Storage System Research Articles

MOF-Derived Nitrogen-Rich Hollow Nanocages as a Sulfur Carrier for High-Voltage Aluminum Sulfur Batteries.

Aluminum-sulfur batteries (ASBs) are emerging as promising energy storage systems due to their safety, low cost, and high theoretical capacity. However, it remains a challenge to overcome voltage hysteresis and short cycle life in the sulfur/Al2S3 conversion reaction, which hinders the development of ASBs. Here, we studied a high-voltage ASB system based on sulfur oxidation in an AlCl3/urea electrolyte. Nitrogen-doped hollow nanocages (HNCs) synthesized from MOF precursors were rationally designed as sulfur/carbon composite electrodes (S@HNC), and the impact of the nitrogen species on the electrochemical performance of sulfur electrodes was systematically investigated. The S@HNC-900 achieved efficient conversion at 1.9 V, delivering a stable capacity of 197.3 mA h g-1 and a Coulombic efficiency of 93.28% after 100 cycles. Furthermore, the S@HNC-900 electrode exhibited exceptional rate capacity and 800th long-term cycling stability, retaining a capacity of 87.1 mA h g-1 at 500 mA g-1. Ex situ XPS and XRD characterizations elucidated the redox mechanism, revealing a four-electron transfer process (S/AlSCl7) at the S@HNC-900 electrode. Density functional theory calculations demonstrated that pyridinic nitrogen-enriched HNC-900 significantly enhanced the sulfur conversion reaction and facilitated the adsorption of sulfur intermediates (SCl3+) on the carbon interface. This work provides critical insights into the high-voltage sulfur redox mechanism and establishes a foundation for the rational design of carbon-based electrocatalysts for the enhancement of ASB performance.

Reinforcement Learning-Enhanced Adaptive Scheduling of Battery Energy Storage Systems in Energy Markets

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy.

Integrating Metal–Organic Frameworks and Polyamide 12 for Advanced Hydrogen Storage Through Powder Bed Fusion

This paper introduces a pioneering approach that combines ex situ synthesis with advanced manufacturing to develop ZIF-67-PA12 Nylon composites with mixed-matrix membranes (MMMs), with the goal of enhancing hydrogen storage systems. One method involves producing MOF-PA12 composite powders through an in situ process, which is then commonly used as a base powder for powder bed fusion (PBF) to fabricate various structures. However, developing the in situ MOF-PA12 matrix presents challenges, including limited spreadability and processability at higher MOF contents, as well as reduced porosity due to pore blockage by polymers, ultimately diminishing hydrogen storage capacity. To overcome these issues, PBF is employed to form PA12 powder into films, followed by the ex situ direct synthesis of ZIF-67 onto these substrates at loadings exceeding those typically used in conventional MMM composites. In this study, ZIF-67 mass loadings ranging from 2 to 30 wt.% were synthesized on both PA12 powder and printed film substrates, with loadings on printed PA12 films extended up to 60 wt.%. ZIF-67-PA12-60(f) demonstrated a hydrogen capacity of 0.56 wt.% and achieved 1.53 wt.% for ZIF-67-PA12-30(p); in comparison, PA12 exhibited a capacity of 0.38 wt.%. This was undertaken to explore a range of ZIF-67 Metal–organic frameworks (MOFs) to assess their impact on the properties of the composite, particularly for hydrogen storage applications. Our results demonstrate that ex situ-synthesized ZIF-67-PA12 composite MMMs, which can be used as a final product for direct application and do not require the use of in situ pre-synthesized powder for the PBF process, not only retain significant hydrogen storage capacities, but also offer advantages in terms of repeatability, cost-efficiency, and ease of production. These findings highlight the potential of this innovative composite material as a practical and efficient solution for hydrogen storage, paving the way for advancements in energy storage technologies.

Experimental Study of a Silica Sand Sensible Heat Storage System Enhanced by Fins

This study aims to assess the thermal performance of silica sand as a heat storage medium within a shell-and-tube sensible heat storage thermal energy system that operates using water as the heat transfer fluid. Two types of silica sand were analyzed, fine sand and coarse sand, to determine which was the best for heat transfer and storage. It was found that the fine sand, which had smaller particles compared to the coarse sand, enhanced the heat transfer in the system. The fine sand required 11.86 h to charge using the benchmark case and 17.58 h to discharge, whereas the coarse sand required 13.36 h to charge and 16.55 h to discharge. Methods of enhancement are also explored by comparing the system performance with the inclusion of four different configurations of copper fins to investigate against a benchmark case without fins in the system with fine sand. When equipped with four radial fins, the system demonstrated a significant enhancement, reducing charging and discharging times by 59.02% and 69.17%, respectively, compared to the baseline. Moreover, the system exhibited an even greater improvement with eight radial fins, cutting charging and discharging times by 63.74% and 78.5%, respectively, surpassing the improvements achieved with four radial fins. The ten annular fins decreased the charging time by 42.58% and the discharge by 62.4%, whereas the twenty annular fins decreased the charging by 56.24% and the discharging by 68.26% when compared to the baseline.

Natural Polyphenol‐Reinforced Ion‐Selective Separators for High‐Performance Lithium‐Sulfur Batteries with High Sulfur Loading and Lean Electrolyte

Ion‐selective separators are promising to inhibit soluble intermediates shuttle in practical lithium‐sulfur (Li‐S) batteries. However, designing and fabricating such high‐performance ion‐selective separators using cost‐effective, eco‐friendly, and versatile methods remains a formidable challenge. Here we present ion‐selective separators fabricated via the spontaneous deposition of green tea‐derived polyphenols onto a polypropylene separator, aimed at enhancing the stability of Li‐S batteries. The resulting natural polyphenol‐reinforced ion‐selective (NPRIS24) separators exhibit rapid Li ion transport and high soluble intermediates inhibition capability with an ultralow shuttle rate of 0.67% for Li2S4, 0.19% for Li2S6 and 0.10% for Li2S8. This superior ion‐selectivity arises from the high electronegativity and strong lithiophilic nature of the phenolic compounds. Consequently, we have achieved high‐performance Li‐S batteries that are steadily cyclable under the challenging conditions of an S loading of 5.7 mg cm−2, an electrolyte‐to‐S ratio of 5.1 μL mg−1, and a 50 μm Li foil anode. Furthermore, the NPRIS24 separator enhances the performance of other Li metal batteries utilizing commercial LiFePO4 (5.3 mg cm−2) and LiNi0.5Co0.2Mn0.3O2 (9.9 mg cm−2) cathodes. This work underscores the potential of utilizing natural polyphenols for the design of advanced ion‐selective separators in energy storage systems.

Data-based power management control for battery supercapacitor hybrid energy storage system in solar DC-microgrid.

This paper addresses the energy management control problem of solar power generation system by using the data-driven method. The battery-supercapacitor hybrid energy storage system is considered to smooth the power fluctuation. A new model-free control method is utilized in the stand-alone photovoltaic DC-microgrid to provide the power to meet the demand load, while guaranteeing the DC bus voltage is stable. Furthermore, the proposed data-based power management control strategy only needs I/O data. Numerical simulations with real data verify the effectiveness of the proposed method.

Natural Polyphenol-Reinforced Ion-Selective Separators for High-Performance Lithium-Sulfur Batteries with High Sulfur Loading and Lean Electrolyte.

Ion-selective separators are promising to inhibit soluble intermediates shuttle in practical lithium-sulfur (Li-S) batteries. However, designing and fabricating such high-performance ion-selective separators using cost-effective, eco-friendly, and versatile methods remains a formidable challenge. Here we present ion-selective separators fabricated via the spontaneous deposition of green tea-derived polyphenols onto a polypropylene separator, aimed at enhancing the stability of Li-S batteries. The resulting natural polyphenol-reinforced ion-selective (NPRIS24) separators exhibit rapid Li ion transport and high soluble intermediates inhibition capability with an ultralow shuttle rate of 0.67% for Li2S4, 0.19% for Li2S6 and 0.10% for Li2S8. This superior ion-selectivity arises from the high electronegativity and strong lithiophilic nature of the phenolic compounds. Consequently, we have achieved high-performance Li-S batteries that are steadily cyclable under the challenging conditions of an S loading of 5.7 mg cm-2, an electrolyte-to-S ratio of 5.1 μL mg-1, and a 50 μm Li foil anode. Furthermore, the NPRIS24 separator enhances the performance of other Li metal batteries utilizing commercial LiFePO4 (5.3 mg cm-2) and LiNi0.5Co0.2Mn0.3O2 (9.9 mg cm-2) cathodes. This work underscores the potential of utilizing natural polyphenols for the design of advanced ion-selective separators in energy storage systems.

State geometric adjustability for interval max‐plus linear systems

AbstractThis article investigates the state geometric adjustability for interval max‐plus linear systems, which means that the state vector sequence is transformed into a geometric vector sequence by using the state feedback control. It is pointed out that the geometric state vector sequence and its common ratio are closely related to the eigenvectors and eigenvalues of the special interval state matrix, respectively. Such an interval state matrix is determined by the eigen‐robust interval matrix, which has a universal eigenvector relative to a universal eigenvalue. The state geometric adjustability is characterized by the solvability of interval max‐plus linear equations, and a necessary and sufficient condition for the adjustability is given. A polynomial algorithm is provided to find the state feedback matrix. Several numerical examples and simulations are presented to demonstrate the results. At the same time, the proposed method is applied for the regulation of battery energy storage systems to optimize the start time of executing tasks for all processing units in each activity.

Integration of renewable energy-powered cold storage solutions for reducing post-harvest food waste in rural agricultural areas

Post-harvest food loss remains a critical challenge in rural agricultural areas, exacerbated by inadequate storage facilities and unreliable energy access. This study develops and optimizes an advanced renewable energy-powered cold storage system tailored for rural settings, integrating solar and wind energy with phase change materials (PCMs) for efficient energy storage. The system incorporates Internet of Things (IoT)-based sensors and artificial intelligence (AI)-driven energy management to maintain optimal storage conditions and enhance energy efficiency. Field trials conducted in Lincolnshire, UK, and Appalachian regions of the US demonstrated significant reductions in post-harvest food loss by an average of 43.5%, extensions in produce shelf-life by up to 300%, and increased income for smallholder farmers by approximately 43%. The system also achieved an 80% reduction in greenhouse gas emissions compared to conventional diesel-powered systems. Economic analyses revealed a shorter payback period and higher return on investment, confirming the system's viability. High user satisfaction and adoption rates indicate the system's practicality and potential for widespread implementation. The findings suggest that integrating renewable energy with smart technologies in cold storage solutions offers a scalable and sustainable approach to enhancing food security, promoting economic growth in rural areas, and supporting environmental objectives globally.

Enhancing Erasure Resilience in Reed-Solomon Codes: Theory and Applications in Data Storage Systems

Reed-Solomon (RS) codes are widely utilized in data storage and communication systems due to their robust error detection and correction capabilities. However, traditional RS codes encounter challenges in addressing the increasing data corruption rates demanded by modern storage systems. This paper aims to overcome these limitations by modifying and extending the structure of traditional RS codes, particularly enhancing their recovery capabilities in the face of significant data loss. We begin by reviewing the theoretical foundations of RS codes and existing extension methods and discussing emerging technologies integrated into RS codes. We then present detailed methodologies for RS code encoding, erasure recovery mechanisms, soft decision decoding, and interleaving coding techniques, followed by a series of experiments designed to test the proposed methods. The experimental results indicate that soft-decision decoding outperforms traditional hard-decision decoding under high signal-to-noise ratio conditions, albeit with increased computational complexity as the list of candidate codewords grows. Interleaved Reed-Solomon (IRS) coding offers improved performance under low signal-to-noise ratio conditions but may introduce additional system complexity due to the interleaving and deinterleaving processes. We conclude with a summary of the research findings, a discussion of the study’s limitations, and suggestions for future research directions.

Enhancing the Efficiency of Distributed Storage Systems through XOR-Optimized Cauchy Reed-Solomon Codes

In today‘s society, we have entered the era of big data and the Internet of Things. The rise of services such as artificial intelligence, cloud storage, and the metaverse has led to increased demands for data transmission and storage. People now require not only greater capacity but also higher efficiency, more powerful hardware, and advanced algorithms. storageThe Reed-Solomon (RS) code, as a highly effective error correction code, is officially applied in fields such as data storage and data transmission. The Cauchy Reed-Solomon (CRS) Code, which is based on the RS code, utilizes a Cauchy matrix instead of the original Vandermonde matrix and replaces the original matrix multiplication with XOR operations. This paper primarily explores how the XOR-optimized Cauchy Reed-Solomon Code improves the efficiency of distributed storage systems, It was discovered that using lookup tables to store frequently used data and operations can further enhance the efficiency of storage systems and its future applications.

Optimal Planning of Renewables Energies Management in Power Energy Systems

Optimal management of renewable energy resources is a priority, especially in a global energy mix where fossil fuels are increasingly exploited. The major challenge associated with these renewable resources lies in their intermittency. Complementarity and optimal management of these resources are therefore essential. This article proposes a model for managing renewable energies in power grid systems with a storage system. The resulting model has been tested. Python 3.10 programming language was used to solve the optimization problem, using mixted integer linear programming. To test the model, a special case study was carried out in the South of Togo, representing almost 96% of the country's electrical loads. In this study, resources were first evaluated for one year, then compared according to their evolution over the years. The results showed that the country's energy potential is considerable, but unevenly distributed. The study showed that in the north and center of the country, solar energy and biomass are the main resources available. In the south, on the other hand, energy potential is based on solar, wind, hydro and biomass. The optimization results obtained for the south of the country have enabled to plan better the management of these resources over the course of the year. The results show a composition of maximum load satisfaction, with 39% from grid compared with 8% from hydro, 10% from wind, 12% from batteries systems and 31% from photovoltaic systems. The storage required for energy management is estimated at 220 kWh, with an optimal annual value for the objective cost function of around 67885.10212 USD. The model thus obtained provides a decision-making tool for the optimal management of renewable resources.

Application of Discrete Variable-Gain-Based Self-Immunity Control to Flywheel Energy Storage Systems

For the study of the trade-off between steady-state error and transient response in control systems for flywheel energy storage, a controller with a discrete variable gain is proposed. This controller aims to adapt to changes in the system state by dynamically adjusting the controller gain to optimize the system’s anti-disturbance performance. Theoretical analysis and mathematical derivation demonstrate that increasing the observer gain can significantly enhance the system’s anti-disturbance capability. However, this increase also results in overshooting, which highlights the limitations of traditional control methods in achieving both system stability and anti-disturbance performance. A discrete variable-gain extended state observer is designed. The gain of this observer can be adaptively adjusted according to a system state value, enabling the effective control of both steady-state error and transient response. Additionally, the stability of the proposed control method was analyzed and verified, ensuring its effectiveness and reliability for practical applications. Finally, the effectiveness of the proposed method in improving system performance is demonstrated by simulation results.

Estimation of hydrogen solubility in aqueous solutions using machine learning techniques for hydrogen storage in deep saline aquifers

The porous underground structures have recently attracted researchers’ attention for hydrogen gas storage due to their high storage capacity. One of the challenges in storing hydrogen gas in aqueous solutions is estimating its solubility in water. In this study, after collecting experimental data from previous research and eliminating four outliers, nine machine learning methods were developed to estimate the solubility of hydrogen in water. To optimize the parameters used in model construction, a Bayesian optimization algorithm was employed. By examining error functions and plots, the LSBoost method with R² = 0.9997 and RMSE = 4.18E-03 was identified as the most accurate method. Additionally, artificial neural network, CatBoost, Extra trees, Gaussian process regression, bagged trees, regression trees, support vector machines, and linear regression methods had R² values of 0.9925, 0.9907, 0.9906, 0.9867, 0.9866, 0.9808, 0.9464, and 0.7682 and RMSE values of 2.13E-02, 2.43E-02, 2.44E-02, 2.83E-02, 2.85E-02, 3.40E-02, 5.68E-02, and 1.18E-01, respectively. Subsequently, residual error plots were generated, indicating the accurate performance of the LSBoost model across all ranges. The maximum residual error was − 0.0252, and only 4 data points were estimated with an error greater than ± 0.01. A kernel density estimation (KDE) plot for residual errors showed no specific bias in the models except for the linear regression model. To investigate the impact of temperature, pressure, and salinity parameters on the model outputs, the Pearson correlation coefficients for the LSBoost model were calculated, showing that pressure, temperature, and salinity had values of 0.8188, 0.1008, and − 0.5506, respectively, indicating that pressure had the strongest direct relationship, while salinity had an inverse relationship with hydrogen solubility. Considering the results of this research, the LSBoost method, alongside approaches like state equations, can be applied in real-world scenarios for underground hydrogen storage. The findings of this study can help in a better understanding of hydrogen solubility in aqueous solutions, aiding in the optimization of underground hydrogen storage systems.

Разложение аммиака на Co-Al2O3 /SiO2 катализаторах: влияние способов восстановления кобальта

The focus on "green" energy requires the search for environmentally friendly energy storage systems. The reason for choosing ammonia as a potential storage for hydrogen is its high energy capacity and the absence of carbon and nitrogen oxide emissions during decomposition. Herein, we tested Co-Al2O3/SiO2 ammonia decomposition catalysts that had been pre-activated via cyclic hydrogenation-carburization-hydrogenation (RCR) and reduction-oxidation-reduction (ROR) procedures versus the conventional cobalt oxides reduction with hydrogen (R). The samples were characterized by H2-TPR, TEM and synchrotron X-ray diffraction techniques, which revealed that the structural properties of the catalysts were not modified by the reaction. Since the activities of the tested catalysts and the effective reaction barriers appeared to be close, the easiest-to-prepare catalyst R was chosen for the long-term catalytic trial (500 h), and it showed excellent performance stability.

Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell

Fuel cell electric vehicles (FCEVs) are among the devices that have emerged in recent years. To provide electricity to the electric motors, they use a proton-exchange membrane fuel cell (PEMFC) as the primary energy source and a secondary source consisting of an energy storage system (battery or supercapacitors). The addition of these sources to the motors and accessories of a vehicle requires the association of static converters to condition the different power sources. In addition, a high-efficiency and enhanced-reliability power converter is essential to connect the PEMFC to the vehicle’s DC bus. This paper proposes a robust feedback controller for a four-phase interleaved boost converter used with PEMFC. The proposed controller has double loops based on a state-feedback controller, and an inner loop which translates the differential equation of the system into a state representation by linearization around its operation points. The reference current is generated by state feedback in the outer loop; the state variable is defined by using a change variable. The strong robustness and highly dynamic characteristics of the proposed controller are demonstrated through its performance in terms of output voltage, source current, and settling time. The findings indicate that the proposed controller achieves a response time of 20 ms, resulting in an over 50% improvement compared to the controllers referenced in the literature. Additionally, it reduces both current and voltage ripple, keeping them each below 10%. Further, the controller gains synthesis is validated using the linear quadratic regulator (LQR) technique as well as boundary conditions, and its robustness is verified, taking into account the uncertainty of various operating conditions and discrepancies in circuit components. A double-loop super-twisting sliding mode controller, a backstepping control algorithm, and a PI controller are selected for comparison and discussion. Subsequently, the effectiveness of the proposed controller is evaluated through simulation with the parameters of a 500 W fuel cell system.

Solar Dryers: Technical Insights and Bibliometric Trends in Energy Technologies

This review article provides a comprehensive analysis of the technical advancements and research trends in solar drying technologies for agricultural products. The study encompasses various innovations in energy storage systems, including phase change materials (PCMs) and the use of computational fluid dynamics (CFD) for optimizing the drying process. Through a bibliometric analysis of 126 scientific papers published between 1984 and 2024, five major research clusters were identified: energy generation, heat transfer, thermal storage, simulation modeling, and the integration of hybrid systems. The results demonstrate a marked increase in scientific output over the past decade, emphasizing a growing interest in the sustainable use of solar energy for drying applications. Key findings highlight that while PCM-based storage solutions significantly enhance the thermal stability of dryers, the high implementation costs and technical complexities limit their adoption, especially in small-scale operations. Similarly, CFD models have proven effective in optimizing air and temperature distribution within dryers; however, their performance is hindered by real-world fluctuations in solar radiation and humidity levels. To address these limitations, future research should focus on the development of cost-effective PCM materials and the improvement of CFD models for dynamic environmental conditions. The review concludes by emphasizing the importance of interdisciplinary collaboration in the design and application of these technologies, recommending the inclusion of real-world case studies to better illustrate the practical implications and economic benefits of solar drying technologies for agricultural production.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF

The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range.

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.