Storage Allocation Scheme Research Articles

Optimal Allocation of Hybrid Energy Storage Capacity Based on ISSA-Optimized VMD Parameters

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.

Hybrid energy storage configuration method for wind power microgrid based on EMD decomposition and two-stage robust approach

Data centers are usually characterized by high energy loads, which raises increasing sustainability concerns in both academic and daily usage. To mitigate the uncertainty and high volatility of distributed wind energy generation, this paper proposes a hybrid energy storage allocation strategy by means of the Empirical Mode Decomposition (EMD) technique and the two-stage robust method. First, this paper conducts the evolution analyses for the over- and under-evaluated uncertainty of wind power fluctuation under different time scales. Second, we employ the EMD technique to configure a high-frequency flywheel energy storage device, realizing the wind power transformation from large fluctuations to small fluctuations and the convergence of the wind power fluctuation curves in minute- and hour levels. Finally, based on the hour-level wind energy stable power curves, we carry out two-stage robust planning for the equipment capacity of low-frequency cold storage tanks and lithium bromide chillers. The case study on a data center microgrid in northeastern China confirms the effectiveness of our proposed strategy.

Consider the optimal allocation planning of energy storage for flexible resources of source network load

The increased penetration of distributed generation such as wind and light in the distribution grid causes dramatic fluctuations in net load, resulting in a sharp increase in the need for distribution grid flexibility. To this end, a distribution grid energy storage optimal allocation method considering source-net-load flexibility resources is proposed. First, a set of uncertain operation scenarios is established from multiple time scales using the K-means method and robust optimization theory; second, an economically flexible multi-objective optimization model with the minimum annual integrated cost and the highest average daily flexibility level of the distribution network as the optimization objectives is established and solved by a multi-objective composite differential evolutionary algorithm with embedded tide calculation. Arithmetic design and simulation are based on the IEEE 33-node distribution network system. The results validate the reasonableness and effectiveness of the proposed model considering source-grid-load flexibility resources in solving the energy storage allocation scheme.

Research on energy storage allocation strategy considering smoothing the fluctuation of renewable energy

Energy storage technology can effectively solve the problems caused by large-scale grid connection of renewable energy with volatility and uncertainty. Due to the high cost of the energy storage system, the research on capacity allocation of energy storage system has important theoretical and application value. In this paper, an optimization method for determining the capacity of energy storage system for smoothing the power output of renewable energy is proposed. First, based on the actual data of Ulanqab, the output characteristics of wind power and photovoltaic power generation are studied, and the K-means algorithm is used to select typical days. Then, the energy storage configuration model is built according to the objective function and constraints. Finally, genetic algorithm is used to solve the optimization model, obtain the corresponding parameters, and complete the configuration of energy storage capacity. Based on the results of renewable energy spectrum analysis, the minimum capacity of the energy storage system that meets the constraint of target power output volatility after compensation by the energy storage system can be optimized. The simulation results show that at 1 and 10 min, the flattened volatility is about 2% and 5%, while the actual penetration volatility is about 20% and 30%. The volatility of the optimized model is greatly reduced, which proves the effectiveness of the proposed strategy.

Dynamic Picking and Storage Optimization of Robotic Picking Systems

With the development of e-commerce and the improvement of logistics requirements, more and more ‘parts-to-picker’ picking systems begin to replace the inefficient ‘picker-to-parts’ picking systems in various scenarios. As the mainstream ‘parts-to-picker’ system, the robotic mobile fulfillment system has been attracting much attention in recent years. In addition to the customer's changing requirements, the rapid response of the picking system to the order is particularly important. In the above context, to seek a breakthrough in the picking system's picking efficiency without increasing the cost of additional equipment, the storage allocation of the pods becomes very important. This article focuses on the dynamic storage allocation of robotic mobile fulfillment system, which has positive theoretical and practical significance. By analyzing the pod storage process of the robotic mobile fulfillment system, a dynamic pod storage allocation model suitable for the robotic mobile fulfillment system is established with the goal of minimizing the pod handling distance. Two dynamic pod storage allocation strategies are proposed for the system. By simulating the picking systems of different scales, the effectiveness of the dynamic storage allocation strategy is verified, which has a certain reference to the operation of the robotic mobile fulfillment system in practice.

Resource Allocation and Consensus of Blockchains in Pervasive Edge Computing Environments

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage and propose a recent block storage allocation scheme for quick retrieval of missing blocks. We develop data migration algorithms to dynamically reallocate data and block storage to adapt topology changes in the network. The proposed blockchain system can also reach consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 18.4 percent less time and consumes 87 percent less battery power when compared with traditional blockchain systems.

Travel time model for multi-deep automated storage and retrieval systems with different storage strategies

Travel time models for automated storage and retrieval systems (AS/RS) are used to define average travel times during storage/retrieval operations in an AS/RS. With an increasing depth of AS/RS racks, storage goods are stored in front of each other. This can lead to relocation operations of blocking goods causing higher travel times. This paper derives analytically and presents four travel time models for multi-deep AS/RS following four storage allocation strategies. Two models handle random strategies, one minimises the variance of storage channel fillings and the fourth maximises this variance. Evaluation and comparison of different models is followed by a discrete event simulation to verify these models. It is shown that the minimal variance strategy achieves the lowest relocation numbers and also the lowest total travel times, the random strategies perform between the minimal and maximal variance strategy.

Retraction Note to: Secure storage allocation scheme using fuzzy based heuristic algorithm for cloud

Retraction Note to: Secure storage allocation scheme using fuzzy based heuristic algorithm for cloud

A machine learning approach for predictive warehouse design

Warehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems.

Double-Shift: A Low-Power DNN Weights Storage and Access Framework based on Approximate Decomposition and Quantization

One major challenge in deploying Deep Neural Network (DNN) in resource-constrained applications, such as edge nodes, mobile embedded systems, and IoT devices, is its high energy cost. The emerging approximate computing methodology can effectively reduce the energy consumption during the computing process in DNN. However, a recent study shows that the weight storage and access operations can dominate DNN's energy consumption due to the fact that the huge size of DNN weights must be stored in the high-energy-cost DRAM. In this paper, we propose Double-Shift, a low-power DNN weight storage and access framework, to solve this problem. Enabled by approximate decomposition and quantization, Double-Shift can reduce the data size of the weights effectively. By designing a novel weight storage allocation strategy, Double-Shift can boost the energy efficiency by trading the energy consuming weight storage and access operations for low-energy-cost computations. Our experimental results show that Double-Shift can reduce DNN weights to 3.96%–6.38% of the original size and achieve an energy saving of 86.47%–93.62%, while introducing a DNN classification error within 2%.

Optimal Energy Storage Allocation Strategy by Coordinating Electric Vehicles Participating in Auxiliary Service Market

The further liberalization of China's electricity market encourages demand-side entities to participate in electricity market transactions. Electric vehicles (EVs) are developing rapidly and have high regulating potential, and are the main force for demand-side participation in the auxiliary service market. Aiming at the problems of dispatching accuracy and economy in EV participation in auxiliary service market, this paper analyzes the bidding strategy and dispatching scheme of EV-storage participation in auxiliary service market, and proposes EV-storage optimal allocation strategy with the goal of economic optimum. In the process of optimal allocation, based on the market rules of third-party subject participation in auxiliary services, the bidding strategy of EV-storage coordinated EV participation in auxiliary services market considering daily load scale changes is designed, while the conditional value at risk (CVaR) method is used to determine the short-term coordinated energy storage capacity and efficiency storage capacity considering the uncertainty of EV response situation; then, based on the annual EV load scale change, the benefit calculation function is constructed by considering various factors such as auxiliary service market revenue, spread revenue, investment cost and market opportunity cost of EV-storage participation in the auxiliary market. Finally, taking EV aggregation participation in the valley-filling ancillary service market as an example, it is verified that the strategy proposed in this paper can effectively improve the responsiveness of EV participation in the ancillary service market and increase the revenue of electric vehicle aggregator (EVA).

Research on optimal allocation strategy of multi-energy storage in regional integrated energy system considering the quantification of low-carbon economic benefits

The dual pressure of energy crisis and environmental pollution has prompted the world’s energy sector to change in the direction of clean, efficient, sustainable and pollution-free, and RIES (Regional Integrated Energy System) has emerged. The deployment of energy storage devices in the RIES can deepen the connection of each energy system, release the spatial and temporal constraints of multiple energy sources, and thus improve the energy utilization and economy of the integrated energy system. Based on the infrastructure and model of the RIES, this paper studied the synergistic optimal configuration of electricity storage, power-to-gas (P2G) technology, cooling storage and heating storage in the case of multi-energy complementation operation of combined cooling, heating and power supply (CCHP) unit and other equipment. Firstly, an optimal configuration model of multiple energy storage in the RIES based on the optimal operational economy was proposed. Then, the economics and feasibility of configuring different energy storage combinations in the system were analyzed. Finally, the benefits of energy storage devices in the regional integrated energy system were quantified and analyzed by the economic indicators and low carbon emissions reduction indicators. By employing an industrial district RIES in northern China as the study case, the operation scheduling scheme and energy storage allocation scheme were solved by the optimization model proposed in this paper. The results show that the RIES containing multiple types of energy storage can consume more distributed new energy, improve economic efficiency and reduce carbon emissions. And this model has a good application prospect.

Optimal Storage Allocation for Delay Sensitivity Data in Electric Vehicle Network

For significant characteristics such as high efficiency and environmental protection, electric vehicles (EVs) have become a new technology trend in the intelligent transportation system (ITS). Data like real time traffic situation and charge point occupation in ITS shows significant real-time characteristics. Distributed storage system, an effective technology to ensure reliable sharing of dynamic data, is first adopted to storage allocation for delay-sensitive data in this article. In order to improve the recovery probability of delay-sensitive data within its timeliness, we establish an access queuing delay model based on the characteristics of sensitive data. Then, we find the optimal storage allocation strategy across distributed storage nodes for data with different delay threshold in terms of the recovery probability. The analysis shows that for data with low real-time performance and delay sensitivity, the maximal symmetric allocation is more excellent. For real-time data with low delay threshold, the minimal allocation is better under the condition of limited storage budget.

Data dissemination with interoperability in IoT network

SummaryInternet of Things (IoT) is connected to heterogeneous devices. Efficient adaptive scheduling with encoding and decoding of data is an unaddressed issue in IoT. This paper processes the data under three major hierarchy: namely, adaptability, scheduling of data, and network coding for that data. The reliable access to the information is ensured by a device which is a primary eminence in IoT. Device must be able to adapt itself according to the changes in the network and to maintain its reliability as well as transparency and seamless access to the resources. To enhance the performance of the data dissemination, the scheduling process is investigated using the spatial grouping in IoT devices; this is achieved by joint spatial and code domain scheduling scheme, and the novel preconfigured access scheme is coined in order to minimize the collision rate of arbitrary access; during the data dissemination, the erasure coding scheme is used for the encoding and decoding of packets which provides optimal redundancy. We carried the simulation using Contiki and it shows the proposed Polymorphic Erasure Coding with Markov decision Adaptability and Neural networks (PECMAN) improves in terms of cost, overhead, and delay when compared with Multi‐user Shared Access (EMUSA), Polynomial‐time Optimal Storage Allocation (OSA) scheme, and Event‐Aware Back pressure Scheduling Scheme (EABS).

RETRACTED ARTICLE: Secure storage allocation scheme using fuzzy based heuristic algorithm for cloud

Cloud computing is a paradigm in the modern era for the development of secure storage and secure job scheduling process in a cloud-based system. The cloud technology provides a user interface in the sense of raw data management with the relational databases to improve flexibility and scalability. Fuzzy-based Heuristic algorithm is proposed for secure storage allocation in a cloud environment. In this work, client systems are clustered using a fuzzy-based rule mechanism. The server generates a distributed public key using RSA for ensuring security and resolve memory recycling issues. It enhances the usage of the client over the cloud data storage in secure manner. It also improves the efficiency of data storage globally by implementing heuristic techniques. The K-nearest neighbors approach is used for efficient query searching for data storage in the clusters by the client. The proposed load-balanced scheduling approach enhances power consumption and efficiency. In the simulation, the results reveal that the proposed scheme provides better performance than existing approaches in terms of load balancing with security.

Research overview of energy storage in renewable energy power fluctuation mitigation

The integration of renewable energy, such as PV and wind power, has exerted great impacts on the power system with its rapid development. If the corresponding energy storage system is configured, the power system could be able to hold a higher proportion of renewable energy. Focusing on energy storage application for the output fluctuation mitigation of renewable energy, this paper first analyses the reason for renewable energy power fluctuation mitigation from the four aspects of frequency, unit ramp, low frequency oscillation and cascading failure. In addition, the fluctuation rate standard of grid-connected renewable energy, the energy storage type and the mitigation topology are introduced. Then a summary and analysis on mitigation strategy and hybrid energy storage allocation strategy are presented. Finally, the demonstration application and development trend of energy storage are analyzed to provide reference for the promotion of energy storage in renewable energy.

Minimizing Power Consumption in Video Servers by the Combined Use of Solid-State Disks and Multi-Speed Disks

Disks use a considerable portion of the total energy consumed by a video server. Solid-state disks (SSDs) can be used as a cache to reduce disk speeds, thereby decreasing the power consumed by disks. However, effective SSD bandwidth management is essential to cope with the high request rates for popular video files stored on SSDs. To address this problem, we propose a new SSD cache management scheme for a video server that uses multi-speed disks. First, we introduce a storage allocation scheme to examine how the presence of an SSD cache affects disk bandwidth consumption. We then propose an SSD bandwidth allocation algorithm to minimize energy consumption by allowing disks to run at lower speeds while achieving jitter-free disk speed transitions and by limiting the number of transitions to ensure disk reliability. Our experimental results demonstrate that our proposed bandwidth allocation algorithm achieves appreciable power savings under various workloads while limiting the number of disk speed changes.

Information-Based Allocation Strategy for GRID-Based Transshipment Automated Container Terminal

In this paper, we introduce a storage allocation strategy for a transshipment container hub using a new automated container terminal called the hybrid GRID system. This study provides a novel approach of developing an efficient storage allocation strategy for new terminal concepts so that the complex formulations can be approximated by simple functions that can be quickly computed. Specifically, the storage allocation strategy is derived from an optimal allocation decision learned from a Mixed Integer Programming (MIP) model. Some input parameters of the MIP model are collected from a simulation model. An index measuring the storage location convenience is proposed and we regress this index with important variables to build an empirical model that provides recommendations on where to allocate containers to storage locations. The advantage of using the empirical approach is that it allows for fast computation which is expected in the dynamic and uncertain port environment. Numerical results show that four variables can significantly affect the performance of the allocation decision. This strategy is shown to be robust for different scenarios in terms of terminal configuration and container workload. In addition, the new strategy performs better than the commonly used strategies found in warehousing literatures, and the solution is close to the optimal allocation decision. The online appendix is available at https://doi.org/10.1287/trsc.2017.0736 .

Storage allocation framework for designing lean buffers in forward-reserve model: a test case

PurposeThe retail revolution swing from traditional distribution to e-tailing services and unprecedented increase in internet adoption insist practitioners to diversely plan warehousing strategies. More than practically required storage space has been identified as wastes, and also it does not improve performance. An organized framework integrating storage design policies, operational performance and customer value improvement for retail-distribution management is lacking. Therefore, the purpose of this paper is to develop broad guidelines to design the “just-right” amount of forward area, i.e., “lean buffer” answering the following questions: “What should be lean buffer size? How effective the forward area is? As per demand variations, which storage waste (SKU) should be allocated with how much storage space? What is the amount of storage waste (SW)? How smooth the material flow is in between reserve-forward area?” for storage allocation in cosmetics distribution centers.Design/methodology/approachAfter forecasting static storage allocation between two planning horizons, if a particular SKU is less or non-moving, then it will cause SW, as the occupied location can be utilized by other competing SKUs, and also it impedes material flow for an instance. A dynamically efficient and self-adaptive, knapsack instance based heuristics is developed in order to make effective storage utilization.FindingsThe existing state-of-the-art under study is supported with a distribution center case, and the study investigates the need of a model adopting lean management approach in storage allocation policies along with test results in LINGO. The sensitivity analysis describes the impact of varying demand and buffer size on performance. The results are compared with uniform and exponential distributed demands, and findings reveal that the proposed heuristics improves efficiency and reduce SWs in forward-reserve area.Originality/valueThe presented model demonstrates a novel thinking of lean adoption in designing storage allocation strategy and its performance measures while reducing wastes and improving customer value. Future research issues are highlighted, which may be of great help to the researchers who would like to explore the emerging field of lean adoption for sustainable retail and distribution operations.

Towards Green IoT Networking: Performance Optimization of Network Coding Based Communication and Reliable Storage

Internet of things (IoT) is expanding its outreach to almost every aspect of our daily life. By utilizing network coding in IoT, the IoT energy consumption can be reduced. Thus it is worthwhile studying and improving the applications in IoT, where network coding is incorporated. In this paper, we optimize the performance of network coding-based communication and reliable storage in two important components of IoT, including the IoT core network, where data is sensed and transmitted, and the distributed cloud storage, where the data generated by the IoT core network is stored. First, we propose an adaptive network coding scheme in the IoT core network to improve the transmission efficiency. We demonstrate the efficacy of the scheme and the performance advantage over existing schemes through simulations. Second, we introduce the optimal storage allocation problem in the network coding-based distributed cloud storage, which aims at searching for the most reliable allocation that distributes the $n$ data components into $N$ data centers, given the failure probability $p$ of each data center. Finally, we propose a polynomial-time optimal storage allocation (OSA) scheme to solve the problem. Both the theoretical analysis and the simulation results show that the storage reliability could be greatly improved by the OSA scheme.