International Transactions on Electrical Energy Systems最新文献

{"title":"Comparative Review of Traditional, Intelligent, and Hybrid MPPT Techniques for Enhanced Photovoltaic System Efficiency Under Dynamic Operating Conditions","authors":"Ahmed G. Abo-Khalil,&nbsp;Abdel-Rahman Al Qawasmi,&nbsp;Al Amir Hassan","doi":"10.1155/etep/6465506","DOIUrl":"https://doi.org/10.1155/etep/6465506","url":null,"abstract":"<p>The global shift towards renewable energy underscores the critical need to maximize the efficiency of photovoltaic (PV) systems, where maximum power point tracking (MPPT) plays a decisive role. This comprehensive review systematically categorizes, analyzes, and benchmarks traditional, intelligent, and hybrid MPPT algorithms under dynamic and nonideal operating conditions, including partial shading, temperature fluctuations, rapid irradiance transients, and real-world stressors such as panel soiling and module aging. Conventional methods like Perturb and Observe (P&amp;O) and incremental conductance (INC), while simple, exhibit significant limitations including steady-state oscillations and susceptibility to local maxima under shading. Intelligent techniques—encompassing fuzzy logic control, artificial neural networks, and metaheuristic algorithms like particle swarm optimization (PSO) and grey wolf optimizer (GWO)—demonstrate superior accuracy, adaptability, and global search capabilities. Hybrid strategies, which synergize global exploration with local refinement, consistently achieve tracking efficiencies exceeding 99% under the specific partial shading patterns and environmental conditions reported in each study. Notably, advanced deep reinforcement learning (DRL) frameworks, particularly memory-augmented architectures such as PPO-LSTM, achieve exceptional adaptability with &gt; 99.3% accuracy in complex multipeak environments. A critical contribution of this review is the systematic identification of four key limitations in standard PV modeling for MPPT evaluation: ill-posed parameter identification, model uncertainty from manufacturing tolerances and aging, inaccurate temperature effects, and the trade-offs of multidiode models. Experimental validations confirm the performance supremacy of intelligent and hybrid methods, though challenges in computational demand and hardware implementation persist. This study consolidates state-of-the-art advancements, providing a foundational roadmap for future research focused on hardware acceleration, uncertainty-aware DRL, multiagent coordination, and lifelong learning to enhance real-time applicability, grid stability, and long-term energy yield in next-generation resilient PV systems.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/6465506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multilevel Boost Converter With Reduced Inductor Current and Its Topological Family","authors":"Jesus E. Valdez-Resendiz,&nbsp;Julio C. Rosas-Caro,&nbsp;Johnny Posada,&nbsp;Jorge A. Brizuela-Mendoza,&nbsp;José G. González-Hernández,&nbsp;Enrique Garza-Arias,&nbsp;Edgar D. Silva-Vera,&nbsp;Fernando Davalos-Hernandez","doi":"10.1155/etep/5147643","DOIUrl":"https://doi.org/10.1155/etep/5147643","url":null,"abstract":"<p>This article introduces a DC–DC topological family; the family of converters is based on a new version of the DC–DC multilevel boost converter, which inductor drains a reduced current. The main advantages of this topology are (i) it has a high voltage gain, its conversion ratio is relatively large without using extreme duty cycles and without using a transformer, and the voltage gain can be extended; (ii) all semiconductors (transistors and diodes) are rated to the same level of voltage, which is lower than the output voltage; and (iii) its input current is continuous (nonpulsating). The family consists of the base topology, the interleaved version, and the variations related to the number of phases in the interleaved version and the voltage-extended versions. Experimental results are provided to corroborate the principle of the converter.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5147643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic, Environment, and Technical Energy Management in Smart Microgrid With Optimal Participation of the Plug-In Electric Vehicles and Adaptive Demand Response","authors":"Mahmud Mohammedi,&nbsp;Khashimova Naima,&nbsp;Komilov Kamol,&nbsp;Elmurodov Gayratjon,&nbsp;Nargiza Juraeva,&nbsp;Saitov Elyor,&nbsp;Aboubaker Khoualed,&nbsp;Samariddin Makhmudov,&nbsp;Turabayeva Kimiyokhon","doi":"10.1155/etep/3535070","DOIUrl":"https://doi.org/10.1155/etep/3535070","url":null,"abstract":"<p>This article presents a multiobjective framework for energy management and scheduling specifically designed for a microgrid that includes electric vehicles (EVs) and demand response (DR). The primary innovations consist of a unique Dependency Reduction Index (DRI) objective function aimed at enhancing the microgrid’s self-sufficiency. The optimization problem is tackled using an Improved Newton–Raphson Method (INRM). The adaptive sigma point approach is employed to represent uncertainties associated with load, EV, and renewable energy sources. The results demonstrate that incorporating the DRI objective significantly enhances the energy independence of the microgrid, resulting in lower operational and emission costs. In summary, the paper claims that the proposed optimization framework successfully balances economic, environmental, and resilience goals, offering a practical and superior solution for modern microgrid operations. The implementation of the DRI objective led to remarkable enhancements in microgrid operational autonomy, achieving a 5.69% reduction in operational costs and a 4.48% decrease in emissions. This illustrates that dependency-aware optimization fundamentally transforms resource allocation strategies, reshaping the microgrid operational model into one that relies less on the grid through the strategic coordination of charging stations and storage systems, fostering a scenario where improved self-sufficiency and economic efficiency support each other rather than conflict. The coordination of charging stations with the microgrid, guided by the DRI, resulted in a 12.78% reduction in DR costs, thanks to the sophisticated management of battery storage, EV, and EB charging stations within a hierarchical storage framework that operates synergistically across various case studies.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/3535070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid PSO–Gazelle Optimization MPPT for a Novel Double Tied Cross-Link Photovoltaic Array Under Partial Shading Conditions","authors":"Santhosh Yedla,&nbsp;Suresh Mikkili,&nbsp;Abhinav Bhattacharjee,&nbsp;Praveen Kumar Bonthagorla","doi":"10.1155/etep/1341132","DOIUrl":"https://doi.org/10.1155/etep/1341132","url":null,"abstract":"<p>Partial shading conditions (PSCs) significantly reduce the output power of photovoltaic (PV) systems owing to mismatch losses and the occurrence of multiple local maximum power points. This study proposes a novel double tied cross-link (DTCL) PV array configuration integrated with a hybrid particle swarm optimization–gazelle optimization (HPSOGO) MPPT algorithm. The proposed DTCL configuration was first compared with the conventional SP, BL, HC, and TCT configurations for six representative PSCs. Subsequently, the performance of the DTCL configuration was evaluated using the P&amp;O, cuckoo search (CS), particle swarm optimization (PSO), gazelle optimization (GO), and HPSOGO MPPT algorithms through simulation and experimental validation. The scalability analysis showed that DTCL retained 98.02% of the average TCT power while reducing the cross-links by 30.12%. Compared with PSO, HPSOGO reduced the average MPP tracking time in simulations from 0.4425 to 0.207 s (53.22%) and improved the average experimental MPPT efficiency from 96.50% to 97.83%, while reducing the tracking time by 34.5%. These results demonstrate that the proposed DTCL–HPSOGO approach provides an effective balance between power extraction, tracking performance, and implementation complexity for PV systems operating under PSCs.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/1341132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theoretical Analysis of Active–Reactive Power Decoupling Control for DSSC Based on Magnetically Coupled Reactors","authors":"Yangqing Dan,&nbsp;Hui Zhong,&nbsp;Chenxuan Wang,&nbsp;Jun Wang,&nbsp;Xiao Tang","doi":"10.1155/etep/8905252","DOIUrl":"https://doi.org/10.1155/etep/8905252","url":null,"abstract":"<p>Distributed Static Synchronous Compensator (DSSC) is essential for flexible power flow control in renewable energy integrated systems but suffers from severe active–reactive power coupling that deteriorates voltage quality and dynamic performance. This paper proposes a hardware-assisted decoupling method using magnetically coupled reactors (MCRs) to physically interrupt the coupling path. The MCR achieves decoupling through two synergistic mechanisms: additional damping from mutual inductance suppresses current change rates and transient energy buffering from auxiliary winding LC circuits absorbs power fluctuations during transients. A comprehensive mathematical model quantifies the relationship between additional damping and system parameters, and a systematic parameter design methodology is developed. Simulation results on a 10-MVA DSSC system demonstrate that the proposed method reduces the coupling coefficient from 0.09 to 0.04 Mvar/MW, achieving 55% decoupling improvement. The DC bus voltage fluctuation decreases by 44%, regulation time is shortened by 25%, and the power factor adjustment range expands by 50%. The solution costs only about one-eighth as much as energy-storage-based alternatives while maintaining robust performance under ± 10% parameter uncertainty, providing a cost-effective approach for enhancing power flow control in modern power systems.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/8905252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconfiguration and Optimal Placement of the DVR and DG Units in Distribution Grid Using Driving Training–Based Optimization and Genetic Algorithm","authors":"Hajar Bagheri Tolabi,&nbsp;Rahil Hosseini","doi":"10.1155/etep/7549216","DOIUrl":"https://doi.org/10.1155/etep/7549216","url":null,"abstract":"<p>This study has introduced a new combination of driving training–based optimization (DTBO) and genetic algorithm (GA) in the framework of fuzzy logic to solve the reconfiguration, along with the optimal allocation of dynamic voltage restorer (DVR) and distributed generation (DG) units’ problem. This is one of the practical tasks with a discrete-continuous nature in the field of power engineering. A multipurpose objective function, including reducing power losses, improving reliability, voltage stability, and operating costs, is considered for solving the mentioned scenario. Furthermore, four other scenarios were conducted using the proposed algorithm and several well-known optimization methods by implementing it on a test network with 70 buses. Uncertainty associated with wind and solar units’ production has been modeled in this study by using appropriate allocation of probability density functions to the wind speed and solar radiation. The results assessment reveals the success of the new GA-DTBO algorithm in solving the proposed complex scenario, along with providing a new topology for switches, and optimal locations and capacities for DG and DVR units with lower losses, more stable voltage, an improved voltage profile, higher reliability, and lower operating cost. Also, the calculated results indicate the relative superiority of the proposed hybrid algorithm in finding the optimal solution with high accuracy and appropriate convergence time compared to other methods.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7549216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Intelligent Framework for Electric Vehicle Energy Management and Charging Infrastructure Using HEDQN and IoT-Based Adaptive Sparse Autoencoder With Multilevel Attention Mechanism","authors":"Ranjusha K. P.,&nbsp;Amutha B.,&nbsp;Rebecca Balasundaram,&nbsp;Ramalingam Venkatesan","doi":"10.1155/etep/3828884","DOIUrl":"https://doi.org/10.1155/etep/3828884","url":null,"abstract":"<p>In this research paper, a combined intelligent framework of two complementary systems for electric vehicle (EV) infrastructure is proposed. The first system focuses upon a hierarchically enhanced deep queue networking (HEDQN) model that decides on EV charging stations, routes vehicles and schedules power from the grid optimally, using hierarchical reinforcement learning in a cloud-connected IoT network. The second system is an IoT-based energy management system (EMS) for hybrid electric vehicles (HEVs) based on adaptive sparse autoencoder with multilevel attention network (ASA-MANet) and a revised random variable-based innovative American Zebra optimization algorithm (RRVI-AZOA) for energy management. Intelligent energy distribution, accurate state-of-charge (SoC) prediction and dynamic power sharing between the internal combustion engine (ICE) and electric motor are achieved with real-time data from vehicle subsystems with an external infrastructure. Experimental results display that the integrated framework is able to predict SoC with 96.4% accuracy, reduces the wait time of EV charging by 61%, increase charging station utilization from 78% to 92%, decrease peak grid-load variation from 34% to 14%, extend battery life by 22% and enable energy feedback from EVs to the grid with 68% of the load shifting during the off-peak period and saving ₹1200 per annum per EV. The experimental results demonstrate the framework as a scalable and sustainable solution for next-generation electric mobility.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/3828884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on a Hierarchical Matching–Based Dynamic Grouping Strategy for User Clusters in Ancillary Service Markets","authors":"Qing Zhu,&nbsp;Xiao Yang,&nbsp;Yujia Liu","doi":"10.1155/etep/8782552","DOIUrl":"https://doi.org/10.1155/etep/8782552","url":null,"abstract":"<p>The participation of user clusters in ancillary service markets based on aggregation has become an essential component for the operation of power systems under high penetration of renewable energy. However, most existing studies focus on the optimization of decentralized load aggregation, with relatively little attention paid to the pairwise combination of already aggregated user clusters. This paper investigates a dynamic grouping strategy for user clusters participating in ancillary service markets. Based on the construction of comprehensive dynamic regulation characteristic indicators for user clusters, a hierarchical matching–based dynamic grouping strategy is proposed. In the outer-layer matching, adjustable power curves of a large number of user clusters are clustered, and the most complementary cluster pairs are selected based on complementarity coefficient calculations. In the inner-layer matching, pairwise combinations of user clusters within each selected set are determined by calculating individual complementarity coefficients. Case studies show that the proposed dynamic grouping strategy can effectively enhance the dynamic regulation capability of the combined user clusters, enabling them to play a more significant role and achieve higher economic benefits in ancillary service markets.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/8782552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Fault Detection and Classification in Active MV Distribution Networks Using Compact Feature–Based Ensemble Learning Under DG Infeed With Field Validation on the Moroccan Grid","authors":"Saad Sarih,&nbsp;Samira Chabaa,&nbsp;Zakaria Boulghasoul,&nbsp;Abdelhadi ElBacha,&nbsp;Abdelouahed Tajer","doi":"10.1155/etep/3918625","DOIUrl":"https://doi.org/10.1155/etep/3918625","url":null,"abstract":"<p>Reliable fault diagnosis in medium-voltage distribution networks remains difficult due to measurement noise, variability in operating conditions, and the limited computational resources typically available in protection hardware. To address these constraints, the present paper introduces a compact and relay-compatible framework that integrates practical feature engineering with supervised learning techniques for real-time fault detection and classification. The approach relies on a set of six descriptors including phase-current ratios, zero-sequence measures, and a synthetic impedance deviation indicator ∆<i>Z</i>. Additionally, we used the zero-to-positive-sequence current ratio <i>I</i>₀/<i>I</i>₁ to ensure robust fault classification in the presence of distributed generation (DG) while maintaining low computational overhead suitable for intelligent electronic devices. We adopted a cross-domain validation protocol: Classifiers are trained on synthetic data generated via MATLAB/Simulink fault scenarios and then evaluated using real disturbance records extracted from digital protection relays in COMTRADE format within the Moroccan MV grid. Our evaluation compared radial basis function neural networks (RBFNN) against random forest (RF) and gradient boosting (GB) models. Among these, RF shows the most consistent generalization, achieving 98.5% accuracy on the available field dataset. The investigation further validates the system’s resilience in active networks, ensuring that the proposed feature set maintains sensitivity where traditional relays fail due to DG infeed and confirming the RF model’s suitability for bidirectional fault-current contributions. Overall, the results indicate that physically interpretable features combined with ensemble learning models offer a practical and scalable pathway for integrating AI-assisted fault diagnosis into modern protection infrastructures.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/3918625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of an IoT-Integrated Virtual Power Plant Framework for Optimized Smart Grid Energy Management and Demand Response","authors":"Muhammad Saad Zafar,&nbsp;Tao Chen,&nbsp;Fahad Riaz,&nbsp;Sohaib Mehboob","doi":"10.1155/etep/3047839","DOIUrl":"https://doi.org/10.1155/etep/3047839","url":null,"abstract":"<p>This study reports a virtual power plant (VPP) architecture that incorporates IoT, which focuses on two significant issues in coordinated distributed energy: (1) the real-time coordination of individual distributed energy resources (DERs) with different operational constraints activated by varying load composition and variations in type of DERs and (2) the description of deployment scenario variations based on the difference in load composition and DER type disparity. In contrast to currently available methods that consider aggregated system behavior, this paper clearly shows how individual DERs, i.e., residential solar panels, commercial batteries, and wind turbines, react to VPP coordination cues using IoT-based monitoring and control. The framework shows that the basic performance between deployment scenarios is that the urban test settings with HVAC-dominated loads (45%) and rooftop solar (60%) realize a cost reduction of 18.4 percent over fiber/4G networking with subsecond delays; the rural deployment settings with agricultural loads (60 percent) and ground-mounted wind farms (45 percent) over LoRaWAN result in a cost reduction of 15.7 percent in spite of their delayed communication delays of 2 to 5 s; suburban deployment settings with balanced residential and commercial loads (40% residential, 30% commercial, 30% HVAC) and a hybrid DER mix (50% rooftop PV, 25% wind, 25% battery storage) over fiber/LTE hybrid networks achieve a cost reduction of 17.2 percent despite the extended peak window spanning 12–8 PM. The effectiveness of the framework is confirmed by the detailed analysis of the individual DER dispatch patterns, i.e., the IoT sensor networks performance (1 min sampling in smart meters, 30 Hz in PMUs) and the performance of communication protocols (fiber, 4G/5G, LoRaWAN) among 10,000 DERs. Findings indicate a 23.7 percent enhancement in the integration of renewable energy and a 31.2 percent decrease in peak loads and clearly indicate how the composition of the load, strategies of DER placement, and the capabilities of the IoT infrastructure influence the coordination effectiveness. The work offers empirical findings on VPP implementation such as IoT device specifications, edge computing considerations, and situation-specific optimization plans, which will lead to increased sustainability and stability on the grid in heterogeneous smart grid settings.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/3047839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}