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        <title>Frontiers in Energy Research | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/energy-research</link>
        <description>RSS Feed for Frontiers in Energy Research | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-14T13:23:06.113+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1802854</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1802854</link>
        <title><![CDATA[Research on the crisis propagation of global LNG port trade]]></title>
        <pubdate>2026-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xingxing Wang</author><author>Xiaoqian Guo</author><author>Depeng Zhu</author>
        <description><![CDATA[Port trade is a core component of the global trade system, with approximately 80% of the volume and 70% of the value of global trade goods being transported via maritime shipping. LNG serves as both a pillar of current energy security and a practical choice for low-carbon transition, while profoundly influencing the global economic and political landscape. The import and supply of LNG involve complex trade networks and maritime transportation systems, though current research primarily focuses on static data and country-level analysis. This study applies complex network theory and utilizes the Kpler port trade statistics database to construct a global LNG port trade network. Based on this, a cascading failure model is developed to investigate the avalanche process of LNG port supply crises within the global trade network and analyze the impacts of supply disruptions at different ports. The findings indicate: (1) Over the past decade, global LNG port trade interconnections have grown increasingly close. (2) During the study period, among the top 10 LNG-exporting ports, those in countries such as Qatar, Malaysia, and Australia demonstrated relatively stable export performance, while the United States showed a notable rise in its LNG export port prominence. (3) When supply risks occur at different ports, the port with the highest export volume does not necessarily incur the most severe impact. (4) European countries are relatively more affected by disruptions at U.S. LNG export ports. These results can assist natural gas trading nations in formulating corresponding trade strategies to ensure the security of natural gas supply.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1773794</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1773794</link>
        <title><![CDATA[Potential for biohydrogen production in the United States: a granular assessment]]></title>
        <pubdate>2026-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Siddhartha Narra</author><author>Anurag Mandalika</author><author>Timothy Fitzgerald</author><author>Brian Snyder</author>
        <description><![CDATA[Hydrogen production is critical both for energy transition ambitions and for the existing petrochemical industry. This study evaluates county-level biohydrogen production potential across the contiguous United States (U.S.) by integrating biomass resource availability from the Billion-Ton 2023 (BT23) study with location data on natural gas pipeline infrastructure and refinery hydrogen demand. A multi-criteria decision analysis (MCDA) framework is applied using weighted factors for biomass production capacity, pipeline accessibility, and refinery proximity using different weighting schemes. Counties are categorized into three tiers across five BT23 scenarios using fixed score thresholds derived from baseline percentiles: Tier 1 (high potential), Tier 2 (medium potential), and Tier 3 (lower potential). National production potential ranges from 5.36 to 13.96 million tonnes H2 annually across 3,100+ counties. County-level uncertainty analysis demonstrates robust tier classifications despite feedstock conversion factor variability (CV: 0%–79%), with 13.7% of counties having confidence intervals crossing the Tier 1 threshold that identifies highest-priority targets. Tier counts vary across scenarios, with the Mature-Market High baseline identifying 310 Tier 1 counties (10%) compared to 243 (7.8%) under Near-Term conditions. Threshold sensitivity analysis testing 19 alternative threshold combinations against the baseline confirms classification robustness, with 85%–98% tier assignment agreement (mean: 92%). The Midwest Corn Belt and Gulf Coast are identified as optimal development corridors because of biomass resource availability and existing pipeline infrastructure. Proximity to refineries that serve as hydrogen demand centers further increases their potential. Although the absolute counts of counties across tiers vary with weighting schemes, overall spatial patterns remain robust with the multi-criteria framework. This analysis informs strategic infrastructure investment and regional biohydrogen development, identifying priority counties for targeted production in the transition to a low-carbon hydrogen economy.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1811822</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1811822</link>
        <title><![CDATA[Active reconfiguration for distribution networks considering safety and source load uncertainty]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hongmei Li</author>
        <description><![CDATA[With the increasing penetration of distributed renewable generation and flexible loads, topology adjustment has been used as a proactive risk-mitigation measure. However, loop-closing operations can readily lead to steady-state overloads and bus-voltage limit violations. To address these issues, an active distribution network reconfiguration method is proposed that accounts for security requirements and source–load uncertainty. First, a probabilistic model considering the correlations among wind power, photovoltaic generation, and loads is established. By combining an improved point estimation method with the Cornish–Fisher expansion, the probabilistic characteristics of nodal voltages and loop-closing currents are efficiently calculated. On this basis, chance constraints are introduced to reformulate the traditional deterministic loop-closing feasibility criterion as probabilistic security constraints. Then, a bi-level solution framework is established. The upper level generates candidate network topologies using the proposed firefly-perturbation chaotic SA-PSO algorithm, while the lower level performs loop-closing current security checks and eliminates infeasible loops by adding cutting constraints. Finally, case studies on a modified IEEE 123-bus distribution system show that, compared to pre-reconfiguration, the proposed method reduces active power loss from 1817 kW to 512 kW, voltage deviation from 0.39 to 0.18, and the load-balancing index from 64.41 to 27.84. In addition, the nodal voltage range is improved from 0.9213 to 1.0797 p.u. before reconfiguration to 0.9743–1.0215 p.u. after reconfiguration. These results verify that the proposed method can effectively enhance operating economy, voltage quality, and operational security under source–load uncertainty.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1823682</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1823682</link>
        <title><![CDATA[Adaptive multi-energy dynamic storage strategy for virtual power plants under normal-extreme weather conditions]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fan Wenyi</author><author>Li Hongtao</author><author>An Jiakun</author><author>Zhao Yang</author>
        <description><![CDATA[IntroductionUnder the advancing “Dual-Carbon” strategy, Virtual Power Plants (VPPs), as core platforms integrating distributed energy resources, adjustable loads, and energy storage devices, play a significant role in enhancing grid flexibility and renewable energy accommodation. However, the development of VPPs also faces numerous challenges in system dispatch and optimization, which hinder their large-scale application. This study aims to propose a multi-timescale dispatch model for VPPs incorporating dynamic multi-energy storage and develop a collaborative solution strategy combined with an improved optimization algorithm to minimize operational costs and reduce carbon emissions.MethodsFirst, an adaptive multi-energy dynamic storage scheme for normal and extreme weather conditions is constructed. By introducing core regulation factors and designing energy adaptive conversion logic, issues related to temporal response and adaptation to extreme scenarios are addressed. Furthermore, this study proposes an improved Superb Fairy-wren Optimization Algorithm (SFOA) by introducing adaptive parameter adjustment and white noise perturbation to enhance its global search capability. Building upon this improved single-objective SFOA, a multi-objective version, namely MOSFOA, is further developed by incorporating an external archive mechanism and crowding distance sorting strategy. The MOSFOA algorithm achieves balanced multi-objective optimization through parameter adaptive adjustment and archive sorting strategies.Results and DiscussionThe results show that, under extreme weather conditions, the system’s economic cost remains stable, with pollution costs for electricity producers decreasing by 78.5%, gas costs by 84.2%, and operational costs of electricity consumption areas reduced by an average of 49%.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1806403</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1806403</link>
        <title><![CDATA[Improving the reliability of urban building energy modelling through automated data processing]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elisa Iliste</author><author>Ergo Pikas</author><author>Targo Kalamees</author>
        <description><![CDATA[Urban building energy modelling is increasingly used to support carbon-neutral planning. However, bottom-up methods rely heavily on administrative building data that were not designed for analytical reuse. This study examines how structural data quality issues in digital building logbooks affect large-scale physics-based modelling and how these possible limitations can be mitigated. Using the Estonian Building Registry as an established logbook example, data completeness, correctness, and consistency are systematically assessed for both semantic and geometric building attributes. Based on the identified issues, an automated data management workflow is developed that combines validation rules, fallback strategies, pattern recognition, and geometric processing to transform administrative data into modelling-ready inputs. The workflow is evaluated in a pilot district of 21 apartment buildings in Tallinn using urban building energy modelling. Application of the workflow reduces heated-area errors from an average of 64%–8%, envelope-area deviations to below 5%, and total-building heat-loss prediction errors to approximately 5% compared with ground-truth geometric and physical reference data, rather than measured energy consumption. The results demonstrate that robust, automated data processing can substantially improve the reliability of urban-scale energy simulations, strengthening the role of building digital logbooks in data-driven renovation planning and policy support for net-zero urban environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1821440</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1821440</link>
        <title><![CDATA[Experimental evaluation of PV performance degradation following damage for hourly generation forecasting]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Suleiman Qasim Abu-Ein</author><author>Mohammad S. Khrisat</author><author>Mohamed Qawaqzeh</author><author>Oleksandr Moroz</author><author>Andriy Pavlov</author><author>Oleksandr Miroshnyk</author>
        <description><![CDATA[The rapid deployment of renewable energy in conflict-affected regions necessitates accurate performance assessment of damaged infrastructure. The primary objective of this research is to develop a high-precision model for forecasting electricity generation in solar power plants (SPPs) that operate under non-nominal parameters due to kinetic damage and partial restoration following missile strikes. The study investigates a grid-tied utility-scale SPP located in the Kharkiv region, Ukraine. The damage event is characterized by kinetic impacts from missile strikes (MS), which resulted in both localized physical degradation (micro-cracks and cell destruction) and a regional increase in atmospheric aerosol optical depth. The results demonstrate that kinetic damage from missile strikes creates a complex degradation profile that cannot be captured by standard aging models. The methodological approach involves a comparative analysis of the system’s operational parameters–specifically the maximum specific hourly average power (MSHAP) – before and after the MS. By utilizing multivariate regression analysis, the relationship between the hourly average solar angle (HASA), azimuth, and power output was quantified. The novelty of this research lies in the development of a synergistic forecasting methodology that, for the first time, integrates localized kinetic damage profiles with regional atmospheric turbidity shifts. Unlike conventional degradation studies, the proposed PGM provides a robust mathematical framework for energy resilience in conflict-affected regions, enabling precise generation forecasting for infrastructure operating under extreme non-nominal conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1755322</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1755322</link>
        <title><![CDATA[A siting and sizing strategy for distribution grid energy storage systems accounting for reliability and battery lifespan]]></title>
        <pubdate>2026-05-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zifen Han</author><author>Zhaoguang Yang</author><author>Zongyang Liu</author><author>Fuwen Wang</author><author>Xudong Lu</author>
        <description><![CDATA[High renewable penetration increases outage risk in distribution networks, while battery degradation during long-term operation significantly affects the economy and reliability of battery energy storage system (BESS) planning. To address this issue, this study proposes a decision-level coupled siting-sizing framework for BESS in distribution networks, in which reliability evaluation and battery degradation are directly embedded into the planning optimization process. A unified objective is constructed by monetizing degradation cost and integrating it with reliability improvement, and the problem is solved using a binary-discrete hybrid particle swarm optimization under typical-day scenarios. Results on the IEEE 33-bus system show that the proposed framework can effectively improve distribution system reliability while reducing the annualized total cost, achieving a better balance among reliability, battery lifetime, and economic performance. The proposed method provides an effective approach for coordinated BESS siting and sizing in distribution networks with explicit consideration of both reliability enhancement and battery lifespan.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2025.1689210</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2025.1689210</link>
        <title><![CDATA[Informer-based power load forecasting model for electrolytic aluminum smelters]]></title>
        <pubdate>2026-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maomao Ding</author><author>Shiyao Cheng</author><author>Tianpeng Xia</author><author>Zhongwei Cai</author><author>Boyang Chen</author><author>Huixian Zhu</author>
        <description><![CDATA[IntroductionAs the most energy-intensive stage in aluminium industries, electrolytic aluminium smelters account for 40% of global industrial load. This significant percentage signifies a crucial source of flexibility on the industrial demand side. Consequently, accurately forecasting its power load is fundamental to unlocking and utilizing its significant regulation potential.MethodsThis study addresses the complex task of mid- to long-term load forecasting for electrolytic aluminum smelters, which requires analyzing yearly patterns, growth trends, and unpredictable fluctuations. By leveraging the advanced features of the Informer mechanism, the proposed approach introduces a multifaceted ensemble strategy. It is characterized by: 1) utilizing a hierarchical decomposition approach to meticulously uncover and emphasize the intrinsic characteristics present in mid- to long-term power load for electrolytic aluminum smelters; 2) employing a dedicated long-sequence time-series data forecasting mechanism to precisely capture and model the underlying trends in the data; 3) integrating an Adversarial Autoencoder and Long Short-Term Memory ensemble model to creatively assimilate and predict the residual components of power load by effectively considering random fluctuations.ResultsThe effectiveness and accuracy of the proposed approach are rigorously validated using historical power load data from some electrolytic aluminum suppliers in China.DiscussionThis validation process involves a comparative analysis with various traditional algorithms, thereby establishing the superior performance and reliability of the proposed strategy in capturing nuances of electrolytic aluminum load forecasting.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1776639</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1776639</link>
        <title><![CDATA[Deep reinforcement learning-enabled methods for large-scale active distribution network planning with forbidden zones]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhenlei Zhang</author><author>Liang Ma</author><author>Bin Bai</author><author>Xuejian Zheng</author><author>Shigong Jiang</author><author>Yushuai Wang</author><author>Yuan Gao</author>
        <description><![CDATA[The global shift toward sustainable energy has accelerated the integration of photovoltaic (PV) systems and battery energy storage systems (BESS) into low-voltage (LV) distribution networks. This transition introduces bidirectional power flows and complex geospatial constraints, such as forbidden zones, which conventional planning methods struggle to address. While deterministic mixed-integer programming (MIP) ensures optimal performance, it is computationally prohibitive for large-scale networks, often requiring hours of calculation. Conversely, traditional heuristics often lack stability and converge to suboptimal solutions under high distributed energy resource (DER) penetration. To bridge this gap, this paper proposes a Proximal Policy Optimization (PPO) reinforcement learning framework for LV distribution network planning. Our method explicitly encodes geospatial forbidden zones into the RL action space to ensure geographic feasibility. The proposed PPO framework was compared against five heuristic algorithms and MIP across three representative networks. Quantitative results demonstrate that PPO achieves near-optimal performance, with total investment costs and network losses remaining within 0.8%–1.0% of the theoretical optimum achieved by MIP. Furthermore, PPO exhibits superior computational efficiency; in a large-scale 69-node network, PPO generates optimal topologies in approximately 200 s, representing a speed-up factor of over 37.5 times compared to MIP, which exceeds 7,500 s. These findings establish PPO as a scalable and robust alternative for real-world grid modernization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1749148</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1749148</link>
        <title><![CDATA[Two-stage day-ahead and intraday scheduling method for remote distribution networks considering transitional weather effects and feasible operation regions]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Tang</author><author>Li Gao</author><author>Zhanxin Yan</author><author>Dawei Li</author><author>Qi Tan</author><author>Linhong Li</author>
        <description><![CDATA[Transitional weather poses a significant challenge to the secure operation of remote distribution networks, as both high penetration of renewable energy and scarce scheduling resources are prevalent in such networks. In this context, this study proposes a two-stage day-ahead and intraday scheduling method for remote distribution networks that considers the impact of transitional weather and the feasible operation region (FOR) of grid-forming energy storage systems (ESSs) and renewable energy. First, both inertia support and reactive power support capabilities for grid-forming (GFM) energy storage systems are considered in remote distribution networks, where the FOR of energy storage systems is modeled. Second, a two-stage optimal scheduling model is constructed by integrating the complementary use of wind turbines (WTs), photovoltaics (PVs), and energy storage systems. In particular, the FOR of renewable energy is included to ensure the safe and reliable performance of the scheduling scheme of remote distribution networks. Both historical wind and solar meteorological data are then fitted and modeled using the least squares method. Based on the impact of transitional weather and the probability distribution of wind and solar outputs, operational scenarios of renewable energy are developed. Finally, the effectiveness of the proposed scheduling method is validated through simulation analysis based on the modified IEEE 33-bus system under different cases.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1811800</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1811800</link>
        <title><![CDATA[Analysis of thermal-hydraulic characteristics and cooling optimization of three-phase concentric HTS cables based on magnetic-thermal-fluid multi-physics coupling]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Siyuan Lu</author><author>Aiqing Ma</author><author>Yunzhi Zhou</author>
        <description><![CDATA[In view of the limitations of the existing studies on the thermal characteristics of HTS (high-temperature superconducting) cables, which mostly adopt a one-way coupling strategy and simplify the heat sources such as AC losses to fixed values, this article establishes a coupled multiphysics three-dimensional model integrating AC loss calculation, fluid dynamics, and heat transfer. This enables precise solutions for the liquid nitrogen cooling process of HTS cables. This article also takes into account the contact resistance of the cable terminals, with comparative analysis revealing significant deviations in thermal distribution predictions by idealized models. Results indicate that the additional temperature rise attributable to contact resistance accounts for over 12% of the total cooling temperature rise. Further analysis shows that increasing inlet flow velocity effectively enhances convective heat transfer and suppresses hot spots at the terminals, though this cooling benefit exhibits a pronounced diminishing marginal effect. Based on this, the study identifies the optimal flow velocity range that balances thermal safety margins with fluid pumping energy consumption. This study not only optimizes traditional models’ prediction of maximum system temperatures but also provides a practical and efficient solution for enhancing heat and fluid transport efficiency in superconducting power facilities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1812366</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1812366</link>
        <title><![CDATA[Experimental investigations of the thermophysical properties of MWCNT–CeO2–Fe3O4 nanofluids]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Goshtasp Cheraghian</author><author>Victor O. Adogbeji</author><author>Chris Govinder</author><author>Mohsen Sharifpur</author>
        <description><![CDATA[Hybrid and tri-hybrid nanofluids have emerged as promising candidates for advanced thermal management due to their enhanced heat transfer capabilities compared to mono-nanoparticle nanofluids and conventional fluids. However, the combined influence of nanoparticle composition and concentration on thermophysical and electrical properties remains insufficiently understood. In this study, nanofluids composed of multi-walled carbon nanotubes (MWCNT), cerium oxide (CeO2), and magnetite (Fe3O4) were synthesized at varying volumetric concentrations and weight ratios, including Fe3O4 (15%)/CeO2 (80%)/MWCNT (5%), Fe3O4 (5%)/CeO2 (80%)/MWCNT (15%), CeO2 (80%)/MWCNT (20%), and Fe3O4 (20%)/CeO2 (80%). The samples were characterized through morphological analysis, thermal conductivity measurements, electrical conductivity evaluation, and viscosity assessment under controlled temperature conditions. The results reveal that thermal conductivity increases with temperature for all formulations, with the CeO2 (80%)/MWCNT (20%) nanofluid exhibiting the highest thermal conductivity. Electrical conductivity was maximized in the tri-hybrid nanofluid Fe3O4 (15%)/CeO2 (80%)/MWCNT (5%), indicating the effectiveness of low MWCNT content, while higher MWCNT concentrations adversely affected electrical performance. Morphological analysis confirmed adequate nanoparticle dispersion, and viscosity increased with nanoparticle concentration, with the 0.1% volume fraction sample showing the highest viscosity. An optimal formulation was identified at low MWCNT concentration (5%) combined with Fe3O4 (15%), achieving a balance between enhanced thermal and electrical performance and manageable viscosity. These findings provide valuable insights for the design and optimization of hybrid nanofluids for energy and heat transfer applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1857436</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1857436</link>
        <title><![CDATA[Correction: Indexing energy and cost of the pretreatment for economically efficient bioenergy generation]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Frontiers Production Office </author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1738386</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1738386</link>
        <title><![CDATA[Harmonic interaction analysis method for multi-machine parallel system of grid-forming converters]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Ming Li</author><author>Yunping Zheng</author><author>Sijia Zong</author><author>Zhumabieke Abai</author><author>Chenglong Lan</author><author>Tuerhong Yaxiaer</author>
        <description><![CDATA[When grid-forming converters operate in parallel with the grid, interactions among converters and between converters and the grid may induce harmonic amplification. Excessive harmonics can negatively impact the operation of both converters and the power grid. Currently, the influence of interactions in multi-converter parallel systems on harmonic generation is difficult to identify and evaluate. To address this issue, this paper proposes an analysis method based on harmonic admittance interaction factors. This method can determine whether the interactions in a multi-converter parallel system will result in harmonic amplification. First, an impedance model of a single grid-forming converter is established and extended to a multi-converter parallel system. Meanwhile, harmonic admittance mutual-interaction factors and self-interaction factors are constructed, based on which the peak values of harmonic admittance interaction factors are analyzed. Then, quantitative conditions for harmonic amplification caused by harmonic interactions in the system are derived using a peak criterion. Finally, MATLAB/Simulink simulations verify the correctness and effectiveness of the proposed analysis method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1760250</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1760250</link>
        <title><![CDATA[Internet-of-Things (IoT) applications in modern power grids]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Mohd. Hasan Ali</author><author>Sai Nikhil Vodapally</author>
        <description><![CDATA[Internet-of-things (IoT)-enabled smart grids modernize electricity infrastructure by integrating sensors, smart meters, and communication technologies to create a two-way, real-time, data-driven network. This enables automated monitoring, self-healing capabilities, efficient renewable energy integration, and improved load management, reducing outages and operational costs. However, there are significant gaps in current IoT-smart grid research, particularly in cybersecurity, interoperability, and scalability. Key challenges include securing, decentralized, and trustworthy IoT-driven monitoring; integrating variable renewable energy sources; establishing unified, standardized communication protocols; and ensuring data management and real-time data processing for large-scale, heterogeneous systems. Also, prior reviews did not clarify applications where IoT can be implemented in smart grids. To overcome the gaps and limitations in existing reviews, this paper presents an in-depth overview of the architecture, key technologies, possible areas of smart grids where IoT devices and technologies can be implemented, and cybersecurity issues of IoT. An extensive bibliography on the IoT applications in smart grids has been provided. Prioritized research gaps have been identified that focus on enhancing cybersecurity, improving interoperability among diverse devices, and managing massive data volumes for real-time stability. The challenges and future work for integration of IoT for smart grids are also discussed. This work benefits the readers and researchers and serves as a basis to understand the benefits of IoT in smart grids and the associated challenges to come over in implementation of these systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1710466</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1710466</link>
        <title><![CDATA[Maritime energy optimization for SDG 7: an integrated quantitative framework]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Khaled Mili</author>
        <description><![CDATA[ObjectiveTo develop and validate a quantitative framework for optimizing maritime renewable energy integration, advancing SDG 7 targets through improved deployment efficiency and socioeconomic impact assessment.Theoretical FrameworkThis research integrates resource optimization theory with sustainable development paradigms, addressing the gap between technical efficiency metrics and SDG 7 implementation. The study establishes explicit connections between maritime energy infrastructure deployment and specific SDG 7 renewable energy expansion targets.MethodStatistical analysis of 47 maritime installations (2019–2024) employing a hierarchical optimization modeling approach (η = 0.76), with high-frequency temporal sampling (Δt = 15 min) and fine-resolution spatial analysis (2.5 km2 grid cells). Technical performance metrics were synthesized with stakeholder assessment data (n = 156) to construct a comprehensive evaluation framework.ResultsImplementation of the Maritime Energy Integration Index (MEII) demonstrated 27.4% enhanced economic returns (95% CI: 24.2%–30.6%) compared to conventional deployment approaches. Stakeholder analysis revealed strong correlation between optimization implementation and community acceptance (τ = 0.72, p < 0.001). Scale-dependent analysis identified differential performance patterns, with large-scale (>50 MW) and small-scale (<10 MW) installations achieving 28.1% and 28.8% improvements respectively through distinct optimization mechanisms.ImplicationsFindings support integrated optimization approaches in maritime energy deployment, contributing directly to SDG 7 targets through measurable improvements in renewable energy integration efficiency, advancing both theoretical understanding and practical implementation strategies.OriginalityThis study introduces a quantitative framework synthesizing 12 key parameters into a unified optimization metric, bridging technical optimization with sustainable development objectives to advance SDG 7 implementation through enhanced deployment strategies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1718662</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1718662</link>
        <title><![CDATA[A study on the dual-layer energy storage configuration in photovoltaic distribution networks considering voltage-loss synergy optimisation]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huiqiang Zhi</author><author>Xiangyu Guo</author><author>Xiao Chang</author><author>Longfei Hao</author>
        <description><![CDATA[To address the challenges of voltage deviation and increased network losses arising from the high integration of photovoltaic (PV) systems in distribution networks, this paper proposes a bi-level optimisation model for configuring distributed energy storage systems (ESS) tailored to high-penetration PV distribution networks. In the proposed model, the upper-level operational layer minimises voltage deviation and network losses to improve voltage quality and reduce power losses, while the lower-level planning layer optimises overall system cost, focusing on the capacity configuration of lithium-based ESS. The bi-level optimisation problem is solved using the Particle Swarm Optimisation (PSO) algorithm to determine the optimal ESS capacity. Case studies on a modified 33-bus distribution system demonstrate that the proposed model effectively reduces voltage deviation by up to 48.1% and network losses by up to 35.1%, while significantly enhancing economic efficiency through coordinated optimisation of operation and planning. This study offers a practical solution for energy storage planning in distribution networks with high PV penetration, balancing technical performance and economic viability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1742642</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1742642</link>
        <title><![CDATA[NanoDistillNet: a lightweight diagnostic model for PV module faults in UAV-based inspection]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weiwei Ma</author><author>Zhuo Zhang</author><author>Xinyan Zhang</author>
        <description><![CDATA[As photovoltaic (PV) power plants expand, UAV-based infrared thermography has become a standard tool for efficient and safe fault diagnosis. However, most deep learning models are too large and computationally heavy for real-time deployment on edge devices. We propose NanoDistillNet, a lightweight model based on knowledge distillation designed for on-board processing. The framework utilizes a teacher network incorporating a Frequency-Adaptive Attention Module (FAAM) and a Kolmogorov-Arnold Network (KAN) classifier to extract cross-scale thermal features from infrared images. A Multi-level Feature Alignment Loss (MFAL) is used to transfer these fine-grained discriminative capabilities by guiding a compact student network to learn the teacher’s multidimensional feature maps. To minimize computational load, the student network is constructed using depthwise separable convolutions. Evaluation on the Jetson Orin Nano Super platform shows that NanoDistillNet requires only 0.122 M parameters while achieving an inference speed of 107 FPS. The model reaches 92.51% accuracy for ten-class fault diagnosis and 97.38% for binary health-state classification, offering a practical edge-end solution for the automated operation and maintenance of solar power stations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1774740</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1774740</link>
        <title><![CDATA[Analysis of China’s power development and the impact of data center energy consumption]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yating Wang</author><author>Hui Lei</author><author>Wenbo Zhang</author><author>Erfei Jia</author><author>Fanglin Guo</author>
        <description><![CDATA[China’s power industry faces critical challenges in balancing rapid economic growth with environmental sustainability amidst surging electricity demand from artificial intelligence (AI), cloud computing, and data centers. This study introduces the Data Center Energy Consumption Ratio (DCECR) as a quantitative metric and comprehensively evaluates China’s “East-to-West Computing” (ETWC) strategy through empirical analysis and predictive modeling. Using official data from 2018 to 2024 and advanced scenario analysis, we quantify that data center electricity consumption increased from 161 TWh (2.35% of total) in 2018, with projections indicating 277–500 TWh by 2030 depending on AI adoption rates and efficiency improvements. Our analysis reveals that China’s renewable energy capacity reached 1.889 billion kW (56% of total) by end-2024, providing foundation for sustainable data center expansion. Through comprehensive carbon emission modeling and economic analysis, we demonstrate that the ETWC strategy can achieve 25%–40% emission reduction per kWh by relocating computational loads to renewable-rich western regions, with potential annual carbon savings of 30–50 million tonnes CO2 by 2030. Sensitivity analysis indicates that the reported 20.8–45 Mt CO2 emission reduction range is subject to uncertainty from time-varying grid emission factors and key parameter assumptions, with load-shifting scenarios potentially yielding 14–52 Mt CO2 savings depending on temporal alignment with renewable generation. This research provides evidence-based insights for policymakers and industry stakeholders to achieve balanced electricity development in China’s digital economy era.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenrg.2026.1710402</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenrg.2026.1710402</link>
        <title><![CDATA[Online short-term multi-user load forecasting based on dynamic recognition of spatiotemporal dependencies]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Minghui Jia</author><author>Changlai Yu</author><author>Litong Wu</author><author>Fangzhou Shao</author>
        <description><![CDATA[To fully and effectively exploit the spatiotemporal correlations among multi-user loads, this paper proposes an online short-term multi-user load forecasting method based on dynamic recognition of spatiotemporal dependencies. A hybrid graph convolutional network-bidirectional long short-term memory (GCN-BiLSTM) model is employed to capture the complex spatiotemporal relationships among multi-user loads. To enable dynamic recognition of spatiotemporal dependencies, a novel graph distance metric is developed by integrating temporal variations in load sequences with spatial correlation changes among multiple users. Temporal variations are quantified through cosine similarity, inverse cosine transformation, and complex-domain mapping to construct a weighting matrix. This matrix is then used to weight the absolute difference between graph adjacency matrices, which quantifies the changes in spatial correlations, thus forming the final graph distance metric. Based on this metric, clustering analysis is performed on the graph-structured training samples to identify typical spatiotemporal dependency patterns. During the forecasting phase, the graph distances between each input sample and these typical patterns are calculated in real time to determine the current spatiotemporal dependency pattern. When a pattern shift is detected, the model is locally fine-tuned using both the most temporally recent historical buffered samples and current-pattern historical buffered samples, thereby enabling more effective adaptive updates of the forecasting model. Case studies demonstrate that the proposed method significantly improves forecasting accuracy for multi-user loads.]]></description>
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