChoiceArticle

Polish Farmers′ Perceptions of the Benefits and Risks of Investing in Biogas Plants and the Role of GISs in Site Selection

by Anna Kochanek, Józef Ciuła, Mariusz Cembruch-Nowakowski and Tomasz Zacłona

Energies 2025, 18(15), 3981; https://doi.org/10.3390/en18153981 - 25 Jul 2025

Viewed by 304

Abstract

In the past decade, agricultural biogas plants have become one of the key tools driving the energy transition in rural areas. Nevertheless, their development in Poland still lags behind that in Western European countries, suggesting the existence of barriers that go beyond technological [...] Read more.

In the past decade, agricultural biogas plants have become one of the key tools driving the energy transition in rural areas. Nevertheless, their development in Poland still lags behind that in Western European countries, suggesting the existence of barriers that go beyond technological or regulatory issues. This study aims to examine how Polish farmers perceive the risks and expected benefits associated with investing in biogas plants and which of these perceptions influence their willingness to invest. The research was conducted in the second quarter of 2025 among farmers planning to build micro biogas plants as well as owners of existing biogas facilities. Geographic Information System (GIS) tools were also used in selecting respondents and identifying potential investment sites, helping to pinpoint areas with favorable spatial and environmental conditions. The findings show that both current and prospective biogas plant operators view complex legal requirements, social risk, and financial uncertainty as the main obstacles. However, both groups are primarily motivated by the desire for on-farm energy self-sufficiency and the environmental benefits of improved agricultural waste management. Owners of operational installations—particularly small and medium-sized ones—tend to rate all categories of risk significantly lower than prospective investors, suggesting that practical experience and knowledge-sharing can effectively alleviate perceived risks related to renewable energy investments. Full article

(This article belongs to the Special Issue Green Additive for Biofuel Energy Production)

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38 pages, 2182 KiB

Open AccessEditor’s ChoiceArticle

Smart Grid Strategies for Tackling the Duck Curve: A Qualitative Assessment of Digitalization, Battery Energy Storage, and Managed Rebound Effects Benefits

by Joseph Nyangon

Energies 2025, 18(15), 3988; https://doi.org/10.3390/en18153988 - 25 Jul 2025

Viewed by 476

Abstract

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the [...] Read more.

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the U.S. electricity market, triggering significant changes in electricity production, transmission, and consumption. Utilities are embracing digital twins and repurposed Utility 2.0 concepts—distributed energy resources, microgrids, innovative electricity market designs, real-time automated monitoring, smart meters, machine learning, artificial intelligence, and advanced data and predictive analytics—to foster operational flexibility and market efficiency. This analysis qualitatively evaluates how digitalization, Battery Energy Storage Systems (BESSs), and adaptive strategies to mitigate rebound effects collectively advance smart duck curve management. By leveraging digital platforms for real-time monitoring and predictive analytics, utilities can optimize energy flows and make data-driven decisions. BESS technologies capture surplus renewable energy during off-peak periods and discharge it when demand spikes, thereby smoothing grid fluctuations. This review explores the benefits of targeted digital transformation, BESSs, and managed rebound effects in mitigating the duck curve problem, ensuring that energy efficiency gains translate into actual savings. Furthermore, this integrated approach not only reduces energy wastage and lowers operational costs but also enhances grid resilience, establishing a robust framework for sustainable energy management in an evolving market landscape. Full article

(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)

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60 pages, 1535 KiB

Open AccessEditor’s ChoiceReview

Renewable Energy Communities (RECs): European and Worldwide Distribution, Different Technologies, Management, and Modeling

by Sandra Corasaniti, Paolo Coppa, Dario Atzori and Ateeq Ur Rehman

Energies 2025, 18(15), 3961; https://doi.org/10.3390/en18153961 - 24 Jul 2025

Viewed by 580

Abstract

Renewable energy communities (RECs) are increasingly recognized as pivotal instruments in the global energy transition, offering decentralized, participatory, and sustainable solutions for energy management, specifically regarding energy production and consumption. The present review provides a comprehensive examination of the REC concept, tracing its [...] Read more.

Renewable energy communities (RECs) are increasingly recognized as pivotal instruments in the global energy transition, offering decentralized, participatory, and sustainable solutions for energy management, specifically regarding energy production and consumption. The present review provides a comprehensive examination of the REC concept, tracing its regulatory evolution, particularly within the European Union through the renewable energy directives (RED II and RED III) and by analyzing its practical implementation across various countries. This paper explores the diverse technologies integrated into REC projects, such as photovoltaic systems, wind turbines, biogas, hydroelectric, and storage solutions, while also considering the socioeconomic frameworks, management models, and local engagement strategies that underpin their success. Key case studies from Europe, Asia, Africa, and Australia illustrate the various approaches, challenges, and outcomes of REC initiatives in different geographic and policy contexts. The analysis also highlights barriers to implementing RECs, including regulatory uncertainty and market integration issues, and identifies the best practices and policies that support REC scalability. By synthesizing current trends and lessons learned, this review aims to inform policymakers, researchers, and practitioners about the transformative role of RECs in achieving decarbonization goals and accomplishing resilient energy systems. Full article

(This article belongs to the Section B: Energy and Environment)

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36 pages, 7620 KiB

Open AccessEditor’s ChoiceReview

Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization

by Han Fu, Amin Mojiri, Junli Wang and Zhe Zhao

Energies 2025, 18(15), 3958; https://doi.org/10.3390/en18153958 - 24 Jul 2025

Viewed by 567

Abstract

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid [...] Read more.

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Full article

(This article belongs to the Section A5: Hydrogen Energy)

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28 pages, 2724 KiB

Open AccessEditor’s ChoiceArticle

Data-Driven Dynamic Optimization for Hosting Capacity Forecasting in Low-Voltage Grids

by Md Tariqul Islam, M. J. Hossain and Md Ahasan Habib

Energies 2025, 18(15), 3955; https://doi.org/10.3390/en18153955 - 24 Jul 2025

Viewed by 305

Abstract

The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. [...] Read more.

The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an

R^{2}

score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions. Full article

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53 pages, 1950 KiB

Open AccessEditor’s ChoiceArticle

Redefining Energy Management for Carbon-Neutral Supply Chains in Energy-Intensive Industries: An EU Perspective

by Tadeusz Skoczkowski, Sławomir Bielecki, Marcin Wołowicz and Arkadiusz Węglarz

Energies 2025, 18(15), 3932; https://doi.org/10.3390/en18153932 - 23 Jul 2025

Viewed by 364

Abstract

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth [...] Read more.

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

(This article belongs to the Section B: Energy and Environment)

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39 pages, 18290 KiB

Open AccessEditor’s ChoiceArticle

Turning Construction, Renovation, and Demolition (CRD) Wood Waste into Biochar: A Scalable and Sustainable Solution for Energy and Environmental Applications

by Aravind Ganesan, Simon Barnabé, Younès Bareha, Simon Langlois, Olivier Rezazgui and Cyrine Boussabbeh

Energies 2025, 18(15), 3902; https://doi.org/10.3390/en18153902 - 22 Jul 2025

Viewed by 422

Abstract

This study investigates the pyrolysis of construction, renovation, and demolition (CRD) wood waste to produce biochar, with a focus on its robustness, scalability, and characterization for energy and environmental applications. Pyrolysis conditions, including the temperature, biomass residence time (BRT), and feedstock mass, were [...] Read more.

This study investigates the pyrolysis of construction, renovation, and demolition (CRD) wood waste to produce biochar, with a focus on its robustness, scalability, and characterization for energy and environmental applications. Pyrolysis conditions, including the temperature, biomass residence time (BRT), and feedstock mass, were varied to evaluate their effects on biochar properties. High-temperature biochars (B800) showed the highest fixed carbon (FC) (87%) and thermostable fraction (TSF) (96%) and the lowest volatile carbon (VC) (9%), with a high carbon content (92%), a large BET surface area (300 m²/g), and a high micropore volume (0.146 cm³/g). However, the hydrogen (0.9%) and oxygen (2.2%) content, Van-Krevelen parameters (H/C: 0.1; O/C: 0.02), and biochar yield (21%) decreased with increasing temperature. Moderate-temperature biochars (B600) have balanced physicochemical properties and yields, making them suitable for adsorption applications. Methyl orange dye removal exceeded 90% under the optimal conditions, with B600 fitting well with the Freundlich isotherm model (R² = 0.97; 1/n = 0.5) and pseudo-second-order kinetic model (R² = 1). The study highlights biochar’s suitability for varied applications, emphasizing the need for scalability in CRD wood pyrolysis. Full article

(This article belongs to the Special Issue Advances in Bio-Energy: Biofuel and Other Value-Added Bioproducts, Production Technology, and Environmental Impact Systems Components)

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54 pages, 3087 KiB

Open AccessEditor’s ChoiceReview

Application of Energy Storage Systems to Enhance Power System Resilience: A Critical Review

by Muhammad Usman Aslam, Md Sazal Miah, B. M. Ruhul Amin, Rakibuzzaman Shah and Nima Amjady

Energies 2025, 18(14), 3883; https://doi.org/10.3390/en18143883 - 21 Jul 2025

Viewed by 447

Abstract

The growing frequency and severity of extreme events, both natural and human-induced, have heightened concerns about the resilience of power systems. Enhancing the resilience of power systems alleviates the adverse impacts of power outages caused by unforeseen events, delivering substantial social and economic [...] Read more.

The growing frequency and severity of extreme events, both natural and human-induced, have heightened concerns about the resilience of power systems. Enhancing the resilience of power systems alleviates the adverse impacts of power outages caused by unforeseen events, delivering substantial social and economic benefits. Energy storage systems play a crucial role in enhancing the resilience of power systems. Researchers have proposed various single and hybrid energy storage systems to enhance power system resilience. However, a comprehensive review of the latest trends in utilizing energy storage systems to address the challenges related to improving power system resilience is required. This critical review, therefore, discusses various aspects of energy storage systems, such as type, capacity, and efficacy, as well as modeling and control in the context of power system resilience enhancement. Finally, this review suggests future research directions leading to optimal use of energy storage systems for enhancing resilience of power systems. Full article

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

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26 pages, 2178 KiB

Open AccessEditor’s ChoiceArticle

Optimizing Agri-PV System: Systematic Methodology to Assess Key Design Parameters

by Kedar Mehta and Wilfried Zörner

Energies 2025, 18(14), 3877; https://doi.org/10.3390/en18143877 - 21 Jul 2025

Viewed by 489

Abstract

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, [...] Read more.

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

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

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32 pages, 3289 KiB

Open AccessEditor’s ChoiceArticle

Optimal Spot Market Participation of PV + BESS: Impact of BESS Sizing in Utility-Scale and Distributed Configurations

by Andrea Scrocca, Roberto Pisani, Diego Andreotti, Giuliano Rancilio, Maurizio Delfanti and Filippo Bovera

Energies 2025, 18(14), 3791; https://doi.org/10.3390/en18143791 - 17 Jul 2025

Viewed by 386

Abstract

Recent European regulations promote distributed energy resources as alternatives to centralized generation. This study compares utility-scale and distributed photovoltaic (PV) systems coupled with Battery Energy-Storage Systems (BESSs) in the Italian electricity market, analyzing different battery sizes. A multistage stochastic mixed-integer linear programming model, [...] Read more.

Recent European regulations promote distributed energy resources as alternatives to centralized generation. This study compares utility-scale and distributed photovoltaic (PV) systems coupled with Battery Energy-Storage Systems (BESSs) in the Italian electricity market, analyzing different battery sizes. A multistage stochastic mixed-integer linear programming model, using Monte Carlo PV production scenarios, optimizes day-ahead and intra-day market offers while incorporating PV forecast updates. In real time, battery flexibility reduces imbalances. Here we show that, to ensure dispatchability—defined as keeping annual imbalances below 5% of PV output—a 1 MW PV system requires 220 kWh of storage for utility-scale and 50 kWh for distributed systems, increasing the levelized cost of electricity by +13.1% and +1.94%, respectively. Net present value is negative for BESSs performing imbalance netting only. Therefore, a multiple service strategy, including imbalance netting and energy arbitrage, is introduced. Performing arbitrage while keeping dispatchability reaches an economic optimum with a 1.7 MWh BESS for utility-scale systems and 1.1 MWh BESS for distributed systems. These results show lower PV firming costs than previous studies, and highlight that under a multiple-service strategy, better economic outcomes are obtained with larger storage capacities. Full article

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

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38 pages, 1945 KiB

Open AccessEditor’s ChoiceReview

Grid Impacts of Electric Vehicle Charging: A Review of Challenges and Mitigation Strategies

by Asiri Tayri and Xiandong Ma

Energies 2025, 18(14), 3807; https://doi.org/10.3390/en18143807 - 17 Jul 2025

Viewed by 1051

Abstract

Electric vehicles (EVs) offer a sustainable solution for reducing carbon emissions in the transportation sector. However, their increasing widespread adoption poses significant challenges for local distribution grids, many of which were not designed to accommodate the heightened and irregular power demands of EV [...] Read more.

Electric vehicles (EVs) offer a sustainable solution for reducing carbon emissions in the transportation sector. However, their increasing widespread adoption poses significant challenges for local distribution grids, many of which were not designed to accommodate the heightened and irregular power demands of EV charging. Components such as transformers and distribution networks may experience overload, voltage imbalances, and congestion—particularly during peak periods. While upgrading grid infrastructure is a potential solution, it is often costly and complex to implement. The unpredictable nature of EV charging behavior further complicates grid operations, as charging demand fluctuates throughout the day. Therefore, efficient integration into the grid—both for charging and potential discharging—is essential. This paper reviews recent studies on the impacts of high EV penetration on distribution grids and explores various strategies to enhance grid performance during peak demand. It also examines promising optimization methods aimed at mitigating negative effects, such as load shifting and smart charging, and compares their effectiveness across different grid parameters. Additionally, the paper discusses key challenges related to impact analysis and proposes approaches to improve them in order to achieve better overall grid performance. Full article

(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)

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28 pages, 3053 KiB

Open AccessEditor’s ChoiceReview

X-in-the-Loop Methodology for Proton Exchange Membrane Fuel Cell Systems Design: Review of Advances and Challenges

by Hugo Lambert, David Hernàndez-Torres, Clément Retière, Laurent Garnier and Jean-Philippe Poirot-Crouvezier

Energies 2025, 18(14), 3774; https://doi.org/10.3390/en18143774 - 16 Jul 2025

Viewed by 263

Abstract

Proton Exchange Membrane Fuel Cells (PEMFCs) are seen as an alternative for heavy-duty transportation electrification. Powered by a green hydrogen source, they can provide high efficiency and low carbon emissions compared to traditional fuels. However, to be competitive, these systems require high reliability [...] Read more.

Proton Exchange Membrane Fuel Cells (PEMFCs) are seen as an alternative for heavy-duty transportation electrification. Powered by a green hydrogen source, they can provide high efficiency and low carbon emissions compared to traditional fuels. However, to be competitive, these systems require high reliability when operated in real-life conditions, as well as safe and efficient operating management. In order to achieve these goals, the X-in-the-loop (also called model-based design) methodology is well suited. It has been largely adopted for PEMFC system development and optimisation, as they are complex multi-component systems. In this paper, a systematic analysis of the scientific literature is conducted to review the methodology implementation for the design and improvement of the PEMFC systems. It exposes a precise definition of each development step in the methodology. The analysis shows that it can be employed in different ways, depending on the subsystems considered and the objectives sought. Finally, gaps in the literature and technical challenges for fuel cell systems that should be addressed are identified. Full article

(This article belongs to the Special Issue Innovations in Fuel Cell Technologies: Advancements and Future Directions)

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30 pages, 4318 KiB

Open AccessEditor’s ChoiceArticle

AI-Enhanced Photovoltaic Power Prediction Under Cross-Continental Dust Events and Air Composition Variability in the Mediterranean Region

by Pavlos Nikolaidis

Energies 2025, 18(14), 3731; https://doi.org/10.3390/en18143731 - 15 Jul 2025

Viewed by 256

Abstract

Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, [...] Read more.

Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, particularly regression trees, the proposed approach evaluates the impact of key environmental variables on PV performance, with an emphasis on atmospheric dust transport and air composition variability. A distinguishing feature of this work is the integration of cross-continental dust events and diverse atmospheric parameters into a structured forecasting model. A new clustering methodology is introduced to classify these inputs and analyze their correlation with PV output, enabling improved feature selection for model training. Importantly, all input parameters are sourced from publicly accessible, internet-based platforms, facilitating wide reproducibility and operational application. The obtained results demonstrate that incorporating dust deposition and air composition features significantly enhances forecasting accuracy, particularly during severe dust episodes. This research not only fills a notable gap in the PV forecasting literature but also provides a scalable model for other dust-prone regions transitioning to high levels of solar energy integration. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, Second Edition)

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24 pages, 10449 KiB

Open AccessEditor’s ChoiceArticle

Quantifying the System Benefits of Ocean Energy in the Context of Variability: A UK Example

by Donald R. Noble, Shona Pennock, Daniel Coles, Timur Delahaye and Henry Jeffrey

Energies 2025, 18(14), 3717; https://doi.org/10.3390/en18143717 - 14 Jul 2025

Viewed by 221

Abstract

Recent studies have shown benefits of using tidal stream and wave energy in the electricity generation mix to improve supply–demand balancing on annual/subannual timeframes. This paper investigates this further by considering the variability of solar photovoltaic, onshore and offshore wind, wave, and tidal [...] Read more.

Recent studies have shown benefits of using tidal stream and wave energy in the electricity generation mix to improve supply–demand balancing on annual/subannual timeframes. This paper investigates this further by considering the variability of solar photovoltaic, onshore and offshore wind, wave, and tidal stream over multiple years. It also considers their ability to match with electricity demand when combined. Variability of demand and generation can have a significant impact on results. Over the sample of five years considered (2015–2019), demand varied by around 3%, and the availability of each renewable technology differed by up to 9%. This highlights the importance of considering multiple years of input data when assessing power system impacts, instead of relying on an ‘average’ year. It is also key that weather related correlations between renewable resources and with demand can be maintained in the data. Results from an economic dispatch model of Great Britain’s power system in 2030 are even more sensitive to the input data year, with costs and carbon emissions varying by up to 21% and 45%, respectively. Using wave or tidal stream as part of the future energy mix was seen to have a positive impact in all cases considered; 1 GW of wave and tidal (0.57% of total capacity) reduces annual dispatch cost by 0.2–1.3% and annual carbon emissions by 2.3–3.5%. These results lead to recommended best practises for modelling high renewable power systems, and will be of interest to modellers and policy makers. Full article

(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)

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60 pages, 3843 KiB

Open AccessEditor’s ChoiceReview

Energy-Efficient Near-Field Integrated Sensing and Communication: A Comprehensive Review

by Mahnoor Anjum, Muhammad Abdullah Khan, Deepak Mishra, Haejoon Jung and Aruna Seneviratne

Energies 2025, 18(14), 3682; https://doi.org/10.3390/en18143682 - 12 Jul 2025

Viewed by 675

Abstract

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. [...] Read more.

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

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30 pages, 6991 KiB

Open AccessEditor’s ChoiceArticle

A Hybrid EV Charging Approach Based on MILP and a Genetic Algorithm

by Syed Abdullah Al Nahid and Junjian Qi

Energies 2025, 18(14), 3656; https://doi.org/10.3390/en18143656 - 10 Jul 2025

Viewed by 383

Abstract

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a [...] Read more.

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

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

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17 pages, 6852 KiB

Open AccessEditor’s ChoiceArticle

Performance Evaluation of Static and Dynamic Compressed Air Reservoirs for Energy Storage

by Alfred Rufer

Energies 2025, 18(14), 3666; https://doi.org/10.3390/en18143666 - 10 Jul 2025

Cited by 1 | Viewed by 366

Abstract

The concept of static and dynamic reservoirs is presented, and their performances are evaluated. The static reservoir is a simple reservoir with constant volume, and the dynamic one has a volume which varies as a function of the position of an internal piston [...] Read more.

The concept of static and dynamic reservoirs is presented, and their performances are evaluated. The static reservoir is a simple reservoir with constant volume, and the dynamic one has a volume which varies as a function of the position of an internal piston coupled to a spring. The spring is compressed when the pressure in the chamber rises and exerts a proportional force on it. The two reservoirs are components to be used in compressed air energy storage systems. The study comprises a model of the compression machine as well as models of the two reservoirs. The filling processes are simulated, and the different variables are represented as a function of time. A reduced scale experimentation set-up is presented, and its behavior is first simulated. Then. the results are compared to the experimental records. Full article

(This article belongs to the Special Issue Intelligent Concepts for the Modelling, Optimization and Control of Smart Energy Systems)

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37 pages, 4004 KiB

Open AccessEditor’s ChoiceArticle

MCDM Optimization-Based Development of a Plus-Energy Microgrid Architecture for University Buildings and Smart Parking

by Mahmoud Ouria, Alexandre F. M. Correia, Pedro Moura, Paulo Coimbra and Aníbal T. de Almeida

Energies 2025, 18(14), 3641; https://doi.org/10.3390/en18143641 - 9 Jul 2025

Viewed by 425

Abstract

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic [...] Read more.

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic feasibility assessed through a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The system is projected to generate approximately 1 GWh annually, with a 98% probability of exceeding 1076 MWh based on Gaussian estimation. Consumption is estimated at 460 MWh, while a 3.8 MWh battery ensures up to 72 h of autonomy. Rooftop panels and green parking arrays, fixed at 13.5° and 59°, minimize visual impact while contributing a surplus of +160% energy injection (or a net surplus of +60% energy after self-consumption). Assuming a battery cost of EUR 200/kWh, each hour of energy storage for the building requires 61 kWh of extra capacity with a cost of 12,200 (EUR/hr.storage). Recognizing environmental variability, these figures represent cross-validated probabilistic estimates derived from both PVsyst and Monte Carlo simulation using Python, reinforcing confidence in system feasibility. A holistic photovoltaic optimization strategy balances technical, economic, and architectural factors, demonstrating the potential of PEBs as a sustainable energy solution for academic institutions. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

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42 pages, 8877 KiB

Open AccessEditor’s ChoiceReview

Artificial-Intelligence-Based Energy Management Strategies for Hybrid Electric Vehicles: A Comprehensive Review

by Bin Huang, Wenbin Yu, Minrui Ma, Xiaoxu Wei and Guangya Wang

Energies 2025, 18(14), 3600; https://doi.org/10.3390/en18143600 - 8 Jul 2025

Viewed by 869

Abstract

The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy distribution between [...] Read more.

The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy distribution between the internal combustion engine and the electric motor. While rule-based and optimization methods have formed the foundation of EMS, their performance constraints under dynamic conditions have prompted researchers to explore artificial intelligence (AI)-based solutions. This paper systematically reviews four main AI-based EMS approaches—the knowledge-driven, data-driven, reinforcement learning, and hybrid methods—highlighting their theoretical foundations, core technologies, and key applications. The integration of AI has led to notable benefits, such as improved fuel efficiency, enhanced emission control, and greater system adaptability. However, several challenges remain, including generalization to diverse driving conditions, constraints in real-time implementation, and concerns related to data-driven interpretability. The review identifies emerging trends in hybrid methods, which combine AI and conventional optimization approaches to create more adaptive and effective HEV energy management systems. The paper concludes with a discussion of future research directions, focusing on safety, system resilience, and the role of AI in autonomous decision-making. Full article

(This article belongs to the Special Issue Optimized Energy Management Technology for Electric Vehicle)

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38 pages, 3052 KiB

Open AccessEditor’s ChoiceReview

Recent Advancements in Understanding Hot Carrier Dynamics in Perovskite Solar Cells

by Muhammad Mujahid, Jonas Gradauskas, Algirdas Sužiedėlis, Edmundas Širmulis and Steponas Ašmontas

Energies 2025, 18(13), 3543; https://doi.org/10.3390/en18133543 - 4 Jul 2025

Viewed by 611

Abstract

A potential field of study for improving the efficiency of next-generation photovoltaic devices hot carriers in perovskite solar cells is investigated in this review paper. Considering their relevance to hot carrier dynamics, the paper thoroughly studies metal halide perovskites’ essential characteristics and topologies. [...] Read more.

A potential field of study for improving the efficiency of next-generation photovoltaic devices hot carriers in perovskite solar cells is investigated in this review paper. Considering their relevance to hot carrier dynamics, the paper thoroughly studies metal halide perovskites’ essential characteristics and topologies. We review important aspects like carrier excitation, exciton binding energy, phonon coupling, carrier excitation, thermalization, and hot hole and hot electron dynamics. We investigate, in particular, the significance of relaxation mechanisms, including thermalization and the Auger heating effect. Moreover, the bottleneck effect and defect management are discussed with an eye on their impact on device performance and carrier behaviour. A review of experimental methods for their use in investigating hot carrier dynamics, primarily transient photovoltage measurements, is included. Utilizing this thorough investigation, we hope to provide an insightful analysis of the difficulties and techniques for reducing the effect of hot carriers in perovskite solar cells and optimizing their performance. Full article

(This article belongs to the Special Issue Perovskite Solar Cells and Tandem Photovoltaics)

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42 pages, 6369 KiB

Open AccessEditor’s ChoiceReview

Review of Post-Combustion Carbon Capture in Europe: Current Technologies and Future Strategies for Largest CO₂-Emitting Industries

by Luísa Marques, Miguel Monteiro, Charles Cenci, Maria Mateus and José Condeço

Energies 2025, 18(13), 3539; https://doi.org/10.3390/en18133539 - 4 Jul 2025

Viewed by 1656

Abstract

Heavy industry is a significant contributor to CO₂ global emissions, accounting for approximately 25% of the total. In Europe, the continent’s largest emitting industries, including steel, cement, and power generation, face significant decarbonization challenges due to multiple interrelated factors. Heavy industry must [...] Read more.

Heavy industry is a significant contributor to CO₂ global emissions, accounting for approximately 25% of the total. In Europe, the continent’s largest emitting industries, including steel, cement, and power generation, face significant decarbonization challenges due to multiple interrelated factors. Heavy industry must achieve carbon neutrality by 2050, as outlined in the 13th United Nations Sustainable Goals. One strategy to achieve this goal involves Carbon Capture Utilization and Storage (CCUS) with post-combustion carbon capture (PCC) technologies playing a critical role. Key methods include absorption, which uses chemical solvents like amines; adsorption, employing solid sorbents; cyclic CO₂ capture, such as calcium looping methods; cryogenic separation, which involves chilling flue gas to liquefy CO₂; and membrane separation, leveraging polymeric materials. Each technology offers unique advantages and challenges, necessitating hybrid approaches and policy support for widespread adoption. In this sense, this review provides a comprehensive overview of the existing European pilot and demonstration units and projects, funded by the EU across several industries. It specifically focuses on PCC. This study examines 111 industrial facilities across Europe, documenting the PCC technologies deployed at plants of varying capacities, geographic locations, and operational stakeholders. The review further evaluates the techno-economic performance of these systems, assessing their potential to advance carbon neutrality in heavy industries. Full article

(This article belongs to the Special Issue Process Optimization of Carbon Capture Technology)

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42 pages, 8136 KiB

Open AccessEditor’s ChoiceReview

From Empirical Measurements to AI Fusion—A Holistic Review of SOH Estimation Techniques for Lithium-Ion Batteries in Electric and Hybrid Vehicles

by Runzhe Shan, Yaxuan Wang, Shilong Guo, Yue Cui, Lei Zhao, Junfu Li and Zhenbo Wang

Energies 2025, 18(13), 3542; https://doi.org/10.3390/en18133542 - 4 Jul 2025

Viewed by 506

Abstract

Accurate assessment of lithium-ion battery state of health (SOH) represents a cross-disciplinary challenge that is critical for the reliability, safety, and total cost of ownership of electric vehicles (EVs) and hybrid electric vehicles (HEVs). This review systematically examines the evolutionary trajectory of SOH [...] Read more.

Accurate assessment of lithium-ion battery state of health (SOH) represents a cross-disciplinary challenge that is critical for the reliability, safety, and total cost of ownership of electric vehicles (EVs) and hybrid electric vehicles (HEVs). This review systematically examines the evolutionary trajectory of SOH estimation methods, ranging from conventional experimental measurement approaches to cutting-edge data-driven techniques. We analyze how these techniques address critical challenges in battery aging and performance evaluation, while discussing their respective advantages across different application scenarios. The paper highlights emerging trends in artificial intelligence-integrated advanced technologies for SOH estimation, along with practical implementation considerations. Special emphasis is placed on key challenges of SOH estimation in EVs/HEVs applications with proposed alternative solutions. By synthesizing current research directions and identifying critical knowledge gaps, this work provides valuable insights for fundamental research and industrial applications in battery health management. Full article

(This article belongs to the Special Issue Electric and Hybrid Vehicles: Technology Trends, Challenges and Opportunities—2nd Edition)

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37 pages, 3802 KiB

Open AccessEditor’s ChoiceReview

Energy Efficiency Optimization of Air Conditioning Systems Towards Low-Carbon Cleanrooms: Review and Future Perspectives

by Xinran Zeng, Chunhui Li, Xiaoying Li, Chennan Mao, Zhengwei Li and Zhenhai Li

Energies 2025, 18(13), 3538; https://doi.org/10.3390/en18133538 - 4 Jul 2025

Viewed by 836

Abstract

The advancement of high-tech industries, notably in semiconductor manufacturing, pharmaceuticals, and precision instrumentation, has imposed stringent requirements on cleanroom environments, where strict control of airborne particulates, microbial presence, temperature, and humidity is essential. However, these controlled environments incur significant energy consumption, with air [...] Read more.

The advancement of high-tech industries, notably in semiconductor manufacturing, pharmaceuticals, and precision instrumentation, has imposed stringent requirements on cleanroom environments, where strict control of airborne particulates, microbial presence, temperature, and humidity is essential. However, these controlled environments incur significant energy consumption, with air conditioning systems accounting for 40–60% of total usage due to high air circulation rates, intensive treatment demands, and system resistance. In light of global carbon reduction goals and escalating energy costs, improving the energy efficiency of cleanroom heating, ventilation, and air conditioning (HVAC) systems has become a critical research priority. Recent efforts have focused on optimizing airflow distribution, integrating heat recovery technologies, and adopting low-resistance filtration to reduce energy demand while maintaining stringent environmental standards. Concurrently, artificial intelligence (AI) methods, such as machine learning, deep learning, and adaptive control, are being employed to enable intelligent, energy-efficient system operations. This review systematically examines current energy-saving technologies and strategies in cleanroom HVAC systems, assesses their real-world performance, and highlights emerging trends. The objective is to provide a scientific basis for the green design, operation, and retrofit of cleanrooms, thereby supporting the industry’s transition toward low-carbon, sustainable development. Full article

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39 pages, 5325 KiB

Open AccessEditor’s ChoiceArticle

Optimal Sizing and Techno-Economic Evaluation of a Utility-Scale Wind–Solar–Battery Hybrid Plant Considering Weather Uncertainties, as Well as Policy and Economic Incentives, Using Multi-Objective Optimization

by Shree Om Bade, Olusegun Stanley Tomomewo, Michael Maan, Johannes Van der Watt and Hossein Salehfar

Energies 2025, 18(13), 3528; https://doi.org/10.3390/en18133528 - 3 Jul 2025

Viewed by 496

Abstract

This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective [...] Read more.

This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective particle swarm optimization (MOPSO), the study simultaneously optimizes three key objectives: economic performance (maximizing net present value, NPV), system reliability (minimizing loss of power supply probability, LPSP), and operational efficiency (reducing curtailment). The optimized HPP (283 MW wind, 20 MW solar, and 500 MWh BESS) yields an NPV of $165.2 million, a levelized cost of energy (LCOE) of $0.065/kWh, an internal rate of return (IRR) of 10.24%, and a 9.24-year payback, demonstrating financial viability. Operational efficiency is maintained with <4% curtailment and 8.26% LPSP. Key findings show that grid imports improve reliability (LPSP drops to 1.89%) but reduce economic returns; higher wind speeds (11.6 m/s) allow 27% smaller designs with 54.6% capacity factors; and tax credits (30%) are crucial for viability at low PPA rates (≤$0.07/kWh). Validation via Multi-Objective Genetic Algorithm (MOGA) confirms robustness. The study improves hybrid power plant design by combining weather predictions, policy changes, and optimizing three goals, providing a flexible renewable energy option for reducing carbon emissions. Full article

(This article belongs to the Special Issue Recent Advances and New Challenges in Solar–Wind Hybrid Energy Systems)

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22 pages, 1380 KiB

Open AccessEditor’s ChoiceReview

Carbon Mineralization in Basaltic Rocks: Mechanisms, Applications, and Prospects for Permanent CO₂ Sequestration

by Ernest Ansah Owusu, Jiyue Wu, Elizabeth Akonobea Appiah, William Apau Marfo, Na Yuan, Xiaojing Ge, Kegang Ling and Sai Wang

Energies 2025, 18(13), 3489; https://doi.org/10.3390/en18133489 - 2 Jul 2025

Viewed by 824

Abstract

Basalt is prevalent in the Earth’s crust and makes up about 90% of all volcanic rocks. The earth is warming at an alarming rate, and there is a search for a long-term solution to this problem. Geologic carbon storage in basalt offers an [...] Read more.

Basalt is prevalent in the Earth’s crust and makes up about 90% of all volcanic rocks. The earth is warming at an alarming rate, and there is a search for a long-term solution to this problem. Geologic carbon storage in basalt offers an effective and durable solution for carbon dioxide sequestration. Basaltic rocks are widely used for road and building construction and insulation, soil amendment, and in carbon storage. There is a need to understand the parameters that affect this process in order to achieve efficient carbon mineralization. This review systematically analyzes peer-reviewed studies and project reports published over the past two decades to assess the mechanisms, effectiveness, and challenges of carbon mineralization in basaltic formations. Key factors such as mineral composition, pH, temperature and pressure are evaluated for their impact on mineral dissolution and carbonate precipitation kinetics. The presence of olivine and basaltic glass also accelerates cation release and carbonation rates. The review includes case studies from major field projects (e.g., CarbFix and Wallula) and laboratory experiments to illustrate how mineralization performs in different geological environments. It is essential to maximize mineralization kinetics while ensuring the formation of stable carbonate phases in order to achieve efficient and permanent carbon dioxide storage in basaltic rock. Full article

(This article belongs to the Collection Feature Papers in Carbon Capture, Utilization, and Storage)

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48 pages, 9186 KiB

Open AccessEditor’s ChoiceReview

A Review on Design, Synthesis and Application of Composite Materials Based on MnO₂ for Energy Storage

by Loukia Plakia and Ioannis A. Kartsonakis

Energies 2025, 18(13), 3455; https://doi.org/10.3390/en18133455 - 1 Jul 2025

Viewed by 516

Abstract

The design, synthesis, and application of composite materials based on manganese dioxide (MnO₂) for energy storage are pivotal in advancing efficient, sustainable, and high-performance energy storage systems. The MnO₂ is widely recognized for its abundance, low cost, environmental friendliness, and [...] Read more.

The design, synthesis, and application of composite materials based on manganese dioxide (MnO₂) for energy storage are pivotal in advancing efficient, sustainable, and high-performance energy storage systems. The MnO₂ is widely recognized for its abundance, low cost, environmental friendliness, and excellent electrochemical properties, making it a promising candidate for use in supercapacitors, batteries, fuel cells, and other energy storage systems. This study offers a comprehensive overview of how various materials influence the performance of MnO₂ as an energy storage medium. Specifically, the design of composite materials is examined with respect to morphological control, integration with conductive additives, doping strategies, and structural engineering, all of which impact the final material properties. Additionally, the influence of diverse synthetic techniques—including hydrothermal synthesis, electrochemical deposition, sol–gel processing, co-precipitation, and templating methods—is evaluated. The latest attempts through which the developed composites showcase improved structural stability, inherent conductivity, and electron mobility compared to the original first material are presented in this review article. The presented results have been quite promising for the synthesis of great-performing materials with improved electrochemical data compared to that of MnO₂ alone, competing with other significant energy storage materials. This review highlights future prospects for the development of state-of-the-art devices, large-scale production applications, and the use of environmentally friendly materials and methods. It is anticipated that this research will provide valuable insights to facilitate further improvements in performance and broaden the scope of practical applications in this rapidly evolving field of composite materials. Full article

(This article belongs to the Special Issue Advances in Electrochemical Power Sources: Systems and Applications)

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30 pages, 2871 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 294

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)

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26 pages, 4104 KiB

Open AccessEditor’s ChoiceArticle

Smart Thermostat Development and Validation on an Environmental Chamber Using Surrogate Modelling

by Leonidas Zouloumis, Nikolaos Ploskas, Nikolaos Taousanidis and Giorgos Panaras

Energies 2025, 18(13), 3433; https://doi.org/10.3390/en18133433 - 30 Jun 2025

Viewed by 260

Abstract

The significant contribution of buildings to the global primary energy consumption necessitates the application of energy management methodologies at a building scale. Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational [...] Read more.

The significant contribution of buildings to the global primary energy consumption necessitates the application of energy management methodologies at a building scale. Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational cost. As future controlling structures tend to become autonomized in building heating layouts, encouraging distributed heating services, the research scope calls for creating lightweight building energy system modeling as well monitoring and controlling methods. Following this notion, the proposed methodology turns a programmable controller into a smart thermostat that utilizes surrogate modeling formed by the ALAMO approach and is applied in a 4-m-by-4-m-by-2.85-m environmental chamber setup heated by a heat pump. The results indicate that the smart thermostat trained on the indoor environmental conditions of the chamber for a one-week period attained a predictive RMSE of 0.082–0.116 °C. Consequently, it preplans the heating hours and applies preheating controlling strategies in real time effectively, using only the computational power of a conventional controller, essentially managing to attain at least 97% thermal comfort on the test days. Finally, the methodology has the potential to meet the requirements of future building energy systems featured in urban-scale RES-based district heating networks. Full article

(This article belongs to the Special Issue Optimizing Energy Efficiency and Thermal Comfort in Building)

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33 pages, 6831 KiB

Open AccessEditor’s ChoiceReview

Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review

by Arman Fathollahi

Energies 2025, 18(13), 3431; https://doi.org/10.3390/en18133431 - 30 Jun 2025

Viewed by 1248

Abstract

The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the [...] Read more.

The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the important role of artificial intelligence and machine learning approaches in managing the developing stability characteristics of smart grids. This work starts with a discussion of the smart grid’s dynamic structures and subsequently transitions into an overview of machine learning approaches that explore various algorithms and their applications to enhance smart grid operations. A comprehensive analysis of frameworks illustrates how machine learning and artificial intelligence solve issues related to distributed energy supplies, load management and contingency planning. This review includes general pseudocode and schematic architectures of artificial intelligence and machine learning methods which are categorized into supervised, semi-supervised, unsupervised and reinforcement learning. It includes support vector machines, decision trees, artificial neural networks, extreme learning machines and probabilistic graphical models, as well as reinforcement strategies like dynamic programming, Monte Carlo methods, temporal difference learning and Deep Q-networks, etc. Examination extends to stability, voltage and frequency regulation along with fault detection methods that highlight their applications in increasing smart grid operational boundaries. The review underlines the various arrays of machine learning algorithms that emphasize the integration of reinforcement learning as a pivotal enhancement in intelligent decision-making within smart grid environments. As a resource this review offers insights for researchers, practitioners and policymakers by providing a roadmap for leveraging intelligent technologies in smart grid control and stability analysis. Full article

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

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30 pages, 6733 KiB

Open AccessEditor’s ChoiceArticle

Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours

by Muhammed Cavus, Huseyin Ayan, Dilum Dissanayake, Anurag Sharma, Sanchari Deb and Margaret Bell

Energies 2025, 18(13), 3425; https://doi.org/10.3390/en18133425 - 29 Jun 2025

Viewed by 473

Abstract

This study presents a novel predictive framework for estimating electric vehicle (EV) charging demand in smart cities, contributing to the advancement of data-driven infrastructure planning through behavioural and spatial data analysis. Motivated by the accelerating regional demand accompanying EV adoption, this work introduces [...] Read more.

This study presents a novel predictive framework for estimating electric vehicle (EV) charging demand in smart cities, contributing to the advancement of data-driven infrastructure planning through behavioural and spatial data analysis. Motivated by the accelerating regional demand accompanying EV adoption, this work introduces HCB-Net: a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Extreme Gradient Boosting (XGBoost) for robust regression. The framework is trained on user-level survey data from two demographically distinct UK regions, the West Midlands and the North East, incorporating user demographics, commute distance, charging frequency, and home/public charging preferences. HCB-Net achieved superior predictive performance, with a Root Mean Squared Error (RMSE) of 0.1490 and an

R^{2}

score of 0.3996. Compared to the best-performing traditional model (Linear Regression,

R^{2} = 0.3520

), HCB-Net improved predictive accuracy by 13.5% in terms of

R^{2}

, and outperformed other deep learning models such as LSTM (

R^{2} = - 0.3756

) and GRU (

R^{2} = - 0.6276

), which failed to capture spatial patterns effectively. The hybrid model also reduced RMSE by approximately 23% compared to the standalone CNN (RMSE = 0.1666). While the moderate

R^{2}

indicates scope for further refinement, these results demonstrate that meaningful and interpretable demand forecasts can be generated from survey-based behavioural data, even in the absence of high-resolution temporal inputs. The model contributes a lightweight and scalable forecasting tool suitable for early-stage smart city planning in contexts where telemetry data are limited, thereby advancing the practical capabilities of EV infrastructure forecasting. Full article

(This article belongs to the Special Issue Sustainable and Low Carbon Development in the Energy Sector)

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25 pages, 1544 KiB

Open AccessEditor’s ChoiceReview

Transformation of the Energy Market in Poland in the Context of the European Union over the Last 20 Years

by Anna Marciniuk-Kluska and Mariusz Kluska

Energies 2025, 18(13), 3410; https://doi.org/10.3390/en18133410 - 28 Jun 2025

Cited by 1 | Viewed by 836

Abstract

The transformation of the energy market in Poland over the last 20 years has been a process deeply rooted in European Union policies and initiatives, including emissions trading (EU ETS), climate and energy packages and the European Green Deal. Poland, historically dependent on [...] Read more.

The transformation of the energy market in Poland over the last 20 years has been a process deeply rooted in European Union policies and initiatives, including emissions trading (EU ETS), climate and energy packages and the European Green Deal. Poland, historically dependent on coal, continues to struggle with systemic problems such as low grid flexibility, ageing infrastructure, high CO₂ emissions and the socio-economic costs of the transition in mining regions. The research methodology is based on analysis of reports, scientific articles, EU documents and statistical data. So far, there is a research gap in the research area, mainly concerning two problems. The first is the lack of a multifaceted, integrated analysis of Poland’s energy transition, taking into account not only technological changes and RES participation, but also systemic problems (infrastructure, policy, social acceptance). The second, in turn, relates to the need to identify the impact of EU regulation as a driving force, not just an obstacle. The objective of the article is to provide a comprehensive analysis of the Polish energy market in the context of the EU over the past 20 years, covering (1) systemic problems of the Polish power sector, (2) the impact of key EU initiatives and regulations, (3) the development of renewable energy sources, (4) the modernisation and digitalisation of the grid, (5) current and future market trends, and (6) the main challenges of the transition. The analysis shows that Poland’s electricity sector is still dominated by coal, but its share is steadily decreasing, from ~85% in 2015 to about 60% in 2023. At the same time, the share of renewable energy sources (mainly wind and photovoltaics) has increased from ~10% to ~27%. Nevertheless, the gap with the EU average remains significant. Full article

(This article belongs to the Special Issue Transformation of Energy Markets: Description, Modeling of Functioning Mechanisms and Determining Development Trends—Third Edition)

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26 pages, 2010 KiB

Open AccessEditor’s ChoiceReview

Development of High-Efficiency and High-Stability Perovskite Solar Cells with Space Environmental Resistance

by Donghwan Yun, Youngchae Cho, Hyeseon Shin and Gi-Hwan Kim

Energies 2025, 18(13), 3378; https://doi.org/10.3390/en18133378 - 27 Jun 2025

Viewed by 1096

Abstract

The rapid growth of the private space industry has intensified the demand for lightweight, efficient, and cost-effective photovoltaic technologies. Metal halide perovskite solar cells (PSCs) offer high power conversion efficiency (PCE), mechanical flexibility, and low-temperature solution processability, making them strong candidates for next-generation [...] Read more.

The rapid growth of the private space industry has intensified the demand for lightweight, efficient, and cost-effective photovoltaic technologies. Metal halide perovskite solar cells (PSCs) offer high power conversion efficiency (PCE), mechanical flexibility, and low-temperature solution processability, making them strong candidates for next-generation space power systems. However, exposure to extreme thermal cycling, high-energy radiation, vacuum, and ultraviolet light in space leads to severe degradation. This study addresses these challenges by introducing three key design strategies: self-healing perovskite compositions that recover from radiation-induced damage, gradient buffer layers that mitigate mechanical stress caused by thermal expansion mismatch, and advanced encapsulation that serves as a multifunctional barrier against space environmental stressors. These approaches enhance device resilience and operational stability in space. The design strategies discussed in this review are expected to support long-term power generation for low-cost satellites, high-altitude platforms, and deep-space missions. Additionally, insights gained from this research are applicable to terrestrial environments with high radiation or temperature extremes. Perovskite solar cells represent a transformative solution for space photovoltaics, offering a pathway toward scalable, flexible, and radiation-tolerant energy systems. Full article

(This article belongs to the Special Issue New Advances in Material, Performance and Design of Solar Cells)

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36 pages, 2642 KiB

Open AccessEditor’s ChoiceArticle

Empirical Evaluation of the Energy Transition Efficiency in the EU-27 Countries over a Decade—A Non-Obvious Perspective

by Jarosław Brodny, Magdalena Tutak and Wieslaw Wes Grebski

Energies 2025, 18(13), 3367; https://doi.org/10.3390/en18133367 - 26 Jun 2025

Cited by 1 | Viewed by 373

Abstract

In response to the escalating challenges of climate change and the urgent need to reduce greenhouse gas emissions, the energy transition has become a central priority of environmental policy worldwide. The European Union (EU), a global leader in implementing sustainable energy solutions, has [...] Read more.

In response to the escalating challenges of climate change and the urgent need to reduce greenhouse gas emissions, the energy transition has become a central priority of environmental policy worldwide. The European Union (EU), a global leader in implementing sustainable energy solutions, has pursued numerous initiatives aimed at advancing energy transformation. This paper presents the results of an empirical study assessing the efficiency of the energy transition process in the EU-27 countries over the 2013–2023 period. The assessment is based on the dynamic changes in selected indicators relevant to the energy transition, including decarbonization of the energy sector, improvements in energy efficiency, the share of renewable energy sources, energy import dependency, greenhouse gas emissions, and the extent of energy poverty. A multidimensional analysis was conducted using a specially developed energy transition efficiency index, where indicator weights were determined through the Analytic Hierarchy Process. The study also examined two distinct sub-periods (2013–2018 and 2018–2023), as well as a series of shorter, two-year intervals (2013–2015, 2015–2017, 2017–2019, 2019–2021, and 2021–2023), enabling a more nuanced analysis of the temporal evolution of transition efforts. Additionally, principal component analysis was employed to classify the EU-27 countries based on the similarity of their energy transition profiles. The findings reveal significant disparities in the pace and scope of energy transition across member states. Luxembourg, Malta, and the Netherlands demonstrated the most dynamic progress during the study period, followed by Sweden, Denmark, Germany, and Estonia. In contrast, Bulgaria, Hungary, Poland, Latvia, Croatia, and Romania recorded the lowest performance. These differences underscore the varying starting points, policy approaches, and implementation speeds among EU countries in achieving energy transition objectives. Full article

(This article belongs to the Special Issue Energy Security and Energy Transition: Towards Sustainable Energy Systems)

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25 pages, 1629 KiB

Open AccessEditor’s ChoiceReview

Biochemical Processes of Lignocellulosic Biomass Conversion

by Stanisław Ledakowicz

Energies 2025, 18(13), 3353; https://doi.org/10.3390/en18133353 - 26 Jun 2025

Viewed by 421

Abstract

After a brief characterisation of lignocellulosic biomass (LCB) in terms of its biochemical structure and the pretreatment techniques used to disrupt lignin structure and decrystallise and depolymerise cellulose, this review considers five main pathways for biochemical biomass conversion: starting with anaerobic digestion to [...] Read more.

After a brief characterisation of lignocellulosic biomass (LCB) in terms of its biochemical structure and the pretreatment techniques used to disrupt lignin structure and decrystallise and depolymerise cellulose, this review considers five main pathways for biochemical biomass conversion: starting with anaerobic digestion to convert various LCB feedstocks into bioproducts; considering the integration of biochemical and thermochemical processes, syngas fermentation, which has been recently developed for biofuel and chemical production, is reviewed; the production of 2G bioethanol and biobutanol from LCB waste is discussed; the literature on biohydrogen production by dark fermentation, photofermentation, and bioelectrochemical processes using microbial electrolysis cells as well as hybrid biological processes is reviewed. The conclusions and future prospects of integrating biochemical and thermochemical conversion processes of biomass are discussed and emphasised. Full article

(This article belongs to the Special Issue Advanced Thermochemical and Biochemical Processes for Biomass Transformation to Biofuels and Biochemicals)

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42 pages, 6265 KiB

Open AccessEditor’s ChoiceReview

A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation

by Ibrahim Engin Taze, Md Armanul Hoda, Irene Miquelez, Payton Maddaloni and Saeed Eftekhar Azam

Energies 2025, 18(13), 3369; https://doi.org/10.3390/en18133369 - 26 Jun 2025

Viewed by 1266

Abstract

This paper presents a comprehensive literature review on the digital twinning of floating offshore wind turbines (FOWTs). In this study, the digital twin (DT) is defined as a dynamic virtual model that accurately mirrors a physical system throughout its lifecycle, continuously updated with [...] Read more.

This paper presents a comprehensive literature review on the digital twinning of floating offshore wind turbines (FOWTs). In this study, the digital twin (DT) is defined as a dynamic virtual model that accurately mirrors a physical system throughout its lifecycle, continuously updated with real-time data and use simulations, machine learning, and analytics to support informed decision-making. The recent advancements and major issues have been introduced, which need to be addressed before realizing a FOWT DT that can be effectively used for life extension and operation and maintenance planning. This review synthesizes relevant literature reviews focused on modeling FOWT and its specific components along with the latest research. It specifically focuses on the structural, mechanical, and energy production components of FOWTs within the DT framework. The state of the art DT for FOWT, or large scale operational civil and energy infrastructure, is not yet matured to perform real-time update of digital replicas of these systems. The main barriers include real-time coupled modeling with high fidelity, the design of sensor networks, and optimization methods that synergize the sensor data and simulations to calibrate the model. Based on the literature survey provided in this paper, one of the main barriers is uncertainty associated with the external loads applied to FOWT. In this review paper, a robust method for inverse analysis in the absence of load information has been introduced and validated by using simulated experiments. Furthermore, the regulatory requirements have been provided for FOWT life extension and the potential of DT in achieving that. Full article

(This article belongs to the Special Issue Development and Validation of Methodologies for Structural Monitoring of Floating Wind Turbines)

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35 pages, 6541 KiB

Open AccessEditor’s ChoiceReview

Biodiesel Production and Life Cycle Assessment: Status and Prospects

by Sergio Nogales-Delgado

Energies 2025, 18(13), 3338; https://doi.org/10.3390/en18133338 - 25 Jun 2025

Viewed by 698

Abstract

Biodiesel synthesis, particularly through transesterification, is a mature technology in constant evolution and update. These innovative changes should be validated from different points of view: economic, social, and, especially, environmental perspectives. In this sense, life cycle assessment (LCA) is the perfect procedure to [...] Read more.

Biodiesel synthesis, particularly through transesterification, is a mature technology in constant evolution and update. These innovative changes should be validated from different points of view: economic, social, and, especially, environmental perspectives. In this sense, life cycle assessment (LCA) is the perfect procedure to verify the sustainability of these advances. This brief review covered the present status and future prospects of life cycle assessment (LCA) applied to biodiesel production. For this purpose, the current energy scenario, along with the foundations of biodiesel production and LCA, has been explained, including current research about the specific application of LCA to biodiesel from various perspectives. As a result, LCA was proven to be a versatile tool that can be easily adapted to biodiesel production, which includes continuous innovative works that should be validated from an environmental perspective. However, the counterpart is the heterogeneity found in LCA studies in general, especially concerning functional units (from 1 MJ to 1 t of biodiesel, for instance) and boundary system selection, mainly due to the wide range of possibilities in biodiesel processing. This fact makes the comparison between works (and general recommendations) difficult, requiring additional research. Nevertheless, further studies will cover the existing gaps in LCA, contributing to completing the outlook on its application to biodiesel. Nevertheless, biodiesel production, compared to diesel, normally presents better environmental impacts in categories like global warming and ozone depletion potential. Full article

(This article belongs to the Special Issue Biodiesel and Biolubricant: Production, Sources and Environmental Impact)

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35 pages, 1661 KiB

Open AccessEditor’s ChoiceArticle

Renewable Energy and CO₂ Emissions: Analysis of the Life Cycle and Impact on the Ecosystem in the Context of Energy Mix Changes

by Sebastian Sobczuk, Agata Jaroń, Mateusz Mazur and Anna Borucka

Energies 2025, 18(13), 3332; https://doi.org/10.3390/en18133332 - 25 Jun 2025

Viewed by 2069

Abstract

This study provides a comprehensive life-cycle assessment (LCA) of renewable energy sources, focusing on the

{CO}_{2}

emissions and ecological impacts associated with photovoltaic (PV) systems and wind energy technologies. The research evaluates emissions from raw material extraction, production, operation, and disposal, as [...] Read more.

This study provides a comprehensive life-cycle assessment (LCA) of renewable energy sources, focusing on the

{CO}_{2}

emissions and ecological impacts associated with photovoltaic (PV) systems and wind energy technologies. The research evaluates emissions from raw material extraction, production, operation, and disposal, as well as the role of energy-storage systems. Photovoltaic systems exhibit life-cycle

{CO}_{2}

emissions ranging between 28–100 [g

{CO}_{2}

eq/kWh], influenced by factors like production energy mix and panel efficiency. Wind turbines demonstrate lower emissions, approximately 7–38 [g

{CO}_{2}

eq/kWh], with variations based on turbine type and operational conditions. Despite low operational emissions, the full environmental impact of renewables includes biodiversity disruptions, land use changes, and material recycling challenges. The findings highlight that while renewable technologies significantly reduce

{CO}_{2}

emissions compared to fossil fuels, their ecological footprint necessitates integrated sustainability strategies. The analysis supports policymakers and stakeholders in making informed decisions for a balanced energy transition, emphasizing the need for continued innovation in renewable technology life-cycle management. Full article

(This article belongs to the Section B: Energy and Environment)

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40 pages, 1622 KiB

Open AccessEditor’s ChoiceReview

A Review of Phase-Change Material-Based Thermal Batteries for Sustainable Energy Storage of Solar Photovoltaic Systems Coupled to Heat Pumps in the Building Sector

by Shafquat Rana and Joshua M. Pearce

Energies 2025, 18(13), 3265; https://doi.org/10.3390/en18133265 - 22 Jun 2025

Viewed by 704

Abstract

Buildings account for about a third of global energy and it is thus imperative to eliminate the use of fossil fuels to power and provide for their thermal needs. Solar photovoltaic (PV) technology can provide power and with electrification, heating/cooling, but there is [...] Read more.

Buildings account for about a third of global energy and it is thus imperative to eliminate the use of fossil fuels to power and provide for their thermal needs. Solar photovoltaic (PV) technology can provide power and with electrification, heating/cooling, but there is often a load mismatch with the intermittent solar supply. Electric batteries can overcome this challenge at high solar penetration rates but are still capital-intensive. A promising solution is thermal energy storage (TES), which has a low cost per unit of energy. This review provides an in-depth analysis of TES but specifically focuses on phase change material (PCM)-based TES, and its significance in the building sector. The classification, characterization, properties, applications, challenges, and modeling of PCM-TES are detailed. Finally, the potential for integrating TES with PV and heat pump (HP) technologies to decarbonize the residential sector is detailed. Although many studies show proof of carbon reduction for the individual and coupled systems, the integration of PV+HP+PCM-TES systems as a whole unit has not been developed to achieve carbon neutrality and facilitate net zero emission goals. Overall, there is still a lack of available literature and experimental datasets for these complex systems which are needed to develop models for global implementation as well as studies to quantify their economic and environmental performance. Full article

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28 pages, 5208 KiB

Open AccessEditor’s ChoiceArticle

The Use of BIM Models and Drone Flyover Data in Building Energy Efficiency Analysis

by Agata Muchla, Małgorzata Kurcjusz, Maja Sutkowska, Raquel Burgos-Bayo, Eugeniusz Koda and Anna Stefańska

Energies 2025, 18(13), 3225; https://doi.org/10.3390/en18133225 - 20 Jun 2025

Viewed by 626

Abstract

Building information modeling (BIM) and thermal imaging from drone flyovers present innovative opportunities for enhancing building energy efficiency. This study examines the integration of BIM models with thermal data collected using unmanned aerial vehicles (UAVs) to assess and manage energy performance throughout a [...] Read more.

Building information modeling (BIM) and thermal imaging from drone flyovers present innovative opportunities for enhancing building energy efficiency. This study examines the integration of BIM models with thermal data collected using unmanned aerial vehicles (UAVs) to assess and manage energy performance throughout a building’s lifecycle. By leveraging BIM’s structured data and the concept of the digital twin, thermal analysis can be automated to detect thermal bridges and inefficiencies, facilitating data-driven decision-making in sustainable construction. The paper examines methodologies for combining thermal imaging with BIM, including image analysis algorithms and artificial intelligence applications. Case studies demonstrate the practical implementation of UAV-based thermal data collection and BIM integration in an educational facility. The findings highlight the potential for optimizing energy efficiency, improving facility management, and advancing low-emission building practices. The study also addresses key challenges such as data standardization and interoperability, and outlines future research directions in the context of smart city applications and energy-efficient urban development. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, Second Edition)

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25 pages, 1725 KiB

Open AccessEditor’s ChoiceReview

Analysis of the Application of Ammonia as a Fuel for a Compression-Ignition Engine

by Wojciech Tutak and Arkadiusz Jamrozik

Energies 2025, 18(12), 3217; https://doi.org/10.3390/en18123217 - 19 Jun 2025

Viewed by 525

Abstract

Piston engines used for powering automobiles as well as machinery and equipment have traditionally relied on petroleum-derived fuels. Subsequently, renewable fuels began to be used in an effort to reduce the combustion of hydrocarbon-based fuels and the associated greenhouse effect. Researchers are currently [...] Read more.

Piston engines used for powering automobiles as well as machinery and equipment have traditionally relied on petroleum-derived fuels. Subsequently, renewable fuels began to be used in an effort to reduce the combustion of hydrocarbon-based fuels and the associated greenhouse effect. Researchers are currently developing technologies aimed at eliminating fuels containing carbon in their molecular structure, which would effectively minimize the emission of carbon oxides into the atmosphere. Ammonia is considered a highly promising carbon-free fuel with broad applicability in energy systems. It serves as an excellent hydrogen carrier (NH₃), free from many of the storage and transportation limitations associated with pure hydrogen. Safety concerns regarding the storage and transport of hydrogen make ammonia an increasingly important fuel also due to its larger hydrogen storage capacity. This manuscript investigates the use of ammonia for powering a dual-fuel engine. The results indicate that the addition of ammonia improves engine performance; however, it may also lead to an increase in NO_x emissions. Due to the limitations of ammonia as a fuel, approximately 40% of the energy input must still be provided by diesel fuel to achieve optimal engine performance and acceptable NO_x emission levels. The presented research findings highlight the significant potential of NH₃ as an alternative fuel for compression-ignition engines. Proper control of the injection strategy or the adoption of alternative combustion systems may offer a promising approach to reducing greenhouse gas emissions while maintaining satisfactory engine performance parameters. Full article

(This article belongs to the Special Issue Renewable Fuels for Internal Combustion Engines: 2nd Edition)

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29 pages, 4263 KiB

Open AccessEditor’s ChoiceArticle

Modeling the Thermodynamics of Oxygen-Enriched Combustion in a GE LM6000 Gas Turbine Using CH₄/NH₃ and CH₄/H₂

by Laith Mustafa, Rafał Ślefarski, Radosław Jankowski, Mohammad Alnajideen and Sven Eckart

Energies 2025, 18(12), 3221; https://doi.org/10.3390/en18123221 - 19 Jun 2025

Viewed by 563

Abstract

Gas turbines are widely used in power generation due to their reliability, flexibility, and high efficiency. As the energy sector transitions towards low-carbon alternatives, hydrogen and ammonia are emerging as promising fuels. This study investigates the thermodynamic and combustion performance of a GE [...] Read more.

Gas turbines are widely used in power generation due to their reliability, flexibility, and high efficiency. As the energy sector transitions towards low-carbon alternatives, hydrogen and ammonia are emerging as promising fuels. This study investigates the thermodynamic and combustion performance of a GE LM6000 gas turbine fueled by methane/hydrogen and methane/ammonia fuel blends under varying levels of oxygen enrichment (21%, 30%, and 40%

O_{2}

by volume). Steady-state thermodynamic simulations were conducted using Aspen HYSYS, and combustion modeling was performed using ANSYS Chemkin-Pro, assuming a constant thermal input of 102 MW. Results show that increasing hydrogen content significantly raises flame temperature and burning velocity, whereas ammonia reduces both due to its lower reactivity. Net power output and thermal efficiency improved with higher fuel substitution, peaking at 43.46 MW and 42.7% for 100%

N H_{3}

. However,

N O_{x}

emissions increased with higher hydrogen content and oxygen enrichment, while

N H_{3}

blends exhibit more complex emission trends. The findings highlight the trade-offs between efficiency and emissions in future low-carbon gas turbine systems. Full article

(This article belongs to the Special Issue The Evolution and Future Prospects of Combustion Engines for Enhancing Energy Efficiency and Minimizing Environmental Impact)

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43 pages, 1295 KiB

Open AccessEditor’s ChoiceReview

Enhancing Building Thermal Performance: A Review of Phase Change Material Integration

by Khaled Alassaad, James Minto and Pieter de Wilde

Energies 2025, 18(12), 3200; https://doi.org/10.3390/en18123200 - 18 Jun 2025

Viewed by 1252

Abstract

Buildings are responsible for over one-third of global energy use and greenhouse gas emissions, with heating and cooling being major contributors. Phase change materials (PCMs) offer a promising passive solution to improve thermal regulation and reduce heating and cooling loads. This review analyses [...] Read more.

Buildings are responsible for over one-third of global energy use and greenhouse gas emissions, with heating and cooling being major contributors. Phase change materials (PCMs) offer a promising passive solution to improve thermal regulation and reduce heating and cooling loads. This review analyses different experimental and simulation-based studies on the integration of PCMs into building structures for enhancing building energy performance. The key variables examined include melting temperature, latent heat capacity, thermal conductivity (λ), PCM positioning (interior, exterior, or embedded), thickness, and climate zone. The results show that PCMs reduce heat transfer by up to 47.6%, stabilize indoor temperatures with up to a 46% reduction in fluctuations, and decrease heating and cooling demands by as much as 31%, depending on component placement and climate. The optimal melting range for moderate climates lies between 22 °C and 28 °C. This review identifies critical trade-offs between PCM quantity, placement, and climatic suitability and provides a matrix of design recommendations for various building types. Full article

(This article belongs to the Special Issue Thermal Comfort and Energy Performance in Building)

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26 pages, 4660 KiB

Open AccessEditor’s ChoiceReview

Ammonia–Hydrogen Dual-Fuel Combustion: Strategies for Optimizing Performance and Reducing Emissions in Internal Combustion Engines

by Cinzia Tornatore, Paolo Sementa and Francesco Catapano

Energies 2025, 18(12), 3159; https://doi.org/10.3390/en18123159 - 16 Jun 2025

Viewed by 946

Abstract

The urgent need to mitigate climate change and reduce greenhouse gas emissions has accelerated the search for sustainable and scalable energy carriers. Among the different alternatives, ammonia stands out as a promising carbon-free fuel, thanks to its high energy density, efficient storage, and [...] Read more.

The urgent need to mitigate climate change and reduce greenhouse gas emissions has accelerated the search for sustainable and scalable energy carriers. Among the different alternatives, ammonia stands out as a promising carbon-free fuel, thanks to its high energy density, efficient storage, and compatibility with existing infrastructure. Moreover, it can be produced through sustainable, green processes. However, its application in internal combustion engines is limited by several challenges, including low reactivity, narrow flammability limits, and high ignition energy. These factors can compromise combustion efficiency and contribute to increased unburned ammonia emissions. To address these limitations, hydrogen has emerged as a complementary fuel in dual-fuel configurations with ammonia. Hydrogen’s high reactivity enhances flame stability, ignition characteristics, and combustion efficiency while reducing emissions of unburned ammonia. This review examines the current status of dual-fuel ammonia and hydrogen combustion strategies in internal combustion engines and summarizes the experimental results. It highlights the potential of dual-fuel systems to optimize engine performance and minimize emissions. It identifies key challenges, knowledge gaps, and future research directions to support the development and widespread adoption of ammonia–hydrogen dual-fuel technologies. Full article

(This article belongs to the Section I1: Fuel)

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38 pages, 8985 KiB

Open AccessEditor’s ChoiceArticle

Impact of Daylight Saving Time on Energy Consumption in Higher Education Institutions: A Case Study of Portugal and Spain

by Ivo Araújo, João Garcia and António Curado

Energies 2025, 18(12), 3157; https://doi.org/10.3390/en18123157 - 16 Jun 2025

Viewed by 524

Abstract

Daylight Saving Time (DST), involving clock shifts forward in spring and backward in autumn, was introduced to promote energy savings. However, its effectiveness remains controversial, especially in buildings with temporary occupancy like academic institutions, which have high daytime use but low summer occupancy. [...] Read more.

Daylight Saving Time (DST), involving clock shifts forward in spring and backward in autumn, was introduced to promote energy savings. However, its effectiveness remains controversial, especially in buildings with temporary occupancy like academic institutions, which have high daytime use but low summer occupancy. This study investigates the impact of DST transitions on energy consumption across seven campuses of two higher education institutions (HEIs) in northern Portugal and Spain, located in different time zones, using measured data from 2023. The analysis accounted for the structural and operational characteristics of each campus to contextualize consumption patterns. Weekly electricity consumption before and after DST changes were compared using independent samples t-tests to assess statistical significance. Results show that the spring transition to DST led to an average energy saving of 1.7%, while the autumn return to standard time caused an average increase of 1.2%. Significant differences (p < 0.05) were found in five of the seven campuses. Descriptive statistics and confidence intervals indicated that only sites with intervals excluding zero exhibited consistent changes. Seasonal energy demand appeared more influenced by academic schedules and thermal comfort needs—particularly heating—than by DST alone. Higher consumption coincided with periods of intense academic activity and extreme temperatures, while lower demand aligned with holidays and longer daylight months. Although DST yielded modest energy savings, its overall impact on academic campus energy use is limited and highly dependent on local conditions. The findings highlight the need to consider regional climate, institutional policies, user behavior, and smart technology integration in future energy efficiency analyses in academic settings. Full article

(This article belongs to the Section B: Energy and Environment)

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41 pages, 4632 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Resilience of Malawi’s Power Grid to the 2022 Tropical Cyclone Ana Using a Combination of the AFLEPT Metric Framework and Resilience Capacities

by Joyce Nyuma Chivunga, Fransisco Gonzalez-Longatt, Zhengyu Lin and Richard Blanchard

Energies 2025, 18(12), 3165; https://doi.org/10.3390/en18123165 - 16 Jun 2025

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Abstract

While power system resilience studies continue to grow due to the criticality of electrical infrastructures, the challenge of inconsistencies in evaluation frameworks remains. Furthermore, the desire for researchers to contribute towards the development of practical assessment frameworks continues to grow. In addition, the [...] Read more.

While power system resilience studies continue to grow due to the criticality of electrical infrastructures, the challenge of inconsistencies in evaluation frameworks remains. Furthermore, the desire for researchers to contribute towards the development of practical assessment frameworks continues to grow. In addition, the locality of resilience issues has challenged researchers to find context-based resilience solutions. This paper addresses these by proposing an assessment framework, which evaluates the five phases of the resilience trapezoid: preventive, absorptive, adaptive, restorative, and transformative. This framework presents metrics for measuring preventive indicators for the anticipating system status, frequency of functionality degradation, how low functionality drops, extension in a degraded state, the promptness of recovery, and system transformation—the AFLEPT model. The AFLEPT framework is applied, with its resilience indicators and capacities, to evaluate the resilience of Malawi’s transmission network to the 2022 Tropical Cyclone Ana (TCA). DigSILENT PowerFactory 2023 SP5 was utilised to support this research. The results indicate significant resilience challenges, manifested by an inadequate generation reserve, significant decline in grid functionality, extended total grid outage hours, longer restoration times, and a lack of transformation. Eight percent of key transmission lines and eighteen percent of power generation infrastructure were completely damaged by the TCA, which lasted up to 25 days and 16 months to, respectively, before restoration. Thus, the analysis reveals gaps in preventive, absorptive, adaptive, restorative, and transformative resilience capacities. The results underscore the need for context-based infrastructural and operational resilience enhancement measures, which have been discussed in this paper. Directions for further research have been proposed, which include exploring multiple grid improvement measures and an economic modelling of these measures. Full article

(This article belongs to the Section F1: Electrical Power System)

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