International Journal of Energy Research最新文献

{"title":"Robust State of Charge Estimation of a Lithium Iron Phosphate Battery Module Using a Dual-UKF Under Various Operational Temperatures","authors":"Jun-Hyeong Kim,&nbsp;Eunji Kwak,&nbsp;Jinho Jeong,&nbsp;Ki-Yong Oh","doi":"10.1155/er/7985949","DOIUrl":"https://doi.org/10.1155/er/7985949","url":null,"abstract":"<p>This paper proposes a robust framework to estimate the state of charge (SOC) of a lithium iron phosphate (LFP) battery module operating under diverse temperature conditions. The proposed framework integrates an enhanced equivalent circuit model (EECM) with a dual unscented Kalman filter (dual-UKF) to address SOC inconsistency and temperature-dependent nonlinearity in LFP modules. Bayesian optimization (BO) is employed to identify both model parameters and filter hyperparameters systematically, ensuring high fidelity and adaptability without manual tuning. The dual-loop UKF architecture simultaneously estimates SOC and updates time-varying parameters, effectively compensating for initialization errors of up to 10%. The proposed framework maintains an SOC root-mean-square error (RMSE) below 1% across a wide temperature range from −15 to 45°C, with a faster convergence observed at low temperatures caused by enhanced observability from increased potential variations. Comprehensive experimental validation using standard dynamic driving cycles and real-world stochastic battery energy storage system (BESS) data confirms the accuracy, robustness, and applicability of the proposed framework. Thus, the proposed framework offers a practical solution for battery management systems (BMSs) in LFP-based battery applications, as it provides accurate monitoring capabilities under diverse environmental and operational uncertainties.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/7985949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Techno-Economic, Life Cycle, and Decision Analyses of Carbon Capture and Utilization–Hydrogen Power Systems for Sustainable Energy Generation","authors":"Ting-Chia Ou,&nbsp;Ting-Yi Hsieh","doi":"10.1155/er/3725889","DOIUrl":"https://doi.org/10.1155/er/3725889","url":null,"abstract":"<p>This study presents an integrated framework to evaluate carbon capture and utilization (CCU)-hydrogen power systems for sustainable development. Techno-economic analysis (TEA), life cycle assessment (LCA), and fuzzy multi-criteria decision-making (Fuzzy-MCDM) are integrated to systematically assess the environmental performance, economic feasibility, and overall sustainability of the proposed system. Results show that the system reduces life cycle environmental impacts by 43.33%–43.46% compared with conventional natural gas combined-cycle plants, with an internal rate of return (IRR) of 24%–25% and a payback period (PP) of approximately 4 years. The analysis demonstrates that integrating CCU with renewable hydrogen shifts carbon emissions from a linear pathway toward a more circular carbon-utilization pathway, supporting climate mitigation and resource efficiency. These findings highlight the potential of integrated low-carbon energy systems to guide policy, improve environmental management, and promote long-term socio-economic sustainability. Overall, the proposed framework provides a practical decision-support tool for comparing alternative energy-system configurations and supporting carbon-neutral energy transitions aligned with global sustainability goals.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/3725889","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Fuzzy Long Short-Term Memory Framework-Based Accurate State-Of-Charge Estimation for Future-Oriented Battery Management in Vehicle-To-Grid System","authors":"Dania Batool,&nbsp;Faiz Majeed,&nbsp;Sein Oh,&nbsp;Jimin Oh,&nbsp;Jonghoon Kim","doi":"10.1155/er/5529603","DOIUrl":"https://doi.org/10.1155/er/5529603","url":null,"abstract":"<p>Accurate state-of-charge (SOC) estimation is critical for vehicle-to-grid (V2G) systems, where bidirectional power flow and dynamic operational transitions impose severe challenges on conventional estimation methods. While long short-term memory (LSTM) networks have shown promise in capturing temporal dependencies, their fixed activation functions limit adaptability under rapidly varying V2G conditions, particularly during charging/discharging mode transitions and irregular load profiles. This paper presents a fuzzy-modulated LSTM (FM-LSTM) framework that dynamically adjusts LSTM sigmoid activation functions in real-time using a fuzzy inference system driven by battery current and voltage measurements. Unlike black-box LSTM implementations, this approach integrates domain knowledge through linguistic rule formulation, enabling the network to adaptively recalibrate its temporal processing based on instantaneous operating conditions. Validated on standardized drive cycles—Urban dynamometer driving schedule (UDDS) and Highway fuel economy test cycle (HWFET)—and a comprehensive V2G dataset encompassing grid-charging, vehicle-discharging, and bidirectional transitions, the FM-LSTM demonstrates substantial performance improvements over conventional LSTM: 44.81% root mean squared error (RMSE) reduction for UDDS, 23.64% for composite V2G profiles, and 12.66% for HWFET. The proposed method achieves estimation accuracy consistently below 1.5% RMSE across all V2G test scenarios at nominal (25°C) operating conditions and remains below 3% RMSE in the evaluated temperature conditions between 0°C and 45°C, including testing at an unseen intermediate temperature, with the adaptive sigmoid modulation effectively addressing the nonlinear dynamics and operational mode transitions that challenge fixed-architecture networks.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/5529603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Balancing Salt Dissociation and Mechanical Robustness in Polymer-in-Salt Solid Electrolytes via a Quaternized Polymer Matrix","authors":"Hyeonchul So,&nbsp;Yu-Gyeong Jeong,&nbsp;Taekyun Kwon,&nbsp;Yueun Kim,&nbsp;HyeJin Yoon,&nbsp;Soonsik Hwang,&nbsp;Ji-Oh Kim,&nbsp;Seungjo Hong,&nbsp;Han Sol Lee,&nbsp;Pilwon Heo,&nbsp;Ju Sung Han,&nbsp;Jin Hong Lee,&nbsp;Kihyun Kim","doi":"10.1155/er/7724614","DOIUrl":"https://doi.org/10.1155/er/7724614","url":null,"abstract":"<p>Polymer-in-salt solid electrolytes (PISSEs) have been regarded as a promising strategy to overcome the low ionic conductivity of solid polymer electrolytes (SPEs). However, developing PISSEs remains challenging due to the intrinsic trade-off between accommodating high lithium salt fractions (&gt;50 wt.%) with sufficient dissociation and maintaining structural stability. Herein, we rationally design a polymer matrix, denoted as FBN, that combines mechanical robustness with enhanced lithium salt dissociation capability by incorporating a rigid main-chain and side-chain-tethered quaternary ammonium moieties. The prepared FBN provides superior toughness, enabling FBN-based PISSEs to form intimate contact with electrodes and stabilize electrode–electrolyte interfaces. In addition, the positive charges in mechanically robust FBN allow lithium bis(fluorosulfonyl)imide (LiFSI) to be sufficiently dissociated up to 80 wt.%, improving the concentration of free Li⁺ and compositional uniformity of the FBN-based PISSEs. As such, the FBN demonstrates high ionic conductivity (4.84 × 10⁻⁴ S cm⁻¹) and enables superior Li⁺ transport behavior during cycles. In particular, the abundant LiFSI content in FBN-based PISSEs facilitates the formation of an inorganic-rich solid electrolyte interphase (SEI) layer, improving electrochemical performances (167.5 mAh g⁻¹ at a 0.5 C after the 100^th cycle) in lithium metal batteries (LMBs) by forming favorable electrode–electrolyte interfaces. This work presents a design strategy of polymer matrices for SPEs containing high lithium salt concentration from the perspective of simultaneously promoting lithium salt dissociation and improving mechanical robustness.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/7724614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Data-Driven Insights Into Feature Engineering and Efficiency Prediction in Lead-Free Rb-Based Perovskite Solar Cells","authors":"Ashir Hasan Nibir,&nbsp;Mohammad Borhan Uddin,&nbsp;Arpon Podder,&nbsp;Usama Ahmed,&nbsp;Md. Saif Hasan,&nbsp;Wasif Abu Dujana","doi":"10.1155/er/8866920","DOIUrl":"https://doi.org/10.1155/er/8866920","url":null,"abstract":"<p>Lead-free perovskite solar cells (PSCs) require systematic identification of device parameters that govern their performance. Here, a machine learning (ML)-assisted SCAPS-1D framework is developed to analyze and optimize the simulated performance of RbGeI₃-based PSCs across different transport-layer configurations. Random forest (RF) and Decision Tree (DT) models exhibited the strongest 10-fold cross-validation (CV) performance, with <i>R</i>² values of 0.9843 ± 0.0044 and 0.9774 ± 0.0079, respectively. SHapley Additive exPlanations (SHAP) analysis identified active-layer thickness as the dominant predictor of power conversion efficiency (PCE), followed by hole-transport layer (HTL)-related parameters and operating temperature. Systematic electron-transport layer (ETL)/HTL screening identified CuSbS₂ as a promising HTL, yielding a maximum simulated PCE of 28.32% within the investigated parameter space. Importantly, held-out ETL/HTL tests revealed substantial variation in out-of-distribution (OOD) generalization, demonstrating that high random CV accuracy does not necessarily translate to reliable prediction of unseen material combinations. The results establish an interpretable computational framework for parameter screening and device optimization of RbGeI₃-based PSCs, while highlighting the need for experimental and independent computational validation.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/8866920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Sustainable Shea Stearic Acid–Based Phase Change Materials for Efficient Thermal Energy Storage","authors":"Loka Prakash Vandana,&nbsp;Gowram Iswarya,&nbsp;Jatoth Heeraman,&nbsp;Ilenia Farina,&nbsp;Bhumiswor Sharma,&nbsp;Praveen Barmavatu","doi":"10.1155/er/2041104","DOIUrl":"https://doi.org/10.1155/er/2041104","url":null,"abstract":"<p>In this study, stearic acid (SA), a bio-based phase change material (PCM) synthesized from shea butter, a renewable vegetable fat extracted from the kernels of <i>Vitellaria paradoxa</i>, was successfully incorporated with expanded graphite (EG) to produce a shape-stabilized SA/EG (80:20) composite PCM. Differential scanning calorimetry (DSC) analysis showed that the SA/EG composite exhibited an initial melting temperature of 69.0°C, a melting peak temperature of 68.6°C, and a melting enthalpy of 186.8 J/g, whereas pure SA exhibited corresponding values of 65.2°C, 74.9°C, and 239.4 J/g, respectively. During cooling, the composite displayed an initial freezing temperature of 62.0°C, a maximum freezing temperature of 64.8°C, and a freezing latent enthalpy of 186.8 J/g, compared with 63.3°C, 58.4°C, and 256.5 J/g for pure SA. The incorporation of EG reduced the melting peak temperature by 8.4% (74.9 to 68.6°C) and increased the maximum freezing temperature by 10.9% (58.4 to 64.8°C), indicating faster heat storage and heat release. Although the latent heat decreased by 22.0% due to the presence of the thermally conductive EG matrix, the composite retained a high thermal energy storage capacity while exhibiting enhanced heat-transfer characteristics, excellent phase stability, and negligible leakage. Furthermore, AVL gas analysis demonstrated reduced greenhouse gas emissions, confirming the environmental sustainability of the developed SA/EG composite PCM for applications such as solar thermal energy storage, energy-efficient buildings, industrial waste–heat recovery, lithium–ion battery thermal management, and other renewable energy systems.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/2041104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Integrated Control of Battery and Ultracapacitor in Electric Vehicles With Regenerative Braking","authors":"N. Saravanan,&nbsp;S. Hosimin Thilagar","doi":"10.1155/er/9967487","DOIUrl":"https://doi.org/10.1155/er/9967487","url":null,"abstract":"<p>This article proposes the centralized control of battery-powered electric vehicles as per reference torque cycle. Power conversion using two sources has an improved voltage regulation of 3%, followed by compensation of battery voltage sag upto 41%. Retention of charge in the battery has improved to 4% for 2 h that increases the battery life and reduces complexity of the battery management system. A nonlinear control structure ensures the stable behavior of a motor in all operating modes of the vehicle. A bidirectional DC–DC converter provides an estimated power as per the requirement to load. This significantly improves battery energy utilization and regulation using ultracapacitor-aided vehicles with regenerative braking. A 24 V, 100 Ahr battery, and 63 F-30 V ultracapacitor are sources connected through a 1 kW bidirectional converter. It is driven by a gate pulse generated by a controller named Spartan6-LX9. The output of the converter is connected to a 1 hp DC motor. An entire system has been verified using a battery with an ultracapacitor and the battery alone. It is proposed to improve voltage regulation and compensate for battery voltage sag, followed by SOC retention improvement. During deceleration regenerative mode, the vehicle enables power flow reversing from load power to source power, which stores the resultant braking energy into an ultracapacitor. The speed and torque of the motor were estimated based on the driving cycle, and the same was controlled by reference load voltage and current by the reset set track (RST) control strategy. The proposed circuit has been simulated using MATLAB/Simulink and experimentally validated.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/9967487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multiobjective Model Predictive Control of Hydrogen Absorption in Solid-State Storage Optimized by Particle Swarm Algorithm","authors":"Zile Jia,&nbsp;Zixuan Wang,&nbsp;Pan Sun,&nbsp;Yifei Wang,&nbsp;Meng Zhao,&nbsp;Jiayuan Tian","doi":"10.1155/er/1558359","DOIUrl":"https://doi.org/10.1155/er/1558359","url":null,"abstract":"<p>Hydrogen is one of the most critical future fuels. To enhance hydrogen storage and transportation capabilities and bridge the gap with hydrogen utilization technologies, this study focuses on the hydrogen absorption process of solid-state hydrogen storage using LaNi₅ as the storage material. Model predictive control (MPC) was employed to optimize and regulate the thermal-pressure management of the container, investigating the regulatory methodology during the solid-state hydrogen storage reaction process and comparing it with proportional–integral-derivative (PID) control under specific operating conditions. By determining the mass source equation—which describes the hydrogen absorption and desorption reaction rates—based on the actual required flow rate at the terminal and performing backward deduction, and by coordinating three regulatory measures (valve opening, cooling power, and pressure regulation), the hydrogen reaction rate was consistently maintained within specified requirements to achieve optimal real-time optimization. Furthermore, a particle swarm optimization (PSO) algorithm was adopted to enhance the regulatory precision of MPC. Sensitivity analysis was conducted using the normalization of multiple reference indices to select the optimal prediction horizon and error coefficients for the objective function. Simulation results demonstrate that the reaction times under the set operating conditions are all within 225 s.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/1558359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Data Leakage From Full-Series Signal Decomposition Inflates the Reported Accuracy of Short-Term Energy Forecasting Hybrids: A Leakage-Controlled Re-Evaluation","authors":"ByungKi Jeon,&nbsp;JaeHyeok Heo,&nbsp;HongJin Joo,&nbsp;Deukwon Kim,&nbsp;JongKyu Kim","doi":"10.1155/er/4433079","DOIUrl":"https://doi.org/10.1155/er/4433079","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Problem&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Most published decomposition-hybrid energy forecasters—a variational mode decomposition (VMD) or empirical-mode-family front end followed by a learned predictor—are evaluated by decomposing the entire series before the train/test split. Because these decompositions are global, noncausal transforms, the features describing any test-period time step then encode that step’s own future, and the reported accuracy is inflated by an unknown amount.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We measure the size of this evaluation artifact under a controlled protocol and attribute it to its cause.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Feature regimes that differ only in the provenance of the decomposition—no decomposition; full-series decomposition (the prevailing protocol); split-then-decompose; rolling causal windows of 30–365 days; and an expanding causal window that keeps the full history but is denied postorigin samples—predict the same targets with the same models on four hourly series (German load, solar, and wind generation, plus the ETTh1 benchmark), using three model families (ridge, LightGBM, and a multilayer perceptron), 10 random seeds, Diebold–Mariano (DM), Wilcoxon, and Friedman significance tests, and dimensionality- and capacity-matched controls.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Full-series decomposition appears to reduce day-ahead mean absolute error (MAE) by up to 94%, yet it understates the error of the identical causal pipeline by 30%–93% in the 11 of 12 dataset–model combinations where the leak takes hold (mean 60% across all 12; significant in 11 of 12). The expanding-window regime retains on average 91% of that gap (88%–104% in 11 of 12 combinations), so the artifact is attributable to look-ahead itself, not to the longer history the full-series protocol enjoys; the popular split-then-decompose correction leaves 77%–101% of the artifact in place; no causal window length from 30 days to the full record recovers the leaky advantage; and the dimensionality- and capacity-matched controls show none of these conclusions is driven by feature count or network size. Honestly decomposed pipelines are no better than the undecomposed baseline in 10 of 12 combinations. The leaky advantage persists essentially undiminished across the full 24-h horizon, giving a one-figure diagnostic for detecting look-ahead, and the same pattern reappears at smaller magnitude when empirical mode decomposition (EMD) replaces VMD.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 ","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/4433079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Cooperative Feeders and Capacity Expansion Planning of Interconnected Microgrids to Enhance the Resilience","authors":"Mohammad Hossein Atazadegan,&nbsp;Esmaeel Rokrok,&nbsp;Meysam Doostizadeh","doi":"10.1155/er/8224817","DOIUrl":"https://doi.org/10.1155/er/8224817","url":null,"abstract":"<p>Distribution network planners continuously encounter significant risks and challenges in their pursuit of ensuring a reliable and uninterrupted power supply for consumers. Natural disasters such as earthquakes, storms, and floods are some of the key threats to power systems, often leading to severe outages. Since conventional reliability assessments alone are insufficient to guarantee the sustained operation of distribution networks under such conditions, the concept of resilience has been introduced as a more comprehensive approach. One of the most effective approaches to improving the resilience of distribution systems is the integration and coordinated operation of interconnected microgrids (MGs). By exploiting locally available distributed energy resources, MGs can maintain power supply to critical and non-critical loads during upstream grid outages. Furthermore, cooperative operation among interconnected MGs enhances their capability to exchange power through available feeders, thereby reducing service interruptions, increasing operational flexibility, and strengthening the overall resilience of the distribution network against large-scale disturbances. This paper proposes a systematic resilience enhancement framework that integrates MG capacity into the coordinated expansion planning of distribution feeders and MGs, with the objective of developing a cost-effective and collaborative strategy for strengthening network resilience in interconnected MG systems. To solve this problem, a decentralized optimization framework is developed. In normal operating conditions, a Stackelberg game-theoretic approach is employed to model peer-to-peer (P2P) energy transactions, where the Nash equilibrium is reached through an iterative bargaining process between buyer and seller MGs. For stochastic conditions, uncertainties in wind turbine generation and load demand are modeled using Weibull and normal distribution functions, respectively, and the problem is solved via the Monte Carlo simulation method. The overall formulation is structured as a mixed-integer linear programming (MILP) problem, solved by the CPLEX 12.9 solver. Simulation results demonstrate that strategic and synchronized expansion of feeders and MG resources significantly enhances resilience while optimizing costs. Compared to conventional islanded operation, implementing non-cooperative and cooperative expansion strategies leads to 12.71% and 15% reductions in total MG costs, respectively. Furthermore, feeder and MG expansion results in cost savings of 88.45%, 78.36%, and 64.73% under islanded, non-cooperative, and cooperative strategies, respectively, compared to a scenario without expansion.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2026 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/8224817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}