Backup Storage Systems Research Articles

Integrated Home Energy Management with Hybrid Backup Storage and Vehicle-to-Home Systems for Enhanced Resilience, Efficiency, and Energy Independence in Green Buildings

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management.

Nonlinear Quadratic Programming Algorithm Modelling for Operation of The Off-Grid Energy Resources System with Backup Storage System

Nonlinear Quadratic Programming Algorithm Modelling for Operation of The Off-Grid Energy Resources System with Backup Storage System

A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

Background: This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ANN algorithm. The ANN algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed. : Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. Objective: The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of an electrical energy storage system and compare the results obtained with the classical control based on the PI regulator. Methods: The state representation of the bidirectional Buck-boost converter was performed in order to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine the charge and discharge current according to a comparison between the power produced and consumed. Results: The simulation results obtained by two control methods can be used to compare and select the appropriate control method to achieve optimal efficiency of the storage system. Conclusion: The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.

Fault-tolerant backup storage system for confidential data in distributed servers

Fault-tolerant backup storage system for confidential data in distributed servers

Understanding the Potential Impact of Climate Change on Hydropower Generation in Rwanda

The primary energy sources used in Rwanda include solar energy, methane gas, diesel and heavy oil generators, hydropower plants, and thermal power plants. Hydropower is a significant renewable low-carbon energy source, generates more than 45% of Rwanda's total electricity. It requires water to produce energy, therefore any modifications to the natural hydrological cycle brought on by climate change have an impact and will continue to have an influence on the production of electricity. Hydropower is sorrow from an increase in extreme weather occurrences and erosion. Therefore, by altering flow (discharge) and head, climate change may have an effect on hydropower. The amount of sediment that is transferred to the reservoir lowers its capacity, and the dry season may result in less water in the reservoir, which has an impact on the hydroelectric power generation process. According to the study, changes in river flow have the greatest impact on how hydropower is affected by climate change. Droughts, seasonal and frequent floods, and greater variability due to climate change are the main contributors. The results show that climate change is likely to have a more pronounced impact on hydropower operations, with reductions in water availability due to climate change expected to result in reduced power generation. Additionally, existing dams will be located in areas at high risk of flooding due to climate change. In order to ensure electricity availability throughout the year, especially during the hot or dry seasons when discharges are often low, the required changes should be performed. These adjustments include using alternate backup sources and storage systems. Water consumers can be conserved and optimized through water resource management, drought resistance, flood protection, and conservation.

Energy Storage and Electric Vehicles: Technology, Operation, Challenges, and Cost-Benefit Analysis

With ever-increasing oil prices and concerns for the natural environment, there is a fast-growing interest in electric vehicles (EVs) and renewable energy resources (RERs), and they play an important role in a gradual transition. However, energy storage is the weak point of EVs that delays their progress. The world’s EV industry is accelerating to faster adoption with appropriate incentives to the EV owners, policy support, and encouraging local manufacturing. The increasing demand for EV’s has presented itself as an authentic alternative to internal combustion engines (ICE). The main feature of the RERs is their variability and intermittency. These drawbacks are overcome by integrating more than one renewable energy source including backup sources and storage systems. This paper presents various technologies, operations, challenges, and cost-benefit analysis of energy storage systems and EVs.

Design and Simulate an Off-Grid PV System with a Battery Bank for EV Charging

This paper presents the control and design of a stand-alone photovoltaic (PV) system with a battery bank for an electric vehicle (EV) battery charging. It also describes the necessary requirements and power electronic converters design for the EV battery charging station. The design of a bidirectional converter to allow for bidirectional power flow control to regulate the charging and discharging of the battery bank is presented. The PV system connected to the battery bank system is used to enhance the power output of renewable energy sources, regulate electrical power to effectively charge batteries, draw maximum power from solar panels, and provide a high-quality DC output for electric vehicle charging. The PV is the primary power source of the system, and a battery bank is used as a backup storage system. A comparison between the performance both of lithium-ion batteries and lead-acid batteries based on using it as a backup storage system for the off-grid PV system is presented. The proposed system has been designed and simulated in the MATLAB-Simulink environment. The effective performance of the proposed station for EV battery charging is achieved through the simulation results. A proposed charging station cost estimation is performed to present a powerful index of the proposed charging station's effectiveness.

Techno-Economic Analysis of a Stand-Alone Hybrid Wind-Power Fuel-Cell Grid System: A Case Study in Shahryar Region of Tehran

Abstract Decreasing fossil energy resources and increasing greenhouse gas emissions increase the need for clean and renewable energy sources day by day. One of the sources of renewable energy is wind power, which has been constantly evolving in recent years. Wind power plants are sometimes unresponsive during peak hours, so a backup storage system seems essential for these power plants. In this study, a hybrid system is presented for connection to wind power plants consisting of fuel cell and hydrogen production, to provide reliable power and valuable by-products. In this paper, a case study is conducted for the desired system in the Shahryar County. The techno-economic optimization of the above system indicates that in the best design (consisting of three wind turbines), the annual production capacity of the hybrid system will be 1795 MWh, of which 12 % is the share of fuel cells. The results show that the hybrid system increases the capacity factor of the wind power plant by 2.8 %. The calculated cost of energy (COE) and the net present cost (NPC) for the hybrid system would amount to $ 0.77 and $ 5 235.066, respectively.

Optimal Balancing and Control of a Dynamic Load Demand in a Grid Connected Hybrid System using Feed–in Tariff Approach

In this paper, we have proposed, designed and simulated a grid connected residential smart home system benefiting from hybrid energy system (HES) for optimal balancing and control of a dynamic load using Feed–in Tariffs (FiT) tariffs by increasing the onsite self–consumption of solar PV system. To achieve the object function, two different PV system configuration scenarios were designed, simulated and analyzed for different case studies. In first scenario, PV system without battery backup storage system (BBSS) under FiT scheme with flat tariffs was investigated for a typical day of winter and summer. In second scenario, PV system with BBSS under FiT scheme with ToU tariffs was investigated again for a typical day of winter and summer. The optimal balancing and dynamic load control can be achieved by increasing the penetration level of solar PV by maximizing the onsite self–consumption of PV system, minimizing the grid electricity import in high ToU tariff periods, charging the battery during low ToU tariffs, discharging the battery during high ToU tariffs and exporting the excessive PV generation in peak PV generation periods especially in summer season. The simulated results suggest that significant revenue can be saved, and optimal balancing and control can be achieved when PV system is configured with BBSS under FiT scheme by utilizing ToU tariffs. The proposed will be more cost effective and optimal control can be achieved if implemented to residential community microgrids by using cooperative game theory approach.

Anomalous Behavior of Anion Exchange Membrane during Operation of a Vanadium Redox Flow Battery

Vanadium redox flow batteries (VRFBs) are becoming an integral component of renewable energy solutions, microgrids, and backup storage systems. The reduction in cell resistance due to advancements in the electrode and overall component design allows flow cells to operate at higher current/power densities. During discharging of an experimental flow cell at higher current densities, under certain conditions, an unexpected voltage drop and subsequent recovery were observed. Similar effects of voltage drop and recovery have been observed in NiCd and primary lithium–thionyl chloride (LTC) batteries but have not yet been reported for the VRFB in peer-reviewed publications. This study aims to understand the causes behind the voltage drop effect in a VRFB under high power density operation.

Optimal and Economic Evaluation of a Stand-alone Microgrid for Electricity and Water Supply for Namibia’s Rural Village

Stand-alone microgrid hold a primary solution for electricity and water supply in remote areas access to National grid is not possible. This paper presents a detailed optimal sizing and economic evaluations of a stand-alone microgrid for a remote village (Amarika) in Namibia. Several renewable energy sources such as wind turbines and photovoltaic arrays were considered with a battery backup storage system and a reverse osmosis desalination plant for water supply. Modelling of the microgrid was done based on the meteorological data, the daily water and energy demand of the village. Particle swarm optimization was employed for the system techno- economic optimization: to determine a suitable microgrid configuration that can be established at minimum cost. Sensitivity analysis of the system was performed to examine the effect of variation of LPSP on LCOE. The results demonstrate that the optimized microgrid configuration and the optimization algorithm are effective and can be adopted in supplying power and water to the village. The levelized cost of electricity proves the economic feasibility of the microgrid. The levelized cost of electricity falls within a 90% standard deviation (σ=0.065) of the mean. This proved to be economically feasible with a 96.5% reliability of power supply.

An implementation of real-time air quality and influenza-like illness data storage and processing platform

Recently, air pollution has become the primary concern in Taiwan as it significantly affected people's health. Some air pollution monitoring, analysis, and prediction systems were proposed to solve the problem. However, there is very little research to see whether the air quality is associated with the Influenza-Like Illness (ILI) disease or not. In this study, a system is needed, in which the air quality data and the influenza-like illness data can be analyzed together to determine their associations accurately and effectively. In this work, a novel integrated platform was implemented by building a cluster environment based on Hadoop, Spark and a visualization environment with ELK Stack as well as a backup storage system based on Ceph object storage architecture. Also, Sqoop and Alluxio were used to solve the inefficiency problem in processing vast amounts of data. The experimental results showed the visualization of air quality and influenza-like illness data collected from 2016 to 2017 in Taichung, Taiwan. Besides, the association analyses and discussion between air quality and influenza-like illness were also presented.

Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System

The increased penetration of renewables is beneficial for power systems but it poses several challenges, i.e., uncertainty in power supply, power quality issues, and other technical problems. Backup generators or storage system have been proposed to solve this problem but there are limitations remaining due to high installation and maintenance cost. Furthermore, peak load is also an issue in the power distribution system. Due to the adjustable characteristics of loads, strategies on demand side such as demand response (DR) are more appropriate in order to deal with these challenges. Therefore, this paper studies how DR programs influence the operation of the multi-microgrid (MMG). The implementation is executed based on a hierarchical energy management system (HiEMS) including microgrid EMSs (MG-EMSs) responsible for local optimization in each MG and community EMS (C-EMS) responsible for community optimization in the MMG. Mixed integer linear programming (MILP)-based mathematical models are built for MMG optimal operation. Five scenarios consisting of single DR programs and DR groups are tested in an MMG test system to evaluate their impact on MMG operation. Among the five scenarios, some DR programs apply curtailing strategies, resulting in a study about the influence of base load value and curtailable load percentage on the amount of curtailed load and shifted load as well as the operation cost of the MMG. Furthermore, the impact of DR programs on the amount of external and internal trading power in the MMG is also examined. In summary, each individual DR program or group could be handy in certain situations depending on the interest of the MMG such as external trading, self-sufficiency or operation cost minimization.

Cryopreserved storage of clonal germplasm in the USDA National Plant Germplasm System

The US Department of Agriculture-Agricultural Research Service (USDA-ARS), National Plant Germplasm System (NPGS) plant collections are a critical source of genetic diversity for breeding and selection of improved crops, including vegetatively propagated plants. Information on these collections is readily accessible to breeders and researchers on the internet from the Germplasm Resources Information Network (GRIN). The clonal collections are at risk for loss due in part to their genetic diversity that makes growing them in one location a challenge, but also because it is difficult to have duplicate collections without incurring great expense. The development of cryopreservation techniques during the last two decades provides a low maintenance form of security backup for these collections. National plant collections for vegetatively propagated crop plants and their wild relatives are maintained by the USDA-ARS, NPGS at 15 sites across the country. These sites include various combinations of field, greenhouse, screenhouse, and in vitro collections. Cryopreserved backup collections in liquid nitrogen storage were instituted in the 1990s, increased greatly in the 2000s with the advent of new techniques, and are continuing today. Collections of dormant buds of temperate trees, shoot tips of in vitro cultures of many crops, and embryonic axes of some large seeded or recalcitrant seeded plants are all part of the clonal backup storage system.

A Lookahead Read Cache: Improving Read Performance for Deduplication Backup Storage

Data deduplication (dedupe for short) is a special data compression technique. It has been widely adopted to save backup time as well as storage space, particularly in backup storage systems. Therefore, most dedupe research has primarily focused on improving dedupe write performance. However, backup storage dedupe read performance is also a crucial problem for storage recovery. This paper designs a new dedupe storage read cache for backup applications that improves read performance by exploiting a special characteristic: the read sequence is the same as the write sequence. Consequently, for better cache utilization, by looking ahead for future references within a moving window, it evicts victims from the cache having the smallest future access. Moreover, to further improve read cache performance, it maintains a small log buffer to judiciously cache future access data chunks. Extensive experiments with real-world backup workloads demonstrate that the proposed read cache scheme improves read performance by up to 64.3%

A Practical Approach in Glycerol Oxidation for the Development of A Glycerol Fuel Cell

The research on electrochemical carbon molecule oxidation started in the past years as new electrochemistries were researched for new fuel cells systems and batteries that may line up as backup energy supply and storage systems in off-grid and on-grid microgrids, as modeled by our group at the University of Twente [1-3]. By lining up these two disciplines we hope to support the bridge between new electrochemical systems on one side, (pilot) production with partner companies, prediction, and validation of systems upon implementation in microgrids by our group. The electrochemical oxidation of glycerol in alkaline aqueous solution has been studied on gold and gold coated metals (Zn-Au and Cu-Au) by voltammetry and EIS (Electrochemical impedance spectroscopy) for possible use in a new fuel cell as an outlet for the excess glycerol that is produced in the biodiesel industry. The observations show that the gold surface may change upon cycling by cyclic voltammetry. Besides, the current density shows non-linear behavior with the square root of the scan rate, implying that the reaction is not totally controlled by diffusion. EIS analysis using the EQUIVCRT software revealed that one out of twenty tested equivalent circuits fitted the data well at potentials of -0.05 V,- 0.15 V and -0.25 V vs. Ag/AgCl, identifying resistors and a Warburg element in parallel with the double layer capacitance, the elements are possibly related to the presence of double layers associated with hydroxypyrovate and oxalate ions. The results are consistent with the low-frequency error fitting analysis (10-4), AC Simulink-Matlab fitting responds and the Kronig-Kramers transform test. The tested Zn-Au and Cu-Au electrodes show similar voltammetry behavior as the gold electrode, as witnessed by the results of cycle analysis and the scan rate analysis. The discharge chronoamperometry test further shows that the Zn-Au electrode and Cu-Au have higher current densities than the gold electrode at a potential of -0.25 V vs. Ag/AgCl (5 mA cm-2, 4.5 mA cm-2, and 3 mA cm-2 respectively).

Survey on Data Deduplication for Cloud Storage to Reduce Fragmentation

Deduplication is an important technique which provides better result to store more information with less space. Cost and maintenance of Information backup storage system for major enterprises can be minimized by storing it on Cloud Storage. Data redundancy between different kinds of data storage gets minimal by utilizing data deduplication method. By giving each application differently and storing the associated information distinctly the overall disk usage can be enhanced to a great level. Cloud backup systems uses data deduplication to eliminate duplicate chunks that are present in multiple files. The duplicate chunks are substituted with the references to already present chunks through deduplication, without storing it again on cloud storage. The successive chunks are actually stored in scattered form in backup system in numerous segments (the storage unit of cloud). KeywordsBackup, Data deduplication, Fragmentation.

A Fast Asymmetric Extremum Content Defined Chunking Algorithm for Data Deduplication in Backup Storage Systems

Chunk-level deduplication plays an important role in backup storage systems. Existing Content-Defined Chunking (CDC) algorithms, while robust in finding suitable chunk boundaries, face the key challenges of (1) low chunking throughput that renders the chunking stage a serious deduplication performance bottleneck, (2) large chunk size variance that decreases deduplication efficiency, and (3) being unable to find proper chunk boundaries in low-entropy strings and thus failing to deduplicate these strings. To address these challenges, this paper proposes a new CDC algorithm called the Asymmetric Extremum (AE) algorithm. The main idea behind AE is based on the observation that the extreme value in an asymmetric local range is not likely to be replaced by a new extreme value in dealing with the boundaries-shifting problem. As a result, AE has higher chunking throughput, smaller chunk size variance than the existing CDC algorithms, and is able to find proper chunk boundaries in low-entropy strings. The experimental results based on real-world datasets show that AE improves the throughput performance of the state-of-the-art CDC algorithms by more than $2.3\times$ , which is fast enough to remove the chunking-throughput performance bottleneck of deduplication, and accelerates the system throughput by more than 50 percent, while achieving comparable deduplication efficiency.

Similarity and Locality Based Indexing for High Performance Data Deduplication

Data deduplication has gained increasing attention and popularity as a space-efficient approach in backup storage systems. One of the main challenges for centralized data deduplication is the scalability of fingerprint-index search. In this paper, we propose SiLo, a near-exact and scalable deduplication system that effectively and complementarily exploits similarity and locality of data streams to achieve high duplicate elimination, throughput, and well balanced load at extremely low RAM overhead. The main idea behind SiLo is to expose and exploit more similarity by grouping strongly correlated small files into a segment and segmenting large files, and to leverage the locality in the data stream by grouping contiguous segments into blocks to capture similar and duplicate data missed by the probabilistic similarity detection. SiLo also employs a locality based stateless routing algorithm to parallelize and distribute data blocks to multiple backup nodes. By judiciously enhancing similarity through the exploitation of locality and vice versa, SiLo is able to significantly reduce RAM usage for index-lookup, achieve the near-exact efficiency of duplicate elimination, maintain a high deduplication throughput, and obtain load balance among backup nodes.

Getting your back-up data back up to date

Regulatory enquiries, e-disclosure exercises and corporate investigations all require data to be quickly and accurately provided. Despite this, many businesses fail to maintain an up-to-date view of their back-up data storage systems, meaning that they can face a nasty surprise in the event of an unforeseen regulatory investigation.