AN OPTIMIZATION FRAMEWORK FOR CLOUD-BASED DATA MANAGEMENT MODEL IN SMART GRID

Smart Grid (SG) is an intelligent electricity network that incorporates advanced information, control and communication technologies to increase the reliability of existing power grid. With advanced communication and information technologies, smart grid deploys complex information management model. This paper presents a cloud service based information management model which opens the issues and benefits from the perspective of both smart grid domain and cloud domain of system model. The overall cost of data management includes storage, computation, upload, download and communication costs which need to be optimized. This paper provides an optimization framework for reducing the overall cost for data management and integration in smart grid model. In this paper, the optimization model focuses on optimizing the size of data items to be stored in the clouds under concern. The types of data items to be stored in the clouds are considered as customer behavior data and Phasor Measurement Units (PMU) data in the smart grid environment. The management model usually comprises of four domains viz., smart grid domain, cloud domain, broker domain and network domain. The present work focuses mainly on smart grid and cloud domain and optimization of cost related to these domains for simplicity of model considered. The proposed model is optimized using various evolutionary optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The results of various techniques when implemented for proposed model are compared in terms of performance measures and a most suitable technique is identified for cloud based data management.

[1]  M. A. El-Sharkawy,et al.  Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system , 2012 .

[2]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  Aditya Ashok,et al.  Cyber-Physical Security Testbeds: Architecture, Application, and Evaluation for Smart Grid , 2013, IEEE Transactions on Smart Grid.

[4]  Daniel M. Batista,et al.  A Survey of Large Scale Data Management Approaches in Cloud Environments , 2011, IEEE Communications Surveys & Tutorials.

[5]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[6]  Mahesh Sooriyabandara,et al.  Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities , 2011, IEEE Communications Surveys & Tutorials.

[7]  Yogesh L. Simmhan,et al.  Cloud-Based Software Platform for Big Data Analytics in Smart Grids , 2013, Computing in Science & Engineering.

[8]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[9]  Chih-Hung Wu,et al.  A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy , 2007, Expert Syst. Appl..

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  Yuan Xiaofang,et al.  Parameters selection of SVM for function approximation based on Differential Evolution , 2007 .

[12]  Judith Kelner,et al.  Resource allocation for distributed cloud: concepts and research challenges , 2011, IEEE Network.

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  S. C. Choube,et al.  Differential evolution applied for reliability optimization of radial distribution systems , 2011 .

[15]  Xi Fang,et al.  Evolving Smart Grid Information Management Cloudward: A Cloud Optimization Perspective , 2013, IEEE Transactions on Smart Grid.

[16]  K. S. Swarup,et al.  Pattern analysis and classification for security evaluation in power networks , 2013 .

[17]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[18]  Xi Fang,et al.  Managing smart grid information in the cloud: opportunities, model, and applications , 2012, IEEE Network.