Operational Flexibility in Smart grid through Cloud computing

Rajeev T. Department of Electrical Engineering, College of Engineering, Trivandrum

mail2rajeevt@gmail.com

Ashok S. Department of Electrical Engineering

National institute of Technology, Calicut ashoks@nitc.ac.in

Abstract—Smart grid technologies promotes renewable energy source integration. This results in huge data handling and processing issues, due to the dynamic and distributed nature of sources and loads. The coordinated operation of generating sources and loads in such environment is challenging and requires substantial support for meeting the distributed storage and processing requirements. The immense potential of cloud computing technology can be utilized to address these issues. The proposed architecture for smart grid data storage applications has been realized in the open source cloud platform. Openstack cloud test bed has been set up for the deployment of applications and tested the performance under varying workload conditions.

Keywords-- Open stack, Dynamic Resource Allocation, Smart grids, Cloud computing, Middleware, Transaction mix, Virtualization

I. INTRODUCTION The demand of data storage, computing and real time operational necessity are increasing steadily. The existing centralized power system is incapable of handling large number of resources and its control functionalities. The present smart grid topology needs modifications to incorporate the requirements associated with coordinated operation of large number of resources, controlling devices, increasing amount of data acquisition and processing. The huge data storage and processing requirements in the smart grid environment demands more capacity, more reliability and the capability to access information from anywhere. The immense potential of cloud computing technology can be utilized to provide a user friendly working environment for smart grid operation Cloud computing allows individuals, teams, and organizations to streamline procurement processes and eliminate the need to duplicate certain computer administrative skills related to setup, configuration, and support. Cloud computing is evolving as a key computing platform for sharing resources that include infrastructures software, applications, and business processes. Many factors such as IT resource, software, hardware, operating systems and net-storage, can be virtualized and manage them in the cloud computing platform. To users, cloud computing is a

Pay-per-Use-on-Demand mode that can conveniently access shared IT resources through Internet. The self service nature of cloud computing allows organizations to create elastic environments that expand and contract based on the workload and target performance parameters. Cloud services are generally classified into three categories (i) infrastructure as a service (ii) Platform as a service and (iii) Software as a service. Virtualization is a core technology behind the cloud computing. It provides important advantages in sharing, manageability, and isolation. Multiple users and applications can share physical resources without affecting one another. Server virtualization turns a node into a set of virtual machines (VM), with each virtual machine appearing to have its own set of virtual resources, such as virtual CPU, Memory, Disk and Network. The logical allocation of resource is accomplished through hypervisor. Different operating system operation is allowed without affecting the performance of others and the operation is distinct in different categories. The full virtualization offers environment for sharing of computer system among multiple users with independent operation and provides improved reliability, security and productivity. Para virtualization permits direct access with cpu hardware and is faster than full virtualization. But simultaneous access of resources by guest operating system is not permitted. The difficulty is that guest operating system needs modifications to interact with underlying hypervisor. Operating system level virtualization is applicable to situations in which there is an offer to a similar set of operating system functionalities to a number of different categories of users with a single system. Open source software gives full freedom with different virtualization technologies as there are no limitations regarding to operating systems. A hardware assisted virtualization solution requires that underlying cpu hardware provides support for virtualization as there is no modifications is needed for guest operating system. Aameek et.al(2008) described the design of an agile data centre with integrated server and storage virtualization technologies. Such data center form key building block for new cloud computing architectures. The authors showed how to leverage the integrated agility for non-disruptive load

2012 International Symposium on Cloud and Services Computing

978-0-7695-4931-6/12 $26.00 © 2012 IEEE

DOI 10.1109/ISCOS.2012.23

21

balancing in data centers across multiple resource layers-servers, switches, and storage nodes. A novel load balancing algorithm called vector Dot is proposed for handling the hierarchical and multidimensional resource constraints in such systems. Experiments under varied conditions demonstrated the end-to-end validity of the system and the ability of Vector Dot to efficiently remove overloads on server, switch and storage nodes. Data storage requirements from end-users are growing, demanding more capacity, more reliability and the capability to access information from anywhere. Cloud storage meets this demand by providing transparent and reliable storage solutions. Most of these solutions are built on distributed infrastructures that rely on data redundancy to guarantee a 100% of data availability. Existing redundancy schemes often assume that resources are homogenous that may increase storage costs in heterogeneous infrastructures. Lluis et.al (2011) analyzed how distributed redundancy schemes can be optimally deployed over heterogeneous infrastructures. The authors proposed a mechanism to measure data availability more precisely in the works.

II. CLOUD COMPUTING IN SMART GRID The emerging smart grid will generate large amount of data due to the wide scale metering, sensing and monitoring operations. Storage, real time analysis and optimization of such a large amount of data are nontrivial task for traditional electric utilities. The analysis of benefits and opportunities of using cloud computing to help information management in the smart grid is explained in [5]. The paper also proposes a model to connect these two domains and discusses some of the issues that must be solved to make the system a realistic one. The cloud computing model for application deployment needs to meet the requirements of data and computing intensive smart grid applications. A cloud computing model for managing the real time streams of smart grid data for the near real time data retrieval needs of the different energy market actors is presented in [1]. The paper also discusses about Smart grid data management based on specific characteristics of cloud computing, such as distributed data management for real time data gathering, parallel processing for real time information retrieval, and ubiquitous access. The coordination of cloud computing and smart power grids is described in[2].The paper discusses on one design probability that can improve load balancing in the grid by carefully distributing the service request among data centers in a cloud computing system. A grid aware service routing in cloud computing for power load balancing is modeled and tested the performance with simulation results. A flexible architecture for data storage, resource allocation and power management and control is presented in [3]. The paper discusses existing issues and necessity of a cloud computing architecture for power management of micro grids. A control strategy is presented in dq reference frame

based on grid line frequency, which can be easily designed to accommodate in the cloud architecture. The study of Smart grid architecture understood that power system demands powerful computing and storage capabilities for the smart devices which are under deployment [8]. Cloud computing in large power grid and cloud data service center is considered as one of the central options which can integrate current infrastructure resources of the enterprise like hardware, high-performance distributed computing and data platform. The ongoing works in the areas of data handling, smart grids and cloud computing reveals that operational complexities and cost are the factors that curtails wide spread implementation of cloud computing technology in smart grid. This work considers an open source platform for providing flexible user friendly architecture for the data storage and processing requirements in smart grid.

III. PROPOSED ARCHITECTURE The open architecture proposed here offers plug and play environment that securely permits to integrate smart grid applications. The architecture is capable of accommodating more features. By incorporating suitable data handling and data messaging services, hardware requirement in smart grid environment can be reduced considerably. The architecture considered here consists of six different modules as shown in figure 1. The storage computing and networking are represented in the infrastructure layer. In the area of computing power sharing the consumer uses “fundamental computing resources” such as processing power, storage, and networking components. End users access cloud based applications through a web browser or a light weight desktop or mobile application while the business software and data are stored on servers at a remote location. Infrastructure as a service delivers basic storage and compute capabilities as standardized services over the network. Servers, storage systems, switches, routers, and other systems are pooled and made available to handle workloads that range from application components to high-

Fig.1: Logical view of Cloud test bed for smart grid

22

performance computing applications. Operating system requires hypervisor and open stack compute control the hypervisors through API server. Kernel virtual machine (KVM) is used as hypervisor in the architecture and there is flexibility for selecting multiple hypervisors for different zones. Middleware allows networked computers to communicate with each other. Open stack is the cloud middleware used in the architecture. More efficient use of the computing capacity is achieved with this, thereby increasing productivity, speedy deployment of different applications, protecting sensitive data while making savings in capital cost. There are three main service families under open stack namely compute infrastructure (nova), storage infrastructure (swift) and imaging service (glance)[7]. Networked on line storage facilities are provided in the architecture, where data is stored in a virtualized pool of storages. The virtualized resources are used by the customers according to the requirement. The resources may span across multiple servers. The storage services may accessed through a web service interface application programming. Swift is an object store to store large number of objects distributed across multiple hardware. Swift has built in redundancy and features like backing up, archiving data/services. It is scalable to multiple data objects and data. The functions includes storing the machine images, working as an independent data container offering elasticity and flexibility of cloud based storage for web applications. The discovery, storage and retrieval of virtual machine images can be provided through Glance, the Open stack Image Service. It includes three components: glance-api, glance-registry and the image store. The glance-api accepts API calls and actual image blobs are placed in the image store. The glance-registry stores and retrieves metadata about images. System has been divided into two sections frontend and backend. They connect to each other through a network, usually the Internet. The hardware/software requirements of the test bed are shown in table1. TABLE I: Hardware/Software requirement

IV. RESULTS AND DISCUSSIONS

The logical configuration of the operating environment for the storage of huge data from smart meter has been realized in the open stack environment. The data storage applications were deployed as instances in the architecture. The test was conducted by applying a user request rate of thousand eight hundred per minute. A linearly increasing rate of 30/s was applied to test the system performance. The performance guarantee of the cloud environment is needed for attracting service entities interest in this area. The result obtained is as shown in figure 2., reveals that data center utilization is dynamically adjusted and the resources are allocated on time. The result also provides percentage of success hits during one test period. The cloud environment succeeds to limit the percentage failure and offered reasonable values of MRT for the cases considered. Open stack dashboard is configured for monitoring the status of instances, resource allocation and the capacities assigned with various applications. Response time is the main performance parameter that the end user can experience directly for the available services. The average response time obtained in the test was 25 ms. The range of variation of response time shows that the open stack environment is not introducing undue system delay in the delivery of service throughout the experiment. The percentage of failed hits for this huge request rate is considerably low which contribute the good performance result.

(a) Success hit rate

(b) Mean response time

Fig.2: Performance result of test bed

Item Recommended Hardware/Software

Cloud Controller

HpProliant,64bitx86,2048KCache,12GB RAM,2x1TBHDD,1GB NIC

Client Node Intel Pentium4 3.20 GHz; 2048 Cache; 8GB RAM; 1TB HDD.2X1GBNIC

OperatingSystem Ubuntu11.10

Hypervisor KVM

Middleware Openstack

Monitoring Web Stress Tool

Testing Smart grid Applications

0

20

40

60

80

100

0 10 20 30 40 50 60

Succ

ess

hit r

ate(

%)

Time(m)

23

V. CONCLUSIONS The rapid development and wide application of cloud computing technologies will provide substantial support for the distributed processing requirements of smart grids. The real time monitoring and control in such environment requires huge data storage and processing. The cloud computing model for smart grid application deployment provides ample flexibility in the operation and it easily integrates the widely distributed resources for meeting the storage and computational requirements. The proposed architecture for application deployment in smart grid environment has been implemented in open stack and tested the performance with storage applications.

REFERENCES

[6] F. katiraei, M. irvani, P.W. lehn, “Microgrid Autonomous operation during and Subsequent to Islanding Process,” IEEE Transactions on Power Delivery .vol.20,No2, Jan 2005,pp.248-257.

[7] Open Stack Beginner's Guide(for Ubuntu - Precise) v3.0, 7 May 2012

[8] Architecture Based on IEC 61850/61499 Intelligent Logical Nodes”, IEEE Transactions on Industrial Electronics,Vol.59,No.5,May 2012

[1] Rusitschka, K. Eger, C. Gerdes, "Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the SmarGrid Domain," in Proc. 2010 IEEE Smart Grid Comm Conf., pp.483-488

[2] Amir-Hamed Mohsenian-Rad, Albert Leo-Garcia “Cordination of Cloud Computing and Smart Power grids,” in proceedings IEEE International conference on Smart grid communications, 2010, pp. 368-372.

[3] Rajeev T., Ashok S, “A Cloud Computing Approach for Power Management of Micro grids,” in proceedings IEEE PES Innovative Smart grid Technologies India(ISGT-India) 2011, pp. 49-52.

[4] R Baomin Xu ning Wang, Chunyan Li,” A Cloud Computing infrastructure on heterogeneous Computing resources” Journal of computers. Vol. 6,No.8, August 2011,pp.1789-1796.

[5] XI Fang, Misra S,Guoliang Xue, Dejun yang, “Managing Smartgrid information in the cloud Opportunities, Model and Applications,IEEE network. Vol 26, No 4,pp 32-38.

24