Energy Storage最新文献

{"title":"Composition-Sensitive Optimization of Mixed Refrigerant Blends for Hydrogen Liquefaction and Storage Using Prediction Based Clustering Method","authors":"Md Shaquib Equbal,&nbsp;Osama Khan,&nbsp;Praveen Pachauri,&nbsp;Mohd Parvez,&nbsp;Taufique Ahamad,&nbsp;Brahmdeo Yadav,&nbsp;Haidar Howari,&nbsp;Ashok Kumar Yadav,&nbsp;Anoop Kumar Shukla","doi":"10.1002/est2.70552","DOIUrl":"https://doi.org/10.1002/est2.70552","url":null,"abstract":"<div>\u0000 \u0000 <p>The growing hydrogen demands have instigated massive developments in the hydrogen liquefaction technology. Hydrogen fuel is potentially seen as a critical aspect toward developing economies and requires substantial improvement in liquefaction mechanisms. The current study explores the hydrogen liquefaction-storage through mixed refrigerants with the goal of increasing overall efficiency of the process. A hybrid method is applied which makes use of Pearson correlation, prediction modeling, and k-means clustering to optimize the process operational parameters. The optimization process showed that an optimal mixed refrigerant composition, MR-6 (Combo 6), which provides an energy consumption of 7.6 kWh/kg, hydrogen storage of 93.2%, and a COP of 3.9. The given composition had strong positive correlations to C3 (<i>R</i> = 0.884) and n-C5 (<i>R</i> = 0.866) with COP. However, a high negative correlation is established with the COP with N₂ (<i>R</i> = −0.888) and Ne (<i>R</i> = −0.874). The cluster 2 gave the best performance results, especially for Combination 6. Further, the ANFIS model has shown that the Gaussian function membership function yielded the best predictions (RMSE 0.15, MAE 0.05), whereas the Trapezoidal function has produced the greatest errors (RMSE 0.25, MAE 0.09) for the liquefaction-based outcomes. The optimum composition is found to be MR-6 with the composition C1(10%), C2(0%), C2*(10%), C3(5%), n-C4(5%), i-C5(5%), n-C5(5%), R14(10%), He (10%), Ne (10%), N₂(5%), R23(15%).</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149356578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Insights Into Functional ABH3 Perovskite Hydrides for Hydrogen Storage Applications","authors":"Muhammad Wajdan Jamal,&nbsp;Aparna Dixit,&nbsp;Ramesh Sharma,&nbsp;Nikhil Shrivastav,&nbsp;Abdullah Almohammedi,&nbsp;Ali El-Rayyes,&nbsp;Dalia H. Elkamchouchi","doi":"10.1002/est2.70550","DOIUrl":"https://doi.org/10.1002/est2.70550","url":null,"abstract":"<div>\u0000 \u0000 <p>Here, we develop and validate a machine-learning (ML) framework for rapid screening and prediction of hydrogen-storage performance in perovskite hydrides (ABH₃), using 17 physicochemical descriptors derived from 1000 DFT-calculated compounds. Efficient solid-state hydrogen storage is critical for sustainable energy, but exhaustive DFT screening of the thousands of possible perovskite-hydride (ABH₃) compositions is computationally prohibitive. Here a ML framework was developed and validated on 1000 DFT-derived ABH₃ compounds described by 17 physicochemical descriptors, benchmarking Random Forest, XGBoost, SVR, Decision Tree, Linear Regression, and a stacking ensemble across five target properties. On a genuinely held-out 200-compound test set, the optimized stacking ensemble reached <i>R</i>² = 0.994 for hydrogen-storage capacity and F1 = ROC-AUC = 1.000 for stability and magnetic-ordering classification; formation energy (<i>R</i>² = 0.755) and bulk modulus (<i>R</i>² = 0.749) proved harder to predict, reflecting genuine descriptor-property limits rather than model failure. A SISSO-style symbolic-regression search distilled the ensemble into a transparent two-term formula (summed A-/B-site atomic mass and its product; <i>R</i>² = 0.838, test), recovering the model's learned physics with no black-box component. SHAP, LIME, permutation importance, and UMAP consistently identify A-/B-site atomic mass as the dominant driver of storage capacity, yielding a design threshold (M_a + Mᵇ ≤ 202.5 amu) satisfied by all 15 top-ranked candidates (KScH₃, NaCoH₃, CaNiH₃, among others). Benchmarked against AB5 (LaNi₅, 1.2–1.5 wt%) and AB2 (TiMn₂, 1.8–2.2 wt%) intermetallic hydrides, the explored ABH₃ space reaches up to 8.19 wt%. Inference across all 1000 compounds completes in under 100 ms after an 18-s training step, versus hours-to-weeks per DFT calculation. A statistically significant but weak, mass-confounded relationship between magnetic ordering and storage capacity is reported, correcting an earlier overstated trade-off.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149356740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Design Optimization of Solar Salt Latent Heat Storage Systems Using Computational Fluid Dynamics and Machine Learning","authors":"Alok Kumar Ray,&nbsp;Vikash Kumar Gupta,&nbsp;Mayank Gautam","doi":"10.1002/est2.70558","DOIUrl":"https://doi.org/10.1002/est2.70558","url":null,"abstract":"<div>\u0000 \u0000 <p>Design optimization of latent heat storage (LHS) systems is essential for improving their charging performance and facilitating efficient integration with renewable energy systems. However, repeated computational fluid dynamics (CFD) simulations for large-scale geometric optimization are computationally intensive and time-consuming due to the nonlinear melting behavior governed by coupled conduction and buoyancy-induced natural convection. To address this limitation, the present study deploys a coupled CFD and machine learning (ML) framework to optimize tube eccentricity (<i>L</i>₁, <i>L</i>₂) and angular orientation (<i>θ</i>) in a shell and multi-tube LHS system. ML models are utilized with CFD-generated datasets for rapid prediction of melting time and efficient design optimization. The results reveal that melting dynamics are strongly governed by the coupled effects of eccentricity and tube angular orientation, which alter the strength of buoyancy-driven convection. Angular tube orientation has a strong effect, as changing <i>θ</i> from 35° to 60° (with <i>L</i>₁ and <i>L</i>₂ nearly constant) increases melting time from 6000 s to 15 000 s. Moreover, at a fixed angular orientation (<i>θ</i> = 50°), reducing the tube eccentricities (from case B to case C) reduces the melting time by 14.9%. The optimal configuration of <i>θ</i> = 35°, <i>L</i>₁ = 34.5 mm, and <i>L</i>₂ = 30 mm (Case K) reduces the total melting time to 6000 s, achieving a 65.7% reduction compared to the least favorable Case E, <i>L</i>₁ = 40 mm, <i>L</i>₂ = 20 mm, and <i>θ</i> = 70°. Four supervised ML models, including Polynomial Regression (PR), Support Vector Regression (SVR), Random Forest Regression (RFR), and XGBoost, are trained and tested using CFD-generated data to predict melting time. The predictive capability of the models is estimated using the mean squared error (MSE) and coefficient of determination (<i>R</i>²) for both the training and test datasets. Among them, SVR showed the best predictive performance (<i>R</i>² = 0.97, MSE = 59.86) while XGBoost showed comparatively lower generalization capability (<i>R</i>² = 0.93, MSE = 201.83). The proposed CFD–ML framework provides a computationally efficient and scalable approach for rapidly optimizing LHS systems.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149259476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Silver Oxide-Reduced Graphene Oxide Nanocomposite Electrodes for High-Performance Alkaline Supercapacitors","authors":"L. Esther Maria Princy,&nbsp;B. K. Nagendraprasad,&nbsp;Vishal Samaje,&nbsp;PadmaPriya Gopalakrishnan,&nbsp;Thiyagaraj Selvaraj","doi":"10.1002/est2.70559","DOIUrl":"https://doi.org/10.1002/est2.70559","url":null,"abstract":"<div>\u0000 \u0000 <p>Silver oxide-reduced graphene oxide (Ag₂O/rGO) nanocomposites were synthesized and investigated as high-performance electrode materials for alkaline supercapacitors. The incorporation of reduced graphene oxide significantly enhanced the electrical conductivity and, based on the observed sheet-like morphology (SEM), is expected to provide a favorable electroactive surface area, facilitating efficient electron transport and ion diffusion. Structural and morphological characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV–visible spectroscopy, scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) confirmed the successful formation of Ag₂O/rGO nanocomposite. Electrochemical performance was evaluated using cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) measurements. The Ag₂O/rGO electrode delivered a high specific capacitance of 1422 F g⁻¹ at a scan rate of 1 mV s⁻¹ and ⁓169 F g⁻¹ at 3.5 A g⁻¹. The electrode also exhibited excellent cycling stability with ⁓90% capacitance retention after 1000 cycles and a coulombic efficiency of approximately 99%. Kinetic analysis revealed a <i>b</i> value of ⁓0.48 and <i>m</i> exponent of ⁓0.5, indicating a predominantly diffusion-controlled Faradaic charge storage mechanism. Furthermore, energy–power scaling yielded an <i>α</i> value of ⁓0.5, suggesting stable rate capability and hybrid electrochemical behavior. These results demonstrate that Ag₂O/rGO nanocomposites are promising electrode materials for next-generation high-performance supercapacitors.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy, Exergy, and Economic Assessment of a Modified Double-Slope Solar Still Using Low-Cost Heat-Storage Materials","authors":"Rahul Agrawal,&nbsp;Kshitij Yugbodh,&nbsp;Rahul Kumar,&nbsp;Akhilesh Kumar,&nbsp;Virendra Rajput","doi":"10.1002/est2.70555","DOIUrl":"https://doi.org/10.1002/est2.70555","url":null,"abstract":"<div>\u0000 \u0000 <p>In many arid and water-scarce regions, the need for new methods of desalination that are inexpensive and environmentally friendly remains high as access to drinking water continues to be insufficient. Solar stills offer a viable solution, but their widespread use is limited due to low freshwater production and poor thermal performance. The current research investigates a traditional single-slope solar still (TSS), a double-slope solar still, and four modified double-slope solar stills (with iron scraps, black Kanchey, shaving blade fins, and pebbles as thermal energy storage, respectively) (MSS-1, MSS-2, MSS-3, MSS-4, MSS-5). In the study, MSS-1 was the unmodified double-slope system. The daily water yield in mL/day for various stills was recorded as follows: TSS: 1330, MSS-1: 1890, MSS-2: 2370, MSS-3: 2450, MSS-4: 3150, and MSS-5: 2250. The MSS-4 system (shaving blades) outperforms TSS, presenting the highest yield, increasing productivity by 136%. The peak energy efficiency for different solar stills was as follows: TSS: 29%, MSS-1: 36.6%, MSS-2: 46.5%, MSS-3: 49.5%, MSS-4: 52.5%, and MSS-5: 38.3%, with MSS-4 achieving the highest value of 58.5%. The maximum exergy efficiency recorded was TSS: 10%, MSS-1: 11.5%, MSS-2: 15.5%, MSS-3: 19.6%, MSS-4: 23.7%, and MSS-5: 17.4%. According to the economic evaluation, the lowest cost of freshwater production for the modified solar stills was for MSS-4 (Rs. 0.98/L), and was less than Rs. 1.85/L for TSS, which is a reduction of about 46.2%. The findings demonstrate that integrating a double-slope configuration with thermal energy storage materials and an enhanced heat-transfer surface area leads to a substantial improvement in freshwater yield.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Investigation of a Hybrid Thermal Management System for LFP Battery Packs Using Ag2O Nano Fluid–Cooled Serpentine Copper Tubes Embedded in Graphite-Enhanced PCM","authors":"Sagar Wankhede,&nbsp;Ajay D. Pingale,&nbsp;Aniruddha Kulkarni,&nbsp;Kunal Kolte,&nbsp;Lalit Jadhav,&nbsp;Lilesh Yadav,&nbsp;Sucheta Kakade,&nbsp;Shweta Suryawanshi","doi":"10.1002/est2.70554","DOIUrl":"https://doi.org/10.1002/est2.70554","url":null,"abstract":"<div>\u0000 \u0000 <p>Effective thermal management is critical to ensure the safety, reliability, and performance of lithium-ion battery modules under high thermal loads. While hybrid phase change material (PCM)–liquid cooling systems have been widely explored, the present study introduces a novel material-level enhancement through the use of an Ag₂O–ethylene glycol Nano fluid, whose application in battery thermal management remains largely unexplored. A compact 4S5P battery module comprising 21 700-type lithium iron phosphate (LFP) cells (14 V, 22.5 Ah) is experimentally investigated under 1C, 1.5C, and 2C discharge rates, along with 1C charging conditions. The proposed hybrid BTMS integrates PCM with active liquid cooling using Ag₂O Nano fluid at varying concentrations (1–5 wt.%), enabling a systematic evaluation of its thermo-physical impact. The results demonstrate that, in the absence of thermal management, the module temperature rapidly increases to approximately 60°C at 2C, indicating severe thermal stress. In contrast, the proposed system significantly enhances heat dissipation, maintaining the maximum cell temperature within 30°C–32°C, thereby achieving a reduction of 5%. Furthermore, increasing nanoparticle concentration improves convective heat transfer, with optimal performance observed at 5 wt.%, reaching temperature to 29°C resulting in improved temperature uniformity across the module. Results demonstrate the effectiveness of Ag₂O nanofluid relative to base fluid under the investigated operating conditions. This study not only demonstrates the feasibility of Ag₂O-based Nano fluids in BTMS but also provides a scalable and high-performance thermal management solution for high-power cylindrical battery modules in electric vehicles and energy storage systems.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Temperature Experimental Characterization of Magnetite-Based Rock-Bed Thermal Battery","authors":"Seyed Soheil Mousavi Ajarostaghi,&nbsp;Sébastien Poncet,&nbsp;Leyla Amiri","doi":"10.1002/est2.70530","DOIUrl":"https://doi.org/10.1002/est2.70530","url":null,"abstract":"<p>Rock bed thermal energy storage (RTES) systems are increasingly used as affordable and scalable solutions for low-temperature heat storage. However, experimental investigations of magnetite packed beds under repeated charge–discharge cycles remain limited. This study experimentally evaluates the thermal performance of a horizontal cylindrical RTES system filled with magnetite and operated as a low-temperature thermal battery for greenhouse applications. Two airflow strategies are compared over two successive cycles of charging and discharging: (i) unidirectional airflow during both charging and discharging (Model 1) and (ii) reversed airflow during discharge (Model 2). Thermal performance is analyzed through temperature stratification, heat transfer dynamics, energy recovery, and dimensionless performance indicators, including the Thermal Efficiency Index (<i>TEI</i>), Temperature Uniformity Index (<i>TUI</i>), Normalized Temperature Standard Deviation (<i>NTSD</i>), and Temperature Gradient Ratio (<i>TGR</i>). Results show that airflow reversal significantly enhances thermal propagation and heat extraction. Model 2 achieves a more pronounced thermal-front response, generally higher <i>TUI</i> and <i>TGR</i> values, and higher energy recovery, delivering up to 30% more recovered heat during the first discharge period and approximately 15.79% more during the second discharge period compared with Model 1. Moreover, Model 2 exhibits higher <i>TEI</i> values across the investigated operating periods, with particularly noticeable advantages during the first discharge and second charging periods. These findings demonstrate that discharge flow reversal is an effective operating strategy for improving the responsiveness and heat-recovery performance of low-temperature magnetite RTES systems.</p>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/est2.70530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Heat Transfer Process With Variable Specific Heat Working Fluids Through Phase Change Latent Heat Adjustment","authors":"Lisheng Pan,&nbsp;Henglong Hao,&nbsp;Weixiu Shi,&nbsp;Zhiyong Huang,&nbsp;Yulong Ding,&nbsp;Nadeem Ahmed Sheikh","doi":"10.1002/est2.70553","DOIUrl":"https://doi.org/10.1002/est2.70553","url":null,"abstract":"<div>\u0000 \u0000 <p>Heat exchange between circulating working fluid and thermal storage medium is an essential process in thermodynamic cycles of energy storage systems. Thermophysical properties of the working fluids therefore play an important role in such systems. Conventional heat-transfer fluids, such as water and thermal oil, have relatively constant specific heat capacities, whereas high pressure CO₂ varies significantly with temperature near the critical region. Such a difference causes a specific heat capacity mismatch between these fluids, leading to temperature difference increase along the heat exchanger. The large temperature difference also increases entropy generation. Here we propose a new method to reduce the irreversibility through the adjustment of equivalent specific heat capacity based on phase-change latent heat. To validate such an approach, a steady-state model counterflow heat exchanger was used for high-pressure CO₂ at 10–15 MPa. Eight paraffins with phase-transition temperatures from 40°C to 58°C were combined to give a phase-change material mixture. A genetic algorithm optimized the paraffin proportions, proportional coefficient, and temperature-shift parameter. The optimized phase change mixture closely matched the temperature-dependent specific heat capacity profile of CO₂ within the investigated range. The best agreement occurred at 11 MPa. Under the same pinch-point temperature difference, the mixed phase-change material mixture showed a lower entropy-generation rate than water. This trend persisted throughout the investigated pressure range. The optimal temperature-shift parameters were 2.8°C, 4.6°C, and 5.2°C, corresponding to pinch-point temperature differences of 3°C, 4°C, and 5°C, respectively. These results show that tailoring the effective specific heat capacity of the phase-change mixture can reduce thermophysical mismatch and heat-transfer irreversibility.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar Photovoltaic Based Modular Electric Vehicle Battery Charging Using Interleaved Boost and Phase-Shifted Full-Bridge Converters","authors":"Ajeet Kumar Rawat,&nbsp;Rajesh Gupta","doi":"10.1002/est2.70556","DOIUrl":"https://doi.org/10.1002/est2.70556","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes direct charging of lithium-ion batteries in electric vehicles (EVs) using standalone solar photovoltaic (PV) systems at distributed charging stations. A PV plant can be operated in an adaptive optimal mode to dynamically regulate its output power to match the aggregate charging power drawn by multiple lithium-ion batteries, as reflected in the DC-link voltage. To stabilize the DC-link voltage and balance power between PV generation and battery load, a master–slave control strategy is used. In this setup, the DC-link voltage controller acts as the master, generating a corrective duty cycle to reduce voltage changes. The MPPT (Maximum Power Point Tracking) controller, acting as the slave, provides the base duty cycle to extract the maximum power possible from the PV array. The resulting composite duty ratio drives a multiple-phase interleaved boost DC–DC converter, improving power conversion efficiency and reducing input current ripple. A distributed phase-shifted full-bridge converter (PSFBC) is used to charge the batteries following the constant current–constant voltage (CC–CV) method. The proposed architecture is implemented using a modular three-leg inverter, which is scalable and allows integration of higher-rated PV arrays and additional battery charging channels without requiring any major modification in the control strategy, owing to the flexibility of the master–slave coordination mechanism. A multiple-modular boost-PSFB converter using four three-phase inverter modules has been used for implementing the four-phase IBC-PSFB converter for 10.44 kW solar PV to charge four 72 V, 100 Ah Li-ion batteries through MATLAB Simulink software. The control algorithm is also validated in real-time using Typhoon-HIL in virtual HIL mode for implementing a two-phase IBC-PSFB converter, based on a practical setup comprising a 2.2 kW solar PV system and two 72 V, 26 Ah Li-ion batteries.</p>\u0000 </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Photovoltaic Power Plants With Battery Energy Storage for Capacity Reserve Auctions in Brazil","authors":"Jorge Alberto Lewis Esswein Júnior,&nbsp;Meliza Helena Panek,&nbsp;Júlio César Passos","doi":"10.1002/est2.70543","DOIUrl":"https://doi.org/10.1002/est2.70543","url":null,"abstract":"<p>The rapid expansion of variable renewable energy in Brazil has induced severe intraday curtailment, characterized by a pronounced “Duck Curve” driven by transmission limits and regional centralization of generation. This study evaluates the techno-economic and regulatory feasibility of utility-scale photovoltaic (PV) plants integrated with battery energy storage systems (BESS) to mitigate curtailment and meet the firm capacity requirements of the Brazilian Capacity Reserve Auctions. Four 30 MW AC (alternating current) hybrid plants, each coupled with a 150 MWh BESS, were simulated across Brazil's four electrical submarkets using the System Advisor Model (SAM) and a customized financial model. Results demonstrate that this configuration guarantees a 4-h daily evening dispatch with a loss of load probability (LoLP) below 0.5%. Furthermore, the system operates at a strict maximum discharging C-rate of 0.20 C, perfectly aligning with manufacturer guidelines to preserve battery lifespan and control depth of discharge (DoD) degradation. Economically, relying exclusively on spot market arbitrage is unviable, as the levelized cost of storage (LCOS) exceeds regulatory price ceilings. However, backed by fixed auction revenues and subsidized financing, the projects achieve highly profitable internal rates of return (IRR) between 23.53% and 24.29% across all regions. Supported by the recent enactment of Law No. 15.269/2025, which formally establishes energy storage as an autonomous sector activity, these findings confirm that BESS deployment under capacity reserve auctions is an immediately viable solution to ensure grid reliability and unlock further renewable expansion.</p>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":"8 8","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/est2.70543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}