Storage Model Research Articles

Experimental validation of a design model for latent thermal energy storage extended with sensible heat

Latent thermal energy storage can be a key technology for a green energy transition by matching fluctuating heat demand and supply. In order to implement a storage system, it needs to be designed which requires estimating the outlet temperature of a system for a given geometry and time history of the heat transfer fluid’s mass flow rate and inlet temperature. Currently, design methods are either overly simplistic, focusing solely on e.g. the phase change time or requiring the solution of partial differential equations which can be computationally expensive. The present paper proposes a novel approach where a latent thermal energy storage system is decomposed into a heat transfer fluid vessel, a sensible storage system and a storage system with only latent heat. Computationally inexpensive models are available for all three of these sub heat exchangers. A heat exchanger model is obtained by connecting the sub heat exchangers in parallel. This novel approach is used to model an industrial scale shell and tube latent thermal energy storage heat exchanger. The predicted outlet temperature is compared to the measured outlet temperature and the design model obtains good agreement.

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

Research on Edge Cloud Data Storage Method of Power Operation Site in Internet of Things Environment Based on Paxos Algorithm

Abstract In order to realize the efficient storage of electric power job site data and ensure the utilization effect of the stored data, a method of electric power job site edge cloud data storage based on the Paxos algorithm is proposed. In this method, power field operation data are collected comprehensively through the edge server and edge controller in the edge computing module, the collected data are processed, and the data storage tasks are allocated reasonably. The allocated data are transferred to the data storage module, which identifies the bad data in the data through a gap statistics algorithm and cluster analysis and retains the valid and normal data. After the primary and secondary nodes are determined based on the Paxos algorithm, the data storage model of power operation site is constructed. After the consistency detection of the reserved data, the data storage of power operation site is completed. The platform management module can analyze the stored data and present the analysis results to the application decision-making module for presentation. The test results show that the maximum time delay and energy consumption are 2.15 s and 42.6 W, respectively, when the method is used for data transmission, which can accurately identify the bad data in the field data of electric power operation, and the reliability index results of data consistency detection are all above 0.95, which ensures the good consistency of the stored power operation site data and effectively completes the power operation site data storage.

Modeling and simulation of xenon storage and supply system for electric propulsion system based on fundamental equation of state

The electric propulsion system (EPS) xenon storage and supply system (SSS) is a complex pipeline network system, which involves the multi-physics coupled fields of transcritical flow, heat transfer, rigid body motion, electromagnetics, and control. Based on the fundamental equation of state, a one-dimensional finite volume model system is proposed for compressible transcritical transient pipe flow. Six typical components of the SSS are modularization modeled by using an object-oriented programming method in Fortran 2018. To evaluate the performance of the algorithm, a simulation model consisting of 77 components is developed for a proportional controlled EPS xenon SSS, and a system-level simulation is performed. Compared with the standard case constructed on the Amesim platform, the model has a pressure rise time error lower than 6%, and the restrictors mass-flow-rate errors of the anode, cathode, and neutralizer are less than 2.8% of the rated flow rate after stabilization. Moreover, the proposed model yields more accurate results at certain operating points. The model system provides an efficient reference for the design and optimization of EPS SSSs, and offers a new approach to simulate complex cryogenic pipe flow systems.

A new method of estimating shelterbelt carbon storage on the regional scale: Combined the single tree carbon storage with tree numbers

The carbon storage potential of planted shelterbelts was gradually recognized in recent years, but there was a lack of shelterbelt knowledge for carbon inventory analyses because of their narrow linear feature and extensive distribution. Considering its own characteristics of the shelterbelt, this paper proposed a new remote sensing model for estimating the carbon storage of farmland shelterbelt (CSBelt) based on the single tree carbon storage and tree numbers. In this model, the single tree carbon storage model was established by shelterbelt age, which could be identified from time series images. Tree numbers of each shelterbelt, as another important parameter in the CSBelt model, was detected by numbers of rows, columns, and preservation rate, which were monitored by high resolution remote sensing image and geographic information system method. Finally, overall accuracy (OA), mean absolute error (MAE), and mean error (ME) was used to validate the remotely identified data. The results showed that, the OA of the single tree carbon storage was 80.4% within the error range of 0.05 MgC/tree, the MAE was 0.036 MgC/tree, and ME was 0.020 MgC/tree; the OA of tree numbers was 71.7% within the error range of 100 trees/km, the MAE was 94 trees/km, and ME was 73 trees/km; the OA of shelterbelt carbon storage was 82.6% within the error range of 100 MgC/km, the MAE was 49.63 MgC/km, and ME was 33.78 MgC/km. Compared to other carbon storage models, this model requires fewer parameters and is not restricted by the stand conditions, which has better universality and realizability. This research is helpful for the accurate assessment of shelterbelt ecosystem carbon storage, which is of great significance for predicting climate change and formulating strategies. The results of this research will also provide data support for shelterbelt management on a regional scale.

Expedition to the blockchain application potential for higher education institutions

In the education sector, blockchain is currently at the end of the peak of inflated expectations in Gartner’s Hype Cycle. Thus, it is crucial to understand whether this technology meets the expectations of Higher Education Institutions (HEIs). We go on an expedition to identify blockchain application scenarios and its potential for HEI administration—the universities are digitalized to just 23.3%.Current information systems research addresses classifications of blockchain-based projects (application level) rather than their technical realization (protocol level). Thus, when evaluating blockchain application scenarios in HEI administration, we intensively consider the technical side of blockchain-based projects. We perform a three-step approach: (1) systematic literature review, (2) qualitative exploratory semi-structured interviews to supplement information on market-ready solutions, and (3) an evaluation of the potential of the blockchain-based projects identified, based on HEI administration requirements.We find that the leading blockchain application scenarios are credential verification and record-sharing. At the protocol level, we obtain equivocal results regarding the technical realization of projects, e.g., their underlying blockchain types and storage models. At the application level, when discussing the potential of different projects, we find that most of them address adaptability, complexity decomposition, and cost reduction requirements between HEIs; interest diversity and stakeholder collaboration between HEIs and business actors; privacy and trust between HEIs and students.

Value Evaluation Model of Multi-Temporal Energy Storage for Flexibility Provision in Microgrids

With the advancement of distributed power generation technology and the deepening of the low-carbon transformation of energy structure, a high proportion of renewable energy has become an inevitable trend in future energy systems, especially for microgrids. However, the volatility and uncertainty associated with renewable energy pose significant challenges to the secure and stable operation of power systems, necessitating the exploration of the flexible regulation of resources. Energy storage, as a crucial flexible resource characterized by technological diversity and a variety of regulation capabilities, has been extensively studied and applied. Nonetheless, the high investment costs and limited returns of energy storage technology, coupled with the ambiguous utility in different scenarios under the current electricity market’s framework, complicate its broader application. To thoroughly analyze the utility of energy storage in facilitating flexible adjustments in microgrids, this study developed a composite weight-TODIM (an acronym in Portuguese for interactive and multi-criteria decision making) model for assessing the utility of energy storage that incorporates heterogeneity in the risk preferences. This model enabled a comparative analysis of the utility of energy storage technology across multiple scenarios, taking the risk preferences of decision-makers into account, thereby providing strategic insights for the application of multi-temporal energy storage in microgrids. The feasibility and effectiveness of the model were validated through a case study analysis.

Modeling and energy management strategy of hybrid energy storage in islanded DC micro-grid

Modeling and energy management strategy of hybrid energy storage in islanded DC micro-grid

An Optimized Data Storage in A Secure Cloud-Edge Collaboration A Fault Tolerance Approach

The rise of edge smart IoT devices has led to the development of edge storage systems (ESS) for efficient access to massive edge data. ESS can reduce the load on cloud centers and improve user experience. However, ESS still faces challenges in improving fault tolerance and efficiency. Thus, there is a need for a secure and efficient fault-tolerant storage scheme. Existing schemes have drawbacks like high edge storage overhead, difficulty in protecting edge data privacy, and low data writing performance. To address these issues, we propose a Hierarchical Cloud-Edge Collaborative Fault-Tolerant Storage (HCEFT) model. This model aims to enhance system robustness, reduce edge storage overhead, and ensure edge data privacy. We also introduce an optimization method for data writing in HCEFT, called ECWSS (Erasure Code data Writing method based on Steiner tree and SDN). This method improves the trade-off between data writing time and traffic consumption. Our scheme improves data robustness, availability, and security. Additionally, the writing optimization method reduces data write time by 13%-67% and network traffic consumption by 20%-62%, enhancing network load balance performance.

Public opinion bunching storage model for dense graph data in social networks1

Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing.

Thermal performance evaluation during charging/discharging cycles of sensible energy storage beds for biogas production process

The breakdown of organic waste, through anaerobic digestion, for production of biogas is a temperature dependent process. The favorable temperature required for efficient production is 35 °C. Attaining the temperature required for this process, under normal conditions, is a challenging task. Hence, the development of an effective heating system is necessary. The warm water from the solar water heater is circulated through the wall of the digester wall using a heat exchanger. The intermittency in the supply of energy, as in the case of solar radiation, creates a mismatch between the demand and the supply of the energy. Hence, development of efficient energy storage system that can be used as stand-by in case of unavailability of the primary energy source.The present work modeled and predicted the thermal performance of sensible heat storage system having 10 MJ capacity which can deliver warm water at 35 °C for 4 hr. The sensible heat storage system unit is a regenerator type heat exchanger which stores/releases heat on passing hot/cold heat transfer fluid through the tubes embedded into it. A mathematical model of a cylindrical configuration with embedded multi-tube is developed employing pebbles and cast iron as storage mediums. The number of embedded charging/discharging tubes in the storage model is optimized based on the charging time using a finite element method based simulation tool (®COMSOL Multiphysics). A parametric study, including charging/discharging time, energy stored/recovered, exergy stored/recovered, and charging/discharging fraction of the system, have been used to evaluate the thermal performance. Further the effect of heat transfer fluid tube diameter and velocity, volumetric heat capacity and thermal conductivity of the storage media on performance of the storage is also investigated.

The integration algorithm of digital resources in business administration based on cluster analysis

The field of business management involves a large amount of data and information sources, including market data, customer data, supply chain data, etc. In order to quantify and analyze different resources, help enterprises better plan and allocate resources, and improve resource utilization efficiency, a clustering analysis based digital resource integration algorithm for business management is studied. Build a business management digital resource integration framework, including data layer, integration layer, and storage layer, to integrate and store data from different sources of business management databases, thereby facilitating unified management and utilization of digital resources by enterprises. The data layer collects data from different business management databases and stores it in the database according to different sources; The integration layer preprocesses the collected data, simply fixes errors and missing information in the data, and improves data quality. Adopting a feature extraction method based on the projection direction uncorrelation strategy of the labeled power set conversion method, the useful feature information of digital resources in enterprise management can be effectively extracted; Based on the two-step clustering analysis method, business management digital resources are clustered according to similar characteristics to complete the classification and integration of business management digital resources, and improve the efficiency of resource utilization; The storage layer adopts the Security Information Diffusion Algorithm (IDA) storage model to store integrated and classified digital resources managed by enterprises, ensuring data security and effectively preventing data leakage and illegal access. The experimental results show that the digital resource structure of business management integrated by this algorithm is clear, with a data redundancy of less than 8% and a difference of less than 11%. The time consumption for data integration is less than 2.11 minutes, indicating good resource integration ability.

Blockchain-based cooperative game bilateral matching architecture for shared storage

The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered around pricing, leading to a scenario where node providers often engage in price competition to obtain greater rewards, resulting in an imbalance in resource allocation for both buyers and sellers. In contrast, a distributed storage model based on cooperative game theory can better facilitate cooperation and resource sharing among node providers. This paper designs a storage resource allocation algorithm based on the stable marriage matching algorithm, demonstrating the stability of this algorithm as a matching solution. The paper also analyzes the differences between cooperative and non-cooperative game models in the market, and explores an equilibrium pricing mechanism guided by supply and demand. Furthermore, the paper introduces a trading mechanism for storage resources, including publication standards and matching schemes, ensuring efficient and trustworthy interaction between storage suppliers and demanders in a decentralized network centered around storage resources, thus enabling the circulation of the value of storage resources. A prototype of a blockchain-based shared storage trading system is implemented in this paper, utilizing bilateral matching for tradings. System evaluation and experimental testing are conducted, with results showing that the average utility value of the matching trading mechanism proposed in this paper outperforms the Double Auction-based matching model under any Poisson distribution (λ = 0.1, 0.2, 0.3, …, 0.9) conditions set in the experiments. Additionally, compared to the traditional approach of directly storing complete data content on the chain, the design proposed in this paper effectively reduces on-chain storage consumption by approximately 27.06%.

Task-Offloading Strategy of Mobile Edge Computing for WBANs

In recent years, mobile edge computing has become one of the popular methods to provide computing resources for the body area network, but existing research only considers the problem of minimizing the cost of offloading when solving the optimization problem of task-offloading, ignoring the trust problem of edge computing nodes, and offloading tasks on edge nodes may cause user information disclosure and reduce the quality of user experience. In response to this situation, this study aims to minimize the average user cost and designs a task-offloading strategy based on the D3QN (dueling double deep Q-network) algorithm in conjunction with the blockchain information security storage model. This strategy uses deep reinforcement learning algorithms to obtain the minimum average offloading cost of the system while considering user latency, energy consumption, and data protection conditions. The experimental simulation results show that compared to traditional schemes and other reinforcement learning-based schemes, this scheme can more effectively reduce the average cost of the system, and the average cost is reduced by 31.25% when reaching convergence. In addition, as the complexity of the model increases, this scheme can provide users with better experience quality, with 53.7% of the 1000 users having a very good experience quality.

Benefit of Multivariate Model Calibration for Different Climatic Regions

AbstractHydrological models are traditionally calibrated against observed discharge. However, for a given model, similar performance for discharge simulation can be achieved through a variety of parameter combinations, some of which produce unrealistic simulations of non‐discharge variables. Thus, considering non‐discharge variables in calibration can help to reduce equifinality and give more realistic simulations. Few studies considered non‐discharge variables in calibration and evaluation and across a large sample of catchments. In this study, we calibrate the lumped SIMHYD model with several combinations of different hydrological variables such as discharge, actual evapotranspiration, soil wetness, and total water storage. The various calibration scenarios are conducted across 550 catchments in Australia spanning arid, tropical and temperate regions. We show that the performance of the discharge‐calibrated model for actual evapotranspiration, soil wetness, and total water storage is strongly related to the co‐seasonality of discharge and the respective variable. Considering actual evapotranspiration, soil wetness, or total water storage in addition to discharge in calibration improves the simulation of the added variable on average by 53%, 58%, and 41%, respectively, which comes at the expense of decrease in discharge performance by 5%. Cross‐benefits, that is, the improved simulation of a variable by considering another variable in calibration, mainly occurs between actual evapotranspiration and total water storage and are most pronounced in humid catchments with highly seasonal soil wetness. We also find that when using all four variables in model calibration, the trade‐off in model performance between discharge and the other variables gets more pronounced with increasing catchment wetness.

Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

Day-Ahead and Intraday Two-Stage Optimal Dispatch Considering Joint Peak Shaving of Carbon Capture Power Plants and Virtual Energy Storage

The anti-peaking characteristics of a high proportion of new energy sources intensify the peak shaving pressure on systems. Carbon capture power plants, as low-carbon and flexible resources, could be beneficial in peak shaving applications. This paper explores the role of carbon capture devices in terms of peak shaving, valley filling, and adjustment flexibility and constructs a virtual energy storage model utilizing various flexible loads on the demand side. Also, it proposes a joint peak shaving strategy involving carbon capture devices and virtual energy storage. Considering the predictive error characteristics of wind power and load across different time scales, this study establishes a day-ahead and intraday two-stage rolling regulation peaking model based on carbon capture power plants and virtual energy storage to fully leverage the diverse response speeds and peak shaving capabilities of various types of flexible loads. Initially, the model calculates the net load curve after implementing a demand response system, aiming to minimize the load peak–valley difference. Subsequently, within the intraday period, it seeks to minimize system operating costs by precisely allocating peak shaving resources as per demand, thus aiming for economic efficiency while ensuring the system’s peak shaving capability.

Standalone hybrid power-hydrogen system incorporating daily-seasonal green hydrogen storage and hydrogen refueling station

In light of the recent developments in the green hydrogen industry, it is essential to investigate its compatibility and integration with existing electrical grids. This task becomes more challenging when considering its implementation in off-grid sites that solely rely on renewable energy. In cases where off-grid sites should provide electricity and hydrogen to a variety of consumers, it is compulsory to have electrical and hydrogen storage devices with long-term storage capabilities. Consequently, a precise programming technique is necessary to manage the storage and consumption of hydrogen and electricity. To investigate such a model, this paper presents a connected electricity-hydrogen grid in which all the required energy is supplied by solar and hydroelectric units. This off-grid system supplies the necessary energy for industrial and residential loads, electric vehicle charging stations, fuelcell car refueling stations, and natural gas pipelines. The power-to-hydrogen (P2H) and hydrogen-to-power (H2P) systems are used to facilitate energy flow between the electrical and hydrogen sectors. A daily-seasonal hydrogen storage is integrated as well. The proposed method contributes by integrating and improving the modeling, management, optimization, and operation of the daily-seasonal hydrogen storage and electrical storage. Its primary objective is to supply electricity and hydrogen to various consumers using solely renewable energy, even in the face of renewable energy fluctuations and outages. The model is mathematically expressed as a mixed integer linear optimization, implemented in GAMS software, and solved by the CPLEX solver. It aims at maximizing profit from selling electricity and hydrogen to the users. Results demonstrate that P2H and H2P units have a complementary operation with the solar system. When there is abundant solar energy, the P2H converts excess clean energy to green hydrogen and H2P operates when solar energy is not available. Seasonal storage also stores hydrogen from spring to autumn and discharges it in winter because of higher hydrogen prices in this season. Such an operation increases the annual profit by about 9%. When the hydroelectricity unit faces an outage in hours 8 to 13, the fuelcell uses the stored hydrogen and comes into operation to compensate hydropower outage. The net present value of profit amounts to $275,179 per year. The total hydrogen required is approximately 49,870 kg per year, with a total electricity requirement of 1190 MWh per year. The levelized revenue from hydrogen is achieved at around $4 per kilogram, while the levelized revenue from electricity is about $0.058 per kilowatt-hour. It is concluded that the integration of seasonal storage effectively reduces costs, boosts profits, and efficiently manages renewable energy fluctuations across different seasons.

A new optimization model for carbon capture utilization and storage (CCUS) layout based on high-resolution geological variability

Carbon capture utilization and storage (CCUS) is expected to play a pivotal role in achieving carbon-neutral pledge. However, the neglect of geological volatility in existing models will lead to obvious bias in the CCUS layout optimization results. Therefore, a new optimization model for CCUS layout is developed based on high-resolution geological variability. Finally, the model is validated by solving the optimal CCUS layout of coal-fired power plants (CFPPs) in a case region till 2060. The obtained results show that (i) To accomplish the same emission reduction targets, the total length of the pipeline network by 2060 in the scenario considering high-resolution geological variability (HGV) will decrease by 12.5% compared with the scenario without geological variability (NGV); (ii) The total crude oil production can be enhanced by about 19.0% in HGV scenario compared with NGV due to the optimized CO2 source-sink matching; (iii) Comparing with the NGV, the CCUS layout under the HGV can decrease 26.1 and 32.7 CNY/tCO2 in capital costs and O&M costs respectively, and ultimately improve the total profit by 149.4 CNY/tCO2. In summary, the proposed new model can solve the optimal CCUS layout and operation more practically and accurately.

Mitigation of Renewable Energy Source Production Variability in Residential Stock through Small-Scale Gas–Liquid Energy Storage Technology Application

Despite renewable energy source integration being a well-established requirement in international policies, energy systems still face some unresolved issues, including the intermittence of production. To tackle this problem, a viable solution could comprise the off-peak storage of electricity production excess, to be consumed later during peak-load hours. The transition from the diffuse pattern of centralized generation to the distributed model, involving energy communities, suggests an additional aspect to manage: the spatial constraints of systems for domestic applications. Compressed-air energy storage represents a promising Power-to-Power technology for small-scale energy integration. This study proposes the application of a gas–liquid energy storage system (GLES) in a residential building, using renewable energy excess from a photovoltaic (PV) array. The performance of the proposed system, whose operation involves the compression of the gaseous mass through a piston operated by mineral oil, was evaluated through energy analysis performed simulating the devices and their coupling with the load profiles of the building. The thermodynamic model of storage was validated using data from an experimental campaign on a prototype. A sensitivity study, acting on the features of the system, such as the compression rate and vessel size, allowed us to compare the absorbed PV energy excess, the coverage of the building energy demand during the expansion phase, and the electrical efficiency of a daily cycle. The results obtained, together with the related economic analysis, were used to quantify the market potential of the proposed solution, to be exploited as a mechanical alternative to conventional electric batteries in dwellings.