Local Storage Research Articles

New hybrid scheme with local battery energy storages and electric vehicles for the power frequency service

This paper proposes a new hybrid scheme using the EV battery and the local battery as a unit, taking an active part in the grid services. Both electric vehicles and grid-scale battery energy storage have been growing fast in recent years. The active combination of these two kinds of energy sectors is challenging but will unlock extra flexibility at the distribution level. Therefore, the EV battery (EVB) and local battery (LB) are studied in a hybrid scheme for the first time. The scheme delivers an improved optimal power schedule for the fast frequency regulation (FFR) in the microgrid. A hybrid power management strategy based on an improved model predictive control (IMPC) in a microgrid is developed. The IMPC is advanced by adding the battery degradation prediction and EV capacity prediction in the loop and designed for optimal power-sharing with the minimum effect on battery lifetime. The EV battery status, which is critical for both the IMPC and the battery degradation quantification, is predicted by the deep learning approach. The proposed hybrid power management with IMPC is verified to be very effective with optimal power-sharing and battery anti-aging control in the microgrid application. • A new hybrid scheme with active combination of the EV and the local energy storage is developed. • An improved MPC is advanced by introducing the EV capacity prediction and battery degradation within the control loop. • The lifetime for both the local battery and the EV battery is proved to be extended under the hybrid scheme.

Provable data deletion from efficient data integrity auditing and insertion in cloud storage

With the widespread popularity of cloud storage, a growing number of data owners are willing to outsource their massive data to remote cloud servers, thus effectively avoiding the heavy burden of storing and managing the massive data by themselves in local physical storage mediums. Despite the tremendous advantages, cloud storage inescapably suffers from some severe security challenges resulting from the separation of outsourced data management and its ownership, such as data integrity, data deletion, and data insertion. In this paper, we aim to deal with the problems of integrity auditing, dynamic insertion and provable deletion over outsourced data. Specifically, we design a novel authentication data structure, namely, number-rank-based Merkle hash tree (NR-MHT), which can achieve data dynamic operations. Meanwhile, NR-MHT can maintain many data blocks in each leaf node, thus effectively solving the problem of the height indefinitely increases with the total number of data blocks. Subsequently, we adopt NR-MHT to propose a new provable data deletion scheme based on efficient data integrity auditing and dynamic data insertion. In our presented scheme, the data owner can not only permanently delete the unnecessary outsourced data blocks to save storage expenditures but also efficiently insert new data blocks to dynamically update the outsourced data set. Meanwhile, we analyze the security to formally demonstrate that our presented scheme can satisfy all of the expected security requirements without interacting with a third party auditor. Finally, we also develop a prototype implementation of our presented scheme and provide an accurate efficiency evaluation. The numerical analysis and experimental results show that compared with the existing schemes, our presented scheme is more attractive in efficiency and practicability.

Dynamic discretization discovery for solving the Continuous Time Inventory Routing Problem with Out-and-Back Routes

In time dependent models, the objective is to find the optimal times (continuous) at which activities occur and resources are utilized. These models arise whenever a schedule of activities needs to be constructed. A common approach consists of discretizing the planning time and then restricting the decisions to those time points. However, this approach leads to very large formulations that are intractable in practice. In this work, we study the Continuous Time Inventory Routing Problem with Out-and-Back Routes (CIRP-OB). In this problem, a company manages the inventory of its customers, resupplying a single product from a single facility during a finite time horizon. The product is consumed at a constant rate (product per unit of time) by each customer. The customers have local storage capacity. The goal is to find the minimum cost delivery plan with out-and-back routes only that ensures that none of the customers run out of product during the planning period. We study the Dynamic Discovery Discretization algorithm (DDD) to solve the CIRP-OB by using partially constructed time-expanded networks. This method iteratively discovers time points needed in the network to find optimal continuous time solutions. We test this method by using randomly generated instances in which we find provable optimal solutions.

Training and pattern recognition by an opto-magnetic neural network

Neuromorphic computing aims to mimic the architecture of the human brain to carry out computational tasks that are challenging and much more energy consuming for standard hardware. Despite progress in several fields of physics and engineering, the realization of artificial neural networks, which combine high operating speeds with fast and low-energy adaptability, remains a challenge. Here, we demonstrate an opto-magnetic neural network capable of learning and classification of digitized 3 × 3 characters exploiting local storage in the magnetic material. Using picosecond laser pulses, we find that micrometer sized synapses absorb well below 100 picojoule per synapse per laser pulse, with favorable scaling to smaller spatial dimensions. We, thus, managed to combine the speed and low-dissipation of optical networks with the low-energy adaptability and non-volatility of magnetism, providing a promising approach to fast and energy-efficient neuromorphic computing.

Automated Indonesian traditional musical instrument '<i>angklung</i>' player based on Raspberry Pi

Angklung is a traditional musical instrument from Indonesia made of bamboo tubes which have different length to produce different tunes. Nowadays, it is losing its popularity as only few young generations are interested in playing this instrument. To get their attention, an electronically controlled angklung is created by setting a three-octave angklung to an automated player consisting of DC motors controlled by Raspberry Pi. This system can download songs from the online cloud and can be played either manually or automatically. User can write songs directly into the system, which can play more than 50 songs automatically and continuously for eight hours. The accuracy of the whole system is 99.8% to 100% and it can download 100 songs from the cloud within 201.9 ms or 11.2 ms from the local storage. In the future, this instrument will be integrated with other traditional music instruments to form an automated traditional musical orchestra.

FASTER STORAGE SYSTEM FOR FIRMS

The access of the data from the cloud storages are somewhat slow when compared to the local storages. But we are not using local storage because we cannot access the data remotely and they are hard to store in a single place (as generally we use cables to transfer files locally). This idea is to increase the data transfer and access speed using wifi for data transfer and storing them locally. Also making the server become cloud storage. In this paper, we take our past work above and beyond and present an inside and out investigation of the vital highlights of future enormous information stockpiling administrations for both unstructured and semi-organized information, what's more, talk about how such administrations ought to be developed and conveyed. We particularly center around the issues of information de-duplication for ventures and private associations. This research is especially significant for unpracticed arrangement suppliers like universities and exploration associations, and will permit them to quickly set up their own enormous information stockpiling administrations.

Snapshot of photovoltaics – February 2022

For the past 10 years photovoltaic electricity generation has been the fastest-growing power generation source worldwide. It took almost six decades to achieve 100 GW of solar energy capacity in 2012, but the 1 TW level is likely to be broken during 2022. Overall investments in solar energy has increased by 19% to USD 205 billion (EUR 171 billion). In 2021 more than 180 GW of new solar photovoltaic electricity generation capacity was installed. After the decline in 2020, the Chinese solar market recovered to 53 GW in 2021. This development and the continuous growth in Europe and the USA as well as in new emerging markets were the main drivers. The number of countries installing more than 1 GW annually has increased to 18 in 2021. The continuation of price reductions in the battery storage sector has again resulted in a growing market for local battery storage systems in solar farms as well as for decentralised photovoltaic electricity generation systems. It is also increasingly clear that apart from classic electricity use, renewable electricity for electric vehicles and for generation of green hydrogen will become more and more important in the future.

Distributed Multistage Optimization of Large-Scale Microgrids Under Stochasticity

Microgrids are recognized as a relevant tool to absorb decentralized renewable energies in the energy mix. However, the sequential handling of multiple stochastic productions and demands, and of storage, make their management a delicate issue. We add another layer of complexity by considering microgrids where different buildings stand at the nodes of a network and are connected by the arcs; some buildings host local production and storage capabilities, and can exchange with others their energy surplus. We formulate the problem as a multistage stochastic optimization problem, corresponding to the minimization of the expected temporal sum of operational costs, while satisfying the energy demand at each node, for all time. The resulting mathematical problem has a large-scale nature, exhibiting both spatial and temporal couplings. However, the problem displays a network structure that makes it amenable to a mix of spatial decomposition-coordination with temporal decomposition methods. We conduct numerical simulations on microgrids of different sizes and topologies, with up to 48 nodes and 64 state variables. Decomposition methods are faster and provide more efficient policies than a state-of-the-art Stochastic Dual Dynamic Programming algorithm. Moreover, they scale almost linearly with the state dimension, making them a promising tool to address more complex microgrid optimal management problems.

Guest Editorial for the Special Section on Instrumentation and Measurement for Smart Energy Systems

The IEEE Open Journal of Instrumentation and Measurement (OJIM) took its first steps in 2022 and opened a new and exciting venue for contributions on emerging topics from experts in the field. One of the long-standing subjects in the context of instrumentation and measurement is represented by power and energy measurements. In recent decades, this, so to speak, rather traditional field of research has received a new and impressive boost from the evolution of the industrial and economic context, but also from the pressing need to face the environmental problems affecting our Planet, whose mitigation demands a green revolution. Pursuing decarbonization means migrating from fossil fuel toward the intensive exploitation of renewable energy sources. A transition of such a magnitude relies on the ubiquitous presence of electricity and requires the electrification of most of our consumption. All these objectives ask for a new paradigm for energy systems, which shall combine centralized and distributed renewable energy generation, local energy storage, integration with electric mobility, communication, advanced automation, etc.

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

Effective networking over wireless media has become extremely essential today as communication between massive Internet of things (IoT) devices is on an increase, thereby leading to a limited spectrum resource for utilisation. Specifically, for healthcare infrastructure in a remote or critical situation, providing uninterrupted communication between the macro base station and IoT devices or user nodes is imperative. However, owing to their limited spectral capacity, unmanned aerial vehicles (UAVs)-based networks can provide an efficient solution and utilise both licensed and unlicensed bands for communication among users or devices. In this paper, our focus is on cache-enabled cognitive networking for secondary users (SUs) that accredits precise communication delivery for critical healthcare systems that are performed by the cognitive UAV (CUAV). In addition, we develop a caching strategy wherein a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in its local and cloud storage by applying a non-orthogonal multiple-access method. In the downlink scenario, the CUAV proactively transmits the requested HP and MP information to the designated SUs considering this entire model over two states, namely effectual state and interference state, which we can realise by any presence or absence of interference. To maximise this system’s energy efficiency, we formulate an optimisation problem to minimise the transmission power and satisfy the target performance in terms of throughput for SUs. We solve the optimisation issue using the Lagrangian approach and the Karush-Kuhn-Tucker conditions. In all simulations, the energy efficiency during the effectual state renders an average performance of approximately 400% better than that of the interference state.

Local strain-induced energy storage as driving force for autogenous scratch closure

In this work we rationalize the relationship between polymer architecture, energy stored during scratch damage, entropy release during healing and gap closure of scratches.

Local structure and ion storage properties of vanadate cathode materials regulated by the pre-alkalization

Alkali metal-ions (A: Li, Na, K) regulate the local structure and long-range order of nanostructured vanadate materials for improved electrochemical performance in zinc-ion batteries.

Overview of Loss Sensitivity Analysis in Modern Distribution Systems

Distribution system planning and operation has seen many structural changes due to the increased participation of consumers in the energy market and the adaptation of new technologies such as distributed energy resources (DERs), electric vehicles (EV) and local energy storage systems (ESSs). Despite the convenience of such technologies and the gradual drop in their prices, new technical challenges (e.g., excessive power losses) have emerged at the system level. Over the past few decades, power loss minimization in distribution systems has gained popularity and the need for loss sensitivity analysis (LSA) frameworks has become a necessity for its successful implementation. Existing work on LSA mostly focuses on system planning aspects through DER optimal placement and sizing. However, enabling LSA-based system operational applications is a vital step toward the successful transition to modern distribution systems (MDSs). Therefore, this paper presents a comprehensive overview on the state of the art in LSA for MDSs. First, the theoretical formulations of existing LSA methods are summarized. Then, the applications of LSA in distribution systems are highlighted. Finally, based on the analysis of literature, open research gaps and future research pathways are discussed.

Data Recovery Approach for Fault-Tolerant IoT Node

Internet of Things (IoT) has a wide range of applications in many sectors like industries, health care, homes, militarily, and agriculture. Especially IoT-based safety and critical applications must be more securable and reliable. Such type of applications needs to be operated continuously even in the presence of errors and faults. In safety and critical IoT applica-tions maintaining data reliability and security is the critical task. IoT suffers from node failures due to limited resources and the nature of deployment which results in data loss consequently. This paper proposes a Data Recovery Approach for Fault Tolerant (DRAFT) IoT node algorithm, which is fully distributed, data replication and recovery implemented through redundant local database storage of other nodes in the network. DRAFT ensures high data availability even in the presence of node failures to preserve the data. When an IoT node fails in any cluster in the network data can be retrieved through redundant storage with the help of neighbor nodes in the cluster. The proposed algorithm is simulated for 100-150 IoT nodes which enhances 5% of network lifetime, and throughput. The performance metrics such as Mean Time to Data Loss (MTTDL), throughput, Network lifetime, and reliability are computed and results are found to be improved.

Compositional stability criteria based on cyclically neutral supply conditions

In this paper we consider stability of large scale interconnected nonlinear systems that satisfy a strict dissipativity property in terms of local storage and supply functions. Existing compositional stability criteria certify global stability by constructing a global Lyapunov function as the (weighted) sum of local storage functions. We generalize these results by unifying spatial composition, i.e., (weighted) sum of local supply functions is neutral, with temporal composition, i.e., (weighted) sum of supply functions over a time cycle is neutral. Two benchmark examples illustrate the benefits of the developed compositional stability criteria in terms of reducing conservatism and constrained distributed stabilization.

Integrity Assurance Method of Multi-Keyword Query for Encrypted Outsourced Data

As the data scale exceeds the petabyte level, more and more users outsource their local databases to third-party service providers to save local storage costs and avoid cumbersome local data management. However, because third-party service providers are not fully trusted, they may leak outsourced data and bring security risks to the personal privacy of data users. The service provider may also return incorrect or incomplete query results to the data user. Therefore, in this paper, we propose a Lightweight and Verifiable Multi-keyword Query Scheme for the integrity verification of multi-keyword query of outsourced data. This scheme supports multi-keyword query integrity verification and does not require a third-party auditor, combining a hash-based accumulator that provides correctness verification and a cuckoo filter that provides integrity verification to form a provably secure composite verification structure, and at the same time, it uses controllable Paillier encryption to support data update operations. This scheme strikes a balance between the cost of verification structure generation, verification object generation, and proof verification cost to enable data users to perform secure multi-keyword query while efficiently verifying the integrity of query results. The security proof and the performance analysis show that the proposed scheme is secure and efficient for practical deployment.

Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study

The introduction of battery electric vehicles (BEV) and the expansion of rooftop photovoltaic (PV) power generation are both progressing at a fast pace to decarbonize the transport and the energy sector in Switzerland. These parallel developments have an enormous synergy potential as the actual decarbonization impact of BEVs depends heavily on the carbon footprint of the power source and the PV expansion requires local storage as a buffer to reduce negative impacts on the distribution grid. We present an empirical analysis based on a detailed 10-month data set of the charging and mobility behavior of 78 BEV users in Switzerland. It is combined with a fine-grained digital surface model of Switzerland to extract the detailed roof geometry and the corresponding rooftop PV generation capacity of each of the BEV owner’s houses.We test four different smart charging strategies with a varying degree of complexity and find that when charging uncontrolled (the strategy used during the study), BEV owners can only cover 15 % of their BEV’s demand using PV generated from the roofs of their own houses. A simple controlled charging approach greatly increases the average coverage to 56 % and up to 90 % or 99 % when using an optimized charging strategy without or with a home battery storage. All charging strategies ensure that the individual mobility behavior of the BEV owners is not affected.We further show that using rooftop PV power generation for BEV charging has a large potential to further decrease the climate impact of BEVs and propose simple adjustments to consider in charging strategies that help to increase the owners’ PV consumption.

Enabling secure mutual authentication and storage checking in cloud-assisted IoT.

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server. Hence, it is necessary to provide a mechanism of mutual authentication for the cloud system to enhance the storage security. In this paper, a secure mutual authentication is proposed for cloud-assisted IoT. Note that the technique of chameleon hash signature is used to construct the authentication. Moreover, the proposed scheme can provide storage checking with the assist of a fully-trusted entity, which highly improves the checking fairness and efficiency. Security analysis proves that the proposed scheme in this paper is correct. Performance analysis demonstrates that the proposed scheme can be performed with high efficiency.

Profiling of Essential Mineral Content, Heavy Metals, and Bacterial Contaminants in Conventional and Organic Eggs Available in the Hypermarkets of the Eastern Province of Saudi Arabia

Eggs are commonly regarded as one of the best sources of various macro and micronutrients, in particular, high biological value (BV) proteins and both saturated and unsaturated fatty acids. Apart from proteins, eggs are excellent sources of various high health value lipid components, carotenoids, minerals, and both water and lipid-soluble vitamins. However, the nutrient-rich nature of this 'superfood' also makes them susceptible to microbial contamination from various sources, such as fecal matter, drinking water for layer chicken, and the surrounding environment. Moreover, drinking water and chicken feed are also sources of heavy metal contamination. The presence of both factors poses serious health concerns for consumers. The main aim of this research was to proximate heavy metals and bacterial contaminants in nine different brands of brown organic and conventional white eggs available in the hypermarkets of the Eastern Province of Saudi Arabia. An Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES) was used to determine the concentration of metals with high sensitivity. Standard plate count was followed by enrichment of bacterial contaminants in a double-strength nutrient broth medium for microbiological assessment. The results indicated that Pb, Cd, Al, and As were present in high concentrations in all egg brands of both types (organic and conventional). The estimated daily intake (EDI) values for Ca, Fe, Mn, and Mg were within the range of WHO-recommended thresholds, and these essential minerals were present in adequate amounts in all egg brands. The two major groups predominating the microbial loads were the family Enterobacteriaceae and Pseudomonas sp. The presence of heavy metals, such as Al, As, Pb, and Cd, in nearly all the brands in amounts that exceeded the established ADI and EDI thresholds was considerably concerning. In particular, the elevated levels of Cd and Pb in group 1 and group 2A probable human carcinogens, respectively, demands that local regulatory authorities investigate the sources of heavy metal contamination and alleviate this considerable risk to human health. Furthermore, this study could be a benchmark for establishing food safety and hygiene standards for local egg production, storage, handling, and transport in Saudi Arabia.

En-AR-PRNS: Entropy-Based Reliability for Configurable and Scalable Distributed Storage Systems

Storage-as-a-service offers cost savings, convenience, mobility, scalability, redundant locations with a backup solution, on-demand with just-in-time capacity, syncing and updating, etc. While this type of cloud service has opened many opportunities, there are important considerations. When one uses a cloud provider, their data are no longer on their controllable local storage. Thus, there are the risks of compromised confidentiality and integrity, lack of availability, and technical failures that are difficult to predict in advance. The contribution of this paper can be summarized as follows: (1) We propose a novel mechanism, En-AR-PRNS, for improving reliability in the configurable, scalable, reliable, and secure distribution of data storage that can be incorporated along with storage-as-a-service applications. (2) We introduce a new error correction method based on the entropy (En) paradigm to correct hardware and software malfunctions, integrity violation, malicious intrusions, unexpected and unauthorized data modifications, etc., applying a polynomial residue number system (PRNS). (3) We use the concept of an approximation of the rank (AR) of a polynomial to reduce the computational complexity of the decoding. En-AR-PRNS combines a secret sharing scheme and error correction codes with an improved multiple failure detection/recovery mechanism. (4) We provide a theoretical analysis supporting the dynamic storage configuration to deal with varied user preferences and storage properties to ensure high-quality solutions in a non-stationary environment. (5) We discuss approaches to efficiently exploit parallel processing for security and reliability optimization. (6) We demonstrate that the reliability of En-AR-PRNS is up to 6.2 times higher than that of the classic PRNS.