Storage Allocation Research Articles

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.

Research on the optimal allocation method of source and storage capacity of integrated energy system considering integrated demand response

The optimal allocation of energy storage capacity is an important issue for integrated energy systems (IES). To reduce the impact of volatility and intermittency of renewable energy sources, the impact of volatility needs to be smoothed out by rational allocation of energy storage. To address the above issues, this paper proposes a method for the optimal allocation of source storage capacity considering integrated demand response(IDR). Firstly, the basic mechanism of IES based on energy hub(EH) is constructed, and the model and coupling relationship of equipment components in the system are introduced. The IDR model is built to optimize the load curve according to the real-time market tariff scheme, and then the confidence interval method is used to analyze the scenery uncertainty in IES and determine the scenery output curve under different confidence levels. On this basis, a bi-level optimization model is built that takes into account both source and storage capacity allocation and operational optimization. The upper-level planning model takes into account the uncertainty of wind power and photovoltaic output, and solves the allocation scheme of energy storage intending to minimize the total planning cost; the lower-level operation optimization model takes into account the output constraints of each unit and optimizes the output of the equipment to minimize the operation cost. Finally, it is verified that considering IDR can reasonably optimize the allocation of the system’s source storage capacity and reduce the system’s investment and operating costs.

Allocation of battery energy storage in power distribution network: Placement, sizing, and scheduling

ABSTRACT The present power distribution infrastructure faces significantly high demand in most of the time hours of the day. Thus, it needs to manage power intelligently in those high-demand hours. Optimal allocation of battery energy storage system (BESS) can foster power management in the distribution system by exchanging energy with it. However, BESS should be strategically allocated to achieve maximum power delivery efficiency with lower investment. BESS units also need to be strategically scheduled to charge in light load hours and discharged in peak demand hours. The present study proposes a strategic planning model that can minimize the cost of BESS allocation and improve the distribution network’s technical performance. The cost function is framed considering battery life, investment in BESS, and investment in the inverter. The distribution system’s performance is realized with a typical network performance index (NPI). The NPI is synthesized from voltage deviation of buses and change in total active power losses in the network. A multiobjective optimization algorithm-based solution technique is presented to comprehend the planning model’s outcome in the most suitable location, size, and battery scheduling scenario. The proposed planning approach has been tested on a 28-bus Indian power distribution network. The result of techno-economic optimization confirms a good trade-off solution with 37.5% of network performance improvement with respect to the base configuration and INR 39.06LAC of total investment. The proposed approach has been verified with the existing BESS allocation model, demonstrating its efficacy.

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

From Spectrum Bonding to Contiguous-Resource Batching Task Scheduling

We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource; some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings.

Optimal allocation of photovoltaic energy storage on user side and benefit analysis of multiple entities

A bi-level optimization configuration model of user-side photovoltaic energy storage (PVES) is proposed considering of distributed photovoltaic power generation and service life of energy storage. The upper layer takes the user’s lowest annual comprehensive cost as the objective function to optimize the capacity of photovoltaic & energy storage and power of energy storage considering the user’s annual electricity bill, basic electricity bill, and investment, operation and maintenance cost of PVES. The lower level takes the minimum daily operation scheduling cost as the objective function to optimize the charging and discharging control strategy of energy storage. The YALMIP solver is used to solve the optimization model, and the effectiveness of the model is verified through an example, which shows that reasonable configuration of photovoltaic & energy storage can reduce the user’s overall annual cost. In the context of the current national electricity price adjustment policy, by analyzing the impact of different time-of-use (TOU) electricity prices and basic electricity charges on the configuration capacity of user-side PVES to find the break-even point of the user’s PVES investment return rate in order to provide guidance to users for installing PVES systems. By analyzing the impact of different TOU electricity prices and basic electricity rates on the benefits of multiple entities, and taking into account the benefits of users, power grids and society at the same time to provide a theoretical basis for formulating a reasonable electricity price system.

Shared community energy storage allocation and optimization

Distributed Energy Resources have been playing an increasingly important role in smart grids. Distributed Energy Resources consist primarily of energy generation and storage systems utilized by individual households or shared among them as a community. In contrast to individual energy storage, the field of community energy storage is now gaining more attention in various countries. However, existing models are either tailored towards optimizing the operations of individual energy storage or do not consider the notion of sharing energy storage within a community. This paper proposes a framework to allocate shared energy storage within a community and to then optimize the operational cost of electricity using a mixed integer linear programming formulation. The allocation options of energy storage include private energy storage and three options of community energy storage: random, diverse, and homogeneous allocation. With various load options of appliances, photovoltaic generation and energy storage set-ups, the operational cost of electricity for the households is minimized to provide the optimal operation scheduling. Computational results are presented on two real use cases in the cities of Ennis, Ireland and Waterloo, Canada, to show the advantage of using community energy storage as opposed to private energy storage and to evaluate the cost savings which can facilitate future deployment of community energy storage. In addition to the electricity operational cost, energy storage utilization and operation fairness are used to compare different allocation options of storage systems.

Evaluation of the Joint Impact of the Storage Assignment and Order Batching in Mobile-Pod Warehouse Systems

The rapid development of e-commerce has impacted the intelligence level of warehouses. The robotic mobile fulfillment system (RMFS) demonstrated that mobile-pod warehouse systems could process a significant number of order-picking operations within the same amount of time as other systems, saving money and improving operational efficiency. The order-picking mode in such a system is “parts-to-picker,” which is becoming both a prevailing operation mode in industrial warehouses and an encouraging research field during these years. One of the most significant fundamental factors in RMFS is the operational efficiency that is affected by storage assignment and order batching. This paper considers the joint impact of the storage assignment policies and order batching policies on order picking process. Our goal is to minimize the moving times of robots, which reflects the order-picking cost or efficiency. We propose using order similarity and item similarity to batch orders and assign item storage locations, respectively. Both the order batching and item grouping are tackled by a clustering model which is an integer linear program. We also develop a policy evaluation model to measure the order picking cost. We conduct numerical tests on six order batching and storage assignment policy combinations. A comparative analysis and an ANOVA analysis are then performed on the test results to compare the performances of these policy combinations. We find that the Weighted Support Count-based storage allocation combining with the correlation-based order batching achieves the best order-picking performance. Also, the more accurate information about the items and orders we can get from the historical data, the more order-picking workload we can save by exploiting the similarity features.

Maximizing self-consumption rates and power quality towards two-stage evaluation for solar energy and shared energy storage empowered microgrids

Substantial penetration levels of intermittent and fluctuated renewable energy resources like PV can cause overcapacity and other operational challenges in the grid. Therefore, energy market actors are directed to the feed-in limitations and restrict the installed capacity. Likewise, the declining incentives make energy storage central to increasing self-consumption. However, economic uncertainties arise concerns about the financial feasibility of energy storage investments. This study presents the techno-economic benefits in increasing PV self-consumption using shared energy storage for a prosumer community under various penetration rates. In the first stage, the optimal energy storage allocations were done using the proposed New Best Algorithm and genetic algorithm with Matlab. Then, the technical performance of the proposed method was simulated with year-long using PSS Sincal. In the second stage, the economic feasibility of increasing PV self-consumption using shared energy storage under various penetration rates is evaluated considering residual energy. The effects of incentives are examined in terms of economic indicators such as payback period, net present value, and internal rate of return. The incentives promote prosumers either with or without energy storage to increase self-consumption. As a result, shared energy storage increased self-consumption up to 11% within the prosumer community. Results and sensitivity analysis are given in detail. The proposed method provides significant economic benefits and improved power quality.

Study on the Influence of Energy Storage on the VRE Station’s Utilization Rate

Abstract With the proposal of carbon peak and neutrality goals, variable renewable energies (VREs mainly wind power and solar) will usher in fast development. To ensure the effective consumption of VREs, it is necessary to improve the flexibility of power system. With the high proportion of VREs, the risk of insufficient flexibility of power system increases, and serious abandonment of VREs may occur. Energy storage is an effective means to improve system flexibility and increase VREs’ utilization rate. At present, many provinces in China have issued requirements for the allocation of energy storage for VRE projects, but there is little literature to analyze the impact of the allocation of energy storage in VRE stations on the utilization rate of VREs. In this paper, the joint optimization model of VRE stations and energy storage is established, which can accurately simulate the equivalent power after energy storage is configured in the VRE station. This model is used to study the impact of energy storage configuration proportion on the utilization rate of the VRE station.

Backhaul-Aware Storage Allocation and Pricing Mechanism for RSU-Based Caching Networks

Remarkable prevalence of in-car entertainment systems empowers vehicular users to download multimedia-enabled contents in transit and creates a new business opportunity for content providers (CPs). However, the timely delivery of requested content is a major concern for CPs to improve the quality of service (QoS) of their subscribe users. Roadside unit (RSU)-based caching appears as a promising solution for CPs wherein CPs proactively store their content at the RSU to reduce the content delivery time. Since the RSUs are enabled with limited storage capacity, the competition among multiple CPs for storage space is unavoidable. Further, the CPs are connected with RSUs using capacity limited backhaul links. Hence the allocation of RSUs’ storage among CPs becomes a fundamental issue in RSU-based caching networks. In this paper, we design a market scenario in which the set of CPs competes for the storage space of RSUs. In the unavailability of utility and cost functions of the CPs and the RSUs, we introduce a market maker to manage the marketplace. Further, we employ iteration-based double-sided auction mechanism to compute the optimal storage allocation and corresponding payment transfer for CPs which maximizes the social welfare of the networks. The simulation results demonstrate the proposed auction mechanism improves the social welfare of the network by at least 29.3% compared to the benchmark schemes. Further, with the help of both analytical and numerical analysis, we show that the proposed auction mechanism also holds vital economical properties.

Trade-offs between succulent and non-succulent epiphytes underlie variation in drought tolerance and avoidance.

Epiphyte communities comprise important components of many forest ecosystems in terms of biomass and diversity, but little is known regarding trade-offs that underlie diversity and structure in these communities or the impact that microclimate has on epiphyte trait allocation. We measured 22 functional traits in vascular epiphyte communities across six sites that span a microclimatic gradient in a tropical montane cloud forest region in Costa Rica. We quantified traits that relate to carbon and nitrogen allocation, gas exchange, water storage, and drought tolerance. Functional diversity was high in all but the lowest elevation site where drought likely limits the success of certain species with particular trait combinations. For most traits, variation was explained by relationships with other traits, rather than differences in microclimate across sites. Although there were significant differences in microclimate, epiphyte abundance, and diversity, we found substantial overlap in multivariate trait space across five of the sites. We found significant correlations between functional traits, many of which related to water storage (leaf water content, leaf thickness, hydrenchymal thickness), drought tolerance (turgor loss point), and carbon allocation (specific leaf area, leaf dry matter content). This suite of trait correlations suggests that the epiphyte community has evolved functional strategies along with a drought avoidance versus drought tolerance continuum where leaf succulence emerged as a pivotal overall trait.

Optimal Allocation of Double-Layer Energy Storage Based on the Dynamic Ancillary Service Price

Optimal Allocation of Double-Layer Energy Storage Based on the Dynamic Ancillary Service Price

Twin-Load Vehicle Dispatching and Storage Allocation Problem of Container Terminal Considering Traffic Congestion

Twin-Load Vehicle Dispatching and Storage Allocation Problem of Container Terminal Considering Traffic Congestion

Vickrey-Clarke-Groves Auction-Based Storage Allocation for Distributed Camera Systems

Video surveillance systems are critical infrastructures and that growing in size and complexity. Storage space is the prime resource in such systems but current surveillance setups are centralized and limited in resources due to security and cost constraints. Allocating the correct amount of storage to each camera considering their large differences in characteristics and video content is challenging. In this paper we propose a game theoretic approach to storage allocation for video surveillance camera systems based on the Vickrey-Clarke-Groves (VCG) auction mechanism.

Research on Application and Benefits of Energy Storage Systems

At present, Energy storage systems are widely used in power supply, power grid and end-users system. There have been extensive application explorations in most application scenarios in China, such as smoothing power fluctuation and output tracking of new energy sources, assisting conventional units to participate in frequency modulation, peak-shaving frequency modulation of the power grid, system standby, peak-valley arbitrage of the load side, as well as demand reduction. This paper firstly investigates the typical operation modes of energy storage applied to new energy consumption scenarios, and analyzes three important technical approaches in order to promote new energy consumption, such as energy storage to stabilize fluctuation, tracking output and peak load balancing. Secondly, to evaluate the economic benefits of energy storage allocation in new energy projects in different regions of China by using the self-developed software for economic analysis of new energy and comprehensive energy system; thereafter the strategic suggestions for energy storage allocation in different regions are proposed. The results show that it is economical for wind power owners to build Pumped Thermal Energy Storage (PTES) and it is even cheaper to purchase PTES by wind power owners than to build electrochemical energy storage (EES) batteries by users when the same conditions of energy storage applies. Owner-built and owner-allocated Photovoltaic (PV) power station is poor in economy. Under the same energy storage condition, when the allocated energy of 1MW PV power generation is less than 200 kW, EES is relatively more economical than PTES.

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%.

Asymptotic analysis of a storage allocation model with finite capacity: Marginal and conditional distributions

We consider a dynamic storage allocation model with a finite number of storage spaces. There are m primary spaces and R secondary spaces. Items arrive according to a Poisson process and an arriving item takes the lowest ranked available space. Each item occupies a storage space for an exponentially distributed amount of time. Letting N1 and N2 denote the numbers of occupied primary and secondary spaces, we examine the steady-state marginal distribution of N2 and derive various conditional limit laws for Prob[N1=k∣N2=r]. The joint process (N1,N2) behaves as a random walk in a lattice rectangle. We scale the storage capacities m and R to be large and study the problem asymptotically as the Poisson arrival rate λ becomes large. We previously derived detailed asymptotic results for the joint distribution Prob[N1=k,N2=r] and we use these results here to study the marginal and conditional distributions.