Storage Optimization Research Articles

The Data Lakehouse: An Evolving Paradigm in Data Architecture

The data lakehouse architecture represents a transformative evolution in data management, addressing critical limitations in traditional big data architectures. This paradigm combines data lake flexibility with data warehouse capabilities, creating a unified platform that eliminates redundant data copies and streamlines processing workflows. By implementing a layered structure—encompassing storage, metadata, catalog, semantic and query optimization components—the lakehouse provides comprehensive support for diverse analytical workloads while maintaining centralized governance. The architecture leverages open file formats, table specifications, and standardized interfaces to enable ACID transactions, time travel capabilities, and efficient query optimization directly on data lake storage. Organizations adopting this architecture can realize significant benefits including cost efficiency through reduced duplication, enhanced analytical flexibility across workload types, improved governance through centralized policies, and strategic advantages from vendor neutrality. The data lakehouse represents not merely an incremental improvement but a fundamental reconceptualization of enterprise data architecture that balances analytical power with operational efficiency.

Integrated optimization of energy storage and green hydrogen systems for resilient and sustainable future power grids

Abstract This study presents a novel multi-objective optimization framework supporting nations sustainability 2030–2040 visions by enhancing renewable energy integration, green hydrogen production, and emission reduction. The framework evaluates a range of energy storage technologies, including battery, pumped hydro, compressed air energy storage, and hybrid configurations, under realistic system constraints using the IEEE 9-bus test system. Results show that without storage, renewable penetration is limited to 28.65% with 1538 tCO2/day emissions, whereas integrating pumped hydro with battery (PHB) enables 40% penetration, cuts emissions by 40.5%, and reduces total system cost to 570 k$/day (84% of the baseline cost). The framework’s scalability is confirmed via simulations on IEEE 30-, 39-, 57-, and 118-bus systems, with execution times ranging from 118.8 to 561.5 s using the HiGHS solver on a constrained Google Colab environment. These findings highlight PHB as the most cost-effective and sustainable storage solution for large-scale renewable integration.

Unreacted shrinking core model for non-catalytic ammonia storage in metal amine particle

This study applies the unreacted shrinking core model (USCM) to elucidate the interaction between metal amine salt and ammonia gas. The governing equations of the model were numerically solved to describe non-catalytic gas-solid reactions. The model considered the effects of diffusion resistance through the product layer and the chemical reaction. The experimental data were compared with the model, and the results confirmed the validity of the USCM model. The impact of various parameters, such as temperature and pressure, on the adsorption/desorption process was investigated. The results revealed that at constant pressure, a more rapid decrease in temperature leads to a higher conversion. Similarly, at a constant temperature, higher pressure positively influences the gas adsorption and increases the conversion. Moreover, it was shown that the maximum improvement in the conversion can be achieved by increasing the operating temperature during the desorption process at constant pressure. The findings presented in this article contribute to a better understanding of the behavior of metal amine salt and ammonia gas systems, providing valuable insights for the design and optimization of ammonia storage.

Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study

This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing.

Optimization of Two-Stage Consensus and Storage of Blockchain-Based Spectrum Sensing in Cognitive Radio Networks

Optimization of Two-Stage Consensus and Storage of Blockchain-Based Spectrum Sensing in Cognitive Radio Networks

Performance analysis and multi-objective optimization of a compressed air piston hydraulic gravity energy storage (CAPHGES) system

Performance analysis and multi-objective optimization of a compressed air piston hydraulic gravity energy storage (CAPHGES) system

Optimization of cascade storage and compression systems in hydrogen refueling stations

Optimization of cascade storage and compression systems in hydrogen refueling stations

Energy Storage Supply Chain Modeling and Optimization: A Systematic Review

Energy Storage Supply Chain Modeling and Optimization: A Systematic Review

A Study on Container Storage Optimization in Yards Based on a Hyper-Heuristic Algorithm with a Q-Learning Mechanism

In the context of a low-carbon economy, scientific methods to reduce carbon emissions have become an important issue for many ports. Carbon emissions in port areas mainly arise from vessels and handling equipment. Therefore, an effective resource assignment and equipment arrangement system could not only reduce carbon emissions, but also improve the port’s operational efficiency. This study considers factors such as the arrival order of container trailers, the cargo weight, and the number of container rehandling operations. The objective is to minimize the carbon emissions and the number of container rehandling operations in ports, for which a mixed-integer linear programming model is built. Both heuristic algorithms and hyper-heuristic algorithms are employed to optimize the container storage plan, and their applicability in storage optimization is compared. The results indicate that hyper-heuristic algorithms outperform heuristic algorithms in terms of solution quality and stability, effectively satisfying the storage requirements of the yard while minimizing the carbon emissions and the number of container rehandling operations. The results provide theoretical support for port enterprises in improving their operational efficiency and achieving their goals regarding low carbon emissions.

Magnetic effects on solid particle dispersion from circular cylinders and double-diffusive convection in a NEPCM-filled porous thermal chamber

Purpose This study aims to examine the effects of magnetic fields, buoyancy ratios (N), Darcy numbers (Da) and thermal radiation parameters (Rd) on the dispersion of solid particles and double-diffusive convection in a porous cavity filled with nano-enhanced phase change materials (NEPCM). The cavity includes three high-temperature and high-concentration circular sources and a fixed cylinder, creating complex convective interactions. The goal is to quantify heat and mass transfer characteristics and improve thermal storage system efficiency by integrating numerical simulations with machine learning for enhanced predictive accuracy. Design/methodology/approach The incompressible smoothed particle hydrodynamics (ISPH) method is used for solving the governing equations of heat, mass and momentum transfer in the porous cavity. The study incorporates the Soret and Dufour effects and examines convective behavior across a range of N, Da and Rd values. To enhance predictive analysis, a machine learning framework based on ensemble regression with bagging is developed to estimate the average Nusselt ( Nu¯) and Sherwood ( Sh¯) numbers with high accuracy. The computational approach is validated against benchmark studies, ensuring reliability in thermal energy storage applications. Findings Results indicate that increasing N enhances thermal and solutal transport, driven by thermal buoyancy effects, whereas negative N values lead to solutal-dominant behavior. Higher Da values promote strong convective mixing, while lower Da induces a conduction-dominated regime, suppressing particle dispersion. Thermal radiation (Rd) significantly enhances heat and mass transfer, optimizing energy transport efficiency. The machine learning model achieved low prediction errors (MSE ∼ 10−3), successfully identifying critical feature interactions such as τ · Rd and τ · N, influencing transfer dynamics. Originality/value This study presents a novel integration of ISPH-based numerical simulations with machine learning to analyze solid particle dispersion and double-diffusive convection in an NEPCM-filled porous cavity. The research uniquely quantifies the interplay between thermal radiation, magnetic effects and convective transport in the presence of multiple heat sources. By leveraging machine learning, the study enhances predictive capabilities for Nusselt and Sherwood numbers, offering practical insights into thermal energy storage optimization. These findings contribute to next-generation heat transfer models, providing a framework for improving thermal management in industrial and renewable energy applications.

Co-Optimization of Market and Grid Stability in High-Penetration Renewable Distribution Systems with Multi-Agent

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios.

Методи денормалізації для сховищ даних IOT: балансування продуктивності запитів і надлишковості даних

This article explores the impact of denormalization techniques on query performance in IoT data warehouses while maintaining acceptable data redundancy. It analyzes normalized and denormalized approaches in a smart home IoT environment using Azure Synapse. Empirical testing (10,000–5 million records) shows that strategic denormalization combined with columnar storage optimization improves performance by up to 94%. Evaluating four key optimization techniques (Join Reduction, Columnar Storage, Query Complexity Optimization, Temporal Scaling Optimization), we find that denormalization initially increases storage needs by 16% (120 GB vs. 103.5 GB), but columnar compression reduces the final storage size by 50.4% (17.1 GB vs. 34.5 GB). The study provides practical insights into balancing query performance and data redundancy in high-speed IoT environments.

Building a Scalable File Upload Platform with AI Capabilities for Enterprise Use

This article explores the architectural considerations and implementation strategies for developing a scalable file upload platform that meets enterprise requirements. It addresses the fundamental challenges of managing large volumes of data across organizational boundaries while ensuring performance, security, and usability. By examining key architectural patterns, storage optimization techniques, advanced upload capabilities, and security implementations, the article provides a framework for building robust file management infrastructure. The integration of artificial intelligence capabilities transforms traditional file storage into intelligent information management systems that extract value from content through automated processing. The combination of scalable infrastructure with AI-driven features creates a powerful foundation for enterprise knowledge management that adapts to organizational needs while maintaining operational efficiency.

Security Analysis with Fog Computing

With numerous benefits of cloud storage such as cost savings, accessibility, scalability etc., users around the world tend to shift their invaluable data to cloud storage. As the data generation rates are increasing, it is a tedious task for cloud storage providers to provide efficient storage. Cloud storage providers uses different techniques to improve storage efficiency and one of leading technique employed by them is de duplication, which claims to be saving 90 to 95% of storage,. Data De duplication technique evolved as an simple storage optimization technique in secondary then widely adapted in primary storage as well as larger storage areas like cloud storage area. Now, data de duplication is widely used by various cloud storage providers. Data once deployed to cloud servers, it’s beyond the security premises of the data owner, thus most of them prefer to outsource their in an encrypted format.

A Neural Network Compiler for Efficient Data Storage Optimization in ReRAM-Based DNN Accelerators

ReRAM-based DNN accelerators have emerged as a promising solution to mitigate the von Neumann bottleneck. While prior research has introduced tools for simulating the hardware behavior of ReRAM’s non-linear characteristics, there remains a notable gap in high-level design automation tools capable of efficiently deploying DNN models onto ReRAM-based accelerators with simultaneous optimization of execution time and memory usage. In this paper, we propose a neural network compiler built on the open-source TVM framework to address this challenge. The compiler incorporates both layer fusion and model partitioning techniques to enhance data storage efficiency. The core contribution of our work is an algorithm that determines the optimal mapping strategy by jointly considering layer fusion and model partitioning under hardware resource constraints. Experimental evaluations demonstrate that the proposed compiler adapts effectively to varying hardware resource limitations, enabling efficient storage optimization and supporting early-stage design space exploration.

Optimizing cold storage for Orius laevigatus: the role of genetic selection and fluctuating thermal regimes in enhancing survival and fecundity

Abstract The optimization of cold storage for biological control agents, such as Orius laevigatus, is critical for addressing mismatches in supply and demand in mass-rearing systems. This study evaluates the survival and reproductive quality of O. laevigatus under various storage conditions, including constant low temperatures (CLTs) and fluctuating thermal regimes (FTRs), with and without food. Additionally, it explores the impact of genetic selection for improved traits on storage tolerance. Selected strains (2POL) adapted to suboptimal diets exhibited significantly higher survival during starvation and storage compared to commercial and wild populations. At 10 °C, improved strains displayed enhanced survival, particularly during prolonged storage periods, and maintained higher post-storage fecundity. Comparisons between temperature regimes revealed that FTRs (e.g., 12–8 °C and 15–6 °C) resulted in higher survival and fecundity after storage than CLTs. The presence of food during storage was found to be crucial for maximizing survival and quality, especially under prolonged storage conditions. These results demonstrate the potential of genetic improvement to enhance the viability and effectiveness of biological control agents under cold storage. Furthermore, FTR protocols offer a promising strategy to balance cold stress with metabolic needs, improving long-term storage outcomes. Further refinement of temperature regimes and feeding strategies could enhance the practical application of FTRs in industrial rearing systems, ensuring efficient and high-quality biological control agent supply.

Optimization of Solar Generation and Battery Storage for Electric Vehicle Charging with Demand-Side Management Strategies

The integration of Electric Vehicles (EVs) with solar power generation is important for decarbonizing the economy. While electrifying transportation reduces Greenhouse Gas (GHG) emissions, its success depends on ensuring that EVs are charged with clean energy, requiring significant increases in photovoltaic capacity and robust Demand-Side Management (DSM) solutions. EV charging patterns, such as home, workplace, and public charging, need adapted strategies to match solar generation. This study analyzes a system designed to meet a unitary hourly average energy demand (8760 MWh annually) using an optimization framework that balances PV capacity and battery storage to ensure reliable energy supply. Historical solar data from 22 years is used to analyze seasonal and interannual fluctuations. The results show that solar PV alone can cover around 30% of the demand without DSM, rising to nearly 50% with aggressive DSM measures, using PV capacities of 1.0–2.0 MW. The optimization reveals that incorporating battery storage can achieve near 100% coverage with PV power of 8.0–9.0 MW. Moreover, DSM reduces required storage from 18 to about 10 MWh. These findings highlight the importance of integrating optimization-based energy management strategies to enhance system efficiency and cost-effectiveness, offering a pathway toward a more sustainable and resilient EV charging infrastructure.

A framework for multi-objective optimization of hybrid energy storage in integrated multi-energy systems at mega seaports

A framework for multi-objective optimization of hybrid energy storage in integrated multi-energy systems at mega seaports

Multi-objective operational optimization of a multi-energy liquid air energy storage (LAES) in a hydrogen-based green energy hub in Singapore

Multi-objective operational optimization of a multi-energy liquid air energy storage (LAES) in a hydrogen-based green energy hub in Singapore

Thermo-hydro-mechanical-chemical coupling effects on the integrated optimization of CO2-EOR and geological storage in a high water-cut reservoir in Xinjiang, China

Thermo-hydro-mechanical-chemical coupling effects on the integrated optimization of CO2-EOR and geological storage in a high water-cut reservoir in Xinjiang, China