Storage Cost Research Articles

Which factors dominate the levelized costs of subsurface hydrogen storage in Pennsylvania, United States

The transition to a hydrogen future in the US will be driven by the demand for low-carbon energy carriers and enabled by the development of a robust hydrogen supply chain. Storing large quantities of hydrogen in the subsurface will likely be vital to this process. Two potential options to develop underground hydrogen storage (UHS) facilities are to repurpose existing subsurface gas storage facilities or to build new facilities. Regardless of the option chosen, the technoeconomics of UHS are not yet well understood. In this paper, we developed a technoeconomic analysis approach and applied it to a dataset of the technical characteristics of the existing underground natural gas storage facilities in the Commonwealth of Pennsylvania. The estimated levelized cost of hydrogen storage calculated for developing a new depleted hydrocarbon site ranged from $0.73 to $1.29/kg, while the cost to convert an existing site within PA’s size range was 67%–99% of a new facility and ranged from $0.72 to $0.88/kg H2. The highest LCHSs are for the Pennsylvania UHS facilities with the smallest capacities, but costs decrease as storage capacity increases only up to 1 Bcf. If cushion gas could be procured from a cheaper source, the median LCHS could be reduced by 36%. If an onsite electricity generation source, like solar or wind, is available to supplement electricity required and the overall price of electricity could be reduced by 50%, the LCHS could be reduced by 11% for a newly constructed storage site and 29% at a repurposed underground gas storage (UGS) site. Our results suggest that if existing UGS facilities in Pennsylvania were converted to store pure H2 rather than natural gas, approximately 88% of the state’s need for annual H2 demand buffering in 2050 might be satisfied given that those facilities operate like current UGS facilities.

A novel blockchain-based system for improving information integrity in building projects from the perspective of building energy performance

Global population growth has long been intertwined with environmental concerns and sustainable development. The building sector, a significant energy consumer worldwide, contributes substantially to total energy consumption and greenhouse gas emissions. With the projected global population projected to reach 9.7 billion by 2050, building energy consumption is expected to rise continuously, emphasizing the need to optimize energy performance for environmental sustainability. Despite efforts by many countries to formulate energy conservation objectives and strategies, empirical evidence reveals a substantial disparity between designed and actual building energy consumption, known as the building energy performance gap (BEPG). This gap poses a significant challenge to energy conservation efforts, and the lack of information integrity is a significant contributor to this gap. To address the challenge of inadequate information integrity in building energy projects, this research proposes an innovative solution based on blockchain technology. By utilizing blockchain's decentralized, transparent, and tamper-resistant nature, the proposed model aims to enhance information transmission mechanisms among stakeholders. Ten key energy-related stakeholders and various information flows within building projects are identified. Based on this, an exploratory architectural information management model incorporating blockchain technology is developed. The model features functionalities such as data recording, retrieval, transmission, and incentive mechanisms to ensure data immutability and reliable access security. Through a case study, the model's performance is evaluated in terms of storage cost, latency, and privacy, demonstrating significant time savings in uploading and transferring files. The proposed blockchain-based model offers a feasible solution to the challenges of inadequate information integrity in building energy projects, promoting collaboration and accurate information transmission. This model contributes to improving project outcomes and advancing sustainable development in the construction industry.

Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization.

An Evaluation of the Levelised Cost of Storage of Buoyancy Energy Storage Technology at Smaller Scales

Buoyancy Energy Storage Technology (BEST) offers a promising solution to the intermittency of renewable energy sources like wind and solar. This paper aims to evaluate the feasibility of using BEST for small- scale energy storage applications. The methodology involves calculating the levelized cost of storage (LCOS) and energy capacity of two BEST variants: Fabric BEST and Reeling BEST. Results indicate that Fabric BEST can store 96 kWh per cycle with an LCOS of $356.73/MWh, while Reeling BEST stores less energy at a significantly higher cost of $683/MWh.

Charge-Selective 2D Heterointerface-Driven Multifunctional Floating Gate Memory for In Situ Sensing-Memory-Computing.

Flash memory, dominating data storage due to its substantial storage density and cost efficiency, faces limitations such as slow response, high operating voltages, absence of optoelectronic response, etc., hindering the development of sensing-memory-computing capability. Here, we present an ultrathin platinum disulfide (PtS2)/hexagonal boron nitride (hBN)/multilayer graphene (MLG) van der Waals heterojunction with atomically sharp interfaces, achieving selective charge tunneling behavior and demonstrating ultrafast operations, a high on/off ratio (108), extremely low operating voltage, robust endurance (105 cycles), and retention exceeding 10 years. Additionally, we achieve highly linear synaptic potentiation and depression, and observe the reversibly gate-tunable transitions between positive and negative photoconductivity. Furthermore, we employed the VGG11 neural network for in situ trained in-sensor-memory-computing to classify the CIFAR-10 data set, pushing accuracy levels comparable to pure digital systems. This work could pave the way for seamlessly integrated sensing, memory, and computing capabilities for diverse edge computing.

Research on time-division multiplexing for error correction and privacy amplification in post-processing of quantum key distribution

The post-processing of quantum key distribution mainly includes error correction and privacy amplification. The error correction algorithms and privacy amplification methods used in the existing quantum key distribution are completely unrelated. Based on the principle of correspondence between error-correcting codes and hash function families, we proposed the idea of time-division multiplexing for error correction and privacy amplification for the first time. That is to say, through the common error correction algorithms and their corresponding hash function families or the common hash function families and their corresponding error-correcting codes, error correction and privacy amplification can be realized by time-division multiplexing with the same set of devices. In addition, we tested the idea from the perspective of error correction and privacy amplification, respectively. The analysis results show that the existing error correction algorithms and their corresponding hash function families or the common privacy amplification methods and their corresponding error-correcting codes cannot realize time-division multiplexing for error correction and privacy amplification temporarily. However, according to the principle of correspondence between error-correcting codes and hash function families, the idea of time-division multiplexing is possible. Moreover, the research on time-division multiplexing for error correction and privacy amplification has some practical significance. Once the idea of time-division multiplexing is realized, it will further reduce the calculation and storage cost of the post-processing process, reduce the deployment cost of quantum key distribution, and help to remote the practical engineering of quantum key distribution.

Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non‐identical Distribution

ABSTRACTThis paper proposes a method to improve the identification performance of linear dynamic systems by utilizing knowledge from samples of non‐identical distribution systems. Traditional identification methods heavily rely on the quality of the dataset, such as sample length and noise level, which constrains their performance due to the assumption of identical distribution. Motivated by the concept of sample‐based transfer learning, we propose a sample transfer identification method and derive the condition to avoid negative transfer. We develop a fast iterative transfer identification method for low storage costs, considering the computational burden imposed by the sample size from the source system. Additionally, based on the fast iterative transfer identification method, considering the need to update the current measurement data model in real time, a fast iterative online sample transfer identification method is explored. Through simulations, we validate the effectiveness and superiority of the proposed methods. The results show that sample transfer identification is superior to non‐transfer identification and fast iterative sample transfer identification effectively reduces the calculation amount when dealing with low quality measurement data.

Exploring the demand for inter-annual storage for balancing wind energy variability in 100% renewable energy systems

As research in 100% renewable energy systems progresses, closing remaining research gaps, such as quantifying inter-annual balancing requirements, is necessary. For this study, inter-annual variations of wind power yield and the resulting balancing requirements are analysed for the energy transition towards 100% renewable energy of the United Kingdom and the Republic of Ireland. The energy system components are determined and cost implications are quantified comparing two options for inter-annual storage: e-hydrogen and e-methane for a low, medium, and high security case, which are expanded over ten years. The results indicate that more than 300 TWhth,LHV and 500 TWhth,LHV of inter-annual gas storage capacity in 2050 for the low and the high security case, respectively are required. For both investigated cases, the annual system costs increase notably compared to the reference scenario without inter-annual storage. For the case of e-hydrogen, the annualised system costs increase by 19% compared to an increase of 7% for e-methane for medium security cases. The difference in cost growth is due to significantly higher hydrogen underground storage costs that overcompensate additional system costs for e-methane production. This study provides an expansion from seasonal to inter-annual storage and a first estimate of inter-annual balancing requirements and costs.

Comparison between the models of potential storage and simulation of the demand for electricity transformer (400/11) in Diyala General Company

Storage models are usually used to determine the optimal order quantity and reorder point by obtaining the lowest total cost of storage to balance the amount of production and the amount of demand for materials, but sometimes these models can not be applied, so the use of simulation is resorted to where accurate and flexible results can be relied upon, to achieve the goal of the study, which is to improve the company's storage system. This research dealt with two cases of the probabilistic storage model, where the first model was applied to the monthly demand data on one of the products of Diyala General Company, which is the distribution transformer (400|11) for the years 2019,2020,2021. The second is the probabilistic storage model by simulating demand and comparing the two models, whichever is better to reach optimal. Where the distribution of demand was found to find the average and standard deviation and apply the model to obtain the indicators of the models. Using the MATLAB program, the statistical analysis of the data was carried out and the indicators of the model were obtained.

Research and Application Progress of Crude Oil Demulsification Technology

The extraction and collection of crude oil will result in the formation of numerous complex emulsions, which will not only decrease crude oil production, raise the cost of extraction and storage, and worsen pipeline equipment loss, but also seriously pollute the environment because the oil in the emulsion can fill soil pores, lower the soil’s permeability to air and water, and create an oil film on the water’s surface to prevent air–water contact. At present, a variety of demulsification technologies have been developed, such as physical, chemical, biological and other new emulsion breaking techniques, but due to the large content of colloid and asphaltene in many crude oils, resulting in the increased stability of their emulsions and oil–water interfacial tension, interfacial film, interfacial charge, crude oil viscosity, dispersion, and natural surfactants have an impact on the stability of crude oil emulsions. Therefore, the development of efficient, widely applicable, and environmentally friendly demulsification technologies for crude oil emulsions remains an important research direction in the field of crude oil development and application. This paper will start from the formation, classification and hazards of crude oil emulsion, and comprehensively summarize the development and application of demulsification technologies of crude oil emulsion. The demulsification mechanism of crude oil emulsion is further analyzed, and the problems of crude oil demulsification are pointed out, so as to provide a theoretical basis and technical support for the development and application of crude oil demulsification technology in the future.

Achieving gigawatt-scale green hydrogen production and seasonal storage at industrial locations across the U.S.

Onsite production of gigawatt-scale wind- and solar-sourced hydrogen (H2) at industrial locations depends on the ability to store and deliver otherwise-curtailed H2 during times of power shortages. Thousands of tonnes of H2 will require storage in regions where subsurface storage is scarce, which may only be possible using liquid organic H2 carriers. We evaluate aboveground system with a focus on providing technical insights into toluene/methylcyclohexane (TOL/MCH) storage systems in locations suitable for gigawatt-scale wind- and solar-powered electrolyzer systems in the United States. Here we show that the levelized cost of storage, at a national median of US dollar $1.84/kg-H2 is spatially heterogeneous, causing minor impact on the cost of H2 supply in the Midwest, and significant impact in Central California and the Southeast. While TOL/MCH may be the cheapest aboveground bulk storage solution evaluated, upfront capital costs, modest energy efficiency, reliance on critical materials and pre-sulfided catalysts, and greenhouse gas emissions from heating are opportunities for further development.

Effective Archival of Salesforce Stale Data: A Strategy for System Optimization and Cost Efficiency

This paper explores the importance of archiving stale data in Salesforce and how implementing an ongoing archival process helps maintain accurate, up-to-date information while optimizing system performance and reducing storage costs. As businesses generate vast amounts of data daily, the accumulation of outdated information can slow system operations, increase storage costs, and lead to data inaccuracies. The paper will outline best practices for Salesforce data archival, including soft archival strategies, hard deletion, data backup procedures, and integration precautions. Examples of account data archival, key performance indicators (KPIs), and cost-saving opportunities will be discussed, along with recommended tools and methods for successful data management. Keywords Salesforce, Data Archival, Stale Data, Data Backup, Soft Archival, Data Management, System Optimization, Cost Savings, AWS Backup, OwnBackup, Salesforce Queries

Recent advancement and human tissue applications of volume electron microscopy.

Structural observations are essential for the advancement of life science. Volume electron microscopy has recently realized remarkable progress in the three-dimensional analyses of biological specimens for elucidating complex ultrastructures in several fields of life science. The advancements in volume electron microscopy technologies have led to improvements, including higher resolution, more stability, and the ability to handle larger volumes. Although human applications of volume electron microscopy remain limited, the reported applications in various organs have already provided previously unrecognized features of human tissues and also novel insights of human diseases. Simultaneously, the application of volume electron microscopy to human studies faces challenges, including ethical and clinical hurdles, costs of data storage and analysis, and efficient and automated imaging methods for larger volume. Solutions including the use of residual clinical specimens and data analysis based on artificial intelligence would address those issues and establish the role of volume electron microscopy in human structural research. Future advancements in volume electron microscopy are anticipated to lead to transformative discoveries in basic research and clinical practice, deepening our understanding of human health and diseases for better diagnostic and therapeutic strategies.

Comparison with Carnot battery of an alternate thermal electricity storage charged by concentrated photovoltaic thermal system and resistance heater

In Carnot Batteries (pumped thermal electricity storage), which is known as a low-cost electricity storage method, electrical energy is stored as thermal energy and then converted back into electrical energy using a heat pump and a heat engine, respectively. In the studies carried out to date, different heat source scenarios, heat storage methods, and methods to improve heat pump and heat engine cycles have been applied to increase efficiency and reduce the cost of the Carnot Battery. This study’s novelty was using a concentrated photovoltaic thermal system and resistance heaters instead of a heat pump for the charge cycle in thermal electricity storage. This novel thermal electricity storage design aimed to reduce the levelized cost of storage of the system by eliminating the need for a heat pump and reducing the volume of the water storage tank. First, parametric analyses were performed for the specific variables of both energy storage systems. Afterward, the Carnot Battery and the proposed system were compared for the same electricity storage capacity. For charge/discharge times ranging from 4 h to 8 h and electricity storage capacities ranging from 0.5 MW to 2 MW, a 31.8 % to 58 % increase in round-trip efficiency and a 7.13 % to 20.67 % decrease in levelized cost of energy storage were achieved with the novel design.

Design of Provably Secure and Lightweight Authentication Protocol for Unmanned Aerial Vehicle systems

Drones also called Unmanned Aerial Vehicles (UAVs) have become more prominent in several applications such as package delivery, real-time object detection, tracking, traffic monitoring, security surveillance systems, and many others. As a key member of IoT, the group of Radio Frequency IDentification (RFID) technologies is referred to as Automatic Identification and Data Capturing (AIDC). In particular, RFID technology is becoming a contactless and wireless technique used to automatically identify and track the tagged objects via radio frequency signals. It also has drawn a lot of attention among researchers, scientists, industries, and practitioners due to its broad range of real-world applications in various fields. However, RFID systems face two key concerns related to security and privacy, where an adversary performs eavesdropping, tampering, modification, and even interception of the secret information of the RFID tags, which may cause forgery and privacy problems. In contrast to security and privacy, RFID tags have very limited computational power capability. To deal with these issues, this paper puts forward an RFID-based Lightweight and Provably Secure Authentication Protocol (LPSAP) for Unmanned Aerial Vehicle Systems. The proposed protocol uses secure Physically Unclonable Functions (PUFs), Elliptic-Curve Cryptography (ECC), secure one-way hash, bitwise XOR, and concatenation operations. We use Ouafi and Phan’s formal security model for analyzing security and privacy features such as traceability and mutual authentication. The rigorous informal analysis is carried out which ensures that our proposed protocol achieves various security and privacy features as well as resists various known security attacks. The performance analysis demonstrates that our proposed protocol outperforms other existing protocols. In addition, Scyther and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool simulation results demonstrates that there is no security attack possible within bounds. Therefore, our proposed LPSAP protocol achieves an acceptable high level of security with the least computational, communication, and storage costs on passive RFID tags.

Economic viability of decentralised battery storage systems for single-family buildings up to cross-building utilisation

Due to the great cost decrease of photovoltaics as well as battery storage, especially in the segment of decentralised home storage, the number of grid-connected battery-supported photovoltaic systems being installed in recent years is steadily increasing. However, the scientific community has intensively discussed that lithium-based battery storage systems cannot yet be operated economically in most cases. This paper addresses the level to which the cost of lithium battery storage needs to decrease in order to be economically viable. For this purpose, the economic viability of battery storage systems in single-family buildings, multi-apartment buildings and across-buildings is analysed on the basis of a linear optimisation model and the method of the internal rate of return. The utilisation of the storage system is optimised for different battery and photovoltaic capacities on the basis of generation and consumption. The internal rate of return method is used to compare the savings resulting from the reduced consumption from the electricity grid with the investment costs and the operation and maintenance costs. In order to be able to estimate the influence of the most important parameters a sensitivity analysis is also carried out. The analysis concludes that, depending on the combination of capacities of photovoltaics, battery storage and in relation to the load profile, the battery storage costs would have to drop by at least 85% in order to generate a certain predefined return over a depreciation period of 25 years. Furthermore, the more different load profiles can be covered directly with photovoltaic electricity, e.g. in a multi-apartment building or across buildings, the less electricity needs to be stored and this reduces the benefit and the utilisation of the battery storage and therefore the specific investment costs must further decrease. Another conclusion that emerges from the sensitivity analysis is that the electricity price and the spread between the electricity price and the feed-in tariff have the greatest influence on the investment costs and profitability. Due to limited space for photovoltaics and simultaneously high consumption, self-consumption is already quite high with cross-building utilisation and can no longer be increased to the necessary extent by the battery storage system, which is why the investment costs must also be lower. The novelty of this paper lies in particular in the fact that it deals with the target costs of battery storage systems in various scenarios for certain rates of return. The analyses in this paper are intended to provide a deeper understanding of the framework conditions for the economic operation of a battery storage system in the aforementioned scenarios. However, this paper does not take into account alternative sources of income other than savings on grid consumption. The possibility of time-variable (grid) tariffs, for example, is also not considered in detail in this paper and should be analysed further in future work.

Experimental study of Lannea microcarpa seed oil as a heat transfer fluid or thermal energy storage material for medium-temperature applications

In this study, we investigated the suitability of Lannea microcarpa seed oil (LaMSO) as a heat transfer fluid (HTF) and thermal energy storage material (TESM) for medium-temperature applications, focusing on its performance in concentrated solar power (CSP) plants. LaMSO demonstrated higher specific heat capacity than both Dowtherm A and Xceltherm 600 across the temperature range of 30 °C to 300 °C. It also exhibited a volumetric thermal energy storage density similar to, or approximately 10 % greater than, that of Dowtherm A and Xceltherm 600 at 210 °C. The oil was subjected to isothermal aging at 210 °C for 500 h and 1000 h, followed by analysis of its thermophysical and chemical properties. Thermogravimetric analysis demonstrated excellent thermal stability, with less than 3 % mass loss after 1000 h of aging. The density remained relatively unchanged, indicating consistent performance as a HTF or TESM. Despite an increase in viscosity at 40 °C, the viscosity at 100 °C remained comparable between the new and aged LaMSO samples. Furthermore, the flash point also showed no significant change, remaining high even after 1000 h of aging. However, challenges arise from the increase in the melting point due to oil saturation, which poses a risk of solidification in pipelines or tanks in certain climates. From the perspective of thermal storage cost per kWh, LaMSO is cheaper than Dowtherm A and Xceltherm 600, being approximately 8 times and 3 times less expensive, respectively. Overall, the findings suggest that LaMSO holds promise as an effective and sustainable alternative for HTF and TESM applications across a wide temperature range, contributing to advancements in renewable energy technology.

Enhancing public opinion monitoring for social hot events with a time series neural network-based logic map

A method for constructing a commercial map of social hotspots based on time-series neural networks is proposed. It explores map construction through three stages: atomic event graph building, fusion, and public opinion analysis. The neural network captures event dynamics and public opinion evolution. An unsupervised learning approach trains the model to explore deep event connections. A time map reflects event trends and opinion changes. A sentence-level event extraction model provides rich information. An empirical mode decomposition method analyzes IMF data to understand intrinsic patterns. Time-series neural networks classify and reconstruct event graphs, constructing a knowledge graph. Experiments show this method reduces storage costs, simplifies prediction, and exhibits robustness. It offers new insights for commercial mapping and contributes to public opinion monitoring.

Electrolysis of low-carbon methanol for point-of-use hydrogen generation: Opportunities and challenges for the direct use of unrefined feedstocks

Low-carbon methanol (LCM) is a promising hydrogen carrier that can reduce the overall transportation and storage costs associated with the industrial use of H2 as a chemical feedstock or fuel. Herein, LCM refers to methanol produced via processes that emit lower greenhouse gases (by 90% or more) relative to conventional methanol production derived from fossil fuels like pipelined natural gas or coal. LCM production pathways can valorize stranded methane as well as from sources such as biomass and biogas (e.g., landfill and digester gases). The distributed nature of these stranded feedstocks lends themselves well to methanol synthesis within a smaller-scale, decentralized infrastructure. While process intensification and relatively cheaper feedstock can support these distributed productions, forgoing economies of scale can significantly increase the cost of conventional upgrading steps such as distillation. Fortunately, these upgrading steps may be unnecessary by targeting specific applications, where high methanol purities are not required, thereby lowering the holistic cost and carbon intensity of an overall process.Here, we consider methanol as a hydrogen carrier and its conversion to H2 at the point of use through an electrolytic process by experimentally comparing the oxidation of methanol, of varying purity levels (e.g., grade (99.9+% purity) and crude forms) using a proton exchange membrane (PEM) electrolyzer. In this study, the supply of crude methanol derives from a distributed gas-to-liquid process, developed by M2X Energy Inc. The impurity content is speciated and quantified within the crude methanol sample, which is used directly with water co-reactants to evaluate the effect of impurity type and concentration on electrolytic performance on commercial Pt–Ru catalysts. Our experimental results show that about 1 wt% of C2–C5 alcohols in the starting crude methanol readily poison the catalyst, leading to an increased electricity consumption by 5.1–9.8 kWh per kg H2 produced when operating the electrolyzer at high current densities (>200 mA cm−2). A sensitivity analysis is developed according to the measured energy penalty, which can be used to decide whether bypassing methanol distillation provides economic benefit based on site-specific electricity and distillation costs.

Optimization of a novel liquid carbon dioxide energy storage system by thermodynamic analysis and use of solar energy and liquefied natural gas

Liquid carbon dioxide energy storage with its advantages in terms of geographical constraints and economic performance has garnered significant attention. In this study, a novel liquid carbon dioxide storage system was proposed which utilizes the waste cold energy from LNG and achieves high liquefaction efficiency. By integrating solar energy, net output of the system is increased. The results show that the net output is sensitive to turbine 1 outlet pressure. Round-trip efficiency and energy storage density increases with the rise of the pump outlet pressure first and subsequently exhibits a gradual decline with an inflection point. With the increase in ambient temperature, the system efficiency shows a slight decrease, yet energy storage density shows a significant increase. At basic operating conditions, the system demonstrates energy storage efficiency of 66.64 %, round-trip efficiency of 74.33 %, and energy storage density of 23.51 kWh/m3. In addition, optimization was conducted. Single-objective optimization results show that round-trip efficiency, energy storage density and levelized cost of storage can reach 89.86 %, 25.37 kWh/m3 and 0.1873 $/kWh, respectively. Multi-objective optimization obtained compromise results of round-trip efficiency and levelized cost of storage of 85.33 % and 0.1947 $/kWh when integrated solar energy, showing a 33.77 % increase and a 13.20 % decrease compared to 63.79 % and 0.2243 $/kWh when not integrated solar energy.