Storage Scheme Research Articles

A molten salt energy storage integrated with combined heat and power system: scheme design and performance analysis

To investigate the flexibility and economic characteristics of a molten salt-combined heat and power (CHP) integrated system under different heat sources, this paper proposes a design scheme for a molten salt-CHP system based on flue gas heat storage, comparing it with main steam heat storage and reheated steam heat source schemes. First, a molten salt heat release sub-loop is designed, where the steam heated by the molten salt can either compensate for heating demands or enter the low-pressure turbine for work, meeting the flexible operation requirements of the unit. Secondly, in response to fluctuations in temperature and pressure parameters of the main steam and reheated steam in the flue gas heat storage scheme, a water-spray desuperheating device is arranged. Finally, a simulation experiment based on a 350 MW CHP was conducted, and the results show that the flue gas molten salt heat storage technology significantly reduces energy loss on the boiler side. Underrated load, compared to the main steam and reheated steam heat storage schemes, the flue gas heat storage scheme has the highest boiler exergy efficiency and total energy utilization efficiency, at 55.8% and 59.1% respectively. During the thermal storage process, the coal consumption index of the flue gas heat storage scheme decreases with increasing load, while conversely, during the heat release process, it increases with the load. The peak-shaving capacity increases with the load, reaching 78.4 MWh under the 75% THA condition. In the steam extraction heating scenario, coupling with the molten salt subsystem can expand the safe heating operation range of the original unit, increasing the maximum peak shaving range by 13.9%, and increasing the maximum heating capacity by 65.4%.

Application of an Optimal Fractional-Order Controller for a Standalone (Wind/Photovoltaic) Microgrid Utilizing Hybrid Storage (Battery/Ultracapacitor) System

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results.

Efficient utilization of abandoned mines for isobaric compressed air energy storage

There are massive abandoned coalmines and corresponding underground space, which provides a viable solution to energy storage of renewable energy generation. Here a novel scheme of isobaric compressed air energy storage (CAES) is proposed to improve the performance of energy storage in underground space. Energy recovery efficiency and energy storage density of isobaric CAES are respectively 70.60 % and 5.74 kWh/m3, while they are 70.56 %, 1.14 kWh/m3 and 60.19 %, 2.46 kWh/m3 respectively for pumped hydro storage and isochoric CAES. Abandoned mining fields can install photovoltaic and wind power, while underground tunnels can storage energy, transforming abandoned mines into a renewable energy support base with electricity generation and storage integrated into a site. Electrical energy with low frequency and high frequency in the base can all be used by driving compressors and heaters respectively. Besides, isobaric CAES can provide space heating and cooling simultaneously through using interstage compression heat and expanding refrigeration technique.

Data-driven simulation methodology for exploring optimal storage location assignment scheme in warehouses

Storage location assignment effects the efficiency of order picking, and thus it is significant for improving operation management in warehouses. Due to the dynamic nature of order picking, system simulation has been recently used for solving Storage Location Assignment Problem (SLAP). However, most previous studies aimed at using simulation to evaluate and verify the optimum storage schemes obtained from mathematical programming, which belongs to “optimize first and then simulation” in essence. In this context, we proposed a novel data-driven simulation methodology for solving SLAP in this study. A four-quadrant judgment criterion is proposed to guide storage locations assignment based on correlation and turnover frequency of items. Moreover, trajectory and blocked heatmaps are involved for exploring the contribution of different factors to order picking efficiency through scenario experiments. A prototype simulator was then developed and applied in a case study to demonstrate its utility. The results obtained reveals the fact that congestion blocking is another key factor impacting global order picking efficiency, and thus considering the correlation between items in storage schemes designing may not always improve the system performance due to the accompanying increases of blocking intensity during the dynamic order picking processes. The findings also demonstrate our inference that the priority of turnover frequency-based storage policy should be higher than correlation between items in designing storage schemes. This work provides new perspectives and valuable demonstration on the evaluating the contribution of different factors to global order picking efficiency as well as exploring optimal storage schemes through emerging data-driven simulation paradigm.

Supercapacitance and Photocatalytic Properties of Keggin Polyoxomolybdated-based 3D Supramolecular Compounds

Constructing efficient electrode materials and photocatalysts to tackle energy scarcity and water pollution is still extremely challenging. Here, four isomorphic Keggin compounds, namely {[M(Tetp)6][H3XMo12O40]2}·6H2O (M=Co/Ni, X=Si/Ge, Co-SiMo12, Ni-SiMo12, Co-GeMo12, Ni-GeMo12, Tetp = 4-[4-(2-Thiophen-2-yl-ethyl)-4H-[1,2,4]triazole-3-yl]-pyridine) were synthesized and directly used as electrode materials for supercapacitors and photocatalysts to reduce Cr (VI). In particular, the combination of polyoxometalates (POMs) that have abundant redox active sites with transition metal-organic compounds (TMCs) improves the internal ion/electron transport efficiency and photocatalytic activity. Specifically, Co-SiMo12 exhibits a higher specific capacitance value (588.46 F g−1 at 0.5 A g−1) and the photocatalytic reduction efficiency (over 90%) of Cr (VI) in 25 min. This work provides a feasible scheme for supercapacitor energy storage and photocatalytic degradation of water pollutants.

Efficiency Enhancement in the Liquefied Natural Gas Storage Scheme: Exploring Thermal Performance for Enhanced Energy Storage Solutions

ABSTRACTThe ongoing transition in the energy sector demands more efficient and reliable energy storage solutions. Our study addresses this need by optimizing the industrial process of liquefied natural gas (LNG) storage, focusing on enhancing thermal performance and energy efficiency. Leveraging a standard LNG storage design, we meticulously evaluated critical supporting variables, modeled key components, and conducted integrated cycle simulations. The primary goal was to minimize the volume of stored gas (achieving a reduction to approximately 1/600th of its gaseous state) while maintaining optimal storage conditions. Our methodology prioritizes insulation over pressure‐bearing factors in large‐scale tanks, aligning with the unique thermal challenges of LNG storage. Simulations were based on methane, which constitutes over 86% of the natural gas in the Middle East, ensuring relevance to the region's resources. The results are promising, with a compression stage reaching a maximum pressure of 2.377, an energy efficiency ratio of 60.71%, and a performance coefficient of 3.188. These findings offer a significant step forward in developing more effective and efficient LNG storage systems, contributing to the broader goal of sustainable energy management.

Hydro-mechanical behaviour of reinforced concrete linings in hydraulic tunnels: steady state

Existing literature covers the behaviour of reinforced concrete (RC) tunnel linings in hydraulic tunnels during filling and steady-state conditions. However, certain aspects remain insufficiently addressed, risking the under-dimensioning of structures, particularly concerning transient behaviour. This paper introduces a comprehensive analytical framework for the design of RC linings in hydraulic tunnels, clarifying conditions that lead to a water-filled gap between the lining and rock mass, significantly impacting system behaviour. The presence or absence of this gap notably affects the number of cracks in the lining, with a substantial increase in cracks when the gap is absent. Contrary to prior assumptions, the paper highlights that the cracking process of the lining is progressive and can stabilize before reaching a fully cracked state, resulting in fewer cracks. Understanding these phases is crucial for setting realistic initial conditions for transient events, particularly concerning lining stiffness. Accurate stiffness determination is essential for evaluating the lining’s response during transients and its fatigue performance. This becomes increasingly significant with the rise of pumped storage schemes for grid stabilization, posing challenges in resistance and fatigue verification of hydraulic tunnel RC linings.

SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks

The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability.

Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system

DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

LSPSS: Constructing Lightweight and Secure Scheme for Private Data Storage and Sharing in Aerial Computing

LSPSS: Constructing Lightweight and Secure Scheme for Private Data Storage and Sharing in Aerial Computing

Suitability Mapping for Subsurface Floodwater Storage Schemes

Suitability Mapping for Subsurface Floodwater Storage Schemes

An Optimized Encryption Storage Scheme for Blockchain Data Based on Cold and Hot Blocks and Threshold Secret Sharing.

In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or "cold" block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers' costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes.

SDN-based security low-latency data storage and distribution scheme for industrial Internet of Things

SDN-based security low-latency data storage and distribution scheme for industrial Internet of Things

Advancements in field development planning through mathematical analysis for reserves estimation, casing design, accidental events and carbon dioxide storage

The objective of this work is to introduce novel methodologies in specific parts of Field Development Plans (FDPs) by leveraging contemporary technological advancements, industry best practices, and accumulated knowledge. The proposed methodologies comprise volumetric analysis by conducting stochastic simulations within a Monte Carlo framework to evaluate the associated uncertainties and a piston-like CO2 storage scheme (based on the material balance approach) aiming to enhance natural gas production and at the same time store CO2, mitigating climate change. Optimization of drilling operations based on appropriate material selection from commercial casing tables and assignment of a failure probability of casing configuration are also included. Another proposed methodology involves spatial risk analysis simulating accidental events based on the points of interest (POIs) and the distance from the event. Finally, a stochastic analysis of project economics is conducted showing uncertainties of cashflows throughout the economic life of the project and assessing the outcomes using various probabilistic performance metrics. The integration of these methodologies offers a robust framework for enhancing the effectiveness of FDPs and their applications, aligning with the evolving demands and complexities of the oil and gas sector. Such methodologies, rooted in mathematical tools aiming to quantify uncertainty in various ways, can increase production, potentially reducing costs through carbon tax incentives and benefit the environment. The applicability of the proposed methodology is demonstrated at the Frigg field, a natural gas reservoir situated in the Norwegian sector of the North Sea. Given its historical development in the 1970s and 1980s, the Frigg field serves as a rich repository of data and information for comparative analysis and fine-tuning of the proposed methodologies. The present study bridges the gap between theoretical advancements and practical applications, promoting innovation and efficiency within the oil and gas field development processes.

An improved rule-based control of battery energy storage for hourly power dispatching of photovoltaic sources

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study.

Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics

Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics

Video security in logistics monitoring systems: a blockchain based secure storage and access control scheme

Video security in logistics monitoring systems: a blockchain based secure storage and access control scheme

Confluence-based suspended sediment fingerprinting: an exploratory approach to determining source to sink sediment contributions in the Tsitsa River catchment, Eastern Cape, South Africa

ABSTRACT In the Tsitsa River catchment of the Eastern Cape Province, South Africa, concerns regarding the sedimentation of a proposed large, multipurpose, in-channel water storage scheme prompted investigations of sub-catchment suspended sediment loads and yields. Confluence-based fingerprinting and source apportionment was undertaken to explore whether this simple approach could identify relative sub-catchment contributions to downstream sediment loads, supplementing modelling and direct sampling. In November 2018, fine sediment sequestered in bed substrates was sampled above and below confluences, using a resuspension technique. The < 63 µm sample fraction underwent analysis of mineral magnetic properties, gamma emitting radionuclide activities and geochemistry to identify suitable tracers. Conservatism and range tests were used to select different combinations of composite fingerprints at seven confluences. Bivariate source apportionment using an unmixing model showed that the approach could be used as a preliminary means of determining relative sub-catchment sediment contributions. Catchment characteristics such as geology and land cover, as well as land tenure and management, were considered as explanatory factors for the difference in sediment contributions. The results for most sub-catchments were broadly similar to those from other studies. Shortcomings of this approach included the reliance on a single sampling effort, and uncertainty associated with sub-catchment sediment transport and residence times.