Energies

26 pages, 1298 KB

Open AccessArticle

Classifying Failures in Distributed Photovoltaic Installations Using a Delphi-Based Dimension-Adjusted Fuzzy SIWEC–MABAC Framework

by Paweł Kut, Katarzyna Pietrucha-Urbanik, Sławomir Rabczak and Karol Nowak

Energies 2026, 19(16), 3881; https://doi.org/10.3390/en19163881 (registering DOI) - 19 Aug 2026

Abstract

Distributed photovoltaic (PV) systems are increasingly important for renewable-energy transformation, prosumer participation, and low-emission electricity markets. However, failures affecting inverters, DC-side components, connectors, protection devices, monitoring units, and PV modules may reduce generation continuity, increase service burden, and weaken user confidence in distributed [...] Read more.

Distributed photovoltaic (PV) systems are increasingly important for renewable-energy transformation, prosumer participation, and low-emission electricity markets. However, failures affecting inverters, DC-side components, connectors, protection devices, monitoring units, and PV modules may reduce generation continuity, increase service burden, and weaken user confidence in distributed generation. Previous PV-failure studies have mainly identified failure modes or ranked them according to maintenance priority, whereas service companies require actionable classes linked with inspection intervals and corrective actions. This study develops and empirically applies a Delphi-based dimension-adjusted fuzzy SIWEC-MABAC decision-support framework for classifying PV installation failures into maintenance action classes. The procedure combines a completed three-round Delphi expert panel, linguistic uncertainty modelling using dimension-adjusted fuzzy sets, SIWEC criterion weighting, and MABAC ranking based on distance from the border approximation area. The empirical SIWEC–MABAC results show that safety/fire impact, downtime duration, and detectability difficulty dominate the service classification. Arc-fault-related DC-side damage and cable insulation degradation are assigned to immediate corrective action, whereas junction-box overheating, inverter hardware failure, melted MC4 connectors, hot-spot formation, and DC circuit-breaker failure require short-term preventive inspection. The validated framework supports service triage and inspection scheduling rather than real-time fault detection. A one-dimensional four-cluster check reproduced the same class membership, and TOPSIS cross-validation showed strong rank agreement with MABAC (Spearman rho = 0.951). Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy in Light of Energy Transition Processes—3rd Edition)

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31 pages, 1818 KB

Open AccessReview

AI-Driven Load Forecasting for Dynamic Tariff Structuring: A Comprehensive Review

by Oluwagbenga Apata, Mukovhe Ratshitanga and Innocent Ewean Davidson

Energies 2026, 19(16), 3880; https://doi.org/10.3390/en19163880 (registering DOI) - 19 Aug 2026

Abstract

Dynamic electricity tariffs are increasingly deployed to manage demand-side flexibility in decarbonising power systems, making AI-driven load forecasting a critical enabler of adaptive pricing. However, existing studies largely treat forecasting and tariff design as independent problems, evaluating models on predictive accuracy alone while [...] Read more.

Dynamic electricity tariffs are increasingly deployed to manage demand-side flexibility in decarbonising power systems, making AI-driven load forecasting a critical enabler of adaptive pricing. However, existing studies largely treat forecasting and tariff design as independent problems, evaluating models on predictive accuracy alone while neglecting the feedback effects through which price signals reshape consumer behaviour and introduce non-stationarity into the very demand distributions forecasts depend upon. This gap leaves practitioners without coherent guidance on how to structure, govern, and adapt forecasting models in price-responsive environments. This paper addresses the gap through a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-informed structured review of peer-reviewed studies spanning statistical, machine learning, deep learning, probabilistic, and reinforcement learning forecasting paradigms. Three principal findings emerge. First, dynamic pricing fundamentally invalidates static forecasting assumptions by inducing a distributional shift in demand data, making robustness to behavioural feedback a first-order requirement for deployment. Second, no single forecasting paradigm simultaneously satisfies the requirements of accuracy, interpretability, uncertainty quantification, and regulatory defensibility that dynamic tariff systems impose; layered, role-specific architectures are therefore operationally necessary. Third, explainability and governance constraints are structural requirements for tariff-oriented forecasting, not optional enhancements, because forecast outputs directly influence economically and socially consequential pricing decisions. Building on these findings, the paper proposes a Forecast-Driven Dynamic Tariff Design Framework that separates forecasting intelligence from pricing authority, embeds uncertainty management as a first-class design element, and positions a governance layer as the mandatory interface between predictive outputs and consumer-facing tariff signals. The framework provides a practical and regulatorily defensible foundation for deploying adaptive, resilient, and equitable electricity tariffs in data-intensive power systems. Full article

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

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33 pages, 16852 KB

Open AccessArticle

Aerodynamic Performance and Wake Characteristics of a Four-Rotor Wind Turbine

by Zhiqiang Li and Jiahao Chen

Energies 2026, 19(16), 3879; https://doi.org/10.3390/en19163879 - 18 Aug 2026

Abstract

The continuous upscaling of offshore wind turbines exacerbates challenges such as blade flutter, stability degradation, and logistical difficulties. To address these issues, this study proposes a parallel multi-rotor wind turbine and systematically investigates its aerodynamic performance and wake characteristics. Based on a 5 [...] Read more.

The continuous upscaling of offshore wind turbines exacerbates challenges such as blade flutter, stability degradation, and logistical difficulties. To address these issues, this study proposes a parallel multi-rotor wind turbine and systematically investigates its aerodynamic performance and wake characteristics. Based on a 5 MW reference turbine, validated Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations incorporating the sliding mesh technique and Shear Stress Transport (SST) k-ω turbulence model are conducted to evaluate twin- and four-rotor configurations under various geometric and rotational layouts. Crucially, this study reveals for the first time that asymmetric rotational configurations induce wind field skewness and modify rotor-edge bypass flow, leading to power imbalance among rotors. Additionally, the twin-rotor configuration enhances total power output with negligible thrust variation. For the four-rotor system, power is highly spacing-dependent: at 1.1 rotor diameter spacing, power increases by approximately 3.0% compared to a single rotor. As spacing increases, rotor coupling weakens, which reduces wake non-uniformity but simultaneously slows wake recovery and decreases power generation. Full article

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

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20 pages, 1703 KB

Open AccessArticle

Cascading Lock-Ins in the Energy Transition of Post-Mining Regions: Towards an Integrated Governance Framework

by Olga Janikowska, Agnieszka Generowicz and Joanna Kulczycka

Energies 2026, 19(16), 3878; https://doi.org/10.3390/en19163878 - 18 Aug 2026

Abstract

The energy transition of post-mining regions requires not only the replacement of fossil fuel-based technologies but also the restructuring of infrastructures, institutions, labour markets, local economies and social relations shaped by mining and conventional energy production. This article develops a conceptual framework of [...] Read more.

The energy transition of post-mining regions requires not only the replacement of fossil fuel-based technologies but also the restructuring of infrastructures, institutions, labour markets, local economies and social relations shaped by mining and conventional energy production. This article develops a conceptual framework of cascading lock-ins through a critical literature review and conceptual synthesis of research on path dependence, carbon lock-in, socio-technical transitions, just transition and regional governance. The framework distinguishes infrastructural, technological, institutional and social lock-ins and examines the mechanisms through which constraints are transmitted and reinforced across these domains. Cascading is defined as a context-dependent process in which a mechanism operating in one domain changes the incentives, capabilities, decisions or expectations in another, potentially generating cumulative effects and feedback. The main contribution of this study lies in shifting attention from the classification of individual barriers to the cross-domain transmission mechanisms through which they reproduce regional transition inertia. The conceptual synthesis suggests that addressing cascading lock-ins requires coordinated interventions linking infrastructural modernisation, technological and economic diversification, institutional reform, skills development and meaningful community participation. Full article

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24 pages, 1317 KB

Open AccessReview

Machine Learning Techniques for Electricity Theft Detection in Smart Grids: A Comprehensive Review

by Oluwagbenga Apata, Mukovhe Ratshitanga and Innocent Ewean Davidson

Energies 2026, 19(16), 3877; https://doi.org/10.3390/en19163877 - 18 Aug 2026

Abstract

Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart [...] Read more.

Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart grid environments, the field lacks a unifying framework that connects algorithm selection to the operational realities of Distribution System Operators (DSOs). Existing reviews catalogue methods and report benchmark metrics without addressing how detection paradigm selection should be aligned with data maturity, regulatory requirements, computational constraints, and institutional capacity. This review addresses that gap by systematically analysing 90 peer-reviewed studies published between 2015 and 2025, identified through structured multi-database searches, screened against explicit eligibility criteria, and graded with a formal five-criterion quality rubric, through a unified adversarial time-series formulation that provides a consistent analytical lens across all major learning paradigms. The analysis covers supervised ensemble methods, unsupervised and semi-supervised anomaly detection, deep learning architectures, including convolutional neural networks, long short-term memory networks and Transformer models, graph neural networks, federated learning, and explainable artificial intelligence. Key findings reveal that no single paradigm achieves optimality across all deployment dimensions simultaneously, that gradient boosting methods deliver near state-of-the-art performance with significantly lower computational overhead than deep learning, and that hybrid architectures achieve AUC-ROC scores of 0.95 to 0.98 on benchmark datasets but require complementary governance mechanisms to satisfy regulatory defensibility requirements. A lifecycle-aligned deployment framework and a layered detection architecture are proposed, offering practitioners a structured pathway from early AMI rollout through to advanced smart grid deployment. The principal outcomes of the review are a formal characterisation of which component of the detection problem each learning paradigm estimates, quality-graded and harmonised benchmark performance ranges, and a quantified illustrative analysis indicating that the proposed layered architecture can improve inspection productivity by roughly an order of magnitude at a fixed field budget. Four priority research challenges are identified: real-time edge detection, continual learning, multi-modal data fusion, and standardised benchmarking. Full article

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

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31 pages, 3045 KB

Open AccessArticle

A Data-Driven Framework for Assessing Second-Life Electric Vehicle Batteries for Stationary Energy Storage Applications

by Marran Al Qwaid, Gobbi Ramasamy and Md Sabbir Hossen

Energies 2026, 19(16), 3876; https://doi.org/10.3390/en19163876 - 18 Aug 2026

Abstract

The increasing adoption of electric vehicles (EVs) is expected to generate a substantial number of retired lithium-ion batteries, creating both environmental challenges and opportunities for second-life energy storage applications. However, the performance variability of retired batteries makes the identification of suitable candidates for [...] Read more.

The increasing adoption of electric vehicles (EVs) is expected to generate a substantial number of retired lithium-ion batteries, creating both environmental challenges and opportunities for second-life energy storage applications. However, the performance variability of retired batteries makes the identification of suitable candidates for repurposing a significant challenge. This study proposes a Battery Stability Index (BSI) framework for evaluating the suitability of second-life EV batteries for stationary energy storage applications supporting EV charging infrastructures. Battery cycling data from Nissan Leaf and Mitsubishi i-MiEV battery packs were analyzed using degradation rate, energy efficiency retention, operational stability, and energy throughput indicators. In addition, EV charging demand data were utilized to assess charging session support capability as a practical deployment-oriented performance metric. The proposed BSI integrates stability, degradation, and throughput characteristics into a unified assessment framework for battery ranking and suitability evaluation. The results demonstrate significant performance differences between the evaluated battery families. Nissan Leaf batteries exhibited lower capacity degradation rates (0.0086) than Mitsubishi i-MiEV batteries (0.0170), maintained higher energy efficiency retention (91.4% versus 79.0%), and achieved substantially greater energy throughput (21,911 Wh versus 6230 Wh). Furthermore, Nissan Leaf batteries supported up to 1.16 EV charging sessions, whereas Mitsubishi i-MiEV batteries supported fewer than 0.34 sessions. Consequently, Nissan Leaf batteries achieved the highest BSI values, with Leaf-1 and Leaf-2 obtaining scores of 0.658 and 0.638, respectively. The findings demonstrate that battery suitability cannot be reliably determined using a single health indicator. The proposed BSI framework provides a comprehensive and practical approach for identifying suitable second-life batteries, supporting battery repurposing decisions, sustainable energy storage deployment, and the integration of second-life batteries within EV charging ecosystems. Full article

(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)

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14 pages, 2337 KB

Open AccessArticle

Coordinated Operation and Stability Limits of Grid-Forming and Grid-Following Inverters in Power System Restoration

by Mohammad Kamran Ikram, Pooyan Alinaghi Hosseinabadi, Mehdi Seyedmahmoudian, Saad Mekhilef and Alex Stojcevski

Energies 2026, 19(16), 3875; https://doi.org/10.3390/en19163875 - 18 Aug 2026

Abstract

As power systems transition from synchronous generation towards inverter-based resources (IBRs), these resources will increasingly need to provide black-start and power system restoration services. This paper evaluates the transient interactions and stability limits of coordinated grid-forming (GFM) and grid-following (GFL) inverter operation during [...] Read more.

As power systems transition from synchronous generation towards inverter-based resources (IBRs), these resources will increasingly need to provide black-start and power system restoration services. This paper evaluates the transient interactions and stability limits of coordinated grid-forming (GFM) and grid-following (GFL) inverter operation during a bottom-up restoration process. Using a parallel–sequential restoration strategy, restoration of the main transmission backbone and formation of a local restoration island with high GFL penetration proceed in parallel, while the energisation events within each restoration path are carried out sequentially, in a modified IEEE 9-bus test system. Electromagnetic transient (EMT) simulations are used to identify the maximum tested stable operating point of the restoration island as GFL capacity is progressively increased. The simulation results demonstrate that, for the studied system and operating conditions, a single 10 MW GFM anchor can maintain stable island operation with up to 110 MW of aggregate GFL capacity, corresponding to a GFM-to-GFL capacity ratio of 1:11. When the aggregate GFL capacity is increased to 120 MW, corresponding to the next tested ratio of 1:12, the restoration island becomes unstable, with the observed instability associated with loss of synchronisation in the GFL phase-locked loops (PLLs). At the 1:11 operating point, the corresponding equivalent short-circuit ratio (ESCR) is 2.5, indicating weak-grid conditions. Finally, the 1:11 restoration island is successfully resynchronised with the main grid, with active power, frequency, and PCC voltage recovering within approximately 1.5 s following the switching events. Full article

(This article belongs to the Special Issue Advanced Control of Power Electronic Systems)

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27 pages, 1186 KB

Open AccessArticle

Conditional Value-at-Risk Optimization in Stochastic Unit Commitment for Energy Aggregator Scheduling

by Pande Popovski, Goran Veljanovski, Metodija Atanasovski, Sofija Nikolova Poceva and Anton Chaushevski

Energies 2026, 19(16), 3874; https://doi.org/10.3390/en19163874 - 18 Aug 2026

Abstract

This paper studies a risk-averse stochastic unit commitment framework for an energy aggregator, operating a portfolio of conventional generators, renewable units, and battery energy storage in a network-constrained environment. Renewable generation and demand uncertainty are represented through a scenario-based extensive-form mixed-integer linear program. [...] Read more.

This paper studies a risk-averse stochastic unit commitment framework for an energy aggregator, operating a portfolio of conventional generators, renewable units, and battery energy storage in a network-constrained environment. Renewable generation and demand uncertainty are represented through a scenario-based extensive-form mixed-integer linear program. To avoid exposure to rare but high cost events, the model incorporates conditional value-at-risk as part of the objective function. The approach captures key market interactions, including day-ahead commitments, imbalance penalties, and power exchange with a neighboring network, while respecting generator constraints, storage dynamics, line flow limits, and bus voltage security. A comprehensive parametric study is conducted to quantify the influence of two risk parameters: the conditional value-at-risk confidence level

α

and the risk-aversion weight

λ

. Using a 300-scenario test set on a modified IEEE 9-bus system, the results show that risk-neutral scheduling exposes the aggregator to larger operational costs in extreme scenarios. Minor levels of risk aversion (0.1–0.5) reduce CVaR and tighten the distribution of costs. Increasing

λ

further yields diminishing returns, while higher

α

values focus risk mitigation on the most severe outcomes. The results demonstrate how CVaR-based stochastic scheduling can support aggregator decision-making by quantifying downside risk under renewable uncertainty. Full article

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

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14 pages, 23301 KB

Open AccessArticle

Sensitivity Study of the Reactivity-Equivalent Physical Transformation Method for Fluoride Salt Coolant Pebble Bed Reactor Lattice

by Yuhan Fan, Minyu Peng, Xin Yang, Yunfei Zhang, Guifeng Zhu, Ye Dai, Rui Yan and Yang Zou

Energies 2026, 19(16), 3873; https://doi.org/10.3390/en19163873 - 18 Aug 2026

Abstract

Conventional dispersed-particle fuel systems, such as TRISO fuel, exhibit typical double heterogeneity. The Reactivity-equivalent Physical Transformation (RPT) method can convert a double-heterogeneity system into a single-heterogeneity system by simplified geometric modelling. In a Pebble-Bed Fluoride-Salt-Cooled Reactor (PB-FHR), the random packing pebbles and the [...] Read more.

Conventional dispersed-particle fuel systems, such as TRISO fuel, exhibit typical double heterogeneity. The Reactivity-equivalent Physical Transformation (RPT) method can convert a double-heterogeneity system into a single-heterogeneity system by simplified geometric modelling. In a Pebble-Bed Fluoride-Salt-Cooled Reactor (PB-FHR), the random packing pebbles and the fluoride-salt coolant further impacted the spatial self-shielding effect, fuel loading and moderating ratio, causing the variation in the RPT radius. In this study, a neutronics model of the TMSR-SF1 pebble lattice was established with OpenMC. The VHM, RPT and random TRISO models were compared and validated from the perspective of neutronics. The effects of pebble lattice structure, pebble packing fraction, FLiBe coolant temperature, TRISO loading fraction, various burnable neutron poisons and boundary albedo on RPT radius were systematically evaluated. Results indicated that RPT radius is unaffected by the pebble packing structure, while it was positively correlated with pebble packing fraction or coolant temperature and was negatively correlated with the moderating ratio. Poison with strongly neutron absorption may reduce RPT radius, while poison with weakly neutron absorption has little effect on RPT radius. Reducing neutron leakage will lead to an increase in a RPT radius. This work would provide an effective view for the improvement of RPT method in PB-FHR, and as the basis for the subsequent full core analysis. Full article

(This article belongs to the Special Issue Advanced Reactor Designs for Sustainable Nuclear Energy)

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18 pages, 2015 KB

Open AccessArticle

Process-Dependent Carbonization Pathways of Mushroom Waste Medium: Mechanistic Insights into Chemical and Structural Evolution

by Sunyoung Woo, Doo Young Oh, Do-Yong Kim and Daegi Kim

Energies 2026, 19(16), 3872; https://doi.org/10.3390/en19163872 - 18 Aug 2026

Abstract

Considering carbon neutrality and fossil fuel depletion, biomass is becoming increasingly important as a renewable and sustainable energy source. However, understanding of process-dependent characteristics relevant to char production remains limited. This study investigated the carbonization of mushroom waste medium (MWM) via conventional carbonization [...] Read more.

Considering carbon neutrality and fossil fuel depletion, biomass is becoming increasingly important as a renewable and sustainable energy source. However, understanding of process-dependent characteristics relevant to char production remains limited. This study investigated the carbonization of mushroom waste medium (MWM) via conventional carbonization (CC; i.e., pyrolysis), hydrothermal carbonization (HTC), and microwave-assisted carbonization (MAC), and evaluated their suitability for desired char properties and target applications. For all methods, increasing reaction temperature led to carbon densification, with decreased oxygen and hydrogen contents and increased carbon and fixed carbon fractions. However, the extent of these transformations depended on the reaction environment. HTC achieved carbon enrichment and the highest higher heating value (HHV) at relatively low temperatures. In contrast, CC required higher temperatures to achieve comparable carbonization levels but showed a marked increase in BET surface area at higher temperatures. MAC exhibited intermediate characteristics under moderate conditions. HTC also facilitated potassium and chlorine removal, which may reduce operational issues during thermal utilization. These results indicate trade-offs among carbon densification, char yield, surface structure, and inorganic matter content. Rather than identifying a universally superior process, this study demonstrates that the suitability of each method depends on the desired properties and applications of MWM-derived char, providing a practical basis for appropriate process selection. Full article

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15 pages, 2943 KB

Open AccessArticle

Research and Application of a Liquid Hydrogen Tank Container Based on a Carbon Fiber Suspension Ring Support

by Xiaoxiang Zhou, Hang Hai, Lin Zhao, Lei Liu, Feng Yang, Yisu Hao and Wei Wei

Energies 2026, 19(16), 3871; https://doi.org/10.3390/en19163871 - 18 Aug 2026

Abstract

Given that large-scale storage and transportation of liquid hydrogen are key to realizing the hydrogen economy, tank containers have attracted much attention for their flexibility. To minimize evaporation losses, efficient support structures are essential for these liquid hydrogen tank containers. Herein, a carbon [...] Read more.

Given that large-scale storage and transportation of liquid hydrogen are key to realizing the hydrogen economy, tank containers have attracted much attention for their flexibility. To minimize evaporation losses, efficient support structures are essential for these liquid hydrogen tank containers. Herein, a carbon fiber-reinforced polymer (CFRP) suspension ring is developed to support the inner vessel of liquid hydrogen tank containers. By using a special resin matrix and optimizing its curing process, the suspension ring capitalizes on a small cross-sectional area and low thermal conductivity, thereby significantly mitigating the cold-bridge heat transferred from the outer vessel to the inner vessel. Experimental results demonstrate that the tensile strength, outgassing rate, and fatigue performance of the suspension ring at both 77 K (liquid nitrogen temperature) and 4 K (liquid helium temperature) can meet the design targets. Notably, its equivalent thermal conductivity was approximately 88% lower than that of a stainless steel structure of the same size. With the integration of this suspension ring into a 40 ft liquid hydrogen tank container, the daily liquid nitrogen evaporation rate was recorded below 0.082%/d. Furthermore, the holding time before the pressure reached 0.14 MPa exceeded 192 h with a 90% liquid hydrogen filling ratio. This work provides key technical support for high thermal insulation, long-endurance liquid hydrogen storage and transportation equipment. Full article

(This article belongs to the Special Issue Advances in Hydrogen Storage and Transportation Equipment)

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20 pages, 2140 KB

Open AccessArticle

Economic Assessment of Recycling High Assay Low Enriched Uranium Sodium Cooled Fast Reactor Used Nuclear Fuels

by Edward Hoffman, Amanda M. Bachmann, Nicolas E. Stauff, Arantxa Cuadra and Cihang Lu

Energies 2026, 19(16), 3870; https://doi.org/10.3390/en19163870 - 18 Aug 2026

Abstract

Sodium-cooled fast reactors (SFRs) are being developed with various fuel cycle strategies, including once-through and recycling options. Initial U.S. deployments are expected to use compact SFR cores fueled with high-assay low-enriched uranium (HALEU). As SFR technology advances, higher fuel burnups and lower enrichments [...] Read more.

Sodium-cooled fast reactors (SFRs) are being developed with various fuel cycle strategies, including once-through and recycling options. Initial U.S. deployments are expected to use compact SFR cores fueled with high-assay low-enriched uranium (HALEU). As SFR technology advances, higher fuel burnups and lower enrichments are anticipated. This study provides an economic assessment of utilizing HALEU in SFRs with a once-through fuel cycle (OTC) compared to recycling used nuclear fuel (UNF) across a range of advanced fuel and reactor designs. As an alternative to current once-through options (FC #1), three recycling alternatives are evaluated: (FC #2) recovered uranium (RU) downblended for pressurized water reactor (PWR) fuel, (FC #3) RU re-enriched for SFR fuel, and (FC #4) RU co-recycled with transuranics (RU/TRU) for SFR fuel. Results indicate that recycling RU from SFR UNF in both fresh PWR and SFR fuel can be economically advantageous compared to the OTC approach, provided the U-235 content remains sufficiently high—a condition met by current and near-term SFR designs. Increased burnup and in situ plutonium production in SFRs reduce overall OTC costs and natural uranium requirements. However, the economic viability of recycling SFR RU depends strongly on the specific UNF composition and the recycling pathway chosen. Recycling both RU and TRU consistently offers the lowest fuel cycle costs across all scenarios, even as fuel and reactor designs advance. These findings provide a framework for identifying when recycling offers economic benefits over direct disposal, informing future fuel cycle decisions for advanced reactors. Full article

(This article belongs to the Special Issue The Nuclear Fuel Cycle)

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19 pages, 3839 KB

Open AccessArticle

A Multi-Scenario Urban Building Energy Modeling Workflow Validated Against Real Monitored Energy Data

by Sara Eslamieh, Martina Ferrando and Alice Denarie

Energies 2026, 19(16), 3869; https://doi.org/10.3390/en19163869 - 18 Aug 2026

Abstract

Urban Building Energy Modeling (UBEM) offers a scalable, physics-based method to simulate energy demand at the district level, enabling data-driven district energy demand planning and optimization. However, translating UBEM into a reliable, openly replicable workflow remains a significant methodological gap. In particular, limited [...] Read more.

Urban Building Energy Modeling (UBEM) offers a scalable, physics-based method to simulate energy demand at the district level, enabling data-driven district energy demand planning and optimization. However, translating UBEM into a reliable, openly replicable workflow remains a significant methodological gap. In particular, limited attention has been devoted to the development of transparent and transferable UBEM workflows capable of systematically quantifying the impact of modeling assumptions on district-scale thermal demand accuracy. This paper presents and validates a five-step UBEM pipeline integrating freely available geospatial data from OpenStreetMap (OSM), archetype-based building characterization, multi-scenario EnergyPlus simulation via the Urban Modeling Interface (UMI) within a structured validation framework. To improve interpretability and reproducibility, a dedicated three-scenario simulation protocol was developed to isolate and quantify the influence of geometry simplifications, archetype assumptions, and weather data fidelity on model accuracy. The workflow is demonstrated through application to a real district heating system (DHS) in northern Italy, encompassing UBEM results validated against monitored consumption data at different temporal resolutions. The refined model achieves a district-scale annual magnitude error of 1.30% between real and simulated data. Persistent limitations in domestic hot water representation and peak load estimation are identified as priorities for future development. Full article

(This article belongs to the Special Issue Efficient Building Energy Systems: Simulation, Optimization and Renewable Energy Integration)

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14 pages, 7288 KB

Open AccessArticle

An Enhanced CDSC-Based Grid Parameter Estimation Method for Distorted Grids

by Yihua Zhu, Jiawei Yu, Chao Luo, Youfeng Zhou, Xuetong Wang, Guangqi Li and Zhiyong Dai

Energies 2026, 19(16), 3868; https://doi.org/10.3390/en19163868 - 18 Aug 2026

Abstract

In modern power systems, DC offset and odd-order harmonics degrade grid phase and frequency estimation accuracy. To address this problem, this paper proposes an enhanced grid parameter estimation scheme based on cascaded delayed signal cancellation (CDSC). Unlike the conventional CDSC, the proposed method [...] Read more.

In modern power systems, DC offset and odd-order harmonics degrade grid phase and frequency estimation accuracy. To address this problem, this paper proposes an enhanced grid parameter estimation scheme based on cascaded delayed signal cancellation (CDSC). Unlike the conventional CDSC, the proposed method introduces a redesigned delay configuration, significantly reducing the inherent filtering delay while preserving the rejection capability for the DC component and targeted odd-order harmonics. Meanwhile, an amplitude-phase compensation strategy is developed to correct the magnitude attenuation and phase distortion introduced by the CDSC filter, which allows accurate extraction of the fundamental grid component. Furthermore, a frequency-adaptive module is integrated to maintain stable grid synchronization under off-nominal frequency deviations. Finally, experimental results verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advanced Control and Optimization of Power Electronic Converters)

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22 pages, 2159 KB

Open AccessArticle

Performance Evaluation and Carbon Emission Reduction Analysis of a Coupled Photovoltaic Thermal and Air Source Heat Pump Heating System in Office Buildings

by Yuxin Zheng, Yabin Jin, Wenhan Song and Zizhen Huang

Energies 2026, 19(16), 3867; https://doi.org/10.3390/en19163867 - 18 Aug 2026

Abstract

PV/T collectors and Air Source Heat Pump (ASHP) are widely studied for building heating, but solar intermittency and ASHP low-temperature frosting limit their large-scale deployment. A novel PV/T-ASHP coupled heating system is proposed to cut building carbon emissions and relieve ASHP performance degradation [...] Read more.

PV/T collectors and Air Source Heat Pump (ASHP) are widely studied for building heating, but solar intermittency and ASHP low-temperature frosting limit their large-scale deployment. A novel PV/T-ASHP coupled heating system is proposed to cut building carbon emissions and relieve ASHP performance degradation in cold zones. Circulating water cools PV/T panels to boost power generation, and the warmed water preheats ASHP evaporator inlet air to reduce frosting and defrosting frequency. With a Xi’an office building as the research object, validated TRNSYS 18.0 models are established for comparative analysis with conventional systems and cross-climate evaluation in Xi’an, Beijing, Shanghai and Chengdu. Results show the new system lifts PV/T combined efficiency by 17.56%, reduces energy consumption by 19.9%, and achieves an average COP of 3.2. Across climate zones, its COP rises 11.5–24.6% and 50-year carbon emissions fall 16.4–26.2%, supporting low-carbon heating promotion for office buildings. Full article

(This article belongs to the Special Issue Advanced Technologies for Energy-Efficient Buildings—2nd Edition)

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24 pages, 1918 KB

Open AccessArticle

Complex-Network-Guided High-Risk Substation Identification and Targeted Mitigation of Transformer DC Bias in Multi-Infeed UHVDC Receiving-End Grids

by Jingbo Song, Huanruo Qi, Chen Chen, Liang Zhang, Xiangyang Yan, Jinfeng Zhang, Bochao Yang, Lei Lan and Yuanjie Li

Energies 2026, 19(16), 3866; https://doi.org/10.3390/en19163866 - 18 Aug 2026

Abstract

In multi-infeed UHVDC receiving-end grids, transformer DC-bias risk is affected not only by the magnitude of grounding-electrode current, but also by the AC-grid topology, substation grounding condition, voltage-level-dependent current limits, and polarity coordination among grounding electrodes. Conventional single-electrode or magnitude-only assessments may therefore [...] Read more.

In multi-infeed UHVDC receiving-end grids, transformer DC-bias risk is affected not only by the magnitude of grounding-electrode current, but also by the AC-grid topology, substation grounding condition, voltage-level-dependent current limits, and polarity coordination among grounding electrodes. Conventional single-electrode or magnitude-only assessments may therefore fail to identify the substations that require priority mitigation. This paper proposes a full-registry high-risk substation identification and targeted mitigation framework for transformer DC bias in multi-infeed UHVDC receiving-end grids. A field–circuit coupling model is established using the earth-resistivity model, grounding-electrode parameters, substation grounding parameters, transformer winding DC resistances, and AC-grid topology. To address earth-resistivity uncertainty, a measurement-based correction procedure is introduced and verified by an engineering field-measurement case. On this basis, the receiving-end grid is represented as a weighted complex network, and high-risk substations are identified by jointly considering network importance, voltage-dependent limits, multi-mode DC-bias exposure, and over-limit severity. A case study with 898 substations is carried out under four representative grounding-electrode operating modes. The results show that CJ single-electrode operation produces no over-limit substation, whereas YZ single-electrode, same-polarity two-electrode, and opposite-polarity two-electrode operation produce 2, 3, and 2 over-limit substations, respectively. Polarity coordination changes the risk pattern: same-polarity operation mainly aggravates the UHV substations NY UHV and ZMD UHV, whereas opposite-polarity operation relieves them but concentrates the 500 kV risk at SMPP and ZT. Guided by the high-risk ranking, installing 2 Ω neutral-point resistors at only two over-limit 500 kV substations reduces the currents at ZT and SMPP from 5.26 A and 5.68 A to 2.55 A and 0.58 A, respectively, bringing all evaluated substations within their limits and avoiding system-wide retrofitting. Full article

(This article belongs to the Special Issue Advanced Grounding Technology for High-Voltage Energy Systems and Critical Energy Infrastructure)

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23 pages, 3902 KB

Open AccessArticle

Evaluation of the Energy and Ecological Effects of a Photovoltaic-Thermal System

by Alicja Siuta-Olcha, Emilia Modrzyńska, Tomasz Ruszniak and Anna Justyna Werner-Juszczuk

Energies 2026, 19(16), 3865; https://doi.org/10.3390/en19163865 - 18 Aug 2026

Abstract

This paper presents a detailed analysis of the operating parameters of a solar active installation with seven photovoltaic-thermal (PV/T) collectors with a total area of 14 m² in a single-family house. A comparative analysis of the work parameters was carried out for [...] Read more.

This paper presents a detailed analysis of the operating parameters of a solar active installation with seven photovoltaic-thermal (PV/T) collectors with a total area of 14 m² in a single-family house. A comparative analysis of the work parameters was carried out for the following two locations: Warsaw (Poland) and Andravida (Greece), based on the research of the solar system model created in the TRNSYS 16 program. Considering the months with the best sunshine, from May to August, the average monthly electricity yield in PV/T solar collectors was 206 kWh (Warsaw) and 248 kWh (Andravida). In July, the monthly generation-to-consumption ratio of the PV/T system under the Polish climate conditions was 82%, and under the Greek climate conditions—99%. The heat recovery from PV/T solar collectors in July in the climate of Greece was estimated at 264 kWh and is 29% higher compared to the heat recovery in a hybrid solar installation located in Poland. The generation of electricity in the PV/T solar system instead of a coal-fired power plant can contribute to the avoidance of the annual emissions of pollutants by: 14.00–19.11 kg of SO₂, 2.72–3.72 kg of NO_X, 5.44–7.43 kg of CO, 1330.86–1816.79 kg of CO₂, and 1.09–1.49 kg of particulate matter, depending on the location. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Systems: Prospect Development and Challenges)

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26 pages, 8727 KB

Open AccessArticle

Game-Theoretic Demand-Side Management for Fair Cost Distribution in Community Energy Storage and Electric Vehicle Charging

by Moin Uddin, Uzair Kazim, Mohsin Ullah, Muhammad Saud Khan and Faraz Ahmad

Energies 2026, 19(16), 3864; https://doi.org/10.3390/en19163864 - 18 Aug 2026

Abstract

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to [...] Read more.

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to meet the resultant higher electricity demand, thus reducing the sustainability of the system. To overcome this challenge, effective DSM techniques integrating renewable energy sources are proposed to efficiently utilize the existing generating capacity. The primary goal is to fairly distribute available resources among smart homes and EV owners using the Shapley value and tau value. In this work, two scenarios are examined. First, a community energy storage (CES) approach is adopted to maximize CES revenue, reduce the grid peak-to-average ratio (PAR), and minimize electricity costs. Second, a coordinated group of EVs is utilized to minimize the impact of charging loads during peak hours while concurrently reducing EV charging costs. Simulation results show a reduction in the grid PAR from 2.468 to 1.799, or 27.1%, together with an average reduction of approximately 3% in the electricity cost of participating smart homes. In the EV scenario, optimal scheduling reduces total charging expenditure by 24.8% and lowers the system peak by 2.65% relative to uncoordinated charging of the same fleet, with the total cost distributed among the vehicles by the Shapley value. Benchmarking against a proportional-to-demand rule shows that the tau-value allocation coincides with proportional sharing, whereas the Shapley allocation shifts 4.2% of the allocation away from the household contributing most to the system peak. The framework provides a fair and individually rational cost allocation layer for community-scale peer-to-peer energy markets. Full article

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13 pages, 2575 KB

Open AccessArticle

Enhancing Insulation Defect Detection in GIS: Comparative Study of Photon Counting, UHF, and Conventional PD Measurement Methods

by Tengfei Li, Qin Xu, Kai Gao, Zhiwen Yuan, Junjie Chen and Chuanyang Li

Energies 2026, 19(16), 3863; https://doi.org/10.3390/en19163863 - 18 Aug 2026

Abstract

High-sensitivity detection of metal contaminants during gas-insulated equipment (GIE) manufacturing is crucial to mitigating insulation risks. In this study, detection tests of metal contaminants are performed using the conventional partial discharge measurement (CPDM), UHF, and photon counting (PC) methods on a 252 kV [...] Read more.

High-sensitivity detection of metal contaminants during gas-insulated equipment (GIE) manufacturing is crucial to mitigating insulation risks. In this study, detection tests of metal contaminants are performed using the conventional partial discharge measurement (CPDM), UHF, and photon counting (PC) methods on a 252 kV GIS chamber. The results indicate that the PC method exhibits high sensitivity to micrometer-sized metal dust, while the UHF sensor performs better in detecting the millimeter-sized single wire-shaped particle. The CPDM method has no sensitivity advantage in either of the above cases. For sub-millimeter-sized block contaminants, all three methods exhibit comparable sensitivity. Moreover, a comprehensive statistical index is introduced to evaluate the discharge activity of different metal defects, enabling a more robust quantitative comparison. Full article

(This article belongs to the Special Issue Advanced Dielectric Materials, Sensing, Monitoring, and Diagnostic Technologies for High-Voltage Equipment in New Power Systems)

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