Distributed Storage Resources Research Articles

A multi-stage sequential load restoration method for under-frequency load shedding with electric vehicles participation

As a low-cost and high-efficiency distributed energy storage resource, electric vehicles (EVs) play an increasingly prominent role in power system regulation. Since EVs are the regulation resources on the demand side, EVs should return to the initial states as soon as possible after completing the grid regulation task to avoid affecting the charging demand. Although EVs can provide a more flexible response to under-frequency load shedding (UFLS) than load shedding, the disorderly recharging of EVs during the load restoration stage may cause a significant second disturbance to the system frequency after UFLS. To ensure frequency stability and simultaneously restore the power supply, a multi-stage sequential load restoration method for UFLS with EVs participation is proposed to coordinate the priority and sequential restoration between the shedding load and recharging power of EVs. In the proposed method, the restoration willingness of EVs is quantified by the membership function, and the EV recharging optimization model with the objective function of maximizing the user’s restoration willingness satisfaction is established based on a two-stage robust under-frequency load shedding framework. Simulation results show that the proposed method can ensure system frequency stability and complete the load restoration quickly, effectively avoiding the secondary frequency drop caused by the disorderly recharging of EVs.

A space variable-scale scheduling method for digital vehicle-to-grid platform under distributed electric energy storage

With the widespread application of digital technology in the intelligent transportation field, the vehicle-to-grid (V2G) pattern, which utilizes electric vehicles (EVs) as the distributed storage resources of electric energy, has won increasingly more attention from authorities and decision-makers to promote the sustainable development of regenerative clean energy. This paper aims to study the interaction and integration problem between EVs and grid for the digital V2G platform, so as to support load aggregators making scheduling decisions. According to the variable-scale data analysis theory, a space–time scale space model is established to describe the demand response behavioral feature of EV users, and the lightning-Scale Transformation method (LST) is also proposed. Compared to the original unidirectional scale transformation modes, the LST is capable of adjusting the space and time observation scale collaboratively. A vehicle-to-grid scheduling method based on the lightning-Scale Transformation (V2G-LST) is put forward, in order to make scheduling plans by considering EV users’ behavioral feature and charger resource constraints. We collect the real dataset from 863 Beijing charging stations using the APIs of Amap, as well as the investigation data of the State Grid Corporation of China to verify the efficacy of our proposed method. Comparative experimental results verify that the V2G-LST could obtain the lower dispatch cost and higher EV charger usage rate than the maximum dispatchable quantity first method EVC-MDQ under both charging and discharging scenarios.

Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning

Most utilities across the world already have demand response (DR) programs in place to incentivise consumers to reduce or shift their electricity consumption from peak periods to off-peak hours usually in response to financial incentives. With the increasing electrification of vehicles, emerging technologies such as vehicle-to-grid (V2G) and vehicle-to-home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, electric vehicles (EV) become distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q-learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue.

From Uninterruptible Power Supply to resilient smart micro grid: The case of a battery storage at telecommunication station

Nowadays, resilient grids meet growing interest for their capability of supplying critical load even in case of power fault coming from grid disturbance and natural disasters. To do this, such grids involve redundant apparatus and predictive control schemes.For high value services, unexpected system unavailability is source of economic losses to the providers. Hence, beside the internal energy storage devices, such plants had better to have redundancy of energy sources (e.g. electrical grid and natural gas network) and tailored power flows control strategies still valid even in the case of energy shortage. On the other hand, distributed storage resources is attracting growing interest to support the power networks in terms of both resiliency and flexibility facing the impact of generation from the Renewable Energy Sources.In this work, a power supply system controller based on Artificial Intelligence was developed and simulated to wisely operate the storage resources to serve the ICT equipment as Uninterruptible Power Supply (above all in case of emergency) as fundamental mission. Secondly, the investigation assessed the capability, to offer ancillary services to the power network increasing its resiliency measured system response in terms of survival time during grid faults and restoration transient time to recover initial service level.

Opportunities and challenges of demand response in active distribution networks

In power systems, the installed generation capacity must exceed the annual peak demand, even though some capacity is kept idle most of the time. However, if it is uneconomical or not feasible to augment a sufficient capacity, the demand might exceed the available capacity. This mandates the system operator to shed the load in order to maintain security of the system. With the advent of advanced smart metering infrastructure, communication between system operator and end‐use customers makes it possible to adjust/curtail/shift the demand with respect to the state of the system. The response of the demand commonly termed as demand response (DR) can be attained either by incentive‐based or price‐based. With the help of DR, the renewable energy generation capacity can be increased by tuning the demand to match the variable and unpredictable power from renewable generation. It can also bring other benefits such as peak shaving, hosting capacity enhancement, and generation cost reduction. Furthermore, electric vehicles, heat pumps, and electric water heater can also be used as distributed storage resources to contribute to ancillary services, such as frequency/voltage regulation, peak‐shaving power or help to integrate fluctuating renewable resources. All these DR modes of operation need conventional regulatory frameworks and market design for capitalizing the available resources. Therefore, the objective of the study is to discuss the DR classification and their control strategies, DR role in microgrids and integration of renewable energy resources. Also, highlighted the opportunities and challenges along with the insights for the research scope associated with DR. WIREs Energy Environ 2018, 7:e271. doi: 10.1002/wene.271This article is categorized under: Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure Energy Policy and Planning > Systems and Infrastructure

Federated data storage system prototype for LHC experiments and data intensive science

Rapid increase of data volume from the experiments running at the Large Hadron Collider (LHC) prompted physics computing community to evaluate new data handling and processing solutions. Russian grid sites and universities’ clusters scattered over a large area aim at the task of uniting their resources for future productive work, at the same time giving an opportunity to support large physics collaborations. In our project we address the fundamental problem of designing a computing architecture to integrate distributed storage resources for LHC experiments and other data-intensive science applications and to provide access to data from heterogeneous computing facilities. Studies include development and implementation of federated data storage prototype for Worldwide LHC Computing Grid (WLCG) centres of different levels and University clusters within one National Cloud. The prototype is based on computing resources located in Moscow, Dubna, Saint Petersburg, Gatchina and Geneva. This project intends to implement a federated distributed storage for all kind of operations such as read/write/transfer and access via WAN from Grid centres, university clusters, supercomputers, academic and commercial clouds. The efficiency and performance of the system are demonstrated using synthetic and experiment-specific tests including real data processing and analysis workflows from ATLAS and ALICE experiments, as well as compute-intensive bioinformatics applications (PALEOMIX) running on supercomputers. We present topology and architecture of the designed system, report performance and statistics for different access patterns and show how federated data storage can be used efficiently by physicists and biologists. We also describe how sharing data on a widely distributed storage system can lead to a new computing model and reformations of computing style, for instance how bioinformatics program running on supercomputers can read/write data from the federated storage.

Novel remote monitoring platform for RES-hydrogen based smart microgrid

In the context of the future power grids – Smart Grids (SGs) – Smart MicroGrids (SMGs) play a paramount role. These ones are very specific portions of the SGs that deal with integration of small-rated distributed energy and storage resources closer to the loads – chiefly within the distribution domain. Data acquisition and monitoring tasks are vital functions that must be developed at every stage of the grid for a proper operation. This paper presents a remote monitoring platform (RMP) to monitor an experimental SMG. It integrates Renewable Energy Sources (RESs) (solar and wind) and hydrogen to operate in isolated regime. The RMP has been developed using the open-source authoring tool Easy Java/Javascript Simulations (EJsS). The interface has been designed to be intuitive and easy-to-use, providing real-time information of all the involved magnitudes over the network. Scalability, easy development, portability and cost effective are the main features of the proposed framework. The microgrid and the proposed monitoring platform are described and the successful results are reported. The remote user executes a ready-to-use file with low computational requirements and is enabled to graphically and numerically track the SMG behaviour. These results prove the suitability of the RMP as an effective means for continuous visualization of the coordinated energy flows of a real SMG.

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

Infrastructure as a service with desktops (DIaaS) based on the extensible mark-up language (XML) is herein proposed to utilize surplus resources. DIaaS is a traditional surplus-resource integrated management technology. It is designed to provide fast work distribution and computing services based on user service requests as well as storage services through desktop-based distributed computing and storage resource integration. DIaaS includes a nondisruptive resource service and an auto-scalable scheme to enhance the availability and scalability of intra-cloud computing resources. A performance evaluation of the proposed scheme measured the clustering performance time for surplus resource utilization. The results showed improvement in computing and storage services in a connection of at least two computers compared to the traditional method for high-availability measurement of nondisruptive services. Furthermore, an artificial server error environment was used to create a clustering delay for computing and storage services and for nondisruptive services. It was compared to the Hadoop distributed file system (HDFS).

Strategies for replica consistency in data grid – a comprehensive survey

SummaryData grid provides an efficient solution for data‐oriented applications that need to manage and process large data sets located at geographically distributed storage resources. Data grid relies on data replicas to enhance the performance and to ensure the fault tolerant results to the users. Replicas are developed to increase the availability of data and to provide better data access. Replicas have their own advantages, but there are a number of issues that must be resolved. Among various existing issues, the critical concern is replica consistency. Various replica consistency strategies are available in the literature. These strategies rationalize and investigate various parameters like bandwidth consumption, access cost, scalability, execution time, storage consumption, staleness, and freshness of replicas. In this paper, several asynchronous replica consistencies are classified and analyzed based on various strategies such as topology, level of abstraction, update propagation, and locality. Some other strategies are also discussed and analyzed like adaptive consistency, quorum‐based consistency, load balancing, and agent‐based economically efficient, check‐pointing, fault tolerance, and conflict management. Parameters on which these strategies are analyzed are methodology, replication classification, consistency, grid topology, environment, evaluation parameters, and performance. Copyright © 2016 John Wiley & Sons, Ltd.

State of the Art and Trends Review of Smart Metering in Electricity Grids

Climate change, awareness of energy efficiency, new trends in electricity markets, the obsolescence of the actual electricity model, and the gradual conversion of consumers to prosumer profiles are the main agents of progressive change in electricity systems towards the Smart Grid paradigm. The introduction of multiple distributed generation and storage resources, with a strong involvement of renewable energies, exposes the necessity of advanced metering or Smart Metering systems, able to manage and control those distributed resources. Due to the heterogeneity of the Smart Metering systems and the specific features of each grid, it is easy to find in the related literature a wide range of solutions with different features. This work describes the key elements in a Smart Metering system and compiles the most employed technologies and standards as well as their main features. Since Smart Metering systems can perform jointly with other activities, these growing initiatives are also addressed. Finally, a revision of the main trends in Smart Metering uses and deployments worldwide is included.

Storage Federations Using Xrootd

In this paper, we introduce a local storage system using Xrootd in which the heterogeneous storage types are combined into a single access point that help clients access to the storage system efficiently without knowing complex architecture underneath. This system is expanding into global storage union federating the storage systems of the WLCG. The WLCG, a collaboration of computing centers to deal with large scale of data produced by the detectors using the LHC at CERN, has distributed storage resources with different protocols and management systems. The heterogeneity of the distributed storage resources prevents efficient access to data and transfer activities between computing centers. The demand on federating storage resources into one single access point has been arising within the WLCG. We introduce the current movement to federating storage resources has been started and expanding into the whole WLCG collaboration upon the successful federation and operation carried by the computing centers supporting ALICE experiment.

Data federation strategies for ATLAS using XRootD

In the past year the ATLAS Collaboration accelerated its program to federate data storage resources using an architecture based on XRootD with its attendant redirection and storage integration services. The main goal of the federation is an improvement in the data access experience for the end user while allowing more efficient and intelligent use of computing resources. Along with these advances come integration with existing ATLAS production services (PanDA and its pilot services) and data management services (DQ2, and in the next generation, Rucio). Functional testing of the federation has been integrated into the standard ATLAS and WLCG monitoring frameworks and a dedicated set of tools provides high granularity information on its current and historical usage. We use a federation topology designed to search from the site's local storage outward to its region and to globally distributed storage resources. We describe programmatic testing of various federation access modes including direct access over the wide area network and staging of remote data files to local disk. To support job-brokering decisions, a time-dependent cost-of-data-access matrix is made taking into account network performance and key site performance factors. The system's response to production-scale physics analysis workloads, either from individual end-users or ATLAS analysis services, is discussed.

Monitoring of a Grid Storage Virtualization Service

Grid computing is commonly used for large scale application requiring huge computation capabilities. In such distributed architectures, the data storage on the distributed storage resources must be handled by a dedicated storage system to ensure the required quality of service. In order to simplify the data placement on nodes and to increase the performance of applications, a storage virtualization layer can be used. This layer can be a single parallel filesystem (like GPFS) or a more complex middleware. The latter is preferred as it allows the data placement on the nodes to be tuned to increase both the reliability and the performance of data access. Thus, in such a middleware, a dedicated monitoring system must be used to ensure optimal performance. In this paper, the authors briefly introduce the Visage middleware – a middleware for storage virtualization. They present the most broadly used grid monitoring systems, and explain why they are not adequate for virtualized storage monitoring. The authors then present the architecture of their monitoring system dedicated to storage virtualization. We introduce the workload prediction model used to define the best node for data placement, and show on a simple experiment its accuracy.

The role of electric vehicles in smart grids

The electrification of transportation is seen as one of the solutions to challenges such as global warming, sustainability, and geopolitical concerns on the availability of oil. From the perspective of power systems, an introduction of plug‐in electric vehicles presents many challenges but also opportunities to the operation and planning of power systems. On the one hand, if vehicles are considered regular loads without flexibility, uncontrolled charging can lead to problems at different network levels endangering secure operation of installed assets. However, with direct or indirect control approaches the charging of vehicles can be managed in a desirable way, e.g., shifted to low‐load hours. Furthermore, vehicles can be used as distributed storage resources to contribute to ancillary services for the system, such as frequency regulation and peak‐shaving power or help integrate fluctuating renewable resources. All these modes of operation need appropriate regulatory frameworks and market design if the flexibility of the vehicles is to be capitalized. In most of the proposed approaches, a so‐called aggregator could be in charge of directly or indirectly controlling the charging of vehicles and serve as an interface with other entities such as the transmission system operator or energy service providers. However, fully decentralized schemes without an aggregator are also conceivable, for instance, to provide primary frequency control. Communication also plays a key role, as in most of the control schemes a significant amount of information needs to be transmitted between vehicles and control entities. The management of electric vehicles as distributed resources fits well in the paradigm of smart grids, where an advanced use of communication technologies and metering infrastructure, increased controllability and load flexibility, and a larger share of fluctuating and distributed resources are foreseen.This article is categorized under: Energy Infrastructure > Economics and Policy Energy Systems Economics > Systems and Infrastructure Energy Systems Analysis > Systems and Infrastructure

One click dataset transfer: toward efficient coupling of distributed storage resources and CPUs

The massive data processing in a multi-collaboration environment with geographically spread diverse facilities will be hardly "fair" to users and hardly using network bandwidth efficiently unless we address and deal with planning and reasoning related to data movement and placement. The needs for coordinated data resource sharing and efficient plans solving the data transfer paradigm in a dynamic way are being more required. We will present the work which purpose is to design and develop an automated planning system acting as a centralized decision making component with emphasis on optimization, coordination and load-balancing.We will describe the most important optimization characteristic and modeling approach based on "constraints". Constraint-based approach allows for a natural declarative formulation of what must be satisfied, without expressing how. The architecture of the system, communication between components and execution of the plan by underlying data transfer tools will be shown. We will emphasize the separation of the planner from the "executors" and explain how to keep the proper balance between being deliberative and reactive. The extension of the model covering full coupling and reasoning about computing resources will be shown.The system has been deployed within STAR experiment over several Tier sites and has been used for data movement in the favour of user analyses or production processing. We will present several real use-case scenario and performance of the system with a comparison to the "traditional" - solved by hands methods. The benefits in terms of indispensable shorter data delivery time due to leveraging available network paths and intermediate caches will be revealed. Finally, we will outline several possible enhancements and avenues for future work.

SBR-k(Sized-based replacement-k) : 데이터 그리드 환경에서 파일 교체

데이터 그리드는 대용량의 데이터 어플리케이션 처리를 위해 지리적으로 분산되어 있는 저장 자원을 제공한다. 대용량을 처리해야 하는 데이터 그리드 환경에서는 기존 웹 캐싱 정책이나 가상 메모리 캐쉬 교체 정책과는 다른 파일 교체 정책이 필요하다. LRU(Least Recently Used)나 LCB-K(Least Cost Beneficial based on K), EBR(Economic-based cache replacement), LVCT(Least Value-based on Caching Time) 같은 기존의 파일 교체 전략은 파일 교체를 위해 추가적인 자원이 필요하거나 미래를 예측해야한다. 본 논문은 이를 해결하기 위해 파일의 크기에 기반하여 파일 교체를 수행하는 SBR-k(Sized-based replacement-k)을 제안한다. 제안된 정책은 불확실한 미래 예측의 부담을 줄이고, 요청된 파일에 대응되는 교체 파일의 개수가 적게 하이 위해 파일 크기를 고려하였다. 성능평가 결과 캐쉬 크기가 작은 경우에 적중률이 비슷하나 캐쉬 크기가 크면 본 논문에서 제시한 정책이 우수함을 보였다. The data grid computing provides geographically distributed storage resources to solve computational problems with large-scale data. Unlike cache replacement policies in virtual memory or web-caching replacement, an optimal file replacement policy for data grids is the one of the important problems by the fact that file size is very large. The traditional file replacement policies such as LRU(Least Recently Used), LCB-K(Least Cost Beneficial based on K), EBR(Economic-based cache replacement), LVCT(Least Value-based on Caching Time) have the problem that they have to predict requests or need additional resources to file replacement. To solve theses problems, this paper propose SBR-k(Sized-based replacement-k) that replaces files based on file size. The proposed policy considers file size to reduce the number of files corresponding to a requested file rather than forecasting the uncertain future for replacement. The results of the simulation show that hit ratio was similar when the cache size was small, but the proposed policy was superior to traditional policies when the cache size was large.

Towards distributed storage resource management using flow control

Deployment of shared storage systems is increasing with rapid adoption of virtualization technologies to provide flexible sharing, isolation, better management and high utilization of resources. Quality of service (QoS) in such environments is quite desirable for meeting IO demands of virtual machines. The lack of QoS support at typical storage arrays, simultaneous access by multiple hosts and concerns regarding under-utilization of resources makes this problem quite challenging. In this paper, we study the problem of providing fairness among hosts accessing a storage array in a distributed manner while maintaining high efficiency. Towards this goal, we investigate whether local latency estimates at each host can be used to detect overload and whether limiting host issue queue lengths can provide fairness across hosts. In principle, the approach is similar to mechanisms used by TCP at each host for flow control. Initial experiments and simulation results for control mechanism provide encouragement to develop a complete framework.

A decision support grid for integrated molecular solvent design and chemical process selection

The process engineering domain makes an interesting application area for grid technologies as problem solving generally involves multiple software and data resources and heavy computations. There are numerous computationally demanding applications that can benefit from grid computing such as molecular simulations and large-scale optimizations. We report on a prototype grid for integrated process and molecular design to demonstrate the potential of grids in the process-engineering arena. The prototype integrates in-house solvent and process design tools with external distributed computation and storage resources as well as web-databases and computer networks. A middleware architecture is proposed in the form of a problem-solving environment as a means of interfacing the heterogeneous grid components. The grid operations and functionalities are exposed to the user through a prototype web-portal that enables the seamless access and utilization of the available resources. The developed prototype is illustrated with an application case.

A distributed storage system with dCache

The LCG collaboration is encompassed by a number of Tier 1 centers. The Nordic LCG Tier 1, operated by NDGF, is in contrast to many other Tier 1 centers distributed over the Nordic countries. A distributed setup was chosen for both political and technical reasons, but also provides a number of unique challenges. dCache is well known and respected as a powerful distributed storage resource manager, and was chosen for implementing the storage aspects of the Nordic Tier 1. In contrast to classic dCache deployments, we deploy dCache over a WAN with limited bandwidth, high latency, frequent network failures, and spanning many administrative domains. These properties provide unique challenges, covering topics such as security, administration, maintenance, upgradability, reliability, and performance. Our initial focus has been on implementing the GFD.47 OGF recommendation (which introduced the GridFTP 2 protocol) in dCache and the Globus Toolkit. Compared to GridFTP 1, GridFTP 2 allows for more intelligent data flow between clients and storage pools, thus enabling more efficient use of our limited bandwidth.

Data location mechanism in agent-based autonomic storage systems

This paper introduces agent-based methodology to build a distributed autonomic storage system infrastructure, and an effectively negotiation mechanism based on agent is applied for data location. We present Availability-based Data Allocation (ADA) algorithm as a data placement strategy to achieve high efficient utilization of storage resources by employing multiple distributed storage resources. We use Bloom filter in each storage device to track the location of data. We present the data lookup strategy that small size of read request is handled directly, and large size of read request is handled by cooperation with storage devices. The performance evaluation shows that the data location mechanism is high available and can work well for heterogeneous autonomic storage systems.