Energies

14 pages, 3021 KB

Open AccessArticle

Multifunctional Benzene-Based Solid Additive for Synergistically Boosting Efficiency and Stability in Layer-by-Layer Organic Photovoltaics

by Junchen Li, Peng He, Wuchao Xie, Yujie Xie, Yongquan Fu, Shutian Huang, Guojuan Lai, Zhen Wang, Fujun Zhang and Xixiang Zhu

Energies 2026, 19(1), 211; https://doi.org/10.3390/en19010211 (registering DOI) - 31 Dec 2025

Abstract

The realization of desirable vertical phase separation, enabled by sequential processing that allows for the separate deposition and targeted regulation of donor and acceptor components to construct a well-defined donor–acceptor (D-A) interface, serves as a pivotal factor governing the performance of layer-by-layer organic [...] Read more.

The realization of desirable vertical phase separation, enabled by sequential processing that allows for the separate deposition and targeted regulation of donor and acceptor components to construct a well-defined donor–acceptor (D-A) interface, serves as a pivotal factor governing the performance of layer-by-layer organic photovoltaics (LOPVs). This study explores the utility of 4-trifluoromethyl benzoic anhydride (4-TBA), a multifunctional benzene-based solid additive, in the PM6/L8-BO LOPV system, focusing on its role in regulating the vertical phase separation of donor-PM6 and acceptor-L8-BO components to form a well-structured D-A interface. To this end, 4-TBA is doped into the donor-PM6 layer, acceptor-L8-BO layer, or both layers, and its effects on device performance are systematically characterized. The results show that simultaneous doping of 0.05 wt% 4-TBA in both PM6 and L8-BO layers yields the optimal performance, with the power conversion efficiency reaching 18.49% compared to the pristine device with a PCE of 17.05%, and this is accompanied by a significant increase in short-circuit current density from 24.71 mA/cm² to 26.65 mA/cm². Additionally, the optimal devices exhibit better stability, as unencapsulated devices retain 76% of their initial PCE after 175 h under ambient conditions compared to 73% for the devices without 4-TBA doping. Essentially, solid additive 4-TBA modulates molecular packing via its interaction between the donor and acceptor molecules and enhances molecular aggregation and hydrophobicity, thereby suppressing bimolecular and trap-assisted recombination, reducing trap density of states, and forming favorable interpenetrating networks. This work validates 4-TBA, which contains benzene rings and other functional groups, as a versatile additive suitable for the LOPV system and offers a generalizable strategy for optimizing LOPV performance by leveraging multifunctional solid additives. Full article

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

► Show Figures

Figure 1

23 pages, 2133 KB

Open AccessArticle

Vulnerability-Driven Multi-Objective Energy Storage Planning Using Enhanced Beluga Whale Optimization for Resilient Distribution Networks

by Huanruo Qi, Chong Zhao, Xiangyang Yan, Weizheng Zhang, Fei Guo, Liang Zhang, Bochao Yang and Hailiang Lu

Energies 2026, 19(1), 210; https://doi.org/10.3390/en19010210 (registering DOI) - 30 Dec 2025

Abstract

The large-scale integration of distributed photovoltaics (DPV) and their inherent uncertainties have significantly increased the operational risks of distribution networks. Moreover, frequent outages caused by extreme events further impose substantial losses on these networks, highlighting the urgent need to enhance their disaster resilience [...] Read more.

The large-scale integration of distributed photovoltaics (DPV) and their inherent uncertainties have significantly increased the operational risks of distribution networks. Moreover, frequent outages caused by extreme events further impose substantial losses on these networks, highlighting the urgent need to enhance their disaster resilience and load-supply capabilities. To address these challenges, this paper proposes an energy storage allocation method that simultaneously considers economic performance and comprehensive vulnerability. First, a vulnerability assessment framework for distribution networks is established from both pre-disaster and post-disaster perspectives. In the pre-disaster stage, an improved electrical betweenness index, voltage deviation index, and network-balance index are employed to identify weak lines and nodes. In the post-disaster stage, based on the identified weak components, two types of scenarios, namely random line failures and worst-case failures, are constructed to emulate extreme events, and an enhanced network supply efficiency index is developed to quantitatively evaluate the network’s recovery capability. Subsequently, a multi-objective optimal allocation model for energy storage is formulated with economic cost and comprehensive vulnerability as objective functions, and an Enhanced Beluga Whale Optimization algorithm is adopted to obtain the optimal siting and sizing of energy storage systems. Case studies on an improved IEEE 33-bus distribution system show that, compared with the no-ESS scheme, the proposed plan yields about a 66.4% reduction in network loss cost, around 22% improvement in average voltage deviation, and a roughly 10% reduction in the comprehensive vulnerability index under normal operation. Under random and targeted line outage scenarios, the proposed scheme also achieves the highest area under curve and average network effectiveness indices and the lowest performance volatility among the benchmark strategies. These results demonstrate that, for the tested IEEE 33-bus system, the vulnerability-driven ESS planning framework can markedly enhance both economic efficiency and resilience to extreme events. Full article

(This article belongs to the Section D: Energy Storage and Application)

► Show Figures

Figure 1

68 pages, 2227 KB

Open AccessArticle

Neural Network Method for Detecting UDP Flood Attacks in Critical Infrastructure Microgrid Protection Systems with Law Enforcement Agencies’ Rapid Response

by Serhii Vladov, Łukasz Ścisło, Anatoliy Sachenko, Jan Krupiński, Victoria Vysotska, Maksym Korniienko, Oleh Uhrovetskyi, Vyacheslav Krykun, Kateryna Levchenko and Alina Sachenko

Energies 2026, 19(1), 209; https://doi.org/10.3390/en19010209 (registering DOI) - 30 Dec 2025

Abstract

This article develops a hybrid neural network method for detecting UDP flooding in critical infrastructure microgrid protection systems. This method combines sequential statistics (CUSUM) and a multimodal convolutional 1D-CNN architecture with a composite scoring criterion. Input features are generated using packet-aggregated one-minute vectors [...] Read more.

This article develops a hybrid neural network method for detecting UDP flooding in critical infrastructure microgrid protection systems. This method combines sequential statistics (CUSUM) and a multimodal convolutional 1D-CNN architecture with a composite scoring criterion. Input features are generated using packet-aggregated one-minute vectors with metrics for packet count, average size, source entropy, and HHI concentration index, as well as compact sketches of top sources. To ensure forensically relevant incident recording, a greedy artefact selection policy based on the knapsack problem with a limited forensic buffer is implemented. The developed method is theoretically justified using a likelihood ratio criterion and adaptive threshold tuning, which ensures control over the false alarm probability. Experimental validation on traffic datasets demonstrated high efficiency, with an overall accuracy of 98.7%, a sensitivity of 97.4%, an average model inference time of 5.3 ms (2.5 times faster than its LSTM counterpart), a controlled FPR of 0.96%, and a reduction in asymptotic detection latency with an increase in intensity from 35 to 12 s. Moreover, with a storage budget of 10 MB, 28 priority bins were selected (their total size was 7.39 MB), ensuring the approximate preservation of 85% of the most informative packets for subsequent examination. This research contribution involves the creation of a ready-to-deploy, resource-efficient detector with low latency, explainable statistical layers, and a built-in mechanism for generating a standardized evidence package to facilitate rapid law enforcement response. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids—2nd Edition)

27 pages, 1022 KB

Open AccessArticle

Energy Substitution Effect and Supply Chain Transformation in China’s New Energy Vehicle Industry: Evidence from DEA-Malmquist and Tobit Model Analysis

by Wei Cheng, Lvjiang Yin, Tianjun Zhang, Tianxin Wu and Qian Sheng

Energies 2026, 19(1), 208; https://doi.org/10.3390/en19010208 (registering DOI) - 30 Dec 2025

Abstract

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is [...] Read more.

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

(This article belongs to the Special Issue Smart and Sustainable Energy Systems: Optimization, Modeling, and Management for Global Energy Challenges)

23 pages, 3716 KB

Open AccessArticle

Spectrum-Dependent Burnable Poison Selection for Enhanced Safety and Neutronic Performance in an Epithermal Supercritical Carbon Dioxide-Cooled Reactor

by Yiming Zhong, Jing Wen, Wenbin Wu, Naibin Jiang, Xiaoqi Zhou, Di Lu, Bin Zhang and Lianjie Wang

Energies 2026, 19(1), 207; https://doi.org/10.3390/en19010207 (registering DOI) - 30 Dec 2025

Abstract

This study investigates the neutronic performance of burnable poisons (BPs) in an epithermal spectrum supercritical carbon dioxide (S-CO₂)-cooled reactor. Twelve candidate BP materials are systematically evaluated, including rare-earth oxides (e.g., HfO₂, Er₂O₃, Eu₂O [...] Read more.

This study investigates the neutronic performance of burnable poisons (BPs) in an epithermal spectrum supercritical carbon dioxide (S-CO₂)-cooled reactor. Twelve candidate BP materials are systematically evaluated, including rare-earth oxides (e.g., HfO₂, Er₂O₃, Eu₂O₃, etc.) and boron-based compounds (B₄C and PACS). The deterministic neutron transport code KYLIN-I with the ENDF/B VI 45-group cross-section library is employed for analysis. According to the calculation results, Eu₂O₃ effectively suppresses the initial k_inf of the epithermal-spectrum fuel assembly to ~1.2 with a relatively low weight fraction (~2.6%) while maintaining a total temperature coefficient (TTC) lower than −1.4 pcm/K throughout the entire burnup period. HfO₂ and Er₂O₃, at approximately 15% weight fraction, achieve TTC values better than −2 pcm/K. Furthermore, both Eu₂O₃ and HfO₂ contribute to maintaining a low, stable power peaking factor (PPF) below 1.24 throughout the burnup process. This study provides a theoretical foundation and technical support for designing an efficient and safe S–CO₂-cooled nuclear reactor. It highlights the importance of selecting BP materials that are well-adapted to the neutron spectrum and optimizing the fuel assembly configuration accordingly. Full article

(This article belongs to the Special Issue Nuclear Engineering and Nuclear Fuel Safety)

18 pages, 3162 KB

Open AccessArticle

Distributionally Robust Game-Theoretic Optimization Algorithm for Microgrid Based on Green Certificate–Carbon Trading Mechanism

by Chen Wei, Pengyuan Zheng, Jiabin Xue, Guanglin Song and Dong Wang

Energies 2026, 19(1), 206; https://doi.org/10.3390/en19010206 (registering DOI) - 30 Dec 2025

Abstract

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability [...] Read more.

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and

\infty

-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

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

► Show Figures

Figure 1

35 pages, 1979 KB

Open AccessReview

Review of Coupled Dynamic Modeling Methods for Floating Offshore Wind Turbines

by Jiahao Chen

Energies 2026, 19(1), 205; https://doi.org/10.3390/en19010205 (registering DOI) - 30 Dec 2025

Abstract

Floating offshore wind turbines (FOWTs) are subjected to multiple environmental loads that induce complex coupled dynamic responses. The development of coupled dynamic methods is therefore essential for FOWT analysis and design and has long attracted significant research attention. This paper presents a comprehensive [...] Read more.

Floating offshore wind turbines (FOWTs) are subjected to multiple environmental loads that induce complex coupled dynamic responses. The development of coupled dynamic methods is therefore essential for FOWT analysis and design and has long attracted significant research attention. This paper presents a comprehensive review of the recent advances in coupled dynamic modeling methods and associated numerical tools for FOWTs. First, the fundamental dynamic components are introduced, including aerodynamics, hydrodynamics, elastodynamics, mooring dynamics, and servodynamics. Next, coupled modeling approaches, such as fully coupled, semi-coupled, and frequency-domain methods, are reviewed and compared in terms of their applicability. The paper then outlines the software tools developed based on these methodologies, along with major international code comparison and validation campaigns. Finally, emerging trends in FOWT coupled dynamics are briefly discussed, including integrated marine energy systems, advanced wake modeling, and the incorporation of artificial intelligence techniques in prediction. This paper systematically synthesizes current knowledge on coupled dynamic methods for FOWTs, providing a foundation for future research while also serving as a practical reference for advancing this area of study. Full article

(This article belongs to the Special Issue Computation Modelling for Offshore Wind Turbines and Wind Farms)

16 pages, 3274 KB

Open AccessArticle

An Adaptive Inertia and Damping Control Strategy for Virtual Synchronous Generators to Enhance Transient Performance

by Wenzuo Tang, Bo Li, Xianqi Shao, Yun Ye, Yue Yu and Jiawei Chen

Energies 2026, 19(1), 204; https://doi.org/10.3390/en19010204 (registering DOI) - 30 Dec 2025

Abstract

Virtual synchronous generator (VSG) technology introduces synthetic rotational inertia and damping into inverter-based systems, thereby enhancing regulation performance under grid-connected operation. However, the output characteristics of VSGs are strongly influenced by virtual inertia and damping. This paper develops a self-tuning inertia–damping coordination mechanism [...] Read more.

Virtual synchronous generator (VSG) technology introduces synthetic rotational inertia and damping into inverter-based systems, thereby enhancing regulation performance under grid-connected operation. However, the output characteristics of VSGs are strongly influenced by virtual inertia and damping. This paper develops a self-tuning inertia–damping coordination mechanism for VSGs. The coupling between virtual inertia and damping with respect to grid power quality is systematically investigated, and a power-angle dynamic response model for synchronous generators (SGs) under extreme operating conditions is established. Building on these results, an improved adaptive control strategy for the VSG’s virtual inertia and damping is proposed. The proposed strategy detects changes in frequency and load power, enabling adaptive tuning of virtual inertia and damping in response to system variations, thereby reducing frequency overshoot while accelerating the dynamic response. The effectiveness of the proposed strategy is validated by hardware-in-the-loop real-time simulations. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

► Show Figures

Figure 1

22 pages, 2790 KB

Open AccessArticle

Partitioned Configuration of Energy Storage Systems in Energy-Autonomous Distribution Networks Based on Autonomous Unit Division

by Minghui Duan, Dacheng Wang, Shengjing Qi, Haichao Wang, Ruohan Li, Qu Pu, Xiaohan Wang, Gaozhong Lyu, Fengzhang Luo and Ranfeng Mu

Energies 2026, 19(1), 203; https://doi.org/10.3390/en19010203 (registering DOI) - 30 Dec 2025

Abstract

With the increasing penetration of distributed energy resources (DERs) and the rapid development of active distribution networks, the traditional centrally controlled operation mode can no longer meet the flexibility and autonomy requirements under the multi-dimensional coupling of sources, networks, loads, and storage. To [...] Read more.

With the increasing penetration of distributed energy resources (DERs) and the rapid development of active distribution networks, the traditional centrally controlled operation mode can no longer meet the flexibility and autonomy requirements under the multi-dimensional coupling of sources, networks, loads, and storage. To achieve regional energy self-balancing and autonomous operation, this paper proposes a partitioned configuration method for energy storage systems (ESSs) in energy-autonomous distribution networks based on autonomous unit division. First, the concept and hierarchical structure of the energy-autonomous distribution network and its autonomous units are clarified, identifying autonomous units as the fundamental carriers of the network’s autonomy. Then, following the principle of “tight coupling within units and loose coupling between units,” a comprehensive indicator system for autonomous unit division is constructed from three aspects: electrical modularity, active power balance, and reactive power balance. An improved genetic algorithm is applied to optimize the division results. Furthermore, based on the obtained division, an ESS partitioned configuration model is developed with the objective of minimizing the total cost, considering the investment and operation costs of ESSs, power purchase cost from the main grid, PV curtailment losses, and network loss cost. The model is solved using the CPLEX solver. Finally, a case study on a typical multi-substation, multi-feeder distribution network verifies the effectiveness of the proposed approach. The results demonstrate that the proposed model effectively improves voltage quality while reducing the total cost by 20.89%, ensuring optimal economic performance of storage configuration and enhancing the autonomy of EADNs. Full article

(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System: 2nd Edition)

► Show Figures

Figure 1

29 pages, 1667 KB

Open AccessArticle

Stress-Based Fatigue Diagnosis of Wind Turbine Blades Using Physics-Informed AI Reduced-Order Modeling

by Jun-Yeop Lee, Minh-Chau Dinh and Seok-Ju Lee

Energies 2026, 19(1), 202; https://doi.org/10.3390/en19010202 (registering DOI) - 30 Dec 2025

Abstract

This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a [...] Read more.

This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a physics-based Soderberg index and a one-class support vector machine (SVM) anomaly detector. The framework is implemented and evaluated using measurements from a 2 MW onshore turbine equipped with blade-root strain gauges and standard SCADA monitoring. Ten-minute operating windows are formed by synchronizing SCADA records with high-frequency strain data, converting strain to stress, and computing DELs via Rainflow counting for flapwise, edgewise, and torsional blade root directions. SCADA inputs are summarized by their 10 min statistics and augmented with yaw misalignment features; these are used to train LightGBM-based ROMs that map operating conditions to directional DELs. On an independent test set, the DEL-ROM achieves coefficients of determination of approximately 0.87, 0.99, and 0.99 for flapwise, edgewise, and torsional directions, respectively, with small absolute errors relative to the measured DELs. The Soderberg index is then used to define conservative Normal/Alert/Alarm classes based on representative material parameters, while a one-class SVM is trained on DEL- and stress-based fatigue features to learn the distribution of normal operation. A simple AND-normal/OR-abnormal rule combines the Soderberg class and SVM label into a hybrid diagnostic decision. Application to the field dataset shows that the proposed framework provides interpretable fatigue-safety margins and reliably highlights operating periods with elevated flapwise fatigue usage, demonstrating its suitability as a scalable building block for digital-twin-enabled condition monitoring and life-extension assessment of wind turbine blades. Full article

(This article belongs to the Special Issue Next-Generation Energy Systems and Renewable Energy Technologies)

25 pages, 2646 KB

Open AccessArticle

Risk-Constrained Optimization Framework for Generation and Transmission Maintenance Scheduling Under Economic and Carbon Emission Constraints

by Huihang Li, Jie Chen, Wenjuan Du, Chiguang Wei, Zhuping Xiang, Hanlong Liu, Xieyu Hu and Yuping Huang

Energies 2026, 19(1), 201; https://doi.org/10.3390/en19010201 (registering DOI) - 30 Dec 2025

Abstract

Power generation and transmission systems face increasing challenges in coordinating maintenance planning under economic pressure and carbon emission constraints. This study proposes an optimization framework that integrates preventive maintenance scheduling with operational dispatch decisions, aiming to achieve both cost efficiency and emission reduction. [...] Read more.

Power generation and transmission systems face increasing challenges in coordinating maintenance planning under economic pressure and carbon emission constraints. This study proposes an optimization framework that integrates preventive maintenance scheduling with operational dispatch decisions, aiming to achieve both cost efficiency and emission reduction. A bi-layer scenario-based mixed-integer optimization model is formulated, where the upper layer determines annual preventive maintenance windows, and the lower layer performs hourly economic dispatch considering renewable generation and demand uncertainty. To manage the exposure to extreme carbon outcomes, a Conditional Value-at-Risk (CVaR) constraint is embedded, jointly controlling economic and environmental risks. A parallel cut-generation decomposition algorithm is developed to ensure computational scalability for large-scale systems. Numerical experiments on six-bus and IEEE 118-bus systems demonstrate that the proposed model reduces total carbon emissions by up to 32.1%, while maintaining cost efficiency and system reliability. The scenario analyses further show that adjusting maintenance schedules according to seasonal carbon intensity effectively balances operation and emission targets. The results confirm that the proposed optimization framework provides a practical and scalable approach for achieving low-carbon, reliable, and economically efficient power system maintenance planning. Full article

(This article belongs to the Special Issue Energy Policies and Energy Transition: Strategies and Outlook)

18 pages, 1589 KB

Open AccessArticle

Do Energy Security Crises Accelerate Decarbonisation? The Case of REPowerEU

by Anastasia Pavlenko and Aleh Cherp

Energies 2026, 19(1), 200; https://doi.org/10.3390/en19010200 (registering DOI) - 30 Dec 2025

Abstract

Energy security crises have historically been turning points for energy systems, exposing vulnerabilities, reshaping policy priorities, and boosting technological change. However, whether—and to what extent—such crises accelerate low-carbon transitions remains contested. This paper examines the effects of the 2022 energy crisis on the [...] Read more.

Energy security crises have historically been turning points for energy systems, exposing vulnerabilities, reshaping policy priorities, and boosting technological change. However, whether—and to what extent—such crises accelerate low-carbon transitions remains contested. This paper examines the effects of the 2022 energy crisis on the European Union (EU)’s energy transition, using policy analysis combined with a quantitative assessment of renewable energy trends, forecasts, and targets. We analyse the ambition, implementation, and outcomes of the REPowerEU plan, the main response to the crisis. In an unprecedented move, REPowerEU securitised renewable energy as a means to reduce dependence on Russian energy imports. However, the plan only moderately increased earlier renewable energy targets and did not reverse declining subsidies despite more forceful implementation measures. Its effects have been uneven across technologies. Already accelerating solar may overshoot its targets, onshore wind might only slightly accelerate beyond its current steady growth, and offshore wind remains constrained by economic and institutional uncertainties. Despite increased subsidies for fossil fuels, coal continued declining, oil remained stable, and natural gas dropped. Overall, REPowerEU sustained rather than transformed the EU’s low-carbon transition, illustrating both the potential and limits of accelerating decarbonisation under security crises. Full article

(This article belongs to the Special Issue Energy Security of the European Union in Light of Sustainable Development)

37 pages, 589 KB

Open AccessReview

Underground Coal Gasification Technology: A Review of Advantages, Challenges, and Economics

by Yancheng Liu, Yan Li, Jihui Jiang, Feng Liu and Yang Liu

Energies 2026, 19(1), 199; https://doi.org/10.3390/en19010199 (registering DOI) - 30 Dec 2025

Abstract

Against the background of global energy transformation and low-carbon development, numerous difficult-to-mine coal resources (e.g., deep, thin coal seams and low-quality coal) remain underdeveloped, leading to potential resource waste. This study systematically summarizes the feasibility of developing these resources via underground coal gasification [...] Read more.

Against the background of global energy transformation and low-carbon development, numerous difficult-to-mine coal resources (e.g., deep, thin coal seams and low-quality coal) remain underdeveloped, leading to potential resource waste. This study systematically summarizes the feasibility of developing these resources via underground coal gasification (UCG) technology, clarifies its basic chemical/physical processes and typical gas supply/gas withdrawal arrangements, and establishes an analytical framework covering resource utilization, gas production quality control, environmental impact, and cost efficiency. Comparative evaluations are conducted among UCG, surface coal gasification (SCG), natural gas conversion, and electrolysis-based hydrogen production. Results show that UCG exhibits significant advantages: wide resource adaptability (recovering over 60% of difficult-to-mine coal resources), better environmental performance than traditional coal mining and SCG (e.g., less surface disturbance, 50% solid waste reduction), and obvious economic benefits (total capital investment without CCS is 65–82% of SCG, and hydrogen production cost ranges from 0.1 to 0.14 USD/m³, significantly lower than SCG’s 0.23–0.27 USD/m³). However, UCG faces challenges, including environmental risks (groundwater pollution by heavy metals, syngas leakage), geological risks (ground subsidence, rock mass strength reduction), and technical bottlenecks (difficult ignition control, unstable large-scale production). Combined with carbon capture and storage (CCS) technology, UCG can reduce carbon emissions, but CCS only mitigates carbon impact rather than reversing it. UCG provides a large-scale, stable, and economical path for the efficient clean development of difficult-to-mine coal resources, contributing to global energy structure transformation and low-carbon development. Full article

37 pages, 2000 KB

Open AccessArticle

An Optimized DRL-GAN Approach for Robust Anomaly Detection in Multi-Scale Energy Systems: Insights from PSML and LEAD1.0

by Anita Ershadi Oskouei, Maral Keramat Dashliboroun, Pardis Sadatian Moghaddam, Nuria Serrano, Francisco Hernando-Gallego, Diego Martín and José Vicente Álvarez-Bravo

Energies 2026, 19(1), 198; https://doi.org/10.3390/en19010198 (registering DOI) - 30 Dec 2025

Abstract

The increasing complexity of multi-scale energy systems makes robust anomaly detection essential to ensure system resilience and operational continuity. Recent advances in DL enable effective modeling of high-dimensional, non-linear energy data by capturing latent spatio-temporal patterns. In this paper, we proposed an optimized [...] Read more.

The increasing complexity of multi-scale energy systems makes robust anomaly detection essential to ensure system resilience and operational continuity. Recent advances in DL enable effective modeling of high-dimensional, non-linear energy data by capturing latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures. Full article

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

► Show Figures

Figure 1

34 pages, 4272 KB

Open AccessReview

Toward Low-Carbon Buildings: Breakthroughs and Challenges in PV–Storage–DC–Flexibility System

by Qihang Jin and Wei Lu

Energies 2026, 19(1), 197; https://doi.org/10.3390/en19010197 (registering DOI) - 30 Dec 2025

Abstract

The photovoltaic–energy storage–direct current–flexibility (PEDF) system provides an integrated pathway for low-carbon and intelligent building energy management by combining on-site PV generation, electrical storage, DC distribution, and flexible load control. This paper reviews recent advances in these four modules and synthesizes quantified benefits [...] Read more.

The photovoltaic–energy storage–direct current–flexibility (PEDF) system provides an integrated pathway for low-carbon and intelligent building energy management by combining on-site PV generation, electrical storage, DC distribution, and flexible load control. This paper reviews recent advances in these four modules and synthesizes quantified benefits reported in real-world deployments. Building-scale systems typically integrate 20–150 kW PV and achieve ~10–18% energy-efficiency gains enabled by DC distribution. Industrial-park deployments scale to 500 kW–5 MW, with renewable self-consumption often exceeding 50% and CO₂ emissions reductions of ~40–50%. Community-level setups commonly report 10–15% efficiency gains and annual CO₂ reductions on the order of tens to hundreds of tons. Key barriers to large-scale adoption are also discussed, including multi-energy coordination complexity, high upfront costs and uncertain business models, limited user engagement, and gaps in interoperability standards and supportive policies. Finally, we outline research and deployment priorities toward open and interoperable PEDF architectures that support cross-sector integration and accelerate the transition toward carbon-neutral (and potentially carbon-negative) built environments. Full article

► Show Figures

Figure 1

21 pages, 3157 KB

Open AccessArticle

Modeling an Industrial Distribution Network with Significant Photovoltaic Integration Using ATP-EMTP

by Katarzyna Nowińska and Krzysztof Kuliński

Energies 2026, 19(1), 196; https://doi.org/10.3390/en19010196 (registering DOI) - 30 Dec 2025

Abstract

This article presents an analysis of the impact of photovoltaic (PV) sources on the effectiveness and selectivity of earth fault protection in a 6 kV industrial distribution network. Simulations were conducted in the ATP-EMTP environment using a model of a generalized, real industrial [...] Read more.

This article presents an analysis of the impact of photovoltaic (PV) sources on the effectiveness and selectivity of earth fault protection in a 6 kV industrial distribution network. Simulations were conducted in the ATP-EMTP environment using a model of a generalized, real industrial network with an isolated neutral point. This model was based on data from real-world cases of earth faults with varying resistance. This study’s main objective was to determine how the power generated by PV sources affects the time and spectral waveforms of currents and zero-sequence voltages, in addition to the network’s overall response to disturbances. The results provide a quantitative assessment of the impact of distributed generation on the operational security of industrial power grids and form a basis for developing recommendations for coordinating protection in power systems integrated with renewable energy sources. Full article

(This article belongs to the Special Issue Energy Security of the European Union in Light of Sustainable Development)

► Show Figures

Figure 1

27 pages, 3766 KB

Open AccessArticle

Optimization of Isolated Microgrid Sizing Considering the Trade-Off Between Costs and Power Supply Reliability

by Caison Ramos, Gustavo Marchesan, Ghendy Cardoso, Jr., Igor Dal Forno, Tiago Pitol Mroginski, Olinto Araújo, Welisson Costa, Rodrigo Gadelha, Vitor Batista, André P. Leão, João Paulo Vieira, Eduardo de Campos, Caio Barroso and Mariana Resener

Energies 2026, 19(1), 195; https://doi.org/10.3390/en19010195 (registering DOI) - 30 Dec 2025

Abstract

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a [...] Read more.

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

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

► Show Figures

Figure 1

25 pages, 4363 KB

Open AccessArticle

Demand Response Potential Evaluation Based on Multivariate Heterogeneous Features and Stacking Mechanism

by Chong Gao, Zhiheng Xu, Ran Cheng, Junxiao Zhang, Xinghang Weng, Huahui Zhang, Tao Yu and Wencong Xiao

Energies 2026, 19(1), 194; https://doi.org/10.3390/en19010194 (registering DOI) - 30 Dec 2025

Abstract

Accurate evaluation of demand response (DR) potential at the individual user level is critical for the effective implementation and optimization of demand response programs. However, existing data-driven methods often suffer from insufficient feature representation, limited characterization of load profile dynamics, and ineffective fusion [...] Read more.

Accurate evaluation of demand response (DR) potential at the individual user level is critical for the effective implementation and optimization of demand response programs. However, existing data-driven methods often suffer from insufficient feature representation, limited characterization of load profile dynamics, and ineffective fusion of heterogeneous features, leading to suboptimal evaluation performance. To address these challenges, this paper proposes a novel demand response potential evaluation method based on multivariate heterogeneous features and a Stacking-based ensemble mechanism. First, multidimensional indicator features are extracted from historical electricity consumption data and external factors (e.g., weather, time-of-use pricing), capturing load shape, variability, and correlation characteristics. Second, to enrich the information space and preserve temporal dynamics, typical daily load profiles are transformed into two-dimensional image features using the Gramian Angular Difference Field (GADF), the Markov Transition Field (MTF), and an Improved Recurrence Plot (IRP), which are then fused into a single RGB image. Third, a differentiated modeling strategy is adopted: scalar indicator features are processed by classical machine learning models (Support Vector Machine, Random Forest, XGBoost), while image features are fed into a deep convolutional neural network (SE-ResNet-20). Finally, a Stacking ensemble learning framework is employed to intelligently integrate the outputs of base learners, with a Decision Tree as the meta-learner, thereby enhancing overall evaluation accuracy and robustness. Experimental results on a real-world dataset demonstrate that the proposed method achieves superior performance compared to individual models and conventional fusion approaches, effectively leveraging both structured indicators and unstructured image representations for high-precision demand response potential evaluation. Full article

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

► Show Figures

Figure 1

21 pages, 435 KB

Open AccessSystematic Review

Design Implications of Headspace Ratio

V_{H S} / V_{t o t}

on Pressure Stability, Gas Composition and Methane Productivity—A Systematic Review

by Meneses-Quelal Orlando

Energies 2026, 19(1), 193; https://doi.org/10.3390/en19010193 (registering DOI) - 30 Dec 2025

Abstract

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative [...] Read more.

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction

V_{H S} / V_{t o t}

, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30–0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5–8% and establishes reference yields of 300–430 NmL CH₄ g⁻¹ VS at 35 °C. At the pilot scale, operation at 3–4 bar absolute increases the CH₄ fraction by 10–20 percentage points relative to ~1 bar, while maintaining yields of 0.28–0.35 L CH₄ g COD⁻¹ and production rates of 0.8–1.5 Nm³ CH₄ m⁻³ d⁻¹ under OLRs of 4–30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2–7.6 and the free NH₃ fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while the integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO₂ solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH₄ m⁻³ d⁻¹, NmL CH₄ g⁻¹ VS, L CH₄ g COD⁻¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling. While this review primarily focuses on batch (discontinuous) anaerobic digesters, insights from semi-continuous and continuous systems are cited for context where relevant to scale-up and headspace dynamics, without expanding the main scope beyond batch systems. Full article

(This article belongs to the Special Issue Research on Conversion for Utilization of the Biogas and Natural Gas)

► Show Figures

Figure 1

Journal Description

Energies

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI