Storage Size Research Articles

Synergies between Carnot battery and power-to-methanol for hybrid energy storage and multi-energy generation

Power-to-methanol (PtMe) technologies and Carnot batteries are two promising approaches for large-scale energy storage. However, the current low efficiency and inadequate profitability of these two technologies, especially concerning green methanol production, pose challenges for their industrial implementation. One solution is to integrate these two technologies to augment the material and energy utilization efficiency through dynamic operational coordination and multi-energy production. This study systematically investigates the synergies of integrating CO2 energy storage (CES) and PtMe for combined heating, power, and methanol generation, aiming to enhance system's efficiency and economic viability. In this context, a novel hybrid energy-driven polygeneration system (CES-PtMe) is developed, featuring a highly integrated methanol production process and a newly proposed CES configuration with heating input and output capabilities. Detailed system simulations assess its performance under steady state, while nonlinear programming models are formulated for optimal storage sizing and energy scheduling considering fluctuating renewable energy. Various scenarios are set to examine the techno-economic potential of the CES-PtMe system, using renewable data from Gansu, China as a case study. The results indicate that the system can achieve a high efficiency of nearly 80% with a renewable penetration exceeding 77%. By incorporating optimal energy scheduling throughout the year, the system shows economic viability in the context of the current fossil energy market. While it has a limited ability to meet user energy demands, the proposed system retains economic benefits compared to standalone technologies.

Flexibility options in a decarbonising iron and steel industry

The decarbonisation of the iron and steel industry is expected to significantly increase its electricity consumption due to higher levels of electrification and the partial shift to hydrogen as iron reductant. With its batch processes, this industry offers large potential for the application of demand response strategies to achieve electricity cost savings. Previous research has primarily focused on investigating the demand response potential for currently operating manufacturing processes and partly for future low-carbon processes. This study aims to consolidate this knowledge and apply it to a modelling analysis that investigates the demand response potential of two new low-carbon technologies: the hydrogen-based direct reduction of iron with electric arc furnace technology (H2-DRI-EAF) and the blast furnace basic oxygen furnace technology retrofitted with carbon capture (BF-BOF-CCUS). A cost optimisation approach is applied to plant configurations with varying parameters relevant for flexibility, such as electrolyser and storage sizes, and in the context of future electricity prices. Multiple price profiles are selected to encompass uncertainties on the development of the power system. The potential for a H2-DRI-EAF plant is 3–27 times higher than for a BF-BOF-CCUS, with electricity costs savings potentials of 35% and 3%, respectively. The study finds that electricity prices have the most significant impact on the profitability of investing in electrolyser overcapacities, which enable operating costs reduction. Therefore, the profitability of these investments are strongly dependent on future power system configurations.

Alloying ratio versus cluster size for reversible hydrogen storage in 3d transition metal doped small Mg clusters: Dispersion corrected DFT study

Alloying ratio versus cluster size for reversible hydrogen storage in 3d transition metal doped small Mg clusters: Dispersion corrected DFT study

Iterative Image Reconstruction Methodology in Optical CT Radiochromic Gel Dosimetry

Modern advancements in radiation therapy require paralleled advancements in the dosimetric tools used to verify dose distributions. Optical computed tomography (CT) imaged radiochromic gel dosimeters provide comprehensive, tissue equivalent, 3D dosimetric information with high spatial resolution and low imaging times. Traditional CT image reconstruction methods (filtered backprojection) do not account for light refraction within the optical CT system reducing the image quality. Iterative reconstruction methods make use of a system matrix that describes this light refraction thus, improving the reconstructed image quality. However, use of iterative reconstruction methods is not widespread, largely due to the impractical storage size of the required system matrix. Furthermore, current iterative reconstruction methods do not address the issue of image degradation due to a single detector element collecting light from multiple raypaths. For optical CT radiochromic gel dosimetry to be used effectively as a radiation therapy treatment plan verification tool, the system must be both practical and accurate. Thus, this work has two main objectives: (i) reduce the size of system matrices by means of polar coordinate discretization in lieu of the traditional Cartesian coordinate discretization, and (ii) reduce image degradation due to multiple raypaths by a novel approach to populating the system matrix that accounts for multiple raypaths.

Standardized benchmark of historical compound wind and solar energy droughts across the Continental United States

As we move towards a decarbonized grid, reliance on weather-dependent energy increases as does exposure to prolonged natural resource shortages known as energy droughts. Compound energy droughts occur when two or more predominant renewable energy sources simultaneously are in drought conditions. In this study we present a methodology and dataset for examining compound wind and solar energy droughts as well as the first standardized benchmark of energy droughts across the Continental United States (CONUS) for a 2020 infrastructure. Using a recently developed dataset of simulated hourly plant level generation which includes thousands of wind and solar plants, we examine the frequency, duration, magnitude, and seasonality of energy droughts at a variety of temporal and spatial scales. Results are presented for 15 Balancing Authorities (BAs), regions of the U.S. power grid where wind and solar are must-take resources by the power grid and must be balanced. Compound wind and solar droughts are shown to have distinct spatial and temporal patterns across the CONUS. BA-level load is also included in the drought analysis to quantify events where high load is coincident with wind and solar droughts. We find that energy drought characteristics are regional and the longest droughts can last from 16 to 37 continuous hours, and up to 6 days. The longest hourly energy droughts occur in Texas while the longest daily droughts occur in California. Compound energy drought events that include load are more severe on average compared to events that involve only wind and solar. In addition, we find that compound high load events occur more often during compound wind and solar droughts that would be expected due to chance. The insights obtained from these findings and the summarized characteristics of energy drought provide valuable guidance on grid planning and storage sizing at the regional scale.

Economic Viability of NaS Batteries for Optimal Microgrid Operation and Hosting Capacity Enhancement under Uncertain Conditions

Recent developments have increased the availability and prevalence of renewable energy sources (RESs) in grid-connected microgrids (MGs). As a result, the operation of an MG with numerous RESs has received considerable attention during the past few years. However, the variability and unpredictability of RESs have a substantial adverse effect on the accuracy of MG energy management. In order to obtain accurate outcomes, the analysis of the MG operation must consider the uncertainty parameters of RESs, market pricing, and electrical loads. As a result, our study has focused on load demand variations, intermittent RESs, and market price volatility. In this regard, energy storage is the most crucial facility to strengthen the MG’s reliability, especially in light of the rising generation of RESs. This work provides a two-stage optimization method for creating grid-connected MG operations. The optimal size and location of the energy storage are first provided to support the hosting capacity (HC) and the self-consumption rate (SCR) of the RESs. Second, an optimal constrained operating strategy for the grid-connected MG is proposed to minimize the MG operating cost while taking into account the optimal size and location of the energy storage that was formerly determined. The charge–discharge balance is the primary criterion in determining the most effective operating plan, which also considers the RES and MG limitations on operation. The well-known Harris hawks optimizer (HHO) is used to solve the optimization problem. The results showed that the proper positioning of the battery energy storage enhances the MG’s performance, supports the RESs’ SCR (reached 100% throughout the day), and increases the HC of RESs (rising from 8.863 MW to 10.213 MW). Additionally, when a battery energy storage system is connected to the MG, the operating costs are significantly reduced, with a savings percentage rate of 23.8%.

Technical and Financial Viability of a 1 MW CSP Power Plant with Organic Rankine Module: Case Study for a Northeastern Brazilian City

In this study, a 1 MWe parabolic trough concentrating solar power plant using an Organic Rankine Cycle to convert thermal power into electricity was simulated. The solar data used is from Fortaleza, the capital city of Ceara, a state in the northeast region of Brazil. The simulation was year-round and used hourly acquired data. Several different configurations differing in number of collectors and size of thermal energy storage were considered and, by the end of the study, according to the technical and financial results, one best configuration was defined. With a net annual energy generation of 4815.50 MWh, a 12.08% total efficiency and a levelized cost of electricity (LCOE) of 186.98 US$/MWh, the best defined configuration was the one with 100 collector assemblies and 48 MWh storage system. Besides that, the capital and OM costs of the various components were compared to define the most expensive to the power plant, being the collectors acquisition the biggest cost, which represents 74% and 58% of capital and OM costs respectively.

Optimal sizing and operation of seasonal ice thermal storage systems

Ice storage systems can be used as an efficient cooling source during summer, as well as a heat source for heat pumps during winter. The non-linear behavior of the heat exchange process in storage makes formulations for optimizing the design and operation of these technologies complex. In this work, we propose a quadratically-constrained mixed-integer programming formulation, that can capture the latent and sensible behavior of the storage and its impact on the delivery of heating and cooling. A building demonstrator integrating an ice storage device was used as a case study. Monitoring data were used to validate the simplified ice storage model employed in the optimization. Results showed that the most common optimal storage cycle requires freezing the water during late winter and when the air temperature falls below 0∘C. Increasing the storage volume increases both storage efficiency and the amount of free cooling available during summer. For these reasons, economies of scale can make larger systems more competitive than smaller ones. Storage size and thermal insulation level affect the duration of the charging and discharging phases. Thermal insulation improves seasonal efficiency and free cooling significantly. A higher CO2 emissions price does not yield significant benefits in terms of emissions reductions. High investment costs and the seasonal variation in CO2 intensity of electricity reduce the economic and environmental competitiveness of long-term ice storage systems, respectively.

Spider Monkey Optimization Based Optimal Sizing of Battery Energy Storage for Micro-Grid

Spider Monkey Optimization Based Optimal Sizing of Battery Energy Storage for Micro-Grid

An Improved Two-Hop Rendezvous Model With Buffer-Aided Relays in Channel-Hopping Cognitive-Radio Wireless Networks for Internet of Things

In this paper, two-hop transmissions in channel-hopping (CH) cognitive-radio wireless networks (CRWNs) with buffer-aided relays are studied. The two-hop relay (rendezvous) model is improved using symmetric CH sequences, which are more suitable for practical CRWNs than the old model using asymmetric ones. A proper buffer storage size in the relays is formulated to minimize the two-hop data-transfer latency. The lower and upper bounds of the steady-state maximum-time-to-rendezvous (MTTR) and throughput with relays are derived with formal proofs. Using the proper buffer size, both metrics can be better controlled and found no worse than twice the original MTTR and throughput without relays. To explore the impacts of utilization probability of Internet-of-Things (IoT) devices to their performances in a small-scale CRWN, the single-region rendezvous-success (RS) rate and throughput are formulated. For a large-scale CRWN, the model is extended to formulate the multi-region RS rate and throughput with and without relays for the first time. Numerical studies of these analytical models are performed and validated with computer simulations. In summary, the transmission range and number of rendezvousing IoT devices are improved in the large-scale CRWN with relays, and the multi-region RS rate and throughput with relays are found better than those without relays.

A hydrophilic Lignin-Based carbon fiber sizing agent assembled with CNTs towards strengthening epoxy resin

The sizing agent has attracted growing attention to improve the interfacial compatibility of carbon fiber reinforced epoxy resin composites (CF/EP), but suffered from poor interfacial adhesion and environmental problems. Here, a hydrophilic lignin-based sizing agent (OLBEDKC) with remarkable storage stability and sizing effect on CF was obtained by the modifying oxidized lignin rich in hydrophilic groups and compounding with carbon nanotubes (CNTs). After treatment with 2.5% OLBEDKC, the hairiness of the CF fabric was reduced by 73.5%, and the surface energy was increased by 43.7%. Moreover, the use of OLBEDKC in CF/EP composites can lead to an increase in their interlaminar shear strength (ILSS), interface shear strength (IFSS), flexural strength, and flexural modulus by 58.4%, 64.8%, 87.9%, and 24.6%, respectively, compared to those without sizing treatment. This increase is attributed to the π-π conjugation and hydrogen bonding among the OLBEDKC, CF, and EP. Furthermore, the mechanical interlocking between the sized CF and EP effectively enhances their interfacial strength. In summary, this study presents a promising and eco-friendly hydrophilic sizing agent based on lignin that possesses high storage stability and exhibits an excellent sizing effect for CF reinforced EP composites.

Fast and private multi-dimensional range search over encrypted data

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

Liquid hydrogen storage design trades for a short-range aircraft concept

Preliminary design trades for the liquid hydrogen storage system of a short-range aircraft are presented. Two promising insulation methods, namely rigid foam and multilayer insulation, are identified as main design drivers. In addition, the maximal pressure and the shape of the hydrogen storage tank influence the aircraft performance and the insulation efficiency. In this study, the hydrogen storage tanks are integrated in wing pods. The main effects driven by the design parameters are addressed using conceptual and preliminary methods: models are carried out for the storage mass, additional drag, propeller efficiency loss and the dynamical thermodynamic behavior of the liquid hydrogen storage. These effects are coupled making an integrated design method necessary. For the sizing of the liquid hydrogen storage, a multidisciplinary workflow is set up including the aircraft sensitivities on the design mission block fuel. The trade-off study reveals the opposing trend between insulation efficiency and aircraft performance. For the insulation architecture based on rigid foam, the penalties implied by the storage tank on aircraft level and the penalties due to vented hydrogen can be balanced and result in minimal block fuel for the design mission. The application of multilayer insulation avoids venting during the design mission, but has an increased penalty on the aircraft performance compared to rigid foam insulation. Besides the criterion of minimal block fuel, the dormancy time is compared, indicating the thermal efficiency. Applying multilayer insulation, the dormancy time can be increased significantly calling for a discussion of operational requirements for hydrogen-powered aircraft.

Source-Load Scenario Generation Based on Weakly Supervised Adversarial Learning and Its Data-Driven Application in Energy Storage Capacity Sizing

The historical measured data of renewable energy sources and loads can be processed in various ways to generate scenarios for energy storage planning. With the development of advanced forecast technology, the valuable reference of massive forecast data accumulated by the prediction platform in sce-nario generation is ignored. To this end, we propose a new par-adigm based on scenario generation for energy storage planning considering source-load uncertainties. First, a novel generative adversarial network (GAN) is constructed under weakly super-vised learning. The network can extract the prediction errors between predicted data and measured data to generate source-load profiles. The loss function of the network is redesigned to adapt to the data generation task. To match the planning pro-cess, scenario generation and reduction algorithms are embed-ded in the GAN. Then, an energy storage sizing model consider-ing battery health constraints is established. Case studies show that the proposed method can accurately portray the local fluc-tuation characteristics of source-load power and properly re-duce conservatism compared with the model-driven method

Design and Implementation of Internet of Things (IoT) Platform Targeted for Smallholder Farmers: From Nepal Perspective

Nepal, a lower-middle-income country in South Asia, predominantly features smallholder farming communities operating on modest land holdings. These smallholders often adhere to traditional farming methods, relying on familial labor, which has become increasingly inefficient in contemporary agricultural landscapes. To enhance their productivity and efficiency, smallholder farmers require affordable and accessible Internet of Things (IoT)-based systems. However, the prevailing IoT solutions in the market primarily cater to large-scale commercial enterprises, rendering them unsuitable for the specific needs and constraints faced by smallholder farmers. In response to this gap, we have introduced a cost-effective, customizable, scalable, and dependable IoT platform tailored expressly for smallholder farmers. This platform empowers them to visualize, monitor, and control real-time data pertaining to their crops, livestock, and other agricultural assets. To ascertain the efficacy and suitability of our proposed platform, we conducted a comparative analysis with existing counterparts such as Blynk IoT and ThingSpeak IoT, evaluating their respective features and application services against standard requirements. Additionally, we subjected our platform to rigorous server load testing, assessing crucial performance parameters including throughput, response time, user capacity, and data sampling rates. Over an observation period spanning an average of 339 days, our platform successfully processed and stored a substantial volume of data, encompassing 817,633 sensor messages, averaging 2412 messages per day, with a cumulative storage size of 14,238.28 KB. Extrapolating from these results, it is noteworthy that an A0 instance with 20 GB of cloud space can adequately accommodate 200 users at a rate of 100 MB per user, which is adequate for the smallholder needs. Furthermore, the purposed platform was deployed inside a polyhouse to perform off-season grafting of citrus plants. The achieved success rate of 84% closely approached the success rate of 90–95% observed during on-season grafting. These empirical findings, coupled with the extensive data gathered during our research, underscore the reliability and performance of our proposed IoT platform for smallholder farmers.

Decentralized Inverse Transparency with Blockchain

Employee data can be used to facilitate work, but their misusage may pose risks for individuals. Inverse transparency therefore aims to track all usages of personal data, allowing individuals to monitor them to ensure accountability for potential misusage. This necessitates a trusted log to establish an agreed-upon and non-repudiable timeline of events. The unique properties of blockchain facilitate this by providing immutability and availability. For power asymmetric environments such as the workplace, permissionless blockchain is especially beneficial as no trusted third party is required. Yet, two issues remain: (1) In a decentralized environment, no arbiter can facilitate and attest to data exchanges. Simple peer-to-peer sharing of data, conversely, lacks the required non-repudiation. (2) With data governed by privacy legislation such as the GDPR, the core advantage of immutability becomes a liability. After a rightful request, an individual’s personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve these issues, we present Kovacs , a decentralized data exchange and usage logging system for inverse transparency built on blockchain. Its new-usage protocol ensures non-repudiation, and therefore accountability, for inverse transparency. Its one-time pseudonym generation algorithm guarantees unlinkability and enables proof of ownership, which allows data subjects to exercise their legal rights regarding their personal data. With our implementation, we show the viability of our solution. The decentralized communication impacts performance and scalability, but exchange duration and storage size are still reasonable. More importantly, the provided information security meets high requirements. We conclude that Kovacs realizes decentralized inverse transparency through secure and GDPR-compliant use of permissionless blockchain.

Integration of ocean thermal energy conversion and pumped thermal energy storage: system design, off-design and LCOS evaluation

Increasing the penetration of Variable Renewable Energy (VRE) in electric generation systems is a fundamental goal in reducing greenhouse gases emission. To reduce power fluctuations in electricity networks and avoid curtailment, large-scale energy storages represent one of the most promising solutions. Thermally-Integrated Pumped Thermal Energy Storage (TI-PTES) systems are an interesting technology that can be used for this scope if the heat source adopted for thermal integration can provide significant thermal power. The ocean temperature gradient in tropical areas is an attractive heat source to be coupled with the PTES system to realise efficient electric storage when integrated with an Ocean Thermal Energy Conversion (OTEC) system. In this study, a heat pump refrigerated by the warm tropical surface water uses electricity surplus from VRE to heat an amount of water contained in an end-life cargo ship used as water storage. The system discharges the stored energy through an ORC cycle refrigerated by the cold deep seawater when VRE production is low. A preliminary sensitivity analysis of the storage size and temperature is proposed through detailed system modelling to define the optimal design and layout. Therefore, the part-load analysis of the system is assessed to characterise the off-design performances and evaluate the potentialities of this system when applied to a plausible case study that includes VRE generation and an electric demand profile. Finally, the Levelised Cost Of Storage (LCOS) is evaluated and compared to other storage technologies. Results show that the round-trip efficiency may achieve values higher than 60 %, and an equivalent electric battery capacity of 20 MWh is feasible using end-life ships acting as energy storage. In contrast, the obtained LCOS of 388 €/MWh is still not competitive in the energy market. However, since tropical areas have high energy prices, considering this application for remote island electrification could be an interesting solution.

SOCIALIZATION OF THE USE OF GOOGLE DRIVE AT PUSKESMAS AMBACANG KOTA PADANG

Computer technology has developed significantly, from Windows application programs to increasingly sophisticated data storage media. In the past, we usually stored data using diskettes with small storage sizes, then Flashdisk and Hardisk appeared, until the entry of a new era, namely Cloud storage technology. Cloud technology itself has broad functions, one of which is as an online data storage medium. Like the familiar cloud-based data storage service, Google Drive can store up to 15GB of data for free. In addition to data storage like Google Drive, there is also cloud data storage, such as DropBox, OneDrive, and others. By using Google Drive, users experience many benefits, including being able to share data with those who need data. There is no need to store files on a flash disk or hard disk, where our files are directly stored on Google servers, which can be used at any time, modified. In using Google Drive, many Ambacang Health Center staff do not understand Google Drive. The purpose of holding this Community Service is so that all staff can understand Google Drive how to use it and its features, the results achieved are that all participants understand and understand the use of Google Drive. This socialization has been carried out 2 times, on May 27 and June 17, 2023, with lecture and discussion methods.

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Digital twins can reflect the dynamical behavior of the identified system, enabling self-diagnosis and prediction in the digital world to optimize the intelligent manufacturing process. One of the key benefits of digital twins is the ability to provide real-time data analysis during operation, which can monitor the condition of the system and prognose the failure. This allows manufacturers to resolve the problem before it happens. However, most digital twins are constructed using discrete-time models, which are not able to describe the dynamics of the system across different sampling frequencies. In addition, the high computational complexity due to significant memory storage and large model sizes makes digital twins challenging for online diagnosis. To overcome these issues, this paper proposes a novel structure for creating the digital twins of cooling fan systems by combining with neural ordinary differential equations and physical dynamical differential equations. Evaluated using the simulation data, the proposed structure not only shows accurate modeling results compared to other digital twins methods but also requires fewer parameters and smaller model sizes. The proposed approach has also been demonstrated using experimental data and is robust in terms of measurement noise, and it has proven to be an effective solution for online diagnosis in the intelligent manufacturing process.

PENGARUH DURASI IRIGASI TETES TERHADAP REDUKSI LENGAS TANAH PADA LAHAN POLYBAG

Soil moisture is an important factor needed by plants for growth, and therefore the amount of soil moisture that can be stored depends on the type of soil or the composition of the soil-forming materials. Compost is one of the nutrients that is often used in the manufacture of planting media on land in the form of polybags, and the amount of compost composition on the soil is thought to greatly affect the ability to store water in the media, both the size and duration of storage. This study aims to examine the effect of using compost on storage of irrigation water in the form of soil moisture and how long it can be stored. in three-storey drip irrigation. The main data analyzed is soil moisture data (w) and the results of the analysis and discussion and conclusions are drawn descriptively. The results of the analysis show that the initial soil moisture content in the range of 19% -25%, can be given additional soil moisture by irrigation of about 12%. The average daily decrease in soil moisture that occurs in soil variation 1 (70%: 30% ) is 3% -4.4% and in soil variation 2 (50%: 50%), the average moisture reduction is around 3% -6%, soil moisture reduction in variation 2 is greater than soil variation 1 for all floors.