Energies

37 pages, 3155 KB

Open AccessEditor’s ChoiceReview

Decarbonising the Inland Waterways: A Review of Fuel-Agnostic Energy Provision and the Infrastructure Challenges

by Paul Simavari, Kayvan Pazouki and Rosemary Norman

Energies 2025, 18(19), 5146; https://doi.org/10.3390/en18195146 - 27 Sep 2025

Viewed by 288

Inland Waterway Transport (IWT) is widely recognised as an energy-efficient freight mode, yet its decarbonisation is increasingly constrained not by propulsion technology, but by the absence of infrastructure capable of delivering clean energy where and when it is needed. This paper presents a [...] Read more.

Inland Waterway Transport (IWT) is widely recognised as an energy-efficient freight mode, yet its decarbonisation is increasingly constrained not by propulsion technology, but by the absence of infrastructure capable of delivering clean energy where and when it is needed. This paper presents a structured review of over a decade of academic, policy and technical literature, identifying systemic gaps in current decarbonisation strategies. The analysis shows that most pilot projects are vessel-specific, and poorly scalable, with infrastructure planning rarely based on vessel-level energy demand data, leaving energy provision as an afterthought. Current approaches overemphasise technology readiness while neglecting the complexity of aligning supply chains, operational diversity, and infrastructure deployment. This review reframes IWT decarbonisation as a problem of provision, not propulsion. It calls for demand-led, demand driven, fuel agnostic infrastructure models and proposes a roadmap that integrates technical, operational, and policy considerations. Without rethinking energy access as a core design challenge—on par with vessel systems and regulatory standards—the sector risks investing in stranded assets and missing climate and modal shift targets. Aligning vessel operations with dynamic, scalable energy delivery systems is essential to achieve a commercially viable, fully decarbonised IWT sector. Full article

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34 pages, 3251 KB

Open AccessEditor’s ChoiceArticle

Stochastic Markov-Based Modelling of Residential Lighting Demand in Luxembourg: Integrating Occupant Behavior and Energy Efficiency

by Vahid Arabzadeh and Raphael Frank

Energies 2025, 18(19), 5133; https://doi.org/10.3390/en18195133 - 26 Sep 2025

Viewed by 210

Abstract

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys [...] Read more.

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys (HETUS) provide a detailed activity-based energy modeling approach, while Bayesian and constraint-based optimization improve data calibration and reduce modeling uncertainties. A Luxembourg-specific stochastic load profile generator links occupant activities to energy loads, incorporating occupancy patterns and daylight illuminance calculations. This study quantifies lighting demand variations across household types, validating results against empirical TUS data with a low mean squared error (MSE) and a minor deviation of +3.42% from EU residential lighting demand standards. Findings show that activity-aware dimming can reduce lighting demand by 30%, while price-based dimming achieves a 21.60% reduction in power demand. The proposed approach provides data-driven insights for energy-efficient residential lighting management, supporting sustainable energy policies and household-level optimization. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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22 pages, 1203 KB

Open AccessEditor’s ChoiceReview

Modelling Syngas Combustion from Biomass Gasification and Engine Applications: A Comprehensive Review

by José Ramón Copa Rey, Andrei Longo, Bruna Rijo, Cecilia Mateos-Pedrero, Paulo Brito and Catarina Nobre

Energies 2025, 18(19), 5112; https://doi.org/10.3390/en18195112 - 25 Sep 2025

Viewed by 656

Abstract

Syngas, a renewable fuel primarily composed of hydrogen and carbon monoxide, is emerging as a viable alternative to conventional fossil fuels in internal combustion engines (ICEs). Obtained mainly through the gasification of biomass and organic waste, syngas offers significant environmental benefits but also [...] Read more.

Syngas, a renewable fuel primarily composed of hydrogen and carbon monoxide, is emerging as a viable alternative to conventional fossil fuels in internal combustion engines (ICEs). Obtained mainly through the gasification of biomass and organic waste, syngas offers significant environmental benefits but also presents challenges due to its lower heating value and variable composition. This review establishes recent advances in understanding syngas combustion, chemical kinetics, and practical applications in spark-ignition (SI) and compression-ignition (CI) engines. Variability in syngas composition, dependent on feedstock and gasification conditions, strongly influences ignition behavior, flame stability, and emissions, demanding detailed kinetic models and adaptive engine control strategies. In SI engines, syngas can replace up to 100% of conventional fuel, typically at 20–30% reduced power output. CI engines generally require a pilot fuel representing 10–20% of total energy to start combustion, favoring dual-fuel (DF) operation for efficiency and emissions control. This work underlines the need to integrate advanced modelling approaches with experimental insights to optimize performance and meet emission targets. By addressing challenges of fuel variability and engine adaptation, syngas reveals promising potential as a clean fuel for future sustainable power generation and transport applications. Full article

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

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15 pages, 132684 KB

Open AccessEditor’s ChoiceArticle

Overcoming Variable Illumination in Photovoltaic Snow Monitoring: A Real-Time Robust Drone-Based Deep Learning Approach

by Amna Mazen, Ashraf Saleem, Kamyab Yazdipaz and Ana Dyreson

Energies 2025, 18(19), 5092; https://doi.org/10.3390/en18195092 - 25 Sep 2025

Viewed by 270

Abstract

Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using [...] Read more.

Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image processing algorithm for snow segmentation. We benchmarked several segmentation models, including MASK R-CNN and the state-of-the-art SAM2 segmentation model. YOLOv11n-seg was selected for its optimal balance of speed and accuracy, achieving 0.99 precision and 0.80 recall. To overcome the unreliability of static thresholding under changing lighting, various dynamic methods were evaluated. Otsu’s algorithm proved most effective, reducing the absolute error of the mean in SCP estimation to just 1.1%, a significant improvement over the 13.78% error from the previous fixed-thresholding approach. The integrated system was successfully validated for real-time performance on live drone video streams, demonstrating a highly accurate and scalable solution for autonomous snow monitoring on PV systems. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)

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21 pages, 1816 KB

Open AccessEditor’s ChoiceArticle

Progress Towards Affordable and Clean Energy: A Comparative Analysis of SDG7 Implementation

by Beata Bieszk-Stolorz and Joanna Landmesser-Rusek

Energies 2025, 18(19), 5078; https://doi.org/10.3390/en18195078 - 24 Sep 2025

Viewed by 363

Abstract

Progress towards Sustainable Development Goal 7 (SDG7) is currently insufficient to achieve. It is particularly important to ensure that all people have access to sustainable, reliable and affordable energy. As SDG7 is linked to other goals, a lack of progress in its implementation [...] Read more.

Progress towards Sustainable Development Goal 7 (SDG7) is currently insufficient to achieve. It is particularly important to ensure that all people have access to sustainable, reliable and affordable energy. As SDG7 is linked to other goals, a lack of progress in its implementation could disrupt the entire sustainable development process. The aim of our article is to compare selected countries around the world in terms of the degree of SDG7 implementation and its dynamics in the years 2000–2022. We assessed the degree of SDG7 implementation using Hellwig’s method in the dynamic approach, and we compared the dynamics of the degree of implementation using the dynamic time warping (DTW) method and hierarchical clustering. The cluster of countries with the highest degree of SDG7 implementation included the European countries of Norway, Sweden and Iceland. The lowest degree of implementation was observed in Belarus, Uzbekistan and Turkmenistan. The dynamic approach to the problem allowed us to conclude that there was an increase in the synthetic measure in all the countries analysed in the period 2000–2022, with the strongest increase observed in the countries with the lowest initial degree of SDG7 implementation (Belarus, Uzbekistan, Turkmenistan). Full article

(This article belongs to the Special Issue Towards Sustainable Energy Development: Energy Efficiency and Economic Growth)

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17 pages, 650 KB

Open AccessEditor’s ChoiceArticle

Optimization of Biomass Delivery Through Artificial Intelligence Techniques

by Marta Wesolowska, Dorota Żelazna-Jochim, Krystian Wisniewski, Jaroslaw Krzywanski, Marcin Sosnowski and Wojciech Nowak

Energies 2025, 18(18), 5028; https://doi.org/10.3390/en18185028 - 22 Sep 2025

Viewed by 336

Abstract

Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its [...] Read more.

Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its complex supply chains efficiently is crucial. Traditional logistics methods often struggle with the dynamic, nonlinear, and data-scarce nature of biomass supply, especially when integrating local and international sources. To address these challenges, this study aims to develop an innovative modular artificial neural network (ANN)-based Biomass Delivery Management (BDM) model to optimize biomass procurement and supply for a fluidized bed combined heat and power (CHP) plant. The comprehensive model integrates technical, economic, and geographic parameters to enable supplier selection, optimize transport routes, and inform fuel blending strategies, representing a novel approach in biomass logistics. A case study based on operational data confirmed the model’s ability to identify cost-effective and quality-compliant biomass sources. Evaluated using empirical operational data from a Polish CHP plant, the ANN-based model demonstrated high predictive accuracy (MAE = 0.16, MSE = 0.02, R² = 0.99) within the studied scope. The model effectively handled incomplete datasets typical of biomass markets, aiding in supplier selection decisions and representing a proof-of-concept for optimizing Central European biomass logistics. The model was capable of generalizing supplier recommendations based on input variables, including biomass type, unit price, and annual demand. The proposed framework supports both strategic and real-time logistics decisions, providing a robust tool for enhancing supply chain transparency, cost efficiency, and resilience in the renewable energy sector. Future research will focus on extending the dataset and developing hybrid models to strengthen supply chain stability and adaptability under varying market and regulatory conditions. 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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21 pages, 2168 KB

Open AccessEditor’s ChoiceArticle

Comparative Study on the Effects of Diesel Fuel, Hydrotreated Vegetable Oil, and Its Blends with Pyrolytic Oils on Pollutant Emissions and Fuel Consumption of a Diesel Engine Under WLTC Dynamic Test Conditions

by Artur Jaworski, Hubert Kuszewski, Dariusz Szpica, Paweł Woś, Krzysztof Balawender, Adam Ustrzycki, Artur Krzemiński, Mirosław Jakubowski, Grzegorz Mieczkowski, Andrzej Borawski, Michał S. Gęca and Arkadiusz Rybak

Energies 2025, 18(18), 5038; https://doi.org/10.3390/en18185038 - 22 Sep 2025

Viewed by 484

Abstract

The search for alternative liquid fuels for compression-ignition (CI) internal combustion engines includes waste-derived fuels such as hydrotreated vegetable oil (HVO) and pyrolytic oils from end-of-life tires (tire pyrolytic oil, TPO) and plastics—polystyrene pyrolytic oil (PSO). The application of these fuels requires meeting [...] Read more.

The search for alternative liquid fuels for compression-ignition (CI) internal combustion engines includes waste-derived fuels such as hydrotreated vegetable oil (HVO) and pyrolytic oils from end-of-life tires (tire pyrolytic oil, TPO) and plastics—polystyrene pyrolytic oil (PSO). The application of these fuels requires meeting a number of criteria, including exhaust pollutant emissions. The scientific objective of this study was to compare pollutant emissions—carbon dioxide (CO₂), carbon monoxide (CO), total hydrocarbons (THC), nitrogen oxides (NO_x), particulate matter (PM)—and fuel consumption of a passenger car CI engine fueled with diesel B7, HVO, and a blend consisting of 90% HVO, 5% TPO, and 5% PSO (vol.), hereinafter referred to as HVO–TPO–PSO. The tests were carried out using a chassis dynamometer equipped for conducting standardized WLTC Class 3b driving cycles, with exhaust gases measured by laboratory-grade analyzers to ensure accuracy and repeatability. Fueling the engine with HVO resulted in the lowest CO₂, CO, THC, NO_x, and PM emissions across all phases of the driving cycle. The addition of pyrolytic oils to HVO increased NO_x and CO₂ levels while maintaining benefits in PM, THC, and CO reduction compared to the B7 reference fuel. The results demonstrated the applicability of HVO–TPO–PSO blends in engine applications while indicating the need for further durability studies. The adopted research approach addresses a significant knowledge gap by providing a unique analysis of the impact of HVO blends with tire and plastic pyrolysis oils on pollutant emissions and internal combustion engine fuel consumption under WLTC 3b operating conditions. Full article

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

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30 pages, 7291 KB

Open AccessEditor’s ChoiceArticle

Energy Criteria in Adaptive Reuse Decision-Making: A Hybrid DEMATEL-ANP Model for Selecting New Uses of a Historic Building in Poland

by Elżbieta Radziszewska-Zielina, Grzegorz Śladowski, Bartłomiej Szewczyk, Małgorzata Fedorczak-Cisak, Alicja Kowalska-Koczwara, Tadeusz Tatara and Krzysztof Barnaś

Energies 2025, 18(18), 5020; https://doi.org/10.3390/en18185020 - 21 Sep 2025

Viewed by 347

Abstract

Historic buildings make up a significant proportion of the existing building stock. Most are characterised by poor technical condition and high energy demand. In Poland, many historic buildings are still in use today, but it is also common to find these buildings subjected [...] Read more.

Historic buildings make up a significant proportion of the existing building stock. Most are characterised by poor technical condition and high energy demand. In Poland, many historic buildings are still in use today, but it is also common to find these buildings subjected to adaptive reuse. Adaptive reuse, often combined with modernisation, is problematic, especially in terms of finding a use that is optimal in the light of use-specific decision criteria. In previous studies, the authors used and developed the potential for the modelling and structural analysis of decision-making problems for the selection of new uses for historic buildings. In this paper, we present a test of this methodology on a Polish historic building. To further the application of our approach in sustainability-focused contexts, we performed the analysis using criteria focused on environmental and energy performance, in addition to other established criteria. In our study, the highest ranking use was a kindergarten, which scored 18% higher than the second-ranked alternative and over 90% higher than the lowest-ranked alternative. Full article

(This article belongs to the Special Issue Green Innovation and Sustainable Energy Transition: Opportunities and Challenges)

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51 pages, 1846 KB

Open AccessEditor’s ChoiceReview

A Review of Methodologies for Photovoltaic Energy Generation Forecasting in the Building Sector

by Omid Pedram, Ana Soares and Pedro Moura

Energies 2025, 18(18), 5007; https://doi.org/10.3390/en18185007 - 20 Sep 2025

Viewed by 530

Abstract

Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated [...] Read more.

Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated to adopt energy management and optimization techniques, where accurate forecasting of PV generation is essential. While the existing literature provides valuable insights, a comprehensive review of methodologies specifically tailored for the forecast of PV generation in buildings remains scarce. This study aims to address this gap by analyzing the forecasting methods, data requirements, and performance metrics employed, with the primary objective of providing an in-depth review of previous research. The findings highlight the critical role of improving PV energy generation forecasting accuracy in enhancing energy management and optimization for individual buildings. Additionally, the study identifies key challenges and opportunities for future research, such as the limited exploration of localized environmental and operational factors (such as partial shading, dust, and dirt); insufficient data on building-specific PV output patterns; and the need to account for variability in PV generation. By clarifying the current state of PV energy forecasting methodologies, this research lays essential groundwork for future advancements in the field. Full article

(This article belongs to the Special Issue Optimal Planning, Integration and Control of Smart Microgrid Systems with Renewable Energy: 2nd Edition)

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28 pages, 2865 KB

Open AccessEditor’s ChoiceArticle

Probabilistic Assessment of Solar-Based Hydrogen Production Using PVGIS, Metalog Distributions, and LCOH Modeling

by Jacek Caban, Arkadiusz Małek and Zbigniew Siemiątkowski

Energies 2025, 18(18), 4972; https://doi.org/10.3390/en18184972 - 18 Sep 2025

Viewed by 906

Abstract

The transition toward low-carbon energy systems requires reliable tools for assessing renewable-based hydrogen production under real-world climatic and economic conditions. This study presents a novel probabilistic framework integrating the following three complementary elements: (1) a Photovoltaic Geographical Information System (PVGIS) for high-resolution, location-specific [...] Read more.

The transition toward low-carbon energy systems requires reliable tools for assessing renewable-based hydrogen production under real-world climatic and economic conditions. This study presents a novel probabilistic framework integrating the following three complementary elements: (1) a Photovoltaic Geographical Information System (PVGIS) for high-resolution, location-specific solar energy data; (2) Metalog probability distributions for advanced modeling of variability and uncertainty in photovoltaic (PV) energy generation; and (3) Levelized Cost of Hydrogen (LCOH) calculations to evaluate the economic viability of hydrogen production systems. The methodology is applied to three diverse European locations—Lublin (Poland), Budapest (Hungary), and Malaga (Spain)—to demonstrate regional differences in hydrogen production potential. The results indicate annual PV energy yields of 108.3 MWh, 124.6 MWh, and 170.95 MWh, respectively, which translate into LCOH values of EUR 9.67/kg (Poland), EUR 8.40/kg (Hungary), and EUR 6.13/kg (Spain). The probabilistic analysis reveals seasonal production risks and quantifies the probability of achieving specific monthly energy thresholds, providing critical insights for designing systems with continuous hydrogen output. This combined use of a PVGIS, Metalog, and LCOH calculations offers a unique decision-support tool for investors, policymakers, and SMEs planning green hydrogen projects. The proposed methodology is scalable and adaptable to other renewable energy systems, enabling informed investment decisions and improved regional energy transition strategies. Full article

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

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29 pages, 7258 KB

Open AccessEditor’s ChoiceArticle

AI-Driven Morphological Classification of the Italian School Building Stock: Towards a Deep Energy Renovation Roadmap

by Giacomo Caccia, Matteo Cavaglià, Fulvio Re Cecconi, Andrea Giovanni Mainini, Marta Maria Sesana and Elisa Di Giuseppe

Energies 2025, 18(18), 4953; https://doi.org/10.3390/en18184953 - 17 Sep 2025

Viewed by 568

Abstract

The Italian school building stock is largely outdated, with structural and technological inadequacies leading to low comfort and high energy consumption. Addressing this challenge requires large-scale renovation supported by an integrated, data-driven approach. This study conducted a nationwide analysis of over 40,000 school [...] Read more.

The Italian school building stock is largely outdated, with structural and technological inadequacies leading to low comfort and high energy consumption. Addressing this challenge requires large-scale renovation supported by an integrated, data-driven approach. This study conducted a nationwide analysis of over 40,000 school buildings. After incomplete or inconsistent records were filtered out, a refined subset was selected. Building forms were reconstructed by cross-referencing GIS data with multiple open data sources. Using supervised machine learning, the research identifies and classifies recurring morphological patterns to define a set of 3D school building archetypes. These archetypes are enriched with spatial configurations and physical characteristics aligned with national educational standards. The result is a macrotypological classification based on form, conceived as part of an operational tool to support policymakers, designers, and public administrations in selecting effective retrofit strategies. This contributes to the creation of large-scale national renovation strategies, as well as Renovation Roadmaps and Digital Building Logbooks in line with the Energy Performance of Buildings Directive (EPBD IV), specifically tailored to the Italian context. The novelty of this work lies in its unprecedented scale and the use of AI to enable fast, replicable assessments of retrofit potential, thereby supporting informed decisions in energy-efficient renovation planning. Full article

(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)

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20 pages, 438 KB

Open AccessEditor’s ChoiceArticle

Optimal Prosumer Storage Management in Renewable Energy Communities Under Demand Response

by Gianni Bianchini, Marco Casini and Milad Gholami

Energies 2025, 18(18), 4904; https://doi.org/10.3390/en18184904 - 15 Sep 2025

Viewed by 390

Abstract

This paper deals with the optimal scheduling of prosumers equipped with energy storage facilities within renewable energy communities, and proposes a novel strategy for optimizing storage usage within a price–volume demand response framework. The problem is formulated as a scalable, low-complexity mixed-integer linear [...] Read more.

This paper deals with the optimal scheduling of prosumers equipped with energy storage facilities within renewable energy communities, and proposes a novel strategy for optimizing storage usage within a price–volume demand response framework. The problem is formulated as a scalable, low-complexity mixed-integer linear program. Furthermore, a heuristic procedure is introduced to ensure redistribution of demand response rewards among participants according to their contribution to achieving demand–response goals. The proposed approach is designed to enhance the benefits for prosumers operating within a community compared to running independently. Full article

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

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22 pages, 1189 KB

Open AccessEditor’s ChoiceReview

EU Bioenergy—Status and Potential

by Manfred Kircher

Energies 2025, 18(18), 4857; https://doi.org/10.3390/en18184857 - 12 Sep 2025

Viewed by 609

Abstract

In the interest of climate protection and the promotion of a sustainable economy, the European Union (EU) is pursuing a policy of transitioning from fossil fuels to renewable energies. The objective of this initiative is twofold: first, to reduce greenhouse gas (GHG) emissions, [...] Read more.

In the interest of climate protection and the promotion of a sustainable economy, the European Union (EU) is pursuing a policy of transitioning from fossil fuels to renewable energies. The objective of this initiative is twofold: first, to reduce greenhouse gas (GHG) emissions, and second, to decrease the reliance on energy imports. This article utilizes publicly accessible databases and studies to assess the extent to which bioenergy (including fuels, heat, and electricity) contributes to these objectives and its long-term potential. Presently, bioenergy constitutes approximately 14% of Europe’s energy supply, with a share of 60% ranking as the foremost source of renewable energy. The evaluation of bioenergy-related GHG is hindered by the fact that the official databases do not satisfy the criteria necessary for scientific analysis. Further expansion of bioenergy should be based on European non-food biomass, as required by the current Renewable Energy Directive REDIII. Taking competing non-energy uses into account, the potential for further growth, contribution to total energy supply, and independence from imports is likely to be limited. As part of the strategic development of bioenergy, there is a considerable need for research into the use of biomass for competing energy and material use pathways, taking into account the different economic potential. To this end, the introduction of a new economic indicator of value creation intensity is proposed. Full article

(This article belongs to the Special Issue Environmental Sustainability and Energy Economy: 2nd Edition)

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55 pages, 7653 KB

Open AccessEditor’s ChoiceArticle

Lifting the Blanket: Why Is Wholesale Electricity in Southeast European (SEE) Countries Systematically Higher than in the Rest of Europe? Empirical Evidence According to the Markov Blanket Causality and Rolling Correlations Approaches

by George P. Papaioannou, Panagiotis G. Papaioannou and Christos Dikaiakos

Energies 2025, 18(18), 4861; https://doi.org/10.3390/en18184861 - 12 Sep 2025

Viewed by 406

Abstract

We investigate the key factors that shape the dynamic evolution of Day-Ahead spot prices of seven European interconnected electricity markets of the Core Capacity Calculation Region, Core CCR (Austria AT, Hungary HU, Slovenia SI, Romania RO), the Southeast CCR (Bulgaria BG, Greece GR) [...] Read more.

We investigate the key factors that shape the dynamic evolution of Day-Ahead spot prices of seven European interconnected electricity markets of the Core Capacity Calculation Region, Core CCR (Austria AT, Hungary HU, Slovenia SI, Romania RO), the Southeast CCR (Bulgaria BG, Greece GR) and the Greece-Italy CCR (GRIT CCR), with emphasis on price surges and discrepancies observed in SEE CCR markets, during the period 2022–2024. The high differences in the prices of the two groups have generated political reactions from the countries that ‘suffer’ from these price discrepancies. By applying Machine Learning (ML) approaches, as Markov Blanket (MB) and Local, causal structures learning (LCSL), we are able of ‘revealing’ the entire path of volatility spillover of both spot price and the Cross-Border Transfer Availabilities (CBTA) between the countries involved, from north to south, thus uncovering i.e., ‘lifting the blanket’, to discover the ‘true’ structure’ of the path of causalities, responsible for the price disparity. The above methods are supported by the ‘mainstream’ approach of computing the correlation of the spot price and CBTA’s volatility curves of all markets, to detect volatility spillover effects across markets. The main findings of this hybrid approach are (a) the volatility of some Core CCRs (AT, HU, RO) markets’ spot price and CBTAs with neighboring countries, ‘uncovered’ to be pivotal, operating as a ‘transmitter’ of volatility ‘disturbances’, over its entire connection and causal path from Core CCR to SEE CCR markets, partially contributing to their price surge, (b) reduced available capacity for cross-border trading of some Core and SEE CCRs (they have not satisfied the minimum 70% requirement margin available for cross-zonal trade, MACZT), combined with local weather and geopolitical conditions, could have exacerbated the impact of the Flow-based Market coupling method (FBMC) used in the Core CCRs, on the prices’ surge of SEE CCR’s countries, e.g., via induced non-intuitive flows. This phenomenon questions the efficiency and reliability of the European Target’s model (TM) in securing ‘homogeneous’ power prices across all interconnected markets, core and peripheral. Full article

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26 pages, 980 KB

Open AccessEditor’s ChoiceArticle

Improvement of Renewable Energy Products by Balancing Quality, Environmental, Societal, and Cost Aspects

by Dominika Siwiec and Andrzej Pacana

Energies 2025, 18(18), 4840; https://doi.org/10.3390/en18184840 - 11 Sep 2025

Viewed by 259

Abstract

The aim of this article is to develop a model that supports the design and improvement of renewable energy products at an early stage of their development (conceptualization and prototyping), while also taking into account key aspects of sustainability. These aspects include quality [...] Read more.

The aim of this article is to develop a model that supports the design and improvement of renewable energy products at an early stage of their development (conceptualization and prototyping), while also taking into account key aspects of sustainability. These aspects include quality (customer satisfaction with product use), environmental impact, social responsibility, and purchase and/or production costs. Hence, this model is named QESC. The model was tested and illustrated for energy storage facilities. According to the proposed modeling process, sixteen key criteria were identified from the quality, environmental, and social aspects. The criteria were based on energy storage catalogs and the ISO 26000 standard. The criteria were described through ten different states (modifications), which represented alternative product solutions (prototypes). The proposed energy storage devices were evaluated using a formalized scoring method (PS, Czechowski). Subsequently, the indicators of quality, environmental, and social aspects were aggregated with the actual cost of the prototypes. A cost analysis was used for this purpose. The results were interpreted considering various aspects depending on the estimated costs of the prototypes. Based on the developed ranking of prototypes, the direction of development of the energy storage devices under consideration was determined. The proposed analysis demonstrated that the most advantageous prototype would be one with a high level of quality and cost compliance, with social and environmental aspects proving less important. The model can support decision making regarding the development of renewable energy products, including being useful for the sustainable development of other products at an early stage of their development. Full article

(This article belongs to the Special Issue Evaluating the Impact of Energy Policies and Energy Economics: Trends, Risks and Challenges)

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17 pages, 1212 KB

Open AccessEditor’s ChoiceArticle

Increasing Economic Benefits in Renewable Energy Communities with Solar PV and Battery Storage Technologies: Insights from New Member Integration

by Jorge Sousa, Sérgio Perinhas, Carla Viveiros and Filipe Barata

Energies 2025, 18(18), 4815; https://doi.org/10.3390/en18184815 - 10 Sep 2025

Viewed by 448

Abstract

Renewable Energy Communities (RECs) play a vital role in driving the transition to sustainable energy systems by facilitating inclusive and cost-effective renewable energy production. They empower citizens to actively participate in the energy sector, promote local energy resource sharing, and improve local energy [...] Read more.

Renewable Energy Communities (RECs) play a vital role in driving the transition to sustainable energy systems by facilitating inclusive and cost-effective renewable energy production. They empower citizens to actively participate in the energy sector, promote local energy resource sharing, and improve local energy balancing efforts. This study presents a model for investment and operational decision-making within an REC framework, enabling multiple members to invest in renewable energy generation and battery energy storage systems. The model determines optimal capacities for each technology, facilitates energy sharing among members, and evaluates both individual and collective economic benefits through an internal electricity sharing price. By examining various scenarios within an established three-member REC, the research identifies key factors influencing the acceptance of a new member into the community. The findings indicate that the economic advantages of expanding the REC are significantly dependent on the characteristics of the prospective new member. Full article

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

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29 pages, 8271 KB

Open AccessEditor’s ChoiceReview

A Review of Offshore Renewable Energy for Advancing the Clean Energy Transition

by Annette von Jouanne, Emmanuel Agamloh and Alex Yokochi

Energies 2025, 18(18), 4798; https://doi.org/10.3390/en18184798 - 9 Sep 2025

Viewed by 930

Abstract

Offshore renewable energy resources are abundant and widely available worldwide, offering significant contributions to the clean energy net-zero carbon emission targets. This paper reviews strong and emerging offshore renewable energy sources, including wind (fixed bottom and floating), hydrokinetic wave and tidal energy, floating [...] Read more.

Offshore renewable energy resources are abundant and widely available worldwide, offering significant contributions to the clean energy net-zero carbon emission targets. This paper reviews strong and emerging offshore renewable energy sources, including wind (fixed bottom and floating), hydrokinetic wave and tidal energy, floating solar photovoltaics (FPVs) and hybrid energy systems. A literature review of recent sources yields a timely comprehensive comparison of the levelized cost of electricity (LCOE), technology readiness levels (TRLs), capacity factors (CFs) and global generation installed and potential, where offshore wind is recognized as being the strongest contributor to the clean energy transition and thus receives the most attention. Offshore wind grid integration, converter technologies, criticality, resiliency and energy storage integration are presented, in addition to challenges and research directions. While wave, tidal and FPV will never dominate the global grid, they have vital roles to play in the global energy transition; thus, they are reviewed, including technologies, installations, potential, challenges and research directions. Offshore hybrid energy systems, combining different offshore renewable energy sources, are also discussed along with example installations. The paper concludes with a discussion of the potential environmental impacts of offshore renewable energy development, including recommendations. Full article

(This article belongs to the Special Issue Sustainable Energy, Environment and Low-Carbon Development)

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18 pages, 7218 KB

Open AccessEditor’s ChoiceArticle

Energy Storage Systems for Fluctuating Energy Sources and Fluctuating Loads—Analysis of Selected Cases

by Marcin Jarnut, Jacek Kaniewski and Mariusz Buciakowski

Energies 2025, 18(18), 4792; https://doi.org/10.3390/en18184792 - 9 Sep 2025

Viewed by 534

Abstract

The dynamic development of energy storage technologies makes it possible to solve many problems related to the negative impact of renewable sources and fluctuating loads on the power and voltage quality parameters at their point of connection to the distribution grid. By absorbing [...] Read more.

The dynamic development of energy storage technologies makes it possible to solve many problems related to the negative impact of renewable sources and fluctuating loads on the power and voltage quality parameters at their point of connection to the distribution grid. By absorbing temporary energy surpluses and covering temporary energy deficits, these technologies enable the smoothing of output power profiles of wind turbines, as well as the reduction in peak power values, for example, in traction substations or fast-charging hubs for electric vehicles. This article discusses the specifics of both applications with particular emphasis on methods for sizing energy storage parameters, methods for their control, and the special effects they allow us to achieve. The methods proposed by the authors allow for the more optimal selection of energy storage parameters in existing energy facilities based on their measured power profiles. The proposed control methods, in turn, allow for not only a reduction in relative changes in power and voltage but also enable an increase in the installed power of wind farms without investing in the modernization of the distribution network, as well as reducing the contracted power of traction substations. The analyses presented in this article are based on power profile measurements of real objects, and the proposed solutions are already being implemented in power infrastructure. Full article

(This article belongs to the Special Issue Advanced Energy Storage Technologies)

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37 pages, 3755 KB

Open AccessEditor’s ChoiceReview

Comparative Performance Analysis of Bioenergy with Carbon Capture and Storage (BECCS) Technologies

by Letizia Cretarola and Federico Viganò

Energies 2025, 18(18), 4800; https://doi.org/10.3390/en18184800 - 9 Sep 2025

Viewed by 566

Abstract

This study presents a comprehensive performance assessment of combustion-based options for Bioenergy with Carbon Capture and Storage (BECCS), widely regarded as key enablers of future climate neutrality. From 972 publications (2000–2025), 16 sources are identified as providing complete data. Seven technologies are considered: [...] Read more.

This study presents a comprehensive performance assessment of combustion-based options for Bioenergy with Carbon Capture and Storage (BECCS), widely regarded as key enablers of future climate neutrality. From 972 publications (2000–2025), 16 sources are identified as providing complete data. Seven technologies are considered: Calcium Looping (CaL), Chemical Looping Combustion (CLC), Hot Potassium Carbonate (HPC), low-temperature solvents (mainly amine-based), molten sorbents, Molten Carbonate Fuel Cells (MCFCs), and oxyfuel. First- and second-law efficiencies are reported for 53 bioenergy configurations (19 reference plants without carbon capture and 34 BECCS systems). Performance is primarily evaluated via the reduction in second-law (exergy) efficiency and the Specific Primary Energy Consumption per CO₂ Avoided (SPECCA), both relative to each configuration’s reference plant. MCFC-based systems perform best, followed by CLC; molten sorbents and oxyfuel also show very good performance, although each is documented by a single source. Low-temperature solvents span a wide performance range—from poor to competitive—highlighting the heterogeneity of this category; HPC performs in line with the average of low-temperature solvents. CaL exhibits modest efficiency penalties alongside appreciable energy costs of CO₂ capture, a counterintuitive outcome driven by the high performance of the benchmark plants considered in the definition of SPECCA. To account for BECCS-specific features (multiple outputs and peculiar fuels), a dedicated evaluation framework with a revised SPECCA formulation is introduced. Full article

(This article belongs to the Special Issue Bioenergy and Waste-to-Energy Technologies to Reach Climate Neutrality)

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23 pages, 803 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Renewable Energy Sources Sector Development in the European Union

by Laima Okunevičiūtė Neverauskienė, Alina Kvietkauskienė, Manuela Tvaronavičienė, Irena Danilevičienė and Dainora Gedvilaitė

Energies 2025, 18(17), 4786; https://doi.org/10.3390/en18174786 - 8 Sep 2025

Viewed by 790

Abstract

The global energy landscape is transforming, driven by the urgent need to address climate change, reduce dependency on fossil fuels, and promote sustainable economic growth. Renewable energy sources (RESs) have emerged as a cornerstone of this transition, offering environmental benefits and significant potential [...] Read more.

The global energy landscape is transforming, driven by the urgent need to address climate change, reduce dependency on fossil fuels, and promote sustainable economic growth. Renewable energy sources (RESs) have emerged as a cornerstone of this transition, offering environmental benefits and significant potential to catalyze economic development. By harnessing inexhaustible natural resources, such as solar, wind, hydro, and biomass, renewable energy systems provide a pathway to achieving energy security, fostering innovation, and generating new economic opportunities. In this article, the economic effect on the RES sector development was examined. The authors defined the set from seven indicators: real GDP growth, unemployment rate, inflation rate, exports of goods and services, government debt, foreign direct investments, and labor cost index, which allowed them to evaluate the EU countries’ economic situation and rank the countries by economic stability level. The results, which were obtained using a multi-criteria evaluation method, show that the EU countries whose economies are the strongest according to the evaluated macroeconomic indicators are Luxembourg, Malta, Estonia, and Ireland. The countries with the lowest scores are Greece, Italy, and Spain. Seeking to evaluate the development level of the RES sector in all ranked EU countries, the analysis of RES sector development during the 2012–2022 period, using these RES indicators—share of renewable energy in gross final energy consumption by sector—in general, in transport, in electricity, and in heating and cooling, was carried out and, through a different multi-criteria method, the countries were ranked by RES development. After the analysis was carried out, it could be stated that the economic situation stability in the country does not directly affect the growth of the RES sector development, and the two rankings by different indicators are heavily uncorrelated. RES sector development can be affected by many other circumstances. RES development is still stagnating in some countries, despite macroeconomic stability, for several reasons: institutional and political barriers, differences in the availability of finance, infrastructure limitations, and technological and human resource shortages. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Economic Growth, Energy Consumption and Carbon Emission)

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26 pages, 1190 KB

Open AccessEditor’s ChoiceArticle

Structural Drivers of Poland’s Renewable Energy Transition (2010–2023): Empirical Insights from Regression and Cluster Analysis

by Bożena Gajdzik, Radosław Wolniak and Wieslaw Wes Grebski

Energies 2025, 18(17), 4754; https://doi.org/10.3390/en18174754 - 6 Sep 2025

Viewed by 874

Abstract

This research investigates the structural drivers of Poland’s energy transition to decarbonization and wider sustainable development goals. With a focus on the period 2010–2023, we use longitudinal regression analysis and cluster-based segmentation to examine the dynamic interactions between investment expenditure, deployed renewable capacity, [...] Read more.

This research investigates the structural drivers of Poland’s energy transition to decarbonization and wider sustainable development goals. With a focus on the period 2010–2023, we use longitudinal regression analysis and cluster-based segmentation to examine the dynamic interactions between investment expenditure, deployed renewable capacity, and innovation expenditure in driving renewable electricity production. Our findings suggest that although installed capacity continues to be the nearest cause of renewable energy output, innovation expenditure has an extraordinarily large marginal effect, acknowledging the system-transformational role of technology innovation in low-carbon systems. Regression specifications suggested that the establishment of Poland’s transformation process is not only guided by the growth in capital, but also by the systemic embedment of knowledge-driven innovation. Cluster analysis reveals three successive stages of sectoral development—initial growth (2010–2013), consistent expansion (2014–2019), and rapid transformation (2020–2023)—with blended policy actions and structural effects. Despite the long shadow of Poland’s coal-linked past and post-2015 stagnation in innovation, the results signal a major move towards a more low-emitting, resilient power system. The report offers empirical facts and prescriptive evidence to guide policy formulation supporting collective, innovation-driven approaches essential for driving energy change in coal-dominated economies. Full article

(This article belongs to the Special Issue Energy Transition and Sustainability: Low-Carbon Economy)

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25 pages, 4628 KB

Open AccessEditor’s ChoiceArticle

Forecasting Electricity Prices Three Days in Advance: Comparison Between Multilayer Perceptron and Support Vector Machine Networks

by Dariusz Borkowski and Michał Jaśkiewicz

Energies 2025, 18(17), 4744; https://doi.org/10.3390/en18174744 - 5 Sep 2025

Viewed by 960

Abstract

Electricity prices are subject to constant changes, mainly owing to the increasing share of unstable renewable energy sources. The ability to predict short-term prices presents significant benefits to both energy consumers and producers. This is crucial for managing the energy in hybrid systems [...] Read more.

Electricity prices are subject to constant changes, mainly owing to the increasing share of unstable renewable energy sources. The ability to predict short-term prices presents significant benefits to both energy consumers and producers. This is crucial for managing the energy in hybrid systems with energy storage. This study presents a methodology for predicting the electricity prices for three days with hourly resolution. The accuracy of the price prediction strongly depends on the stability and repeatability of the analysed energy market. The Polish market, characterised by a dynamically changing energy mix, where the selection of the training period and the training, validation, and test sets are crucial, is assessed. Two periods are analysed: 2019–2021, which is a period of stable prices, and 2022–2024, which is a period of high price variability. The multilayer perceptron (MLP) network and support vector machine (SVM) are trained using three sets of data: time, weather, and prices of various energy sources. The analysis indicates the correlation of data and their impact on the accuracy of the price forecast. Dedicated data processing, network model structures, and training techniques are used. The comparison between prediction accuracies shows the advantages of the SVM network, whose prediction error is lower by 45% for the period of stable prices and by 20% for the period of variable prices when compared with the MLP network. The results indicate a significant increase in accuracy when various types of training data, such as weather or energy prices, are considered. Full article

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20 pages, 3390 KB

Open AccessEditor’s ChoiceArticle

Pattern-Aware BiLSTM Framework for Imputation of Missing Data in Solar Photovoltaic Generation

by Minseok Jang and Sung-Kwan Joo

Energies 2025, 18(17), 4734; https://doi.org/10.3390/en18174734 - 5 Sep 2025

Cited by 1 | Viewed by 841

Abstract

Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems [...] Read more.

Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R² value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R² = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control. Full article

(This article belongs to the Special Issue Renewable Energy Development in Distribution Networks: Optimization, Assessment and Design of Renewable Plants)

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22 pages, 1104 KB

Open AccessEditor’s ChoiceArticle

Improving CO₂ Capture Efficiency Through Novel CLOU-Based Fuel Reactor Configuration in Chemical Looping Combustion

by Anna Zylka, Jaroslaw Krzywanski, Tomasz Czakiert, Marcin Sosnowski, Karolina Grabowska, Dorian Skrobek and Lukasz Lasek

Energies 2025, 18(17), 4640; https://doi.org/10.3390/en18174640 - 1 Sep 2025

Viewed by 671

Abstract

Climate change and global decarbonization targets drive the search for more efficient and cost-effective combustion technologies. Chemical looping combustion (CLC) using solid oxygen carriers with chemical looping with oxygen uncoupling (CLOU) functionality has attracted growing interest due to its inherent potential for CO [...] Read more.

Climate change and global decarbonization targets drive the search for more efficient and cost-effective combustion technologies. Chemical looping combustion (CLC) using solid oxygen carriers with chemical looping with oxygen uncoupling (CLOU) functionality has attracted growing interest due to its inherent potential for CO₂ capture without requiring additional separation processes. This study introduces a conceptual proof-of-concept design of a novel fuel reactor design for a dual-fluidized bed CLC system operating with solid fuels. The new configuration incorporates a perforated conveyor for circulating CLOU-type oxygen carriers, thereby avoiding direct contact between the carriers and the fuel–ash mixture. This approach prevents the slip of unburned fuel and ash into the air reactor, minimizes the loss of oxygen carriers, and improves combustion efficiency. The proposed reactor concept enables the generation of flue gas with a high CO₂ concentration, which facilitates its subsequent capture and reduces the energy penalty associated with traditional CCS techniques. The improved phase separation and better control of oxygen carrier residence time contribute to enhanced system performance and reduced operating costs. Preliminary process simulations conducted in the CeSFaMB environment, using boundary and initial conditions from a CLC test rig, were included to illustrate the potential of the design. Experimental validation is outside the scope of this study and will be presented in future work once the dedicated test facility is operational. This contribution should therefore be regarded as a conceptual proof-of-concept study, and experimental validation together with techno-economic benchmarking will be reported in follow-up publications once the dedicated test facility is operational. Full article

(This article belongs to the Special Issue Innovative Approaches in Carbon Capture and Utilization for Sustainable Industrial Processes)

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25 pages, 2510 KB

Open AccessEditor’s ChoiceReview

Grid-Forming Converters for Renewable Generation: A Comprehensive Review

by Muhammad Waqas Qaisar and Jingyang Fang

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

Viewed by 1588

Abstract

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

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

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

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21 pages, 2243 KB

Open AccessEditor’s ChoiceArticle

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

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

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

Viewed by 583

Abstract

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

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

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

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12 pages, 1513 KB

Open AccessEditor’s ChoiceArticle

Impedance Spectroscopy for Interface Trap Effects Evaluation in Dopant-Free Silicon Solar Cells

by Ilaria Matacena, Laura Lancellotti, Eugenia Bobeico, Iurie Usatii, Marco della Noce, Elena Santoro, Pietro Scognamiglio, Lucia V. Mercaldo, Paola Delli Veneri and Santolo Daliento

Energies 2025, 18(17), 4558; https://doi.org/10.3390/en18174558 - 28 Aug 2025

Viewed by 501

Abstract

This work investigates the effect of interface traps on the impedance spectra of dopant-free silicon solar cells. The studied device consists of a crystalline silicon absorber with an a-Si:H/MoOx/ITO stack as the front passivating hole-collecting contact and an a-Si:H/LiF/Al stack as the rear [...] Read more.

This work investigates the effect of interface traps on the impedance spectra of dopant-free silicon solar cells. The studied device consists of a crystalline silicon absorber with an a-Si:H/MoOx/ITO stack as the front passivating hole-collecting contact and an a-Si:H/LiF/Al stack as the rear passivating electron-collecting contact. Experimental measurements, including illuminated current–voltage (I–V) characteristics and impedance spectroscopy, were performed on the fabricated devices and after a soft annealing treatment. The annealed cells exhibit an increased open-circuit voltage and a larger Nyquist plot radius. To interpret these results, a numerical model was developed in a TCAD environment. Simulations reveal that traps located at the p/i interface (MoOx/i-a-Si:H) significantly affect the impedance spectra, with higher trap concentrations leading to smaller Nyquist plot circumferences. The numerical impedance curves were aligned to the experimental data, enabling extraction of the interfacial traps concentration. The results highlight the sensitivity of impedance spectroscopy to interfacial quality and confirm that the performance improvement after soft annealing is primarily due to reduced defect density at the MoOx/i-a-Si:H interface. Full article

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

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31 pages, 4510 KB

Open AccessEditor’s ChoiceArticle

Anaerobic Digestion and Solid Oxide Fuel Cell Integration: A Comprehensive Dimensioning and Comparative Techno-Energy-Economic Assessment of Biomethane Grid Injection vs. Cogeneration

by Orlando Corigliano, Leonardo Pagnotta and Petronilla Fragiacomo

Energies 2025, 18(17), 4551; https://doi.org/10.3390/en18174551 - 27 Aug 2025

Viewed by 1062

Abstract

The objective of this paper is to study and analyze an integrated anaerobic digester (AD)–solid oxide fuel cell (SOFC) system, to achieve an energy-efficient waste-to-energy solution. A detailed numerical modeling is developed for plant dimensioning and energy evaluations. The calculation pathway involves determining [...] Read more.

The objective of this paper is to study and analyze an integrated anaerobic digester (AD)–solid oxide fuel cell (SOFC) system, to achieve an energy-efficient waste-to-energy solution. A detailed numerical modeling is developed for plant dimensioning and energy evaluations. The calculation pathway involves determining operational parameters based on specific variables such as the net electric power produced by the SOFC system or the amount of biogas produced by the AD. Three types of biomass—sewage sludge, slaughter waste, and the organic fraction of municipal solid waste (OFMSW)—are considered. The reactor volume required is approximately 24,000 m³ per 1 kg/s of biogas, processing a daily organic substrate of around 900 m³. The calculations reveal a SOFC electric efficiency of 51% and a thermal efficiency of 39%, under the most favorable conditions. In the integrated AD-SOFC layout, net electrical and thermal efficiencies of 47% and 35%, respectively, are achieved. The economic analysis evaluates the investment feasibility under current incentive schemes, considering both the standalone sale of biomethane and the sale of electricity and thermal energy through SOFC integration. A case study evaluates a biomethane facility producing 508 Sm³/h, integrated with an SOFC system capable of generating 2.36 MW_el and 1.74 MW_th of electric and thermal powers. Various scenarios are examined using net present value (NPV) and payback period (PB) analyses. Results show that the PB for the biomethane-only case is 6.46 years. When integrating the SOFC system, the PB is slightly longer—6.58 years in the most favorable scenario—while it increases to 11.55 years under the most likely scenario. Full article

(This article belongs to the Special Issue Selected Papers from the 32nd European Biomass Conference and Exhibition (EUBCE 2024))

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20 pages, 1743 KB

Open AccessEditor’s ChoiceArticle

Deep Reinforcement Learning Approaches the MILP Optimum of a Multi-Energy Optimization in Energy Communities

by Vinzent Vetter, Philipp Wohlgenannt, Peter Kepplinger and Elias Eder

Energies 2025, 18(17), 4489; https://doi.org/10.3390/en18174489 - 23 Aug 2025

Viewed by 928

Abstract

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. [...] Read more.

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. While mixed-integer linear programming (MILP) remains the standard for operational optimization and demand response in such systems, its computational burden limits scalability and responsiveness under real-time or uncertain conditions. Reinforcement learning (RL), by contrast, offers a model-free, adaptive alternative. However, its application to real-world energy system operation remains limited. This study explores the application of a Deep Q-Network (DQN) to a real residential EC, which has received limited attention in prior work. The system comprises three single-family homes sharing a centralized heating system with a thermal energy storage (TES), a PV installation, and a grid connection. We compare the performance of MILP and RL controllers across economic and environmental metrics. Relative to a reference scenario without TES, MILP and RL reduce energy costs by 10.06% and 8.78%, respectively, and both approaches yield lower total energy consumption and CO₂-equivalent emissions. Notably, the trained RL agent achieves a near-optimal outcome while requiring only 22% of the MILP’s computation time. These results demonstrate that DQNs can offer a computationally efficient and practically viable alternative to MILP for real-time control in residential energy systems. Full article

(This article belongs to the Special Issue Smart Energy Management and Sustainable Urban Communities)

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25 pages, 4162 KB

Open AccessEditor’s ChoiceArticle

Spaces, Energy and Shared Resources: New Technologies for Promoting More Inclusive and Sustainable Urban Communities

by Fabrizio Cumo, Elisa Pennacchia, Patrick Maurelli, Flavio Rosa and Claudia Zylka

Energies 2025, 18(16), 4410; https://doi.org/10.3390/en18164410 - 19 Aug 2025

Viewed by 587

Abstract

Renewable Energy Communities (RECs) are central to Europe’s strategy for reducing greenhouse gas emissions and advancing a sustainable, decentralized energy system. RECs aim to transform consumers into prosumers—individuals who both produce and consume energy—thereby enhancing energy efficiency, local autonomy, and citizen engagement. This [...] Read more.

Renewable Energy Communities (RECs) are central to Europe’s strategy for reducing greenhouse gas emissions and advancing a sustainable, decentralized energy system. RECs aim to transform consumers into prosumers—individuals who both produce and consume energy—thereby enhancing energy efficiency, local autonomy, and citizen engagement. This study introduces a novel Geographic Information System (GIS)-based methodology that integrates socio-economic and spatial data to support the design of optimal REC configurations. QGIS 3.40.9 “Batislava” tool is used to simulate site-specific energy distribution scenarios, enabling data-driven planning. By combining a Composite Energy Vulnerability Index (CEVI), Rooftop Solar Potential (RSP), and the distribution of urban gardens (UGs), the approach identifies priority urban zones for intervention. Urban gardens offer multifunctional public spaces that can support renewable infrastructures while fostering local resilience and energy equity. Applied to the city of Rome, the methodology provides a replicable framework to guide REC deployment in vulnerable urban contexts. The results demonstrate that 11 of the 18 highest-priority areas already host urban gardens, highlighting their potential as catalysts for collective PV systems and social engagement. The proposed model advances sustainability objectives by integrating environmental, social, and spatial dimensions—positioning RECs and urban agriculture as synergistic tools for inclusive energy transition and climate change mitigation. Full article

(This article belongs to the Special Issue Sustainable Built Environment: Energy Efficiency Technologies, Performance Evaluation and Indoor Environment Quality)

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31 pages, 1954 KB

Open AccessEditor’s ChoiceArticle

Forecasting Short-Term Photovoltaic Energy Production to Optimize Self-Consumption in Home Systems Based on Real-World Meteorological Data and Machine Learning

by Paweł Kut and Katarzyna Pietrucha-Urbanik

Energies 2025, 18(16), 4403; https://doi.org/10.3390/en18164403 - 18 Aug 2025

Cited by 1 | Viewed by 629

Abstract

Given the growing number of residential photovoltaic installations and the challenges of self-consumption, accurate short-term PV production forecasting can become a key tool in supporting energy management. This issue is particularly significant in systems without energy storage, where excess production is fed back [...] Read more.

Given the growing number of residential photovoltaic installations and the challenges of self-consumption, accurate short-term PV production forecasting can become a key tool in supporting energy management. This issue is particularly significant in systems without energy storage, where excess production is fed back into the grid, reducing the profitability of prosumer investments. This paper presents an approach to forecasting short-term energy production in residential photovoltaic installations, based on real meteorological data and the use of machine learning methods. The analysis is based on measurement data from a functioning PV installation and a local weather station. This study compares three models: classical linear regression, Random Forest and the XGBoost algorithm. The method of data preparation, the model training process and the assessment of their effectiveness based on real energy production measurements are presented. This paper also includes a practical calculation example and an analysis of selected days in order to compare the forecast results with the actual production. Of the three models compared, the highest accuracy was achieved for XGBoost, with an MAE = 1.25 kWh, RMSE = 1.93 kWh, and coefficient of determination R² = 0.94. Compared to linear regression, this means a 66% reduction in MAE and a 41% reduction in the Random Forest model, confirming the practical usefulness of this method in a real-world environment. The proposed approach can be used in energy management systems in residential buildings, without the need to use energy storage, and can support the development of a more conscious use of energy resources on a local scale. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy in Light of Energy Transition Processes—2nd Edition)

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26 pages, 675 KB

Open AccessEditor’s ChoiceArticle

Energy Efficiency Starts in the Mind: How Green Values and Awareness Drive Citizens’ Energy Transformation

by Marcin Awdziej, Dariusz Dudek, Bożena Gajdzik, Magdalena Jaciow, Ilona Lipowska, Marcin Lipowski, Jolanta Tkaczyk, Radosław Wolniak and Robert Wolny

Energies 2025, 18(16), 4331; https://doi.org/10.3390/en18164331 - 14 Aug 2025

Viewed by 637

Abstract

Background: Understanding the psychological drivers of the energy transition is essential for accelerating the shift to low-carbon societies. The aim of this study is to examine how green consumer values (GCV), energy-saving knowledge (KES) and consumer energy awareness (CEA) jointly shape pro-environmental energy [...] Read more.

Background: Understanding the psychological drivers of the energy transition is essential for accelerating the shift to low-carbon societies. The aim of this study is to examine how green consumer values (GCV), energy-saving knowledge (KES) and consumer energy awareness (CEA) jointly shape pro-environmental energy behaviors (EEB), while accounting for citizens’ perceived cost barriers (PESC). Methods: We conducted a nationally representative Computer-Assisted Web Interviewing (CAWI) survey of 1405 Polish households and employed structural-equation modeling to test an integrated framework linking values, awareness, knowledge, perceived costs and two behavioral domains: high-commitment efficiency investments and low-cost curtailment actions. Results: The structural-equation model confirms that green consumer value significantly enhance both knowledge of energy-saving (β = 0.434) and consumer energy awareness (β = 0.185), thereby driving two distinct pro-environmental pathways: high-commitment efficiency investments (energy efficiency behavior) (β = 0.488) and curtailment behaviors (β = 0.355). Green consumer value also reduces perception of energy-saving costs (β = −0.344), yet these costs themselves exert strong inhibitory effects on both energy efficiency behavior (β = −0.213) and curtailment behaviors (β = −0.302). Conclusions: Our findings validate an integrated value–awareness–behavior framework, demonstrating that fostering green values and improving informational access are critical to enhancing energy-saving practices, while cost-reduction measures remain indispensable. Policymakers should combine value-based education, transparent feedback tools and targeted financial incentives to unlock citizens’ full potential in driving the energy transition. Full article

(This article belongs to the Special Issue Energy Transformation from the Perspectives of the Individual Citizen and the Market)

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28 pages, 4927 KB

Open AccessEditor’s ChoiceReview

A Review on Perovskite/Silicon Tandem Solar Cells: Current Status and Future Challenges

by Jingyu Huang and Lin Mao

Energies 2025, 18(16), 4327; https://doi.org/10.3390/en18164327 - 14 Aug 2025

Viewed by 4580

Abstract

Perovskite/Si tandem solar cells (PSTSCs) have emerged as a leading candidate for surpassing the Shockley–Queisser (SQ) efficiency limit inherent to single-junction silicon solar cells. Following their inaugural demonstration in 2015, perovskite/Si tandem solar cells have experienced remarkable technological progression, reaching a certified power [...] Read more.

Perovskite/Si tandem solar cells (PSTSCs) have emerged as a leading candidate for surpassing the Shockley–Queisser (SQ) efficiency limit inherent to single-junction silicon solar cells. Following their inaugural demonstration in 2015, perovskite/Si tandem solar cells have experienced remarkable technological progression, reaching a certified power conversion efficiency of 34.9% by 2025. To elucidate pathways for realizing the full potential of perovskite/Si tandem solar cells, this review commences with an examination of fundamental operational mechanisms in multi-junction photovoltaic architectures. Subsequent sections systematically analyze technological breakthroughs across three critical PSTSC components organized by an optical path sequence: (1) innovations in perovskite photoactive layers through component engineering, additive optimization, and interfacial modification strategies; (2) developments in charge transport and recombination management via advanced interconnecting layers; and (3) silicon subcell architectures. The review concludes with a critical analysis of persistent challenges in device stability, scalability, structural optimization and fabrication method, proposing strategic research directions to accelerate the transition from laboratory-scale achievements to commercially viable photovoltaic solutions. Full article

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

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27 pages, 1605 KB

Open AccessEditor’s ChoiceArticle

Using Hydro-Pneumatic Energy Storage for Improving Offshore Wind-Driven Green Hydrogen Production—A Preliminary Feasibility Study in the Central Mediterranean Sea

by Oleksii Pirotti, Diane Scicluna, Robert N. Farrugia, Tonio Sant and Daniel Buhagiar

Energies 2025, 18(16), 4344; https://doi.org/10.3390/en18164344 - 14 Aug 2025

Viewed by 704

Abstract

This paper presents a preliminary feasibility study for integrating hydro-pneumatic energy storage (HPES) with off-grid offshore wind turbines and green hydrogen production facilities—a concept termed HydroGenEration (HGE). This study compares the performance of this innovative concept system with an off-grid direct wind-to-hydrogen plant [...] Read more.

This paper presents a preliminary feasibility study for integrating hydro-pneumatic energy storage (HPES) with off-grid offshore wind turbines and green hydrogen production facilities—a concept termed HydroGenEration (HGE). This study compares the performance of this innovative concept system with an off-grid direct wind-to-hydrogen plant concept without energy storage, both under central Mediterranean wind conditions. Numerical simulations were conducted at high temporal resolution, capturing 10-min fluctuations of open field measured wind speeds at an equivalent offshore wind turbine (WT) hub height over a full 1-year, seasonal cycle. Key findings demonstrate that the HPES system of choice, namely the Floating Liquid Piston Accumulator with Sea Water under Compression (FLASC) system, significantly reduces Proton Exchange Membrane (PEM) electrolyser (PEMEL) On/Off cycling (with a 66% reduction in On/Off events), while maintaining hydrogen production levels, despite the integration of the energy storage system, which has a projected round-trip efficiency of 75%. The FLASC-integrated HGE solution also marginally reduces renewable energy curtailment by approximately 0.3% during the 12-month timeframe. Economic analysis reveals that while the FLASC HPES system does introduce an additional capital cost into the energy chain, it still yields substantial operational savings exceeding EUR 3 million annually through extended PEM electrolyser lifetime and improved operational efficiency. The Levelized Cost of Hydrogen (LCOH) for the FLASC-integrated HGE system, which is estimated to be EUR 18.83/kg, proves more economical than a direct wind-to-hydrogen approach with a levelized cost of EUR 21.09/kg of H₂ produced. This result was achieved through more efficient utilisation of wind energy interfaced with energy storage as it mitigated the natural intermittency of the wind and increased the lifecycle of the equipment, especially that of the PEM electrolysers. Three scenario models were created to project future costs. As electrolyser technologies advance, cost reductions would be expected, and this was one of the scenarios envisaged for the future. These scenarios reinforce the technical and economic viability of the HGE concept for offshore green hydrogen production, particularly in the Mediterranean, and in regions having similar moderate wind resources and deeper seas for offshore hybrid sustainable energy systems. Full article

(This article belongs to the Special Issue Advanced Methods for Hydrogen Production, Storage and Utilization, 2nd Edition)

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16 pages, 2773 KB

Open AccessEditor’s ChoiceArticle

Enhancing Fuel Cell Hybrid Electric Vehicle Energy Management with Real-Time LSTM Speed Prediction

by Matthieu Matignon, Mehdi Mcharek, Toufik Azib and Ahmed Chaibet

Energies 2025, 18(16), 4340; https://doi.org/10.3390/en18164340 - 14 Aug 2025

Viewed by 579

Abstract

This paper presents an innovative approach to optimize real-time energy management in fuel cell electric vehicles (FCEVs) through an integrated EMS (iEMS) framework based on a nested concept. Central to our method are two LSTM-based speed prediction models, trained and validated on open-source [...] Read more.

This paper presents an innovative approach to optimize real-time energy management in fuel cell electric vehicles (FCEVs) through an integrated EMS (iEMS) framework based on a nested concept. Central to our method are two LSTM-based speed prediction models, trained and validated on open-source datasets to enhance adaptability and efficiency. The first model, trained on a 27 h real-time database, is embedded within the iEMS for dynamic real-time operation. The second model assesses the impact of incorporating external traffic data on the prediction accuracy, offering a systematic approach to refining speed prediction models. The results demonstrate significant improvements in fuel efficiency and overall performance compared to existing models. This study highlights the promise of data-driven AI models in next-generation FCEV energy management, contributing to smarter and more sustainable mobility solutions. Full article

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

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15 pages, 1496 KB

Open AccessEditor’s ChoiceArticle

Simultaneous Reductions in NO_x Emissions, Combustion Instability, and Efficiency Loss in a Lean-Burn CHP Engine via Hydrogen-Enriched Natural Gas

by Johannes Fichtner, Jan Ninow and Joerg Kapischke

Energies 2025, 18(16), 4339; https://doi.org/10.3390/en18164339 - 14 Aug 2025

Viewed by 711

Abstract

This study demonstrates that hydrogen enrichment in lean-burn spark-ignition engines can simultaneously improve three key performance metrics, thermal efficiency, combustion stability, and nitrogen oxide emissions, without requiring modifications to the engine hardware or ignition timing. This finding offers a novel control approach to [...] Read more.

This study demonstrates that hydrogen enrichment in lean-burn spark-ignition engines can simultaneously improve three key performance metrics, thermal efficiency, combustion stability, and nitrogen oxide emissions, without requiring modifications to the engine hardware or ignition timing. This finding offers a novel control approach to a well-documented trade-off in existing research, where typically only two of these factors are improved at the expense of the third. Unlike previous studies, the present work achieves simultaneous improvement of all three metrics without hardware modification or ignition timing adjustment, relying solely on the optimization of the air–fuel equivalence ratio

λ

. Experiments were conducted on a six-cylinder engine for combined heat and power application, fueled with hydrogen–natural gas blends containing up to 30% hydrogen by volume. By optimizing only the air–fuel equivalence ratio, it was possible to extend the lean-burn limit from

λ \approx 1.6

to

λ > 1.9

, reduce nitrogen oxide emissions by up to 70%, enhance thermal efficiency by up to 2.2 percentage points, and significantly improve combustion stability, reducing cycle-by-cycle variationsfrom 2.1% to 0.7%. A defined

λ

window was identified in which all three key performance indicators simultaneously meet or exceed the natural gas baseline. Within this window, balanced improvements in nitrogen oxide emissions, efficiency, and stability are achievable, although the individual maxima occur at different operating points. Cylinder pressure analysis confirmed that combustion dynamics can be realigned with original equipment manufacturer characteristics via mixture leaning alone, mitigating hydrogen-induced pressure increases to just 11% above the natural gas baseline. These results position hydrogen as a performance booster for natural gas engines in stationary applications, enabling cleaner, more efficient, and smoother operation without added system complexity. The key result is the identification of a

λ

window that enables simultaneous optimization of nitrogen oxide emissions, efficiency, and combustion stability using only mixture control. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy and Fuel Cell Technologies)

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23 pages, 2126 KB

Open AccessEditor’s ChoiceArticle

Sustainability Assessment of Energy System Transition Scenarios in Gotland: Integrating Techno-Economic Modeling with Environmental and Social Perspectives

by Sahar Safarian, Maria Lidberg and Mirjam Särnbratt

Energies 2025, 18(16), 4315; https://doi.org/10.3390/en18164315 - 13 Aug 2025

Viewed by 608

Abstract

Gotland has been designated by the Swedish government as a pilot region for the transition to a sustainable, fossil-free energy system by 2030. This transformation emphasizes local renewable energy production and system independence. Within this context, this study investigates the role of industrial [...] Read more.

Gotland has been designated by the Swedish government as a pilot region for the transition to a sustainable, fossil-free energy system by 2030. This transformation emphasizes local renewable energy production and system independence. Within this context, this study investigates the role of industrial waste heat as a resource to improve energy efficiency and support sector integration between electricity, heating, and industry. A mixed-methods approach was used, combining techno-economic energy system modeling, life cycle assessment, spatial GIS data, and stakeholder input. The study develops and analyzes future carbon-neutral energy scenarios for Gotland’s energy system. Industrial waste heat can significantly reduce primary energy demand, particularly in scenarios with expanded industry, carbon capture, and increased sector integration—such as through district heating. In such cases, up to 3000–4000 GWh/year of low-temperature industrial residual heat becomes available, offering substantial potential to improve overall energy efficiency. The scenarios highlight synergies and trade-offs across environmental, economic, and social dimensions, emphasizing the importance of coordinated planning. Scenarios with offshore wind enable energy exports and industrial growth but raise challenges related to emissions and public acceptance, while scenarios without cement production reduce environmental impact but weaken local economic resilience. Limitations of the study include the exclusion of global supply chain impacts and assumptions about future technological costs. The study underscores the need for integrated planning, regulatory innovation, and stakeholder collaboration to ensure a just and resilient transition for Gotland. Full article

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26 pages, 3774 KB

Open AccessEditor’s ChoiceArticle

Low-Carbon Industrial Heating in the EU and UK: Integrating Waste Heat Recovery, High-Temperature Heat Pumps, and Hydrogen Technologies

by Pouriya H. Niknam

Energies 2025, 18(16), 4313; https://doi.org/10.3390/en18164313 - 13 Aug 2025

Viewed by 5064

Abstract

This research introduces a two-stage, low-carbon industrial heating process, leveraging advanced waste heat recovery (WHR) technologies and exploiting waste heat (WH) to drive decentralised hydrogen production. This study is supported by a data-driven analysis of individual technologies, followed by 0D modelling of the [...] Read more.

This research introduces a two-stage, low-carbon industrial heating process, leveraging advanced waste heat recovery (WHR) technologies and exploiting waste heat (WH) to drive decentralised hydrogen production. This study is supported by a data-driven analysis of individual technologies, followed by 0D modelling of the integrated system for technical and feasibility assessment. Within 10 years, the EU industry will be supported by two main strategies to transition to low-carbon energy: (a) shifting from grid-mix electricity towards fully renewable sources, and (b) expanding low-carbon hydrogen infrastructure within industrial clusters. On the demand side, process heating in the industrial sector accounts for 70% of total energy consumption in industry. Almost one-fifth of the energy consumed to fulfil the process heat demand is lost as waste. The proposed heating solution is tailored for process heat in industry and stands apart from the dual-mode residential heating system (i.e., heat pump and gas boiler), as it is based on integrated and simultaneous operation to meet industry-level reliability at higher temperatures, focusing on WHR and low-carbon hydrogen. The solution uses a cascaded heating approach. Low- and medium-temperature WH are exploited to drive high-temperature heat pumps (HTHPs), followed by hydrogen burners fuelled by hydrogen generated on-site by electrolysers, which are powered by advanced WHR technologies. The results revealed that the deployment of the solution at scale could fulfil ~14% of the process heat demand in EU/UK industries by 2035. Moreover, with further availability of renewable energy sources and clean hydrogen, it could have a higher contribution to the total process heat demand as a low-carbon solution. The economic analysis estimates that adopting the combined heating solution—benefiting from the full capacity of WHR for the HTHP and on-site hydrogen production—would result in a levelised cost of heat of ~EUR 84/MWh, which is lower than that of full electrification of industrial heating in 2035. Full article

(This article belongs to the Special Issue Advanced Sustainable Industrial Heating: Technologies and Applications)

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23 pages, 781 KB

Open AccessEditor’s ChoiceReview

Operational Roles of Artificial Intelligence in Energy Security: A Triangulated Review of Abstracts (2021–2025)

by Małgorzata Gawlik-Kobylińska

Energies 2025, 18(16), 4275; https://doi.org/10.3390/en18164275 - 11 Aug 2025

Viewed by 1358

Abstract

The operational roles of artificial intelligence in energy security remain inconsistently defined across the scientific literature. To address this gap, the present review examines 165 peer-reviewed abstracts published between 2021 and 2025 using a triangulated methodology that combines trigram frequency analysis, manual qualitative [...] Read more.

The operational roles of artificial intelligence in energy security remain inconsistently defined across the scientific literature. To address this gap, the present review examines 165 peer-reviewed abstracts published between 2021 and 2025 using a triangulated methodology that combines trigram frequency analysis, manual qualitative coding, and semantic clustering with sentence embeddings. Eight core roles were identified: forecasting and prediction, optimisation of energy systems, renewable energy integration, monitoring and anomaly detection, grid management and stability, energy market operations/trading, cybersecurity, and infrastructure and resource planning. According to the results, the most frequently identified roles, based on the average distribution across all three methods, are forecasting and prediction, optimisation of energy systems, and energy market operations/trading. Roles such as cybersecurity and infrastructure and resource planning appear less frequently and are primarily detected through manual interpretation and semantic clustering. Trigram analysis alone failed to capture these functions due to terminological ambiguity or diffuse expression. However, correlation coefficients indicate high concordance between manual and semantic methods (Spearman’s ρ = 0.91), confirming the robustness of the classification. A structured typology of AI roles supports the development of more coherent analytical frameworks in energy research. Future research incorporating full texts, policy taxonomies, and real-world use cases may help integrate AI more effectively into energy security planning and decision support environments. Full article

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27 pages, 2225 KB

Open AccessEditor’s ChoiceReview

Ionic Liquids and Poly (Ionic Liquids) for CO₂ Capture: A Comprehensive Review

by Jui Kharade and Karen Lozano

Energies 2025, 18(16), 4257; https://doi.org/10.3390/en18164257 - 11 Aug 2025

Viewed by 1246

Abstract

The rising concentration of atmospheric carbon dioxide (CO₂), driven largely by fossil fuel combustion, is a major contributor to global climate change and ocean acidification. As conventional CO₂ capture technologies, primarily amine-based solvents, face challenges such as high energy requirements, [...] Read more.

The rising concentration of atmospheric carbon dioxide (CO₂), driven largely by fossil fuel combustion, is a major contributor to global climate change and ocean acidification. As conventional CO₂ capture technologies, primarily amine-based solvents, face challenges such as high energy requirements, volatility, and degradation, there is an urgent need for alternative materials that are both efficient and sustainable. Ionic liquids (ILs) and poly (ionic liquids) (PILs) have emerged as promising candidates due to their unique physicochemical properties, including negligible vapor pressure, high thermal and chemical stability, structural tunability, and strong CO₂ affinity. This review provides a comprehensive overview of recent advancements in the design, synthesis, and application of ILs and PILs for CO₂ capture. We examine the mechanisms of CO₂ absorption in IL and PIL systems, analyze the structure-property relationships influencing capture performance, and compare their advantages and limitations relative to conventional solvents. Special attention is given to the role of functional groups, anion/cation selection, and polymeric architectures in enhancing CO₂ uptake and reducing regeneration energy. Finally, the review highlights current challenges and future research directions for scaling up IL and PIL-based technologies in industrial carbon capture and sequestration systems. Full article

(This article belongs to the Section B3: Carbon Emission and Utilization)

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21 pages, 2150 KB

Open AccessEditor’s ChoiceArticle

Collaborative Microgrids as Power Quality Improvement Nodes in Electricity Networks

by Michel Leseure, Hanaa Feleafel and Jovana Radulovic

Energies 2025, 18(15), 4197; https://doi.org/10.3390/en18154197 - 7 Aug 2025

Cited by 1 | Viewed by 492

Abstract

This paper explores the integration of microgrids within utility networks and distinguishes selfish from collaborative microgrids. Research has shown that selfish microgrids tend to increase volatility of order updates to power generators, whereas collaborative microgrids decrease that volatility, resulting in smoother, more controllable [...] Read more.

This paper explores the integration of microgrids within utility networks and distinguishes selfish from collaborative microgrids. Research has shown that selfish microgrids tend to increase volatility of order updates to power generators, whereas collaborative microgrids decrease that volatility, resulting in smoother, more controllable operations of networks. This paper proposes an analytical formula linking power volatility to power quality, i.e., to issues such as voltage dips, surges, and transients. These are known risks for disrupting the operation of utility grids, causing instability and jeopardising efficiency and reliability. As collaborative microgrids reduce volatility, they improve power quality. That argument is extended to propose that collaborative microgrids can act as quality improvements agents within wider networks. Full article

(This article belongs to the Special Issue Grid Integration of Renewable Energy: Latest Advances and Prospects)

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24 pages, 19050 KB

Open AccessEditor’s ChoiceArticle

Innovative Deposition of AZO as Recombination Layer on Silicon Nanowire Scaffold for Potential Application in Silicon/Perovskite Tandem Solar Cell

by Grażyna Kulesza-Matlak, Marek Szindler, Magdalena M. Szindler, Milena Kiliszkiewicz, Urszula Wawrzaszek, Anna Sypień, Łukasz Major and Kazimierz Drabczyk

Energies 2025, 18(15), 4193; https://doi.org/10.3390/en18154193 - 7 Aug 2025

Cited by 1 | Viewed by 706

Abstract

Transparent conductive aluminum-doped zinc oxide (AZO) films were investigated as potential recombination layers for perovskite/silicon tandem solar cells, comparing the results of atomic layer deposition (ALD) and magnetron sputtering (MS) on vertically aligned silicon nanowire (SiNW) scaffolds. Conformality and thickness control were examined [...] Read more.

Transparent conductive aluminum-doped zinc oxide (AZO) films were investigated as potential recombination layers for perovskite/silicon tandem solar cells, comparing the results of atomic layer deposition (ALD) and magnetron sputtering (MS) on vertically aligned silicon nanowire (SiNW) scaffolds. Conformality and thickness control were examined by cross-sectional SEM/TEM and profilometry, revealing fully conformal ALD coatings with tunable thicknesses (40–120 nm) versus tip-capped, semi-uniform MS films (100–120 nm). Optical transmission measurements on glass substrates showed that both 120 nm ALD and MS layers exhibit interference maxima near 450–500 nm and 72–89% transmission across 800–1200 nm; the thinnest ALD films reached up to 86% near-IR transparency. Four-point probe analysis demonstrated that ALD reduces surface resistance from 1150 Ω/□ at 40 nm to 245 Ω/□ at 120 nm, while MS layers achieved 317 Ω/□ at 120 nm. These results delineate the balance between conformality, transparency, and conductivity, providing design guidelines for AZO recombination interfaces in next-generation tandem photovoltaics. Full article

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

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45 pages, 767 KB

Open AccessEditor’s ChoiceArticle

The Economic Effects of the Green Transition of the Greek Economy: An Input–Output Analysis

by Theocharis Marinos, Maria Markaki, Yannis Sarafidis, Elena Georgopoulou and Sevastianos Mirasgedis

Energies 2025, 18(15), 4177; https://doi.org/10.3390/en18154177 - 6 Aug 2025

Viewed by 2833

Abstract

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs [...] Read more.

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition. Full article

(This article belongs to the Special Issue Economic and Financial Transformations in the Global Energy Transition: Market Mechanisms, Systemic Risks, and Adaptive Policy Frameworks)

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29 pages, 2505 KB

Open AccessEditor’s ChoiceArticle

Battery Energy Storage Systems: Energy Market Review, Challenges, and Opportunities in Frequency Control Ancillary Services

by Gian Garttan, Sanath Alahakoon, Kianoush Emami and Shantha Gamini Jayasinghe

Energies 2025, 18(15), 4174; https://doi.org/10.3390/en18154174 - 6 Aug 2025

Cited by 2 | Viewed by 4205

Abstract

Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of [...] Read more.

Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of BESS’ participation in frequency control ancillary service (FCAS) markets. This review synthesises the current state of knowledge on the evolution of the energy market and the role of battery energy storage systems in providing grid stability, particularly frequency control services, with a focus on their integration into evolving high-renewable-energy-source (RES) market structures. Specifically, solar PV and wind energy are emerging as the main drivers of RES expansion, accounting for approximately 61% of the global market share. A BESS offers greater flexibility in storage capacity, scalability and rapid response capabilities, making it an effective solution to address emerging security risks of the system. Moreover, a BESS is able to provide active power support through power smoothing when coupled with solar photovoltaic (PV) and wind generation. In this paper, we provide an overview of the current status of energy markets, the contribution of battery storage systems to grid stability and flexibility, as well as the challenges that BESS face in evolving electricity markets. Full article

(This article belongs to the Special Issue Innovations and Recent Trends in Power Systems: Smart Grids, Energy Storage Systems and Electric Vehicle Integrations)

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18 pages, 2108 KB

Open AccessEditor’s ChoiceArticle

Machine Learning Forecasting of Commercial Buildings’ Energy Consumption Using Euclidian Distance Matrices

by Connor Scott and Alhussein Albarbar

Energies 2025, 18(15), 4160; https://doi.org/10.3390/en18154160 - 5 Aug 2025

Viewed by 661

Abstract

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods [...] Read more.

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient and Flexible Zero/Positive Energy Buildings, Communities and Districts)

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24 pages, 3337 KB

Open AccessEditor’s ChoiceArticle

Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation

by Cristina Ventura, Giuseppe Marco Tina and Santi Agatino Rizzo

Energies 2025, 18(15), 4161; https://doi.org/10.3390/en18154161 - 5 Aug 2025

Viewed by 583

Abstract

In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability [...] Read more.

In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid: 2nd Edition)

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18 pages, 603 KB

Open AccessEditor’s ChoiceArticle

Leveraging Dynamic Pricing and Real-Time Grid Analysis: A Danish Perspective on Flexible Industry Optimization

by Sreelatha Aihloor Subramanyam, Sina Ghaemi, Hessam Golmohamadi, Amjad Anvari-Moghaddam and Birgitte Bak-Jensen

Energies 2025, 18(15), 4116; https://doi.org/10.3390/en18154116 - 3 Aug 2025

Cited by 1 | Viewed by 453

Abstract

Flexibility is advocated as an effective solution to address the growing need to alleviate grid congestion, necessitating efficient energy management strategies for industrial operations. This paper presents a mixed-integer linear programming (MILP)-based optimization framework for a flexible asset in an industrial setting, aiming [...] Read more.

Flexibility is advocated as an effective solution to address the growing need to alleviate grid congestion, necessitating efficient energy management strategies for industrial operations. This paper presents a mixed-integer linear programming (MILP)-based optimization framework for a flexible asset in an industrial setting, aiming to minimize operational costs and enhance energy efficiency. The method integrates dynamic pricing and real-time grid analysis, alongside a state estimation model using Extended Kalman Filtering (EKF) that improves the accuracy of system state predictions. Model Predictive Control (MPC) is employed for real-time adjustments. A real-world case studies from aquaculture industries and industrial power grids in Denmark demonstrates the approach. By leveraging dynamic pricing and grid signals, the system enables adaptive pump scheduling, achieving a 27% reduction in energy costs while maintaining voltage stability within 0.95–1.05 p.u. and ensuring operational safety. These results confirm the effectiveness of grid-aware, flexible control in reducing costs and enhancing stability, supporting the transition toward smarter, sustainable industrial energy systems. Full article

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25 pages, 2100 KB

Open AccessEditor’s ChoiceArticle

Flexible Demand Side Management in Smart Cities: Integrating Diverse User Profiles and Multiple Objectives

by Nuno Souza e Silva and Paulo Ferrão

Energies 2025, 18(15), 4107; https://doi.org/10.3390/en18154107 - 2 Aug 2025

Viewed by 457

Abstract

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, [...] Read more.

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

(This article belongs to the Special Issue The Future of Energy Systems: Integration of Energy Technologies in Distribution Grids)

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16 pages, 1504 KB

Open AccessEditor’s ChoiceArticle

Tuning the Activity of NbOPO₄ with NiO for the Selective Conversion of Cyclohexanone as a Model Intermediate of Lignin Pyrolysis Bio-Oils

by Abarasi Hart and Jude A. Onwudili

Energies 2025, 18(15), 4106; https://doi.org/10.3390/en18154106 - 2 Aug 2025

Viewed by 502

Abstract

Catalytic upgrading of pyrolysis oils is an important step for producing replacement hydrocarbon-rich liquid biofuels from biomass and can help to advance pyrolysis technology. Catalysts play a pivotal role in influencing the selectivity of chemical reactions leading to the formation of main compounds [...] Read more.

Catalytic upgrading of pyrolysis oils is an important step for producing replacement hydrocarbon-rich liquid biofuels from biomass and can help to advance pyrolysis technology. Catalysts play a pivotal role in influencing the selectivity of chemical reactions leading to the formation of main compounds in the final upgraded liquid products. The present work involved a systematic study of solvent-free catalytic reactions of cyclohexanone in the presence of hydrogen gas at 160 °C for 3 h in a batch reactor. Cyclohexanone can be produced from biomass through the selective hydrogenation of lignin-derived phenolics. Three types of catalysts comprising undoped NbOPO₄, 10 wt% NiO/NbOPO₄, and 30 wt% NiO/NbOPO₄ were studied. Undoped NbOPO₄ promoted both aldol condensation and the dehydration of cyclohexanol, producing fused ring aromatic hydrocarbons and hard char. With 30 wt% NiO/NbOPO₄, extensive competitive hydrogenation of cyclohexanone to cyclohexanol was observed, along with the formation of C₆ cyclic hydrocarbons. When compared to NbOPO₄ and 30 wt% NiO/NbOPO₄, the use of 10 wt% NiO/NbOPO₄ produced superior selectivity towards bi-cycloalkanones (i.e., C₁₂) at cyclohexanone conversion of 66.8 ± 1.82%. Overall, the 10 wt% NiO/NbOPO₄ catalyst exhibited the best performance towards the production of precursor compounds that can be further hydrodeoxygenated into energy-dense aviation fuel hydrocarbons. Hence, the presence and loading of NiO was able to tune the activity and selectivity of NbOPO₄, thereby influencing the final products obtained from the same cyclohexanone feedstock. This study underscores the potential of lignin-derived pyrolysis oils as important renewable feedstocks for producing replacement hydrocarbon solvents or feedstocks and high-density sustainable liquid hydrocarbon fuels via sequential and selective catalytic upgrading. Full article

(This article belongs to the Special Issue Recent Advances in Biomass Energy Torrefaction, Pyrolysis and Gasification Technologies)

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28 pages, 2448 KB

Open AccessEditor’s ChoiceArticle

ATENEA4SME: Industrial SME Self-Evaluation of Energy Efficiency

by Antonio Ferraro, Giacomo Bruni, Marcello Salvio, Milena Marroccoli, Antonio Telesca, Chiara Martini, Federico Alberto Tocchetti and Antonio D’Angola

Energies 2025, 18(15), 4094; https://doi.org/10.3390/en18154094 - 1 Aug 2025

Viewed by 401

Abstract

Promoting energy efficiency in the Italian production sector is significantly hampered by the lack of knowledge, the scarcity and the limited distribution of tools for supporting energy audits in small and medium-sized enterprises (SMEs) in a wide range of Italian economic sectors (industry, [...] Read more.

Promoting energy efficiency in the Italian production sector is significantly hampered by the lack of knowledge, the scarcity and the limited distribution of tools for supporting energy audits in small and medium-sized enterprises (SMEs) in a wide range of Italian economic sectors (industry, tertiary sector, transport). The Advanced Tool for ENErgy Audit for SMEs, ATENEA4SME, is intended to help SMEs promote energy-efficiency projects, supports energy audits and self-evaluation of energy consumption. The tool uses an original mathematical model that takes into account the results of questionnaires and a multi-criteria analysis to generate recommendations for energy efficiency investments. This article will give a thorough explanation of the tool, emphasizing and outlining the sections as well as the procedures to get the ultimate summary of the energy usage of the enterprises under investigation and the potential for energy saving. From a technological and financial perspective, the tool helps to remove obstacles to the development of energy-efficiency measures. In this article, the IT and methodological structure of the tool will therefore be extensively described, and its operation for the context of SMEs will be illustrated, with application cases. Ample space will be allocated to the dissemination campaign and the replicability of the tool for all economic sectors of the industrial and tertiary sectors. Full article

(This article belongs to the Special Issue Energy Saving, Efficiency, and Conversion in Renewable and Sustainable Energy)

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