Capacity Of Storage System Research Articles

Techno-economic Analysis of Large-Scale Battery Energy Storage System for Stationary Applications in South Africa

Abstract Renewable energy sources (RES), such as solar photovoltaics (PV), are vital to South Africa's efforts to achieve low-carbon emission targets while addressing the energy crisis. Despite the intermittent nature of solar PV systems, reliable energy-storage solutions are essential to ensure a steady power supply. This study examinesthree electrochemical storage technologies, lithium ferro hosphate (LFP),p lead acid (Pb acid), and vanadium redox flow batteries (VRFB) in combination with PV systems to determine the most suitable option for large-scale stationary applications. Using Hybrid Optimization Model for Electric Renewables (HOMER Pro) software two scenarios of Battery Energy Storage System (BESS) capacities were modeled and simulated: Scenario A with 1.17 MWh and Scenario B with 2.34 MWh. Results demonstrate that LFP BESS offers better economic performance than Pb-acid and VRFB BESS alternatives, particularly for large-scale energy storage storage deployments. Additionally, this study highlights the critical role of storage technologies in reducing CO2 emissions and achieving sustainability targets. Based on a comprehensive assessment of technical, economic and environmental factors, LFP batteries are recommended as the most techno-economically efficient solution for large-scale energy storage systems, reinforcing the need for informed decision-making in future energy infrastructure development.

HAIPO: Hybrid AI Algorithm-Based Post-Fabrication Optimization for Modern 3D NAND Flash Memory

To successfully meet the various requirements of modern storage systems, NAND flash memory should be highly optimized by precisely tuning a huge number of internal operating parameters. Although 3D NAND flash memory succeeds in increasing the capacity of storage systems, its complex architecture and unique error behavior make such optimization a more difficult and time-consuming process during NAND manufacturing. In this paper, we introduce HAIPO, a novel methodology for post-fabrication optimization of NAND flash memory, which is an essential step in the manufacturing process of modern 3D NAND flash memory to simultaneously meet various requirements on reliability, performance, yield, etc. HAIPO is based on simple machine-learning approaches that consist of (i) a lightweight deep-learning (DL) model to generate initial device parameters and (ii) an evolutionary algorithm (EA) to explore device parameters automatically. To more effectively explore device parameters, we introduce three key guidelines for each generation in the EA: (1) domain-specific rules, (2) recent optimization results, and (3) online Bayesian simulation, respectively, to enable quick optimization for a huge number of device parameters within the limited product turnaround time (TAT). In addition, we integrate two optimization modules with HAIPO to improve optimization efficiency even in environments with severe process variation. We demonstrate the feasibility and effectiveness of HAIPO using real 320 3D TLC/QLC NAND flash chips, showing significant performance and reliability improvements by up to 8.8% and 12% on average, respectively, within a quite limited optimization TAT.

Optimizing fountain codes for DNA data storage

Fountain codes, originally developed for reliable multicasting in communication networks, are effectively applied in various data transmission and storage systems. Their recent use in DNA data storage systems has unique challenges, since the DNA storage channel deviates from the traditional Gaussian white noise erasure model considered in communication networks and has several restrictions as well as special properties. Thus, optimizing fountain codes to address these challenges promises to improve their overall usability in DNA data storage systems. In this article, we present several methods for optimizing fountain codes for DNA data storage. Apart from generally applicable optimizations for fountain codes, we propose optimization algorithms to create tailored distribution functions of fountain codes, which is novel in the context of DNA data storage. We evaluate the proposed methods in terms of various metrics related to the DNA storage channel. Our evaluation shows that optimizing fountain codes for DNA data storage can significantly enhance the reliability and capacity of DNA data storage systems. The developed methods represent a step forward in harnessing the full potential of fountain codes for DNA-based data storage applications. The new coding schemes and all developed methods are available under a free and open-source software license.

Multiplexed Random Access Approach to DNA Microspheres for High‐Capacity Data Storage

AbstractWith the rapid growth of modern information data, DNA has emerged as a novel medium for data storage due to its durability, replicability, sustainability, and high density. This study focuses on the compatibility, high capacity, and random access of DNA data storage systems. A calcium phosphate (CaP) mineralized fluorescent SiO2 DNA (FSD@CaP) microspheres is developed by encapsulating fluorescence‐labeled encoding DNA files onto the surface of the SiO2 microspheres that are intrinsically sized and fluorescent as addressed two‐dimensional (2D) barcodes. The various sizes of SiO2 and various fluorescence‐labeled encoding DNA files forming 2D barcodes of FSD@CaP. The 2D barcodes enhance the system's compatibility while allowing multiplexed random access to DNA files. The high‐capacity DNA storage permits effective single access to the DNA file using a minimal number of FSD@CaP microspheres, without impacting the matrix of the system. Ultimately, random access to arbitrary DNA files within a complex pool of distinct 2D barcode‐tagged FSD@CaP microspheres is demonstrated by utilizing a flow cytometer for multiplexed sorting. Consequently, the strategy of this storage system provides a scalable concept for compatibility and multiplexed random access to DNA data sets.

Using Carbon Dioxide for Subsea Long-Duration Energy Storage

This paper investigates the operating benefits and limitations of utilizing carbon dioxide in hydro-pneumatic energy storage systems, a form of compressed gas energy storage technology, when the systems are deployed offshore. Allowing the carbon dioxide to transition into a two-phase fluid will improve the storage density for long-duration energy storage. A preliminary comparative study between an air-based and a carbon dioxide-based subsea hydro-pneumatic energy storage system is first presented. The analysis is based on thermodynamic calculations assuming ideal isothermal conditions to quantify the potential augmentation in energy storage capacity for a given volume of pressure containment when operating with carbon dioxide in lieu of air. This is followed by a transient thermal analysis of the carbon dioxide-based hydro-pneumatic energy storage system, taking into account the real scenario of a finite thermal resistance for heat exchange between the gas and the surrounding seawater. Results from numerical modelling revealed that the energy storage capacity of a carbon dioxide-based subsea hydro-pneumatic energy storage system operating under ideal isothermal conditions can be theoretically increased by a factor of 2.17 compared to an identical air-based solution. The numerical modelling revealed that, under real conditions under which transient effects resulting from a finite thermal resistance are accounted for, the achievable factor is lower, depending on the charging and discharging time, the initial temperature, and whether a polyethene liner for corrosion prevention is considered or not.

Инновационные магнитные наноматериалы для улучшения хранения энергии

Background: Finding effective energy storage technologies is crucial for transitioning to sustainable energy systems. Magnetic nanoparticles have emerged as options due to their distinctive magnetic characteristics, which can considerably improve the performance of energy storage systems. Objective: This research aims to investigate innovative magnetic nanoparticles and assess to increase the efficiency and capacity of energy storage systems. The emphasis is on developing materials with optimal magnetic characteristics and incorporating them into current energy storage methods. Methods: Co-precipitation and thermal breakdown were used to create a range of new magnetic nanomaterials. Vibrational sample magnetometry, X-ray diffraction, and cyclic voltammetry were used to determine these materials' magnetic characteristics, structural integrity, and electrochemical performance. Subsequent integration into supercapacitors and lithium-ion batteries was carried out to evaluate energy storage capacity. Results: The synthesized magnetic nanoparticles showed improved magnetic saturation and charge-discharge characteristics compared to standard materials. When used in supercapacitors, they increased capacitance by 20% and improved cycle stability by 25%. Similarly, these nanomaterials improved the energy density of lithium-ion batteries by 15% and increased their longevity by 30%. Conclusion: The unique magnetic nanoparticles created in this work significantly improve the performance of energy storage devices. The increased capacitance, energy density, and operational stability indicate that these materials for future energy storage applications. Further study is required to optimize these materials for commercial application and investigate their scalability and environmental effects.

Developing an optimization framework for capacity planning of hydrogen-based residential energy hub

— This paper introduces a comprehensive modeling approach for optimizing the capacities of various energy converters and storage systems within a residential Energy Hub (EH). The model focuses on minimizing energy supply costs to customers while maximizing the utilization of renewable and hydrogen-based technologies. Through the optimization process, we determined that implementing CHP units in the EH could reduce total costs by approximately 78% with green tax incentives and by 46.7% without them. Additionally, the inclusion of an incentive to the EH resulted in a 27.2% reduction in total costs imposed on subscribers.The study also evaluates the impact of uncertainties in photovoltaic (PV) electricity generation, demonstrating the benefits of using stochastic optimization over deterministic methods. The results indicate that the economic advantage of this approach over deterministic methods is 86.75%–95.74% greater, depending on the scenario. Sensitivity analysis further reveals that the investment costs of fuel cells significantly influence the optimal capacity design and economic viability of the EH model.

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.

Extremely-Compressed SSDs with I/O Behavior Prediction

As the data volume continues to grow exponentially, there is an increasing demand for large storage system capacity. Data compression techniques effectively reduce the volume of written data, enhancing space efficiency. As a result, many modern SSDs have already incorporated data compression capabilities. However, data compression introduces additional processing overhead in critical I/O paths, potentially affecting system performance. Currently, most compression solutions in flash-based storage systems employ fixed compression algorithms for all incoming data without leveraging differences among various data access patterns. This leads to sub-optimal compression efficiency. This article proposes a data-type-aware Flash Translation Layer (DAFTL) scheme to maximize space efficiency without compromising system performance. First, we propose an I/O behavior prediction method to forecast future access on specific data. Then, DAFTL matches data types with distinct I/O behaviors to compression algorithms of varying intensities, achieving an optimal balance between performance and space efficiency. Specifically, it employs higher-intensity compression algorithms for less frequently accessed data to maximize space efficiency. For frequently accessed data, it utilizes lower-intensity but faster compression algorithms to maintain system performance. Finally, an improved compact compression method is proposed to effectively eliminate page fragmentation and further enhance space efficiency. Extensive evaluations using a variety of real-world workloads, as well as the workloads with real data we collected on our platforms, demonstrate that DAFTL achieves more data reductions than other approaches. When compared to the state-of-the-art compression schemes, DAFTL reduces the total number of pages written to the SSD by an average of 8%, 21.3%, and 25.6% for data with high, medium, and low compressibility, respectively. In the case of workloads with real data, DAFTL achieves an average reduction of 10.4% in the total number of pages written to SSD. Furthermore, DAFTL exhibits comparable or even improved read and write performance compared to other solutions.

Research on the Design of a MIMO Management System for Lithium-Ion Batteries Based on Radiation–Conductivity–Convection Coupled Thermal Analysis

In this study, the heat transfer model of a radiation–conduction–convection coupled lithium-ion battery pack is established through theoretical analysis. The temperature distribution and flow field distribution inside the battery pack are obtained by simulation using ANSYS Fluent software 2022 R1, and the reasonableness of the simulation model is verified with an experiment. This study also analyzes in detail the improvement effect of adding heat dissipation ribs, applying heat dissipation coatings, and adjusting the fan speed on the heat dissipation performance of the system. Under the same heat sink rib height conditions, the relationship between its thickness and total heat dissipation and thermal efficiency is studied in depth, and the temperature distribution of the cell under different rib thicknesses is obtained. At the same time, the emissivity of the heat sink coating under different coating thicknesses was measured by infrared thermography, and the relevant design values were determined through simulation experiments. Finally, based on the experimental test results of fan performance, a corresponding control strategy is proposed to construct an efficient and high-performance multiple-input multiple-output (MIMO) battery thermal management system. The experimental results show that optimizing the structure of the forced air cooling system through the above measures can ensure that the Li-ion battery operates within the efficient operating temperature range, thus extending its cycle life, improving its stability, and reducing the risk of thermal runaway. Meanwhile, the problem of excessive temperature difference between different modules is improved, and the output capacity of the energy storage system is increased.

Autocatalyzed Kinetics of 6-Electron Electroreduction of Iodic Acid Studied by Rotating Disk Electrode Technique

The 6-electron electrochemical reduction of IO3− to I− represents a breakthrough for the development of next-generation redox flow batteries, offering substantially higher energy densities for oxidizer storage. Our study reveals that on a glassy carbon (GC) electrode in acidic electrolytes, HIO3 undergoes an autocatalyzed electrochemical reduction to I−. This process is mediated by the formation of a thin iodine layer on the electrode, acting as an intermediate and a catalyst. Under steady-state conditions, the iodine layer forms via a comproportionation reaction (HIO3 + I− + 5H+ = I2 (s) + 3H2O). Initially, the iodine layer is generated through the slow direct electrochemical reduction of HIO3 on pristine GC. Once established, this layer significantly enhances the rate of iodate reduction. On voltammetry curves, it is clearly observable as a step-wise current surge to reach a plateau. The limiting current density on the GC seemingly aligns with the Levich equation, varying with the RDE rotation rate. Earlier, we demonstrated the electrochemical oxidation of I− back to HIO3 using an H2/HIO3 flow cell, showcasing a full cycle that underpins the feasibility of this approach for energy storage. This study advances the understanding of iodate electroreduction and underscores its role in enhancing the capacity of next-generation energy storage systems.

Optimal Allocation of Hybrid Energy Storage Capacity Based on ISSA-Optimized VMD Parameters

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.

Desain Baterai Lithium-Ion Pengaruh Iradian Dan Temperatur Menggunakan PV Syst and Meteonorm Software

Indonesia is a country with significant potential for developing solar energy. However, one of the challenges in the electricity sector is the reliance on fossil fuels as the primary energy source. To mitigate the impact of this dependence on fossil fuels, it is crucial to explore new sources of renewable electricity. Solar cells, which convert sunlight or ultraviolet (UV) rays into electrical current, offer a promising solution. Sunlight is a sustainable and abundant energy source, making it a viable option for future energy production. Due to the nature of solar cells, which depend on sunlight, the output they produce is variable and influenced by changing weather conditions. Therefore, energy storage solutions, such as lithium-ion batteries, are needed to store the electricity generated by solar cells. This study will examine the characteristics and efficiency of solar cell input on the capacity of lithium-ion battery storage systems, focusing on the relationship between sunlight intensity and the generated current and voltage. An off-grid photovoltaic (PV) system relies solely on solar energy, utilizing a series of photovoltaic modules. Consequently, sunlight intensity significantly impacts the solar cell's performance by affecting the voltage and current produced, which in turn influences the system's performance. This research aims to assess the efficiency, advantages, and disadvantages of comparing the output results between series and parallel configurations of solar cells to meet desired needs. Testing and data collection will be conducted using simulations with PV Syst and Meteonorm software.

Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources

The significance of microgrid systems has grown considerably. This research proposes an innovative approach to manage uncertainty in microgrids by employing energy storage systems as the exclusive flexible resource. To address this challenge, a mathematical problem is formulated as a two-stage stochastic programming model, considering two uncertainties in the microgrid: wind and photovoltaic production. The microgrid system encompasses multiple components, including a diesel generator, a microturbine, wind and photovoltaic power generation, an energy storage system, and the microgrid’s demand. Notably, the microgrid exhibits two distinctive features: (i) the complete integration of wind and photovoltaic production, and (ii) the utilisation of an energy storage system as the sole flexible resource. The objective is to minimise the expected cost of the microgrid system while determining the optimal capacity of the energy storage system to meet the energy balance constraint. This constraint takes into account the varying scenarios of wind and photovoltaic production. The decisions are taking for a duration of 8760 h, a long-term evaluation. A case study is presented for actual data from Greece and the results show high volatility of the renewable energy sources implies higher energy storage system capacity as a sole flexible source for avoiding renewable curtailment.

Selection of energy storage systems for a special purpose rail vehicle based on simulation analysis

The issue of power supply to electric rail vehicles leads to a separation of the rail network into electrified and unelectrified portions, where the sections lacking electrification exclude the operation of electric rail vehicles powered from the overhead lines. The potential solution to this problem was found in adding energy storage systems to electric rail vehicles, to allow them some range of travel beyond the electrified lines. A simulation analysis of a special purpose rail vehicle travelling across a non-electrified section of railway line was conducted to assess the energy consumption rate and the necessary energy storage capacity. Three energy storage solutions were simulated, showing the travel range they can provide, with the aim of finding the lowest battery capacity solution that would still allow the vehicle to safely complete the simulated drive. The final selection of energy storage system capacity was done based on the assumed expected range outside electrified railway weighed against the mass and cost of the extra energy storage system added to the vehicle. For a vehicle with a mass of 65 tons a battery system with a capacity of 600 Ah was found to be sufficient.

Self-operation and low-carbon scheduling optimization of solar thermal power plants with thermal storage systems

Photo thermal power generation, as a renewable energy technology, has broad development prospects. However, the operation and scheduling of photo thermal power plants rarely consider their internal structure and energy flow characteristics. Therefore, this study explains the structure of a solar thermal power plant with a thermal storage system and analyzes its main energy flow modes to establish a self-operation and low-carbon scheduling optimization model for the solar thermal power plant. The simulation results of the example showed that for the self-operating model oriented towards power generation planning and peak valley electricity prices, the existence of a thermal storage system could improve the power generation capacity and revenue of the photovoltaic power plant. For example, when the capacity of the thermal storage system was greater than 6 h, the penalty for insufficient power generation in the simulation result was 0 $, and the maximum increase in revenue reached 84.9% as the capacity of the thermal storage system increased. In addition, when the capacity of the thermal storage system increased from 0 to 8 h, the comprehensive operating cost decreased from 1635.2 k $ to 1224.6 k $, and the carbon emissions decreased from 26.4 × 103 ton to 22.1 × 103 ton. Compared with the existing literature, this study provides a more comprehensive and systematic solution through detailed energy flow analysis and optimization model. The research has practical and far-reaching significance for promoting the development of clean energy technology, improving the sustainable utilization of renewable energy, and optimizing the overall performance of the energy system.

Multilevel Information Encryption Based on Thermochromic Perovskite Microcapsules via Orthogonal Photic and Thermal Stimuli Responses.

Increasing modal variations of stimulus-responsive materials ensure the high capacity and confidentiality of information storage and encryption systems that are crucial to information security. Herein, thermochromic perovskite microcapsules (TPMs) with dual-variable and quadruple-modal reversible properties are designed and prepared on the original oil-in-fluorine (O/F) emulsion system. The TPMs respond to the orthogonal variations of external UV and thermal stimuli in four reversible switchable modes and exhibit excellent thermal, air, and water stability due to the protection of perovskites by the core-shell structure. Benefiting from the high-density information storage TPMs, multiple information encryptions and decryptions are demonstrated. Moreover, a set of devices are assembled for a multilevel information encryption system. By taking advantage of TPMs as a "private key" for decryption, the signal can be identified as the corresponding binary ASCII code and converted to the real message. The results demonstrate a breakthrough in high-density information storage materials as well as a multilevel information encryption system based on switchable quadruple-modal TPMs.

Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system

The recycling of lithium-ion batteries (LIBs) from electric vehicles (EVs) for augmenting the capacity of battery energy storage systems (BESS) presents a sustainable approach to leverage investment in LIBs, mitigating economic losses. This study highlights the critical role of accurate capacity estimation and remaining-useful-life (RUL) prediction in determining optimal replacement and augmentation schedules. We introduce a semi-empirical (SE) model that integrates experimental aging data with an adaptive method and an electrochemical model to capture the degradation trends of LIBs accurately. The adoption of the Moore-Penrose pseudoinverse (Pinv) method, suited for the SE model's multi-parameter, single-output nature, significantly improves capacity estimation accuracy, achieving a maximum error below 0.2 Ah. This improved SE (ISE) model accounts for capacity trends and addresses non-stationary phenomena, including noise and signal distortion during parameter identification and capacity recovery phases.To address these challenges, we propose using discrete wavelet transform (DWT) for the decomposition of non-stationary signals, adjusting the window size to signal characteristics for enhanced noise reduction. The application of DWT to denoise α and β parameters, characterized by their non-constant periodicity and noise interference, facilitates the development of DWT combined with the ISE (DWT-ISE) aging model for early RUL prediction. Experimental validation reveals that our DWT-based approach minimizes root-mean square error (RMSE) and mean absolute error (MAE) for early RUL prediction, demonstrating the model's superiority at 300 cycles for identification and achieving the lowest prediction error at 400 cycles. Furthermore, comparison results of other models (Particle filter, and long short-term memory) reveal that our DWT-ISE aging model exhibits the best RMSE and MAE on limited data. Specifically, the RMSE and MAE values for RUL prediction, underscored as 0.032 % and 0.027 % respectively at 300 cycles, are confirmed as the highest evaluation metrics.

Suitability of late-life lithium-ion cells for battery energy storage systems

The globally installed capacity of battery energy storage systems (BESSs) has increased steadily in recent years. Lithium-ion cells have become the predominant technology for BESSs due to their decreasing cost, increasing cycle life, and high efficiency. However, the cells are subject to degradation due to a multitude of cell internal aging mechanisms, which result in reduced capacity, efficiency, and usable nominal power range over a BESS life cycle. Towards their end-of-life, the cells often show a steep increase in their degradation rate, called nonlinear aging. This work investigates how these ”late-life” lithium-ion cells perform in typical BESS applications. We show how decreased capacity, efficiency, and nominal power range impact the profitability of a home storage system for self-consumption increase (SCI) and a large-scale storage system for energy arbitrage (EA). A physicochemical aging model, which accounts for lithium-plating-induced nonlinear aging, is developed and parameterized based on an experimental cell aging study. The aging study and model show that cells that have entered the nonlinear aging phase can transition back to a significantly reduced degradation rate through adapted operating conditions, namely a reduced charging rate and operating voltage window. The case study highlights that the battery cells enter the nonlinear aging phase significantly earlier when used for the EA application compared to the SCI application. However, by adapting the operating conditions below 80% state of health, the obtainable net present value over the investigated ten-year timeframe in the EA application increases by 39.8%, and the lifetime is significantly prolonged.