Energies

25 pages, 1579 KB

Open AccessEditor’s ChoiceArticle

Properties of Pellets from Forest and Agricultural Biomass and Their Mixtures

by Mariusz Jerzy Stolarski, Michał Krzyżaniak and Ewelina Olba-Zięty

Energies 2025, 18(12), 3137; https://doi.org/10.3390/en18123137 - 14 Jun 2025

Cited by 2 | Viewed by 707

Pellets can be produced not only from forest dendromass but also from agricultural dendromass derived from short rotation coppice (SRC) plantations, as well as surplus straw from cereal and oilseed crops. This study aimed to determine the thermophysical properties and elemental composition of [...] Read more.

Pellets can be produced not only from forest dendromass but also from agricultural dendromass derived from short rotation coppice (SRC) plantations, as well as surplus straw from cereal and oilseed crops. This study aimed to determine the thermophysical properties and elemental composition of 16 types of pellets produced from four types of forest biomass (Scots pine I, alder, beech, and Scots pine II), four types of agricultural biomass (SRC willow, SRC poplar, wheat straw, and rapeseed straw), and eight types of pellets from mixtures of wood biomass and straw. Another aim of the study was to demonstrate which pellet types met the parameters specified in three standards, categorizing pellets into thirteen different classes. As expected, pellets produced from pure Scots pine sawdust exhibited the best quality. The quality of the pellets obtained from mixtures of dendromass and straw deteriorated with an increase in the proportion of cereal straw or rapeseed straw in relation to pure Scots pine sawdust and SRC dendromass. The bulk density of the pellets ranged from 607.9 to 797.5 kg m⁻³, indicating that all 16 pellet types met the requirements of all six classes of the ISO standard. However, it was determined that four types of pellets (rapeseed, wheat, and two others from biomass mixtures) did not meet the necessary requirements of the Premium and Grade 1 classes. The ash content ranged from 0.44% DM in pellets from pure Scots pine sawdust to 5.00% DM in rapeseed straw pellets. Regarding ash content, only the pellets made from pure Scots pine sawdust met the stringent requirements of the highest classes, A1, Premium, and Grade 1. In contrast, all 16 types of pellets fulfilled the criteria for the lower classes, i.e., Utility and Grade 4. Concerning the nitrogen (N) content, seven types of pellets met the strict standards of classes A1 and Grade 1, while all the pellets satisfied the less rigorous requirements of classes B and Grade 4. Full article

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

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29 pages, 5868 KB

Open AccessEditor’s ChoiceArticle

Assessing the Potential of a Hybrid Renewable Energy System: MSW Gasification and a PV Park in Lobito, Angola

by Salomão Joaquim, Nuno Amaro and Nuno Lapa

Energies 2025, 18(12), 3125; https://doi.org/10.3390/en18123125 - 13 Jun 2025

Viewed by 2302

Abstract

This study investigates a hybrid renewable energy system combining the municipal solid waste (MSW) gasification and solar photovoltaic (PV) for electricity generation in Lobito, Angola. A fixed-bed downdraft gasifier was selected for MSW gasification, where the thermal decomposition of waste under controlled air [...] Read more.

This study investigates a hybrid renewable energy system combining the municipal solid waste (MSW) gasification and solar photovoltaic (PV) for electricity generation in Lobito, Angola. A fixed-bed downdraft gasifier was selected for MSW gasification, where the thermal decomposition of waste under controlled air flow produces syngas rich in CO and H₂. The syngas is treated to remove contaminants before powering a combined cycle. The PV system was designed for optimal energy generation, considering local solar radiation and shading effects. Simulation tools, including Aspen Plus v11.0, PVsyst v8, and HOMER Pro software 3.16.2, were used for modeling and optimization. The hybrid system generates 62 GWh/year of electricity, with the gasifier contributing 42 GWh/year, and the PV system contributing 20 GWh/year. This total energy output, sufficient to power 1186 households, demonstrates an integration mechanism that mitigates the intermittency of solar energy through continuous MSW gasification. However, the system lacks surplus electricity for green hydrogen production, given the region’s energy deficit. Economically, the system achieves a Levelized Cost of Energy of 0.1792 USD/kWh and a payback period of 16 years. This extended payback period is mainly due to the hydrogen production system, which has a low production rate and is not economically viable. When excluding H₂ production, the payback period is reduced to 11 years, making the hybrid system more attractive. Environmental benefits include a reduction in CO₂ emissions of 42,000 t/year from MSW gasification and 395 t/year from PV production, while also addressing waste management challenges. This study highlights the mechanisms behind hybrid system operation, emphasizing its role in reducing energy poverty, improving public health, and promoting sustainable development in Angola. Full article

(This article belongs to the Special Issue New Challenges and Prospects of Smart Energy Systems for Sustainable Communities)

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28 pages, 4174 KB

Open AccessEditor’s ChoiceArticle

Multi-Energy-Microgrid Energy Management Strategy Optimisation Using Deep Learning

by Wenyuan Sun, Shuailing Ma, Yufei Zhang, Yingai Jin and Firoz Alam

Energies 2025, 18(12), 3111; https://doi.org/10.3390/en18123111 - 12 Jun 2025

Viewed by 770

Abstract

Renewable power generation is unpredictable due to its intermittency, making grid-connected microgrids difficult to operate, control, and manage. Currently used prediction models for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with the carbon trading mechanism are inefficient as they cannot account for [...] Read more.

Renewable power generation is unpredictable due to its intermittency, making grid-connected microgrids difficult to operate, control, and manage. Currently used prediction models for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with the carbon trading mechanism are inefficient as they cannot account for all eventualities and are not well studied. Therefore, a two-stage robust optimisation model based on Bidirectional Temporal Convolutional Networks (BiTCN) and Transformer prediction for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with a carbon trading mechanism is proposed to solve this problem. First, BiTCN extracts implicit wind speed and wind power output sequences from historical data and feeds it into the Transformer model for point prediction using the attention mechanism. Ablation computation modelling is then performed. The proposed prediction model’s Mean Absolute Error (MAE) is found to be 1.3512, and its R² is 0.9683, proving its efficacy and reliability. Second, the proposed model is used to perform interval prediction in two typical scenarios: high wind power and low wind power. After constructing the robust optimisation model uncertainty set based on the prediction results, simulation experiments are performed on the proposed optimisation model. The simulation results suggest that the proposed optimisation model enhances renewable energy use, emissions reductions, microgrid operating costs, and system reliability. The study also reveals that the total system cost and carbon emission cost in the low wind scenario are 283% (2.83 times) and 314% (3.14 times) higher than in the high wind scenario; hence, a significant percentage of renewable energy is needed for microgrid stability. Full article

(This article belongs to the Special Issue Advances in Control System for Power Electronics, Microgrids, and Smart Grids)

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54 pages, 6107 KB

Open AccessEditor’s ChoiceReview

A New Framework of Vehicle-to-Grid Economic Evaluation: From Semi-Systematic Review of 132 Prior Studies

by Chengquan Zhang, Hiroshi Kitamura and Mika Goto

Energies 2025, 18(12), 3088; https://doi.org/10.3390/en18123088 - 11 Jun 2025

Cited by 2 | Viewed by 1717

Abstract

Vehicle-to-Grid (V2G) technology enables electric vehicles (EVs (Unless otherwise specified, Electric Vehicles (EVs) in this study refer to the totality of BEVs, PHEVs, and other battery-equipped vehicles that have the potential to participate in V2G)) to interact with renewable energy sources, positioning it [...] Read more.

Vehicle-to-Grid (V2G) technology enables electric vehicles (EVs (Unless otherwise specified, Electric Vehicles (EVs) in this study refer to the totality of BEVs, PHEVs, and other battery-equipped vehicles that have the potential to participate in V2G)) to interact with renewable energy sources, positioning it as a key driver of energy system decentralization. While V2G holds significant potential for enhancing grid stability and economic efficiency, its large-scale deployment requires a robust economic assessment. However, existing research predominantly focuses on technical feasibility, lacking comprehensive economic evaluations due to the complexity of V2G system architectures. To bridge this gap, we propose the BSTP (Business-Stakeholders-Technology-Policy) V2G economic evaluation framework and the VRR (Value Realization Rate) methodology, employing a Semi-Systematic Co-Design Approach. This framework systematically characterizes the evolution of V2G business models, the interactions among key stakeholders, the influence of technological and policy factors, and the criteria for economic feasibility assessment. Furthermore, we identify a “Big Models, No Trials” issue in V2G economic research, where large-scale theoretical models lack empirical validation. To address this challenge and ensure the practical applicability of our framework, we define six core challenges that must be resolved for a rigorous economic evaluation of V2G. Our findings provide a structured foundation for future research and policy development, offering insights that could accelerate the transition to decentralized energy systems. Full article

(This article belongs to the Special Issue New Trends in Energy, Climate and Environmental Research, 2nd Edition)

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64 pages, 3370 KB

Open AccessEditor’s ChoiceReview

Review of Film Cooling Techniques for Aerospace Vehicles

by Edidiong Michael Umana and Xiufeng Yang

Energies 2025, 18(12), 3058; https://doi.org/10.3390/en18123058 - 10 Jun 2025

Cited by 1 | Viewed by 3036

Abstract

Film cooling, a vital method for controlling surface temperatures in components subjected to intense heat, strives to enhance efficiency through innovative technological advancements. Over the last several decades, considerable advancements have been made in film cooling technologies for applications such as liquid rocket [...] Read more.

Film cooling, a vital method for controlling surface temperatures in components subjected to intense heat, strives to enhance efficiency through innovative technological advancements. Over the last several decades, considerable advancements have been made in film cooling technologies for applications such as liquid rocket engines, combustion chambers, nozzle sections, gas turbine components, and hypersonic vehicles, all of which operate under extreme temperatures. This review presents an in-depth investigation of film cooling, its applications, and its key mechanisms and performance characteristics. The review also explores design optimization for combustion chamber components and examines the role of gaseous film cooling in nozzle systems, supported by experimental and numerical validation. Gas turbine cooling relies on integrated methods, including internal and external cooling, material selection, and coolant treatment to prevent overheating. Notably, the cross-flow jet in blade cooling improves heat transfer and reduces thermal fatigue. Film cooling is an indispensable technique for addressing the challenges of high-speed and hypersonic flight, aided by cutting-edge injection methods and advanced transpiration coolants. Special attention is given to factors influencing film cooling performance, as well as state-of-the-art developments in the field. The challenges related to film cooling are reviewed and presented, along with the difficulties in resolving them. Suggestions for addressing these problems in future research are also provided. Full article

(This article belongs to the Special Issue Heat and Mass Transfer: Theory, Methods, and Applications)

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34 pages, 5161 KB

Open AccessEditor’s ChoiceArticle

Robust Adaptive Fractional-Order PID Controller Design for High-Power DC-DC Dual Active Bridge Converter Enhanced Using Multi-Agent Deep Deterministic Policy Gradient Algorithm for Electric Vehicles

by Seyyed Morteza Ghamari, Daryoush Habibi and Asma Aziz

Energies 2025, 18(12), 3046; https://doi.org/10.3390/en18123046 - 9 Jun 2025

Cited by 1 | Viewed by 1123

Abstract

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter [...] Read more.

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a Fractional-order PID (FOPID) controller to benefit from the simple structure of classical PID controllers with lower complexity and improved flexibility because of additional filtering gains adopted in this method. However, for a FOPID controller to operate effectively under real-time conditions, its parameters must adapt continuously to changes in the system. To achieve this adaptability, a Multi-Agent Reinforcement Learning (MARL) approach is adopted, where each gain of the controller is tuned individually using the Deep Deterministic Policy Gradient (DDPG) algorithm. This structure enhances the controller’s ability to respond to external disturbances with greater robustness and adaptability. Meanwhile, finding the best initial gains in the RL structure can decrease the overall efficiency and tracking performance of the controller. To overcome this issue, Grey Wolf Optimization (GWO) algorithm is proposed to identify the most suitable initial gains for each agent, providing faster adaptation and consistent performance during the training process. The complete approach is tested using a Hardware-in-the-Loop (HIL) platform, where results confirm accurate voltage control and resilient dynamic behavior under practical conditions. In addition, the controller’s performance was validated under a battery management scenario where the DAB converter interacts with a nonlinear lithium-ion battery. The controller successfully regulated the State of Charge (SOC) through automated charging and discharging transitions, demonstrating its real-time adaptability for BMS-integrated EV systems. Consequently, the proposed MARL-FOPID controller reported better disturbance-rejection performance in different working cases compared to other conventional methods. Full article

(This article belongs to the Special Issue Power Electronics for Smart Grids: Present and Future Perspectives II)

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20 pages, 6305 KB

Open AccessEditor’s ChoiceArticle

Controlled Growth of α-Al₂O₃ Nanofilm on FeCrAl Alloy as an Effective Cr Barrier for Solid Oxide Fuel Cell (SOFC) Cathode Air Pre-Heaters

by Kun Zhang, Ahmad El-Kharouf and Robert Steinberger-Wilckens

Energies 2025, 18(12), 3055; https://doi.org/10.3390/en18123055 - 9 Jun 2025

Viewed by 696

Abstract

Solid oxide fuel cell (SOFC) systems often employ metallic cathode air pre-heaters (CAPHs), frequently made from alloys with high chromium (Cr) content, to recover thermal energy from exhaust gases and pre-heat incoming air and fuel. Cr evaporation from metallic CAPHs can poison SOFC [...] Read more.

Solid oxide fuel cell (SOFC) systems often employ metallic cathode air pre-heaters (CAPHs), frequently made from alloys with high chromium (Cr) content, to recover thermal energy from exhaust gases and pre-heat incoming air and fuel. Cr evaporation from metallic CAPHs can poison SOFC cathodes, reducing their durability. To mitigate this, we investigated controlled pre-oxidation of a FeCrAl alloy (alloy 318) to form a protective alumina scale by self-growing, assessing its impact on and oxidation resistance and Cr retention capability for CAPH applications. The effects of pre-oxidation were investigated across a temperature range of 800 to 1100 °C and dwelling times of 0.5 to 4 h. The formed oxide scales were characterised using gravimetry in combination with advanced analytic techniques, such as SEM/EDX, STEM/EDX, TEM, and XRD. Subsequently, the pre-oxidised FeCrAl alloys were characterised with respect to the oxidation rate and Cr₂O₃ evaporation in a tubular furnace at 850 °C, with 6.0 L/min air flow and 3 vol% H₂O to simulate the SOFC cathode environment. TEM analysis confirmed that the FeCrAl alloys formed alumina scales with 10 nm and 34 nm thickness after 1 h of pre-oxidation at 900 and 1100 °C, respectively. The corrosion and Cr₂O₃ evaporation rates of the FeCrAl alloy at 850 °C in humidified air were shown to be dramatically decreased by pre-oxidation. It was found that the mechanisms of oxidation and Cr₂O₃ evaporation were found to be controlled by the formation of different alumina phases during the pre-oxidation. Measurements of Cr₂O₃ evaporation and weight gain revealed that the alloy 318 pre-treated at 1100 °C for 1 h will form an α-Al₂O₃ scale, leading to a 98% reduction of the oxidation rate and 90% reduction of Cr₂O₃ evaporation compared to the non-oxidised alloy 318 under simulated SOFC cathode conditions. Full article

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

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27 pages, 3996 KB

Open AccessEditor’s ChoiceArticle

Global Maximum Power Point Tracking of Photovoltaic Systems Using Artificial Intelligence

by Rukhsar, Aidha Muhammad Ajmal and Yongheng Yang

Energies 2025, 18(12), 3036; https://doi.org/10.3390/en18123036 - 8 Jun 2025

Viewed by 968

Abstract

Recently, artificial intelligence (AI) has become a promising solution to the optimization of the energy harvesting and performance of photovoltaic (PV) systems. Traditional maximum power point tracking (MPPT) algorithms have several drawbacks on tracking the global maximum power point (GMPP) under partial shading [...] Read more.

Recently, artificial intelligence (AI) has become a promising solution to the optimization of the energy harvesting and performance of photovoltaic (PV) systems. Traditional maximum power point tracking (MPPT) algorithms have several drawbacks on tracking the global maximum power point (GMPP) under partial shading conditions (PSCs). To track the GMPP, AI enabled methods stand out over other traditional solutions in terms of faster tracking dynamics, lesser oscillation, higher efficiency. However, such AI-based MPPT methods differ significantly in various applications, and thus, a full picture of AI-based MPPT methods is of interest to further optimize the PV energy harvesting. In this paper, various AI-based global maximum power point tracking (GMPPT) techniques are then implemented and critically compared by highlighting the advantages and disadvantages of each technique under dynamic weather conditions. The comparison demonstrates that the hybrid AI techniques are more reliable, which offer higher efficiency and better dynamics to handle PSCs. According to the benchmarking, a modified particle swarm optimization (PSO) GMPPT algorithm is proposed, and the experimental results validate its ability to achieve GMPPT with faster dynamics and higher efficiency. This paper is intended to motivate engineers and researchers by offering valuable insights for the selection and implementation of GMPPT techniques and to explore the AI techniques to enhance the efficiency and reliability of PV systems by providing fresh perspectives on optimal AI-based GMPPT techniques. Full article

(This article belongs to the Section F3: Power Electronics)

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29 pages, 2472 KB

Open AccessEditor’s ChoiceArticle

Prospective Assessment of Life Cycle, Quality, and Cost for Electric Product Improvement: Supporting Prototyping and Conceptualization by Employing CQ-LCA

by Dominika Siwiec and Andrzej Pacana

Energies 2025, 18(12), 3038; https://doi.org/10.3390/en18123038 - 8 Jun 2025

Cited by 2 | Viewed by 685

Abstract

The process of conceptualisation and prototyping of electric energy products is demanding due to the need for a multifaceted approach to product design. This task becomes even more complex during sustainable development, within which supporting techniques are sought. Energy conversion products such as [...] Read more.

The process of conceptualisation and prototyping of electric energy products is demanding due to the need for a multifaceted approach to product design. This task becomes even more complex during sustainable development, within which supporting techniques are sought. Energy conversion products such as electric motorcycles require special attention due to their impact on energy efficiency, environmental emissions, and operating and production costs. The research gap refers to the lack of a model to aggregate these aspects simultaneously. The objective of the research was to develop a CQ-LCA model (Cost–Quality–Life Cycle Assessment) supporting the creation of alternative product solutions and their evaluation in terms of the following: (i) environmental impact in the life cycle (LCA), (ii) quality, and (iii) production and/or purchase costs. The model was developed in seven main stages and tested for electric motorcycles and their ten prototypes, which are examples of modern products that convert electrical energy into mechanical energy. Using the EDAS method, the quality of electric motorcycle prototypes was calculated. Then, by the LCA method according to ISO 14040, the CO₂ emissions were estimated and modelled adequately to quality change. Next, by the parametric model based on the static method and the cost value function, including the nominal least squares method, the cost was estimated adequately to quality and environmental change. The model provided a qualitative and quantitative interpretation of electric motorcycle prototypes (CQ-LCA), allowing for the consideration of product characteristics, such as engine power, charging time, and battery capacity, but also environmental impacts and costs. The originality is the provision of a multi-aspect morphological analysis, after which different scenarios of product solutions. The model can be useful for various commonly used energy-converting products. Full article

(This article belongs to the Special Issue Vehicles, Drives and Transportation Systems and Processes: Opportunities and Challenges in the Context of the Environment)

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51 pages, 4952 KB

Open AccessEditor’s ChoiceReview

Energy-Efficient Near-Field Beamforming: A Review on Practical Channel Models

by Haoran Ni, Mahnoor Anjum, Deepak Mishra and Aruna Seneviratne

Energies 2025, 18(11), 2966; https://doi.org/10.3390/en18112966 - 4 Jun 2025

Cited by 1 | Viewed by 2672

Abstract

The unprecedented expansion of wireless networks has resulted in spectrum sharing between numerous connected devices, demanding advanced interference management and higher energy consumption, which exacerbates the carbon footprint. Near-field communication emerges as a promising solution to these challenges as it enables precise signal [...] Read more.

The unprecedented expansion of wireless networks has resulted in spectrum sharing between numerous connected devices, demanding advanced interference management and higher energy consumption, which exacerbates the carbon footprint. Near-field communication emerges as a promising solution to these challenges as it enables precise signal focusing which reduces power consumption by providing higher spatial multiplexing gains. This review explores how near-field (NF) multiple-input multiple-output (MIMO) beamforming can enhance energy efficiency by optimizing beamfocusing and minimizing unnecessary energy expenditure. We discuss the latest advancements in near-field beamforming, emphasizing energy-efficient strategies and sustainable practices. Recognizing which practical channel models are better suited for near-field communication, we delve into the integration of Electromagnetic Information Theory (EIT) as a joint model for realistic applications. We also discuss the channel models for near-field beamforming, incorporating EIT to provide a comprehensive overview of current methodologies. We further analyze the strengths and limitations of existing channel models and discuss the state-of-the-art models which address existing gaps. We also explore opportunities for the practical deployment of energy-efficient near-field beamforming systems. By summarizing future research directions, this review aims to advance the understanding and application of sustainable energy practices in near-field communication technologies. Full article

(This article belongs to the Special Issue Advances in Energy Harvesting Systems)

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26 pages, 1046 KB

Open AccessEditor’s ChoiceArticle

Unpacking Market Barriers to Energy Efficiency in Emerging Economies: Policy Insights and a Business Model Perspective from Jordan

by Rund Awwad, Scott Dwyer and Andrea Trianni

Energies 2025, 18(11), 2944; https://doi.org/10.3390/en18112944 - 3 Jun 2025

Viewed by 968

Abstract

Energy efficiency (EE) remains an underexploited opportunity in many developing economies, where a complex interplay of policy, institutional, and market-related challenges limit its implementation at scale. This study explores the structural, economic, and policy-related constraints affecting the EE market in Jordan, a country [...] Read more.

Energy efficiency (EE) remains an underexploited opportunity in many developing economies, where a complex interplay of policy, institutional, and market-related challenges limit its implementation at scale. This study explores the structural, economic, and policy-related constraints affecting the EE market in Jordan, a country with a high dependence on imported energy. Using a multi-framework approach, we apply the political, economic, social, technological, environmental, and legal (PESTEL) framework to categorize these barriers, complemented by Brown’s business model (BM) typology to enhance the analytical depth. Primary data were collected through semi-structured interviews with key market actors. The findings highlight issues such as economic volatility, regulatory fragmentation, and the structural biases associated with donor-driven interventions, which contribute to an uneven and loosely regulated market environment in which businesses face significant scaling challenges. This study reflects on international experience to explore how strategies from other contexts might inform markets’ adaptation in emerging economies. This study concludes with targeted policy recommendations aimed at clarifying regulatory pathways and supporting more effective market delivery. This research contributes to ongoing policy discourse by highlighting how context-specific BM innovations might help address systemic barriers, while potentially supporting national energy goals. Full article

(This article belongs to the Section C: Energy Economics and Policy)

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33 pages, 1827 KB

Open AccessEditor’s ChoiceReview

Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid

by Naveed Ali Brohi, Gokul Thirunavukkarasu, Mehdi Seyedmahmoudian, Kafeel Ahmed, Alex Stojcevski and Saad Mekhilef

Energies 2025, 18(11), 2922; https://doi.org/10.3390/en18112922 - 2 Jun 2025

Viewed by 1477

Abstract

The increasing penetration of distributed energy resources (DERs) such as solar photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) in low-voltage (LV) and medium-voltage (MV) distribution networks is reshaping traditional grid operations. This shift introduces challenges including voltage violations, [...] Read more.

The increasing penetration of distributed energy resources (DERs) such as solar photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) in low-voltage (LV) and medium-voltage (MV) distribution networks is reshaping traditional grid operations. This shift introduces challenges including voltage violations, thermal overloading, and power quality issues due to bidirectional power flows. Hosting capacity (HC) assessment has become essential for quantifying and optimizing DER integration while ensuring grid stability. This paper reviews state-of-the-art HC assessment methods, including deterministic, stochastic, time-series, and AI-based approaches. Techniques for enhancing HC—such as on-load tap changers, reactive power control, and network reconfiguration—are also discussed. A key focus is the emerging concept of dynamic operating envelopes (DOEs), which enable real-time allocation of HC by dynamically adjusting import/export limits for DERs based on operational conditions. The paper examines the benefits, challenges, and implementation of DOEs, supported by insights from Australian projects. Technical, regulatory, and social aspects are addressed, including network visibility, DER uncertainty, scalability, and cybersecurity. The study highlights the potential of integrating DOEs with other HC enhancement strategies to support efficient, reliable, and scalable DER integration in modern distribution networks. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges in Zero-Energy Districts)

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34 pages, 3449 KB

Open AccessEditor’s ChoiceArticle

Impacts of Inertia and Photovoltaic Integration on Existing and Proposed Power System Transient Stability Parameters

by Ramkrishna Mishan, Xingang Fu, Chanakya Hingu and Mohammed Ben-Idris

Energies 2025, 18(11), 2915; https://doi.org/10.3390/en18112915 - 2 Jun 2025

Viewed by 605

Abstract

The integration of variable distributed energy sources (DERs) can reduce overall system inertia, potentially impacting the transient response of both conventional and renewable generators within electrical grids. Although transient stability indicators—for instance, the Critical Clearing Time (CCT), fault-induced short-circuit current ratios, and machine [...] Read more.

The integration of variable distributed energy sources (DERs) can reduce overall system inertia, potentially impacting the transient response of both conventional and renewable generators within electrical grids. Although transient stability indicators—for instance, the Critical Clearing Time (CCT), fault-induced short-circuit current ratios, and machine parameters, including subtransient–transient reactances and associated time constants—are influenced by total system inertia, their detailed evaluation remains insufficiently explored. These parameters provide standardized benchmarks for systematically assessing the transient stability performance of conventional and photovoltaic (PV) generators as the penetration level of distributed PV systems (PVD1) increases. This study explores the relationship between conventional stability parameters and system inertia across different levels of PV penetration. CCT, a key metric for transient stability assessment, incorporates multiple influencing factors and typically increases with higher system inertia, making it a reliable comparative indicator for evaluating the effects of PV integration on system stability. To investigate this, the IEEE New England 39-bus system is adapted by replacing selected synchronous machines with PVD1 PV units and adjusting the PV penetration levels. The resulting system behavior is then compared to that of the original configuration to evaluate changes in transient stability. The findings confirm that transient and subtransient reactances, along with their respective time constants under fault conditions, are shaped not only by the characteristics of the generator on the faulted line but also by the surrounding network structure and overall system inertia. The newly introduced sensitivity parameters offer insights by capturing trends specific to conventional versus PV-based generators under different inertia scenarios. Notably, transient parameters show similar responsiveness to inertia variations to subtransient ones. This paper demonstrates that in certain scenarios, the integration of low-inertia PV generators may generate insufficient energy, which is not above critical energy during major disturbances, resulting surviving fault and subsequently an infinite CCT. While the integration of PV generators can be beneficial for their own operational performance, it may adversely impact the dynamic behavior and fault response of conventional synchronous generators within the system. This highlights the need for effective planning and control of DER integration to ensure reliable power system operation through accurate selection and application of both conventional and proposed transient stability parameters. Full article

(This article belongs to the Special Issue Renewable Sources and Storage: Grid Impact, Modeling, and Integration Strategies)

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30 pages, 2741 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies

by Shafie Bahman and Hamidreza Zareipour

Energies 2025, 18(11), 2908; https://doi.org/10.3390/en18112908 - 1 Jun 2025

Viewed by 932

Abstract

Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, [...] Read more.

Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, daily, monthly, and yearly levels. The model integrates climate and economic indicators and employs tailored forecasting techniques at each resolution, including XGBoost and ARIMAX. Initially incoherent forecasts across time scales are reconciled using advanced methods such as Ordinary Least Squares (OLS), Weighted Least Squares with Series Variance Scaling (WLS_V), and Structural Scaling (WLS_S) to ensure consistency. Using historical data from Alberta, Canada, the proposed approach improves the accuracy of deterministic forecasts and enhances the reliability of probabilistic forecasts, particularly when using the OLS reconciliation method. These results highlight the value of temporal hierarchy structures in producing high-resolution long-horizon load forecasts, providing actionable insights for utilities and policymakers involved in long-term energy planning and system optimization. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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28 pages, 3905 KB

Open AccessEditor’s ChoiceReview

Research and Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles: A Review

by Fan Wang, Yina Hong and Xiaohuan Zhao

Energies 2025, 18(11), 2873; https://doi.org/10.3390/en18112873 - 30 May 2025

Cited by 3 | Viewed by 1220

Abstract

Hybrid electric vehicles have received more and more attention owing to energy saving and environmental protection. Optimized energy-management strategies are critical to improve vehicle energy efficiency and reduce the emissions of hybrid electric vehicles. This study summarized the research status of energy-management strategies [...] Read more.

Hybrid electric vehicles have received more and more attention owing to energy saving and environmental protection. Optimized energy-management strategies are critical to improve vehicle energy efficiency and reduce the emissions of hybrid electric vehicles. This study summarized the research status of energy-management strategies for hybrid electric vehicles and analyzed the energy allocation and modeling methods of hybrid power systems. The principles, advantages, and limitations of rule-based and optimized and learning-based energy-management strategies were compared. It is found that the optimized energy-management strategies can improve fuel economy by approximately 6% compared with the rule-based energy-management strategies. The learning-based energy-management strategies can reduce fuel consumption by about 5.2~17%. This study can provide a theoretical basis and practical guidance for the efficient design and optimization of hybrid electric vehicle energy-management systems, which can promote the development and application of related technologies. Full article

(This article belongs to the Special Issue Intelligent Energy Technical Studies About Automobiles, New Energy Vehicles and Key Systems in the Energy Field)

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27 pages, 3064 KB

Open AccessEditor’s ChoiceReview

Energy-Efficient Management of Urban Water Distribution Networks Under Hydraulic Anomalies: A Review of Technologies and Challenges

by Bowen Duan, Jinliang Gao, Huizhe Cao and Shiyuan Hu

Energies 2025, 18(11), 2877; https://doi.org/10.3390/en18112877 - 30 May 2025

Viewed by 869

Abstract

Urban water distribution systems face growing challenges from energy inefficiencies caused by hydraulic anomalies, such as pipe aging, bursts, demand variability, and suboptimal pump and valve operations. This review systematically evaluates current technologies for energy-efficient management of WDNs under such conditions, structured around [...] Read more.

Urban water distribution systems face growing challenges from energy inefficiencies caused by hydraulic anomalies, such as pipe aging, bursts, demand variability, and suboptimal pump and valve operations. This review systematically evaluates current technologies for energy-efficient management of WDNs under such conditions, structured around both basic and applied technologies. Basic technologies include real-time monitoring, data acquisition, and hydraulic modeling with CFD simulation. Applied technologies focus on demand forecasting, pressure management for energy optimization, and leakage anomaly detection. Case studies demonstrate the practical value of these approaches. Despite recent advances, challenges persist in data interoperability, real-time optimization complexity, scalability, and forecasting uncertainty. Future research should emphasize adaptive AI algorithms, integration of digital twin platforms with control systems, hybrid optimization frameworks, and renewable energy recovery technologies. This review provides a comprehensive foundation for the development of intelligent, energy-efficient, and resilient urban water distribution systems through integrated, data-driven management strategies. Full article

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31 pages, 6927 KB

Open AccessEditor’s ChoiceReview

An Overview of Recent AI Applications in Combined Heat and Power Systems

by Ashkan Safari and Arman Oshnoei

Energies 2025, 18(11), 2891; https://doi.org/10.3390/en18112891 - 30 May 2025

Viewed by 1024

Abstract

Combined heat and power (CHP) systems are among the important components for enhancing energy efficiency and sustainability by simultaneously generating electricity and useful thermal energy, reducing waste and costs. Consequently, the effective control of these systems is considered important. To that end, this [...] Read more.

Combined heat and power (CHP) systems are among the important components for enhancing energy efficiency and sustainability by simultaneously generating electricity and useful thermal energy, reducing waste and costs. Consequently, the effective control of these systems is considered important. To that end, this paper provides a comprehensive review of the intelligent methodologies applied to CHP systems, emphasizing their prevalence in the USA and Europe through statistical insights. It outlines the mathematical foundations of CHP systems, analyzing the advancements in intelligent control methods for optimal planning, economic dispatch, and cost minimization. Artificial Intelligence (AI) models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Random Forest, are described and applied to a simulated CHP system. The Key Performance Indicators (KPIs) derived from these models demonstrate their efficacy for optimizing CHP performance. This paper also highlights the impact of AI-driven models for enhancing CHP system efficiency, while identifying the challenges in AI-CHP integration and envisioning CHP systems as important components of future sustainable energy systems. Full article

(This article belongs to the Special Issue Novel Applications of Power Converters for Energy Storage and Grid Integration)

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35 pages, 1927 KB

Open AccessEditor’s ChoiceArticle

Lights, Policy, Action: A Multi-Level Perspective on Policy Instrument Mix Interactions for Community Energy Initiatives

by Aamina Teladia and Henny van der Windt

Energies 2025, 18(11), 2823; https://doi.org/10.3390/en18112823 - 29 May 2025

Cited by 2 | Viewed by 1015

Abstract

Community energy initiatives (CEIs) have the potential to accelerate energy transitions, but their scalability depends heavily on the alignment of policies across various governance levels. This study offers a comprehensive analysis of the multi-level policy instrument mix (PIM) supporting CEIs in the Netherlands, [...] Read more.

Community energy initiatives (CEIs) have the potential to accelerate energy transitions, but their scalability depends heavily on the alignment of policies across various governance levels. This study offers a comprehensive analysis of the multi-level policy instrument mix (PIM) supporting CEIs in the Netherlands, using the Multi-Level Perspective (MLP) to conceptualize CEIs as niche innovations within the broader energy regime. Our findings reveal that while national, regional, and local policies in the Netherlands align with overarching decarbonization and community involvement goals, significant misalignments persist. Specifically, the 50 percent local ownership ambition is inconsistently enforced, and grid infrastructure bottlenecks continue to hinder project implementation. These gaps underscore the need for improved coordination and clear role definitions across governance levels. In contrast, well-aligned policy instruments (such as coherent subsidy schemes and regional plans under the national Climate Act) have played a tangible role in supporting the growth of CEIs. This multi-level analysis contributes valuable insights not only for the Netherlands but also for countries seeking to integrate CEIs into their energy strategies. We conclude that a cohesive policy framework—combining top-down targets, bottom-up empowerment, and cross-level collaboration—is essential to empower communities and accelerate a just energy transition. Full article

(This article belongs to the Special Issue Energy Policies and Sustainable Development)

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35 pages, 1579 KB

Open AccessEditor’s ChoiceArticle

Feasibility Analysis of Storage and Renewable Energy Ancillary Services for Grid Operations

by Evyatar Littwitz and Ofira Ayalon

Energies 2025, 18(11), 2836; https://doi.org/10.3390/en18112836 - 29 May 2025

Viewed by 1412

Abstract

This study examines the feasibility of deploying renewable energy sources and storage systems to provide ancillary services (ASs), traditionally supplied by conventional power systems, in an electric-island power grid. As renewable energy penetration grows, grid stability becomes increasingly challenged as reduced system inertia [...] Read more.

This study examines the feasibility of deploying renewable energy sources and storage systems to provide ancillary services (ASs), traditionally supplied by conventional power systems, in an electric-island power grid. As renewable energy penetration grows, grid stability becomes increasingly challenged as reduced system inertia and higher variability occur. The study focuses on Israel, which currently lacks operational AS markets. This research explores regulatory, economic, and technical mechanisms to enable renewables and storage systems to provide such services, using a comparative analysis of Germany and California, US, as use cases, along with interview analysis with experts from the Israeli energy sector. The findings highlight, on the one hand, notable regulatory and infrastructural barriers limiting the ability of alternative sources to provide ancillary services. On the other hand, the feasibility and importance of integrating renewables and storage, as regulatory adjustments, market-based procurement mechanisms, and incentive schemes, are to be undertaken. Adopting a structured AS market in Israel, influenced by international best practices, can improve grid resilience, allowing higher renewable integration and supporting long-term energy security and sustainability. Full article

(This article belongs to the Special Issue Energy and Environmental Economic Theory and Policy)

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33 pages, 610 KB

Open AccessEditor’s ChoiceReview

Energy-Aware Machine Learning Models—A Review of Recent Techniques and Perspectives

by Rafał Różycki, Dorota Agnieszka Solarska and Grzegorz Waligóra

Energies 2025, 18(11), 2810; https://doi.org/10.3390/en18112810 - 28 May 2025

Cited by 3 | Viewed by 4721

Abstract

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO₂ emissions are drawing critical attention. The [...] Read more.

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO₂ emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future. Full article

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

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31 pages, 6374 KB

Open AccessEditor’s ChoiceArticle

An Electric Vehicle Charging Simulation to Investigate the Potential of Intelligent Charging Strategies

by Max Faßbender, Nicolas Rößler, Markus Eisenbarth and Jakob Andert

Energies 2025, 18(11), 2778; https://doi.org/10.3390/en18112778 - 27 May 2025

Cited by 1 | Viewed by 872

Abstract

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to [...] Read more.

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to simulate charging scenarios. A rule-based control strategy is applied to assess six configurations for a supermarket parking lot charging point. Key findings include the highest profit being achieved with two fast chargers. In scenarios with a 50 kW grid connection limit, combining fast chargers with stationary battery storage proves effective. Conversely, mobile charging robots generate lower revenue, though grid peak limitations have minimal impact. The study highlights the potential of the simulation environment to optimise charging layouts, refine operational strategies, and develop energy management algorithms. This work demonstrates the utility of the simulation framework for analyzing diverse charging solutions, offering insights into cost efficiency and user satisfaction. The results emphasise the importance of tailored strategies to balance grid constraints, profitability, and user needs, paving the way for intelligent EV charging infrastructure development. Full article

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

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55 pages, 2227 KB

Open AccessEditor’s ChoiceReview

Is Green Hydrogen an Environmentally and Socially Sound Solution for Decarbonizing Energy Systems Within a Circular Economy Transition?

by Patrizia Ghisellini, Renato Passaro and Sergio Ulgiati

Energies 2025, 18(11), 2769; https://doi.org/10.3390/en18112769 - 26 May 2025

Cited by 3 | Viewed by 2185

Abstract

Green hydrogen (GH₂) is expected to play an important role in future energy systems in their fight against climate change. This study, after briefly recalling how GH₂ is produced and the main steps throughout its life cycle, analyses its current [...] Read more.

Green hydrogen (GH₂) is expected to play an important role in future energy systems in their fight against climate change. This study, after briefly recalling how GH₂ is produced and the main steps throughout its life cycle, analyses its current development, environmental and social impacts, and a series of case studies from selected literature showing its main applications as fuel in transportation and electricity sectors, as a heat producer in high energy intensive industries and residential and commercial buildings, and as an industrial feedstock for the production of other chemical products. The results show that the use of GH₂ in the three main areas of application has the potential of contributing to the decarbonization goals, although its generation of non-negligible impacts in other environmental categories requires attention. However, the integration of circular economy (CE) principles is important for the mitigation of these impacts. In social terms, the complexity of the value chain of GH₂ generates social impacts well beyond countries where GH₂ is produced and used. This aspect makes the GH₂ value chain complex and difficult to trace, somewhat undermining its renewability claims as well as its expected localness that the CE model is centred around. Full article

(This article belongs to the Collection Energy-Efficient Chemistry)

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45 pages, 1253 KB

Open AccessEditor’s ChoiceArticle

Governance, Energy Policy, and Sustainable Development: Renewable Energy Infrastructure Transition in Developing MENA Countries

by Michail Michailidis, Eleni Zafeiriou, Apostolos Kantartzis, Spyridon Galatsidas and Garyfallos Arabatzis

Energies 2025, 18(11), 2759; https://doi.org/10.3390/en18112759 - 26 May 2025

Viewed by 1286

Abstract

This study provides a comparative analysis of the environmental and economic performance of Oman, Egypt, and Morocco, focusing on the critical interplay between their economic structures, governance frameworks, and energy policies. Morocco stands out as a regional leader in renewable energy, driven by [...] Read more.

This study provides a comparative analysis of the environmental and economic performance of Oman, Egypt, and Morocco, focusing on the critical interplay between their economic structures, governance frameworks, and energy policies. Morocco stands out as a regional leader in renewable energy, driven by significant investments in solar, wind, and hydroelectric projects, positioning itself as a model for clean energy transition. Egypt, despite its rapid industrialization and urbanization, faces mounting environmental pressures that challenge its economic diversification efforts. Oman, heavily dependent on hydrocarbons, confronts significant sustainability risks due to its reliance on fossil fuels, despite the political stability that could support renewable integration. The research underscores that while these nations share common challenges, including regulatory weaknesses and energy policy inconsistencies, their distinct economic contexts demand tailored approaches. Morocco’s path to energy leadership must focus on integrating renewables across all sectors, enhancing grid infrastructure, and expanding green technology innovations to maintain momentum. Egypt should prioritize scaling up renewable infrastructure, reducing dependency on fossil fuels, and investing in clean technology to address its carbon footprint. For Oman, the strategic diversification of its economy, combined with aggressive renewable energy integration, is critical to reducing CO₂ emissions and mitigating climate impacts. This study contributes novel insights by highlighting the role of political stability, institutional quality, and policy coherence as critical enablers of long-term sustainability. It also identifies the importance of regional cooperation and knowledge sharing to overcome shared challenges like data limitations, geopolitical complexities, and methodological gaps in sustainability assessments. The findings advocate for a multi-method approach, integrating economic modeling, life-cycle analysis, and policy evaluation, to guide future sustainability efforts and foster resilient, low-carbon economies in the MENA region. Full article

(This article belongs to the Special Issue The Future of Renewable Energy: 2nd Edition)

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25 pages, 1106 KB

Open AccessEditor’s ChoiceReview

Voltage Regulation Strategies in Photovoltaic-Energy Storage System Distribution Network: A Review

by Qianwen Dong, Xingyuan Song, Chunyang Gong, Chenchen Hu, Junfeng Rui, Tingting Wang, Ziyang Xia and Zhixin Wang

Energies 2025, 18(11), 2740; https://doi.org/10.3390/en18112740 - 25 May 2025

Cited by 2 | Viewed by 1277

Abstract

With the increasing penetration of distributed photovoltaic-energy storage system (PV-ESS) access distribution networks, the safe and stable operation of the system has brought a huge impact, in which the voltage regulation of PV-ESS distribution networks is more prominent. This paper comprehensively reviews the [...] Read more.

With the increasing penetration of distributed photovoltaic-energy storage system (PV-ESS) access distribution networks, the safe and stable operation of the system has brought a huge impact, in which the voltage regulation of PV-ESS distribution networks is more prominent. This paper comprehensively reviews the voltage over-run mechanism in the PV-ESS distribution network and combs through the current mainstream voltage regulation strategies, of which two strategies of direct voltage regulation and current optimization are summarized. At the same time, this paper discusses the advantages and limitations of centralized, distributed, multi-timescale, voltage-reactive joint optimization and other regulation methods and focuses on the analysis of heuristic algorithms and algorithms based on deep reinforcement learning in the voltage regulation of the relevant research progress. Finally, this paper points out the main challenges currently facing voltage regulation in PV-ESS distribution networks, including cluster dynamic partitioning technologies, multi-timescale control of hybrid voltage regulation devices, and synergistic problems of demand-side resources, such as electric vehicle participation in voltage regulation, etc., and gives an outlook on future research directions. The aim of this paper is to provide a theoretical basis and practical guidance for voltage regulation of PV-ESS distribution networks and to promote the intelligent construction and sustainable development of power grids. Full article

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

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41 pages, 686 KB

Open AccessEditor’s ChoiceReview

Reinforcement Learning in Energy Finance: A Comprehensive Review

by Spyros Giannelos

Energies 2025, 18(11), 2712; https://doi.org/10.3390/en18112712 - 23 May 2025

Cited by 4 | Viewed by 1970

Abstract

The accelerating energy transition, coupled with increasing market volatility and computational advances, has created an urgent need for sophisticated decision-making tools that can address the unique challenges of energy finance—a gap that reinforcement learning methodologies are uniquely positioned to fill. This paper provides [...] Read more.

The accelerating energy transition, coupled with increasing market volatility and computational advances, has created an urgent need for sophisticated decision-making tools that can address the unique challenges of energy finance—a gap that reinforcement learning methodologies are uniquely positioned to fill. This paper provides a comprehensive review of the application of reinforcement learning (RL) in energy finance, with a particular focus on option value and risk management. Energy markets present unique challenges due to their complex price dynamics, seasonality patterns, regulatory constraints, and the physical nature of energy commodities. Traditional financial modeling approaches often struggle to capture these intricacies adequately. Reinforcement learning, with its ability to learn optimal decision policies through interaction with complex environments, has emerged as a promising alternative methodology. This review examines the theoretical foundations of RL in financial applications, surveys recent literature on RL implementations in energy markets, and critically analyzes the strengths and limitations of these approaches. We explore applications ranging from electricity price forecasting and optimal trading strategies to option valuation, including real options and products common in energy markets. The paper concludes by identifying current challenges and promising directions for future research in this rapidly evolving field. Full article

(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy)

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29 pages, 5625 KB

Open AccessEditor’s ChoiceArticle

Lower-Carbon Substitutes for Natural Gas for Use in Energy-Intensive Industries: Current Status and Techno-Economic Assessment in Lithuania

by Aurimas Lisauskas, Nerijus Striūgas and Adolfas Jančauskas

Energies 2025, 18(11), 2670; https://doi.org/10.3390/en18112670 - 22 May 2025

Cited by 2 | Viewed by 942

Abstract

Significant shortfalls in meeting the climate mitigation targets and volatile energy markets make evident the need for an urgent transition from fossil fuels to sustainable alternatives. However, the integration of zero-carbon fuels like green hydrogen and ammonia is an immense project and will [...] Read more.

Significant shortfalls in meeting the climate mitigation targets and volatile energy markets make evident the need for an urgent transition from fossil fuels to sustainable alternatives. However, the integration of zero-carbon fuels like green hydrogen and ammonia is an immense project and will take time and the construction of new infrastructure. It is during this transitional period that lower-carbon natural gas alternatives are essential. In this study, the industrial sectors of Lithuania are analysed based on their energy consumption. The industrial sectors that are the most energy-intensive are food, chemical, and wood-product manufacturing. Synthetic natural gas (SNG) has become a viable substitute, and biomethane has also become viable given a feedstock price of 21 EUR/MWh in the twelfth year of operation and 24 EUR/MWh in the eighth year, assuming an electricity price of 140 EUR/MWh and a natural gas price of 50 EUR/MWh. Nevertheless, the scale of investment in hydrogen production is comparable to the scale of investment in the production of other chemical elements; however, hydrogen production is constrained by its high electricity demand—about 3.8 to 4.4 kWh/Nm³—which makes it economically viable only at negative electricity prices. This analysis shows the techno-economic viability of biomethane and the SNG as transition pathways towards a low-carbon energy future. Full article

(This article belongs to the Special Issue International Finance and Energy Markets: Energy Supplying, Prices, Investments and Trends)

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32 pages, 2613 KB

Open AccessEditor’s ChoiceReview

Energy Storage Systems: Scope, Technologies, Characteristics, Progress, Challenges, and Future Suggestions—Renewable Energy Community Perspectives

by Shoaib Ahmed and Antonio D’Angola

Energies 2025, 18(11), 2679; https://doi.org/10.3390/en18112679 - 22 May 2025

Viewed by 2346

Abstract

A paradigm transition from centralized to decentralized energy systems has occurred, which has increased the deployment of renewable energy sources (RESs) in renewable energy communities (RECs), promoting energy independence, strengthening local resilience, increasing self-sufficiency, and moving toward CO₂ emission reduction. However, the [...] Read more.

A paradigm transition from centralized to decentralized energy systems has occurred, which has increased the deployment of renewable energy sources (RESs) in renewable energy communities (RECs), promoting energy independence, strengthening local resilience, increasing self-sufficiency, and moving toward CO₂ emission reduction. However, the erratic and unpredictable generation of RESs like wind, solar, and other sources make these systems necessary, and a lot of interest in energy storage systems is increasing because they have rapidly become the cornerstone of modern energy infrastructure, and there is a trend towards using more RESs and decentralization, resulting in increased self-sufficiency. Additionally, ESS is increasingly being installed at or close to the point of energy generation and consumption, like within residences, buildings, or community microgrids, instead of at centralized utility-scale facilities, referred to be decentralized. By storing and using energy in the same location, this localized deployment reduces transmission losses, facilitates quicker response to changes in demand, and promotes local autonomy in energy management. Since the production of renewable energy is naturally spread, decentralizing storage is crucial to optimizing efficiency and dependability. This article also focuses on energy storage systems, highlighting the role and scope of ESSs along with the services of ESSs in different parts of the power system network, particularly in renewable energy communities (RECs). The classification of various ESS technologies and their key features, limitations, and applications is discussed following the current technological and significant information trends and discussing the ESS types for the RECs with different options as per the capacity, like small, medium, and large scale. It covers the overall scenario and progress, like overall European ESS installed capacity, and the work relevant to ESSs in RECs with different aspects, following the literature review. Additionally, it draws attention to the gaps and significant challenges related to ESS technologies and their deployment. Key future suggestions have also been given as per the current trends of technological information and significant information that may affect those trends globally in the future and would be helpful in the growth of ESSs integration in RECs. The authors also suggest the role of the government, stakeholders, and supportive policies that can aid in the implementation of ESS technologies in RECs. Full article

(This article belongs to the Special Issue Modern Technologies for Renewable Energy Development and Utilization: 4th Edition)

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28 pages, 6056 KB

Open AccessEditor’s ChoiceArticle

A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques

by Kaiyao Jiang and Yuji Yamada

Energies 2025, 18(11), 2680; https://doi.org/10.3390/en18112680 - 22 May 2025

Viewed by 1740

Abstract

Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consumer electricity demand [...] Read more.

Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consumer electricity demand to uncertain variables such as temperature and solar radiation, in addition to complex factors such as planned generator outages and operational constraints. Consequently, the prediction of imbalance signals using linear models is inherently challenging and requires the adaptation of more advanced methods in practice. This study comprehensively analyzes imbalance signal dynamics and develops practical forecasting tools using Machine Learning (ML) techniques. By incorporating a diverse range of features—including lagged imbalance data, weather forecast errors specific to Japan, and temporal patterns—we demonstrate that the prediction accuracy of imbalance signals is significantly improved compared to a baseline reflecting random forecasts based on class distribution observed during the initial training period. Furthermore, the proposed approach identifies the key drivers of hourly imbalance signals, while leveraging out-of-sample forecasting models. Based on these findings, we conclude that the use of multiple predictive models enhances the robustness and reliability of our forecasts, offering actionable tools for improving forecasting accuracy in real-world operations and contributing to a more stable and efficient electricity market. Full article

(This article belongs to the Section C: Energy Economics and Policy)

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29 pages, 5334 KB

Open AccessEditor’s ChoiceArticle

Optimal Multi-Area Demand–Thermal Coordination Dispatch

by Yu-Shan Cheng, Yi-Yan Chen, Cheng-Ta Tsai and Chun-Lung Chen

Energies 2025, 18(11), 2690; https://doi.org/10.3390/en18112690 - 22 May 2025

Viewed by 554

Abstract

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the [...] Read more.

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

(This article belongs to the Special Issue Advanced Energy Technologies and Energy Savings: Low Emissions and High Efficiency)

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51 pages, 4396 KB

Open AccessEditor’s ChoiceReview

A Review of CO₂ Clathrate Hydrate Technology: From Lab-Scale Preparation to Cold Thermal Energy Storage Solutions

by Sai Bhargav Annavajjala, Noah Van Dam, Devinder Mahajan and Jan Kosny

Energies 2025, 18(10), 2659; https://doi.org/10.3390/en18102659 - 21 May 2025

Cited by 2 | Viewed by 1707

Abstract

Carbon dioxide (CO₂) clathrate hydrate is gaining attention as a promising material for cold thermal energy storage (CTES) due to its high energy storage capacity and low environmental footprint. It shows strong potential in building applications, where space cooling accounts for [...] Read more.

Carbon dioxide (CO₂) clathrate hydrate is gaining attention as a promising material for cold thermal energy storage (CTES) due to its high energy storage capacity and low environmental footprint. It shows strong potential in building applications, where space cooling accounts for nearly 40% of total energy use and over 85% of electricity demand in developed countries. CO₂ hydrates are also being explored for use in refrigeration, cold chain logistics, supercomputing, biomedical cooling, and defense systems. With the growing number of applications in mind, this review focuses on the thermal behavior of CO₂ hydrates and their environmental impact. It highlights recent efforts to reduce formation pressure and temperature using chemical promoters and surfactants. This paper also reviews key experimental techniques used to study hydrate properties, including Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), high-pressure differential scanning calorimetry (HP-DSC), and the T-history method. In lifecycle comparisons, CO₂ hydrate systems show better energy efficiency and lower carbon emissions than traditional ice or other phase-change materials (PCMs). This review also discusses current commercialization challenges such as high energy input during formation and promoter toxicity. Finally, practical strategies to move CO₂ hydrate-based CTES from lab-scale studies to real-world cooling and temperature control applications are discussed. Full article

(This article belongs to the Special Issue New Advances in Indoor Acoustics and Thermal Comfort for Sustainable Buildings)

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34 pages, 5896 KB

Open AccessEditor’s ChoiceArticle

Networked Multi-Agent Deep Reinforcement Learning Framework for the Provision of Ancillary Services in Hybrid Power Plants

by Muhammad Ikram, Daryoush Habibi and Asma Aziz

Energies 2025, 18(10), 2666; https://doi.org/10.3390/en18102666 - 21 May 2025

Cited by 1 | Viewed by 735

Abstract

Inverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control [...] Read more.

Inverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control services. This paper presents a novel networked multi-agent deep reinforcement learning (N—MADRL) scheme for optimal dispatch and frequency control services. First, we develop a model-free environment consisting of a photovoltaic (PV) plant, a wind plant (WP), and an energy storage system (ESS) plant. The proposed framework uses a combination of multi-agent actor-critic (MAAC) and soft actor-critic (SAC) schemes for optimal dispatch of active power, mitigating frequency deviations, aiding reserve capacity management, and improving energy balancing. Second, frequency stability and optimal dispatch are formulated in the N—MADRL framework using the physical constraints under a dynamic simulation environment. Third, a decentralised coordinated control scheme is implemented in the HPP environment using communication-resilient scenarios to address system vulnerabilities. Finally, the practicality of the N—MADRL approach is demonstrated in a Grid2Op dynamic simulation environment for optimal dispatch, energy reserve management, and frequency control. Results demonstrated on the IEEE 14 bus network show that compared to PPO and DDPG, N—MADRL achieves 42.10% and 61.40% higher efficiency for optimal dispatch, along with improvements of 68.30% and 74.48% in mitigating frequency deviations, respectively. The proposed approach outperforms existing methods under partially, fully, and randomly connected scenarios by effectively handling uncertainties, system intermittency, and communication resiliency. Full article

(This article belongs to the Collection Artificial Intelligence and Smart Energy)

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43 pages, 1501 KB

Open AccessEditor’s ChoiceReview

State and Perspectives of Biomethane Production and Use—A Systematic Review

by Małgorzata Pawłowska, Magdalena Zdeb, Marta Bis and Lucjan Pawłowski

Energies 2025, 18(10), 2660; https://doi.org/10.3390/en18102660 - 21 May 2025

Cited by 2 | Viewed by 2809

Abstract

In the face of increasingly frequent natural disasters resulting from climate change and disruptions in the supply chains of energy resources, the demand for energy carriers based on locally sourced renewable resources is growing. Biomethane, derived from biomass and having multiple uses in [...] Read more.

In the face of increasingly frequent natural disasters resulting from climate change and disruptions in the supply chains of energy resources, the demand for energy carriers based on locally sourced renewable resources is growing. Biomethane, derived from biomass and having multiple uses in the energy sector, fully meets these conditions. Analyses of the development and spatial distribution of biomethane production plants, the prevalence of methods of its production, and directions of applications, made on the basis of the data gained from official databases and research papers, are the main subjects of the paper. Additionally, the advantages and disadvantages of biomethane production, taking into account the results of the life cycle assessments, and the prospects for development of the biomethane market, facing regulatory and policy challenges, are considered. The results of the review indicate that biomethane production is currently concentrated in Europe and North America, which together generate over 80% of the globally produced biomethane. An exponential growth of the number of biomethane plants and their production capacities has been observed over the last decade. Assuming that the global strategies currently adopted and the resulting regional and national regulations on environmental and socio-economic policies are maintained, the further intensive development of the biomethane market will be expected in the near future. Full article

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25 pages, 2228 KB

Open AccessEditor’s ChoiceArticle

Green Hydrogen Production from Biogas or Landfill Gas by Steam Reforming or Dry Reforming: Specific Production and Energy Requirements

by Dhruv Singh, Piero Sirini and Lidia Lombardi

Energies 2025, 18(10), 2631; https://doi.org/10.3390/en18102631 - 20 May 2025

Cited by 4 | Viewed by 1429

Abstract

Biogas is a crucial renewable energy source for green hydrogen (H₂) production, reducing greenhouse gas emissions and serving as a carbon-free energy carrier with higher specific energy than traditional fuels. Currently, methane reforming dominates H₂ production to meet growing global [...] Read more.

Biogas is a crucial renewable energy source for green hydrogen (H₂) production, reducing greenhouse gas emissions and serving as a carbon-free energy carrier with higher specific energy than traditional fuels. Currently, methane reforming dominates H₂ production to meet growing global demand, with biogas/landfill gas (LFG) reform offering a promising alternative. This study provides a comprehensive simulation-based evaluation of Steam Methane Reforming (SMR) and Dry Methane Reforming (DMR) of biogas/LFG, using Aspen Plus. Simulations were conducted under varying operating conditions, including steam-to-carbon (S/C) for SMR and steam-to-carbon monoxide (S/CO) ratios for DMR, reforming temperatures, pressures, and LFG compositions, to optimize H₂ yield and process efficiency. The comparative study showed that SMR attains higher specific H₂ yields (0.14–0.19 kgH₂/Nm³), with specific energy consumption between 0.048 and 0.075 MWh/kg of H₂, especially at increased S/C ratios. DMR produces less H₂ than SMR (0.104–0.136 kg H₂/Nm³) and requires higher energy inputs (0.072–0.079 MWh/kg H₂), making it less efficient. Both processes require an additional 1.4–2.1 Nm³ of biogas/LFG per Nm³ of feed for energy. These findings provide key insights for improving biogas-based H₂ production for sustainable energy, with future work focusing on techno–economic and environmental assessments to evaluate its feasibility, scalability, and industrial application. Full article

(This article belongs to the Special Issue Biomass, Biofuels and Waste: 3rd Edition)

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28 pages, 3898 KB

Open AccessEditor’s ChoiceArticle

Assessing the Implications of Integrating Small Modular Reactors in Modern Power Systems

by Christos K. Simoglou, Ioannis M. Kaissas and Pandelis N. Biskas

Energies 2025, 18(10), 2578; https://doi.org/10.3390/en18102578 - 16 May 2025

Cited by 1 | Viewed by 834

Abstract

This paper investigates the long-term impact of integrating emerging Small Modular Reactors (SMR) in modern power systems. A chronological simulation of the Greek day-ahead market and real-time balancing market with fine time granularity is conducted for a future 20-year period (2032–2051) under four [...] Read more.

This paper investigates the long-term impact of integrating emerging Small Modular Reactors (SMR) in modern power systems. A chronological simulation of the Greek day-ahead market and real-time balancing market with fine time granularity is conducted for a future 20-year period (2032–2051) under four SMR penetration scenarios using a specialized integrated market simulation software. Simulation results indicate that SMR units can be regarded as a promising electricity generation solution in the forthcoming energy transition landscape. The introduction of up to 3 GW of SMR capacity is projected to significantly decrease reliance on gas imports by up to 62%, reduce carbon emissions by up to 52%, and lower overall electricity costs for end-consumers by up to 21% as compared to a baseline scenario without SMRs. It is anticipated that SMR units are expected to leverage their operating advantages and generally achieve positive financial results when participating directly in the wholesale market. However, their economic viability is highly dependent on their initial capital expenditure and other operating cost components, which at present are highly uncertain. Full article

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

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45 pages, 11703 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Self-Assembled Monolayers as Hole-Transport Layers in Inverted Perovskite Solar Cells

by Yuchen Yuan, Houlin Li, Haiqiang Luo, Yang Zhang, Xiaoli Li, Ting Jiang, Yajie Yang, Lei Liu, Baoyan Fan and Xia Hao

Energies 2025, 18(10), 2577; https://doi.org/10.3390/en18102577 - 16 May 2025

Cited by 1 | Viewed by 4203

Abstract

The hole-transport layer (HTL) plays a pivotal role in engineering high-performance inverted perovskite solar cells (PSCs), as it governs both hole extraction/transport dynamics and critically impacts the crystallization quality of the perovskite absorber layer in device architectures. Recent advancements have highlighted self-assembled monolayers [...] Read more.

The hole-transport layer (HTL) plays a pivotal role in engineering high-performance inverted perovskite solar cells (PSCs), as it governs both hole extraction/transport dynamics and critically impacts the crystallization quality of the perovskite absorber layer in device architectures. Recent advancements have highlighted self-assembled monolayers (SAMs) as promising candidates for next-generation HTL materials in inverted PSCs due to their intrinsic advantages over conventional counterparts. These molecularly engineered interfaces demonstrate superior characteristics including simplified purification processes, tunable molecular structures, and enhanced interfacial compatibility with device substrates. This review systematically examines the progress, existing challenges, and future prospects of SAM-based HTLs in inverted photovoltaic systems, aiming to establish a systematic framework for understanding their structure–property relationships. The review is organized into three sections: (1) fundamental architecture of inverted PSCs, (2) molecular design principles of SAMs with emphasis on head-group functionality, and (3) recent breakthroughs in SAM-engineered HTLs and their modification strategies for HTL optimization. Through critical analysis of performance benchmarks and interfacial engineering approaches, we elucidate both the technological merits and inherent limitations of SAM implementation in photovoltaic devices. Furthermore, we propose strategic directions for advancing SAM-based HTL development, focusing on molecular customization and interfacial engineering to achieve device efficiency and stability targets. This comprehensive work aims to establish a knowledge platform for accelerating the rational design of SAM-modified interfaces in next-generation optoelectronic devices. Full article

(This article belongs to the Collection Review Papers in Solar Energy and Photovoltaic Systems)

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48 pages, 3194 KB

Open AccessEditor’s ChoiceReview

A Review and Comparative Analysis of Solar Tracking Systems

by Reza Sadeghi, Mattia Parenti, Samuele Memme, Marco Fossa and Stefano Morchio

Energies 2025, 18(10), 2553; https://doi.org/10.3390/en18102553 - 14 May 2025

Cited by 4 | Viewed by 5573

Abstract

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms [...] Read more.

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Full article

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

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26 pages, 1052 KB

Open AccessEditor’s ChoiceReview

Study on Biodiesel Production: Feedstock Evolution, Catalyst Selection, and Influencing Factors Analysis

by Fangyuan Zheng and Haeng Muk Cho

Energies 2025, 18(10), 2533; https://doi.org/10.3390/en18102533 - 14 May 2025

Cited by 6 | Viewed by 2739

Abstract

As fossil fuel depletion and environmental pollution become increasingly severe, biodiesel has emerged as a promising renewable alternative to conventional diesel due to its biodegradability, low sulfur emissions, and high combustion efficiency. This paper provides a comprehensive review of the evolution of biodiesel [...] Read more.

As fossil fuel depletion and environmental pollution become increasingly severe, biodiesel has emerged as a promising renewable alternative to conventional diesel due to its biodegradability, low sulfur emissions, and high combustion efficiency. This paper provides a comprehensive review of the evolution of biodiesel feedstocks, major production technologies, and key factors influencing production efficiency and fuel quality. It traces the development of feedstocks from first-generation edible oils, second-generation non-edible oils and waste fats, to third-generation microalgal oils and fourth-generation biofuels based on synthetic biology, with a comparative analysis of their respective advantages and limitations. Various production technologies such as transesterification, direct esterification, supercritical alcohol methods, and enzyme-catalyzed transesterification are examined in terms of reaction mechanisms, process conditions, and applicability. The effects of critical process parameters including the alcohol-to-oil molar ratio, reaction time, and temperature on biodiesel yield and quality are discussed in detail. Particular attention is given to the role of catalysts, including both homogeneous and heterogeneous types, in enhancing conversion efficiency. In addition, life cycle assessment (LCA) is briefly considered to evaluate the environmental impact and sustainability of biodiesel production. This review serves as a valuable reference for improving biodiesel production technologies, advancing sustainable feedstock development, and promoting the commercial application of biodiesel. Full article

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

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31 pages, 2108 KB

Open AccessEditor’s ChoiceArticle

Evaluating the Impact of Frequency Decomposition Techniques on LSTM-Based Household Energy Consumption Forecasting

by Maissa Taktak and Faouzi Derbel

Energies 2025, 18(10), 2507; https://doi.org/10.3390/en18102507 - 13 May 2025

Viewed by 614

Abstract

Accurate energy consumption forecasting is essential for efficient power grid management, yet existing deep learning models struggle with the multi-scale nature of energy consumption patterns. Contemporary approaches like LSTM and GRU networks process raw time series directly, failing to distinguish between distinct frequency [...] Read more.

Accurate energy consumption forecasting is essential for efficient power grid management, yet existing deep learning models struggle with the multi-scale nature of energy consumption patterns. Contemporary approaches like LSTM and GRU networks process raw time series directly, failing to distinguish between distinct frequency components that represent different physical phenomena in household energy usage. This study presents a novel methodological method that systematically decomposes energy consumption signals into low-frequency components representing gradual trends and daily routines and high-frequency components capturing transient events, such as appliance switching, before applying predictive modeling. Our approach employs computationally efficient convolution-based filters—uniform and binomial—with varying window sizes to separate these components for specialized processing. Experiments on two real-world datasets at different temporal resolutions (1 min and 15 min) demonstrate significant improvements over state-of-the-art methods. For the Smart House dataset, our optimal configuration achieved an R² of 0.997 and RMSE of 0.034, substantially outperforming previous models with R² values of 0.863. Similarly, for the Mexican Household dataset, our approach yielded an R² of 0.994 and RMSE of 13.278, compared to previous RMSE values exceeding 82.488. These findings establish frequency decomposition as a crucial preprocessing step for energy forecasting as it significantly improve the prediction in smart grid applications. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)

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31 pages, 372 KB

Open AccessEditor’s ChoiceReview

Privacy-Preserving Machine Learning for IoT-Integrated Smart Grids: Recent Advances, Opportunities, and Challenges

by Mazhar Ali, Moharana Suchismita, Syed Saqib Ali and Bong Jun Choi

Energies 2025, 18(10), 2515; https://doi.org/10.3390/en18102515 - 13 May 2025

Viewed by 1713

Abstract

Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies [...] Read more.

Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. Integrated IoT devices directly exchange data with the SG cloud, which increases the vulnerability and security threats to the energy system. The review aims to provide a comprehensive analysis of privacy-preserving machine learning (PPML) applications in IoT-Integrated SGs, focusing on non-intrusive load monitoring, fault detection, demand forecasting, generation forecasting, energy-management systems, anomaly detection, and energy trading. The study also highlights the importance of data privacy and security when integrating these applications to enable intelligent decision-making in smart grid domains. Furthermore, the review addresses performance issues (e.g., accuracy, latency, and resource constraints) associated with PPML techniques, which may impact the security and overall performance of IoT-integrated SGs. The insights of this study will provide essential guidelines for in-depth research in the field of IoT-integrated smart grid privacy and security in the future. Full article

(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)

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33 pages, 4339 KB

Open AccessEditor’s ChoiceReview

Review of Electrochemical Systems for Grid Scale Power Generation and Conversion: Low- and High-Temperature Fuel Cells and Electrolysis Processes

by Tingke Fang, Annette von Jouanne and Alex Yokochi

Energies 2025, 18(10), 2493; https://doi.org/10.3390/en18102493 - 12 May 2025

Cited by 1 | Viewed by 1194

Abstract

This review paper presents an overview of fuel cell electrochemical systems that can be used for clean large-scale power generation and energy storage as global energy concerns regarding emissions and greenhouse gases escalate. The fundamental thermochemical and operational principles of fuel cell power [...] Read more.

This review paper presents an overview of fuel cell electrochemical systems that can be used for clean large-scale power generation and energy storage as global energy concerns regarding emissions and greenhouse gases escalate. The fundamental thermochemical and operational principles of fuel cell power generation and electrolyzer technologies are discussed with a focus on high-temperature solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs) that are best suited for grid scale energy generation. SOFCs and SOECs share similar promising characteristics and have the potential to revolutionize energy conversion and storage due to improved energy efficiency and reduced carbon emissions. Electrochemical and thermodynamic foundations are presented while exploring energy conversion mechanisms, electric parameters, and efficiency in comparison with conventional power generation systems. Methods of converting hydrocarbon fuels to chemicals that can serve as fuel cell fuels are also presented. Key fuel cell challenges are also discussed, including degradation, thermal cycling, and long-term stability. The latest advancements, including in materials selection research, design, and manufacturing methods, are also presented, as they are essential for unlocking the full potential of these technologies and achieving a sustainable, near zero-emission energy future. Full article

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

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26 pages, 1729 KB

Open AccessEditor’s ChoiceReview

Research Progress on Energy-Saving Technologies and Methods for Steel Metallurgy Process Systems—A Review

by Jiacheng Cui, Gang Meng, Kaiqiang Zhang, Zongliang Zuo, Xiangyu Song, Yuhan Zhao and Siyi Luo

Energies 2025, 18(10), 2473; https://doi.org/10.3390/en18102473 - 12 May 2025

Cited by 1 | Viewed by 1326

Abstract

Against the backdrop of global energy crises and climate change, the iron and steel industry, as a typical high energy consumption and high-emission sector, faces rigid constraints for energy conservation and emission reduction. This paper systematically reviews the research progress and application effects [...] Read more.

Against the backdrop of global energy crises and climate change, the iron and steel industry, as a typical high energy consumption and high-emission sector, faces rigid constraints for energy conservation and emission reduction. This paper systematically reviews the research progress and application effects of energy-saving technologies across the entire steel production chain, including coking, sintering, ironmaking, steelmaking, continuous casting, and rolling processes. Studies reveal that technologies such as coal moisture control (CMC) and coke dry quenching (CDQ) significantly improve energy utilization efficiency in the coking process. In sintering, thick-layer sintering and flue gas recirculation (FGR) technologies reduce fuel consumption while enhancing sintered ore performance. In ironmaking, high-efficiency pulverized coal injection (PCI) and hydrogen-based fuel injection effectively lower coke ratios and carbon emissions. Integrated and intelligent innovations in continuous casting and rolling processes (e.g., endless strip production, ESP) substantially reduce energy consumption. Furthermore, the system energy conservation theory, through energy cascade utilization and full-process optimization, drives dual reductions in comprehensive energy consumption and carbon emission intensity. The study emphasizes that future advancements must integrate hydrogen metallurgy, digitalization, and multi-energy synergy to steer the industry toward green, high-efficiency, and low-carbon transformation, providing technical support for China’s “Dual Carbon” goals. Full article

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

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50 pages, 7037 KB

Open AccessEditor’s ChoiceReview

Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review

by Lei Zhang, Yuxing Yuan, Su Yan, Hang Cao and Tao Du

Energies 2025, 18(10), 2465; https://doi.org/10.3390/en18102465 - 11 May 2025

Viewed by 1062

Abstract

With the increasing liberalization of energy markets, the penetration of renewable clean energy sources, such as photovoltaics and wind power, has gradually increased, providing more sustainable energy solutions for energy-intensive industrial sectors or parks, such as iron and steel production. However, the issues [...] Read more.

With the increasing liberalization of energy markets, the penetration of renewable clean energy sources, such as photovoltaics and wind power, has gradually increased, providing more sustainable energy solutions for energy-intensive industrial sectors or parks, such as iron and steel production. However, the issues of the intermittency and volatility of renewable energy have become increasingly evident in practical applications, and the economic performance and operational efficiency of localized microgrid systems also demand thorough consideration, posing significant challenges to the decision and management of power system operation. A smart microgrid can effectively enhance the flexibility, reliability, and resilience of the grid, through the frequent interaction of generation–grid–load. Therefore, this paper will provide a comprehensive summary of existing knowledge and a review of the research progress on the methodologies and strategies of modeling technologies for intelligent power systems integrating renewable energy in industrial production. Full article

(This article belongs to the Special Issue Modeling Analysis and Optimization of Energy System)

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54 pages, 6836 KB

Open AccessEditor’s ChoiceReview

Statistical Foundations of Generative AI for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions

by Elinor Ginzburg-Ganz, Eden Dina Horodi, Omar Shadafny, Uri Savir, Ram Machlev and Yoash Levron

Energies 2025, 18(10), 2461; https://doi.org/10.3390/en18102461 - 11 May 2025

Cited by 1 | Viewed by 1453

Abstract

With the rapid advancement of deep learning, generative artificial intelligence (Gen-AI) has emerged as a powerful tool, unlocking new prospects in the power systems sector. Despite the evident success of these methods and the rapid growth of this field in the power systems [...] Read more.

With the rapid advancement of deep learning, generative artificial intelligence (Gen-AI) has emerged as a powerful tool, unlocking new prospects in the power systems sector. Despite the evident success of these methods and the rapid growth of this field in the power systems community, there is still a pressing need for a deeper understanding of how different evaluation metrics relate to the underlying statistical structure of the models. Another related important question is what tools can be used to quantify the different uncertainties, which are inherent in these problems, and stem not only from the physical system but also from the nature of the generative model itself. This paper attempts to address these challenges and provides a comprehensive review of existing evaluation metrics for generative models applied in various power system tasks. We analyze how these metrics align with the statistical properties of the models and explore their strengths and limitations. We also examine different sources of uncertainty, distinguishing between uncertainties inherent to the learning model, those arising from measurement errors, and other sources. Our general aim is to promote a better understanding of generative models as they are being applied in power systems to support this fascinating growing trend. Full article

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

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35 pages, 4428 KB

Open AccessEditor’s ChoiceArticle

An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids

by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín and Sarvenaz Sadat Khatami

Energies 2025, 18(10), 2435; https://doi.org/10.3390/en18102435 - 9 May 2025

Viewed by 994

Abstract

The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false [...] Read more.

The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false alarm rates and inefficient fault detection. To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. This optimization ensures improved accuracy, faster convergence, and better generalization to unseen data. The proposed DRL-NSABC model is evaluated using four benchmark datasets: smart grid, advanced metering infrastructure (AMI), smart meter, and Pecan Street, widely recognized in anomaly detection research. A comparative analysis against state-of-the-art deep learning (DL) models, including RL, CNN, RNN, the generative adversarial network (GAN), the time-series transformer (TST), and bidirectional encoder representations from transformers (BERT), demonstrates the superiority of the proposed DRL-NSABC. The proposed DRL-NSABC model achieved high accuracy across all benchmark datasets, including 95.83% on the smart grid dataset, 96.19% on AMI, 96.61% on the smart meter, and 96.45% on Pecan Street. Statistical t-tests confirm the superiority of DRL-NSABC over other algorithms, while achieving a variance of 0.00014. Moreover, DRL-NSABC demonstrates the fastest convergence, reaching near-optimal accuracy within the first 100 epochs. By significantly reducing false positives and ensuring rapid anomaly detection with low computational overhead, the proposed DRL-NSABC framework enables efficient real-world deployment in smart power distribution systems without major infrastructure upgrades and promotes cost-effective, resilient power grid operations. Full article

(This article belongs to the Special Issue Flexible and Secure Operation of Multi-Scenario Integrated Energy System Coupled with Electricity and Hydrogen)

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29 pages, 4235 KB

Open AccessEditor’s ChoiceReview

Wide-Bandgap Subcells for All-Perovskite Tandem Solar Cells: Recent Advances, Challenges, and Future Perspectives

by Qiman Li, Wenming Chai, Xin Luo, Weidong Zhu, Dazheng Chen, Long Zhou, He Xi, Hang Dong, Chunfu Zhang and Yue Hao

Energies 2025, 18(10), 2415; https://doi.org/10.3390/en18102415 - 8 May 2025

Viewed by 1681

Abstract

All-perovskite tandem solar cells (APTSCs) offer a promising pathway to surpassing the efficiency limits of single-junction photovoltaics. The wide-bandgap (WBG) subcell, serving as the top absorber, plays a critical role in optimizing light harvesting and charge extraction in tandem architectures. This review comprehensively [...] Read more.

All-perovskite tandem solar cells (APTSCs) offer a promising pathway to surpassing the efficiency limits of single-junction photovoltaics. The wide-bandgap (WBG) subcell, serving as the top absorber, plays a critical role in optimizing light harvesting and charge extraction in tandem architectures. This review comprehensively summarizes recent advancements in WBG subcells, focusing on material design, defect passivation strategies, and interfacial engineering to address challenges such as phase instability, halide segregation, and voltage losses. Key innovations, including compositional tuning, additive engineering, and charge transport layer optimization, are critically analyzed for their contributions to efficiency and stability enhancement. Despite significant progress, challenges remain regarding scalability, long-term stability under illumination, and cost-effective fabrication. Future research directions include the development of lead-reduced perovskites, machine learning-guided material discovery, and scalable deposition techniques. This review provides insights into advancing WBG subcells toward high-efficiency, stable, and eco-friendly APTSCs for next-generation solar energy applications. Full article

(This article belongs to the Special Issue Advanced Developments of Photovoltaic Devices and Perovskite Solar Cells: 2nd Edition)

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27 pages, 2530 KB

Open AccessEditor’s ChoiceReview

Recent Advances in Electrified Methane Pyrolysis Technologies for Turquoise Hydrogen Production

by Hossein Rohani, Galina Sudiiarova, Stephen Matthew Lyth and Arash Badakhsh

Energies 2025, 18(9), 2393; https://doi.org/10.3390/en18092393 - 7 May 2025

Viewed by 4092

Abstract

The global campaign to reach net zero will necessitate the use of hydrogen as an efficient way to store renewable electricity at large scale. Methane pyrolysis is rapidly gaining traction as an enabling technology to produce low-cost hydrogen without directly emitting carbon dioxide. [...] Read more.

The global campaign to reach net zero will necessitate the use of hydrogen as an efficient way to store renewable electricity at large scale. Methane pyrolysis is rapidly gaining traction as an enabling technology to produce low-cost hydrogen without directly emitting carbon dioxide. It offers a scalable and sustainable alternative to steam reforming whilst being compatible with existing infrastructure. The process most commonly uses thermal energy to decompose methane (CH₄) into hydrogen gas (H₂) and solid carbon (C). The electrification of this reaction is of great significance, allowing it to be driven by excess renewable electricity rather than fossil fuels, and eliminating indirect emissions. This review discusses the most recent technological advances in electrified methane pyrolysis and the relative merits of the mainstream reactor technologies in this space (plasma, microwave, fluidised bed, and direct resistive heating). This study also examines the economic viability of the process, considering energy costs, and the market potential of both turquoise hydrogen and solid carbon products. Whilst these technologies offer emission-free hydrogen production, challenges such as carbon deposition, reactor stability, and high energy consumption must be addressed for large-scale adoption. Future research should focus on process optimisation, advanced reactor designs, and policy frameworks to support commercialisation. With continued technological innovation and sufficient investment, electrified methane pyrolysis has the potential to become the primary route for sustainable production of hydrogen at industrial scale. Full article

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

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29 pages, 4735 KB

Open AccessEditor’s ChoiceArticle

Offshore Wind Farm Economic Evaluation Under Uncertainty and Market Risk Mitigation

by Antonio C. Caputo, Alessandro Federici, Pacifico M. Pelagagge and Paolo Salini

Energies 2025, 18(9), 2362; https://doi.org/10.3390/en18092362 - 6 May 2025

Viewed by 1074

Abstract

Renewable energy systems (RES) are strongly affected by many sources of uncertainty and variability. Nevertheless, traditional technical and economic evaluation methods often neglect uncertainty by deterministically assuming average nominal values, using simple sensitivity analysis to explore effects of changing conditions, or limiting to [...] Read more.

Renewable energy systems (RES) are strongly affected by many sources of uncertainty and variability. Nevertheless, traditional technical and economic evaluation methods often neglect uncertainty by deterministically assuming average nominal values, using simple sensitivity analysis to explore effects of changing conditions, or limiting to a few sources of uncertainty. Furthermore, long-term variability and changing scenarios during the life of the system are not considered. This leads to inaccurate estimation of inherent investment risk. To address this gap, this work proposes a framework for the economic evaluation of offshore wind farms, considering the effects of both epistemic and aleatory uncertainty. Uncertainty of correlations used to model the system, the variability of resources and energy prices, as well as the use of a financial hedging tool to cope with market risk, the impact of failures and disruptive events, the changing of long-term scenarios during the system’s life, and the wake effect due to wind direction variability are all considered. As demonstrated through an example of an application, this methodology will be useful to practitioners and academics to achieve a more realistic assessment of the profitability of the investment based on a more comprehensive propagation of uncertainty. Full article

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

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27 pages, 15839 KB

Open AccessEditor’s ChoiceReview

A Systematic Review of Sensitivity Analysis in Building Energy Modeling: Key Factors Influencing Building Thermal Energy Performance

by Rajendra Roka, António Figueiredo, Ana Vieira and Claudino Cardoso

Energies 2025, 18(9), 2375; https://doi.org/10.3390/en18092375 - 6 May 2025

Cited by 3 | Viewed by 1894

Abstract

Improving building thermal energy performance is essential to reducing energy consumption, minimizing carbon emissions, and enhancing occupants’ thermal comfort. For this purpose, there is an increasing research interest in this field of building energy performance. This review aims to present a precise and [...] Read more.

Improving building thermal energy performance is essential to reducing energy consumption, minimizing carbon emissions, and enhancing occupants’ thermal comfort. For this purpose, there is an increasing research interest in this field of building energy performance. This review aims to present a precise and systematic overview of the sensitivity analysis in optimizing the thermal energy performance of buildings. The investigation covers various aspects, including sensitivity analysis techniques, key measures and variables, objectives and criteria, software tools, optimization methods, climate zones, building typology, and climate change effects. The findings reveal that sensitivity analysis is a powerful technique for optimizing energy performance and identifying adaptive strategies such as dynamic shading, reflective coatings, and efficient HVAC set points to address climate change. Most of the study also highlights that the temperature set point is the key influential parameter in both heating-dominant and cooling-dominant climate zones. This review offers critical insights on advancing sustainable building design, informing policy, and guiding future research in energy-efficient building solutions. Full article

(This article belongs to the Special Issue Energy and Buildings: Thermal Storage, Emissions, Transports, Control and Their Effects)

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43 pages, 814 KB

Open AccessEditor’s ChoiceReview

Regulating AI in the Energy Sector: A Scoping Review of EU Laws, Challenges, and Global Perspectives

by Bo Nørregaard Jørgensen and Zheng Grace Ma

Energies 2025, 18(9), 2359; https://doi.org/10.3390/en18092359 - 6 May 2025

Cited by 2 | Viewed by 3147

Abstract

Using the PRISMA-ScR methodology, this scoping review systematically analyzes how EU laws and regulations influence the development, adoption, and deployment of AI-driven digital solutions in energy generation, transmission, distribution, consumption, and markets. It identifies key regulatory barriers such as stringent risk assessments, cybersecurity [...] Read more.

Using the PRISMA-ScR methodology, this scoping review systematically analyzes how EU laws and regulations influence the development, adoption, and deployment of AI-driven digital solutions in energy generation, transmission, distribution, consumption, and markets. It identifies key regulatory barriers such as stringent risk assessments, cybersecurity obligations, and data access restrictions, along with enablers like regulatory sandboxes and harmonized compliance frameworks. Legal uncertainties, including AI liability and market manipulation risks, are also examined. To provide a comparative perspective, the EU regulatory approach is contrasted with AI governance models in the United States and China, highlighting global best practices and alignment challenges. The findings indicate that while the EU’s risk-based approach to AI governance provides a robust legal foundation, cross-regulatory complexity and sector-specific ambiguities necessitate further refinement. This paper proposes key recommendations, including the integration of AI-specific energy sector guidelines, acceleration of standardization efforts, promotion of privacy-preserving AI methods, and enhancement of international cooperation on AI safety and cybersecurity. These measures will help strike a balance between fostering trustworthy AI innovation and ensuring regulatory clarity, enabling AI to accelerate the clean energy transition while maintaining security, transparency, and fairness in digital energy systems. Full article

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

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25 pages, 2250 KB

Open AccessEditor’s ChoiceArticle

Simulation of Heat Pump with Heat Storage and PV System—Increase in Self-Consumption in a Polish Household

by Jakub Szymiczek, Krzysztof Szczotka and Piotr Michalak

Energies 2025, 18(9), 2325; https://doi.org/10.3390/en18092325 - 2 May 2025

Cited by 2 | Viewed by 1780

Abstract

The use of renewables in heat production requires methods to overcome the issue of asynchronous heat load and energy production. The most effective method for analyzing the intricate thermal dynamics of an existing building is through transient simulation, utilizing real-world weather data. This [...] Read more.

The use of renewables in heat production requires methods to overcome the issue of asynchronous heat load and energy production. The most effective method for analyzing the intricate thermal dynamics of an existing building is through transient simulation, utilizing real-world weather data. This approach offers a far more nuanced understanding than static calculations, which often fail to capture the dynamic interplay of environmental factors and building performance. Transient simulations, by their nature, model the building’s thermal behavior over time, reflecting the continuous fluctuations in temperature, solar radiation, and wind speed. Leveraging actual meteorological data enables the simulation model to faithfully capture system dynamics under realistic operational scenarios. This is crucial for evaluating the effectiveness of heating, ventilation, and air conditioning (HVAC) systems, identifying potential energy inefficiencies, and assessing the impact of various energy-saving measures. The simulation can reveal how the building’s thermal mass absorbs and releases heat, how solar gains influence indoor temperatures, and how ventilation patterns affect heat losses. In this paper, a household heating system consisting of an air source heat pump, PV, and buffer tank is simulated and analyzed. The 3D model accurately represents the building’s geometry and thermal properties. This virtual representation serves as the basis for calculating heat losses and gains, considering factors such as insulation levels, window characteristics, and building orientation. The approach is based on the calculation of building heat load based on a 3D model and EN ISO 52016-1 standard. The heat load is modeled based on air temperature and sun irradiance. The heating system is modeled in EBSILON professional 16.00 software for the calculation of transient 10 min time step heat production during the heating season. The results prove that a buffer tank with the right heat production control system can efficiently increase the auto consumption of self-produced PV electric energy, leading to a reduction in environmental effects and higher economic profitability. Full article

(This article belongs to the Special Issue Advances in Refrigeration and Heat Pump Technologies)

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