Guest Editorial Special Section on Cloud Computing in ... - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 14, NO. 3, MARCH 2018

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Guest Editorial Special Section on Cloud Computing in Smart Grid Operation and Management HE future power network will be designed to accommodate and integrate all types of distributed renewable energy resources, storage units, and flexible demand response loads in the existing conventional grid. Also it should be able to perform intelligent energy management utilizing advancement in computation and communication to cater the needs of ever growing energy demand in secure manner. This leads the transition of conventional power grid into smart grid. However, performance of the smart grid utilizing automated, intelligent, and integrated functional blocks with widely interconnected distributed energy resources is dependent on advanced communication network, sensors, computing, and information technologies. There is a need for reliable and efficient communication network and computing infrastructure for the robust, affordable, and secure supply of electric power through smart grid operation. A huge amount of raw data is collected by sensors (e.g., smart meters) from the end user and different parts of the network. Subsequently, this big amount of data must be processed, analyzed, and stored in cost effective ways. An enormous pool of computing resources and storage must be provided to compute using this big data. The conventional data storage and its computational approach for such big, distributed, heterogeneous, complex state data will be challenging. Cloud computing has been explored to address the issues related to security in smart grid operation [1]. The computational needs for smart grid applications can be met using cloud computing architecture. Presently, successful implementation or adequate research on cloud computing with application in power industry cannot be found much. The related technologies are evolving in different capacities; technical, economical, and code/standard compliance. A comprehensive survey on different cloud computing applications for the smart grid architecture is focused in three different areas—energy management, information management, and security can be found in [2] and [3]. The challenges existing in implementation of smart grid operation successfully and current research problems in these three areas are addressed in their work. Multiagent architecture is addressed in [4]. The feasibility study on monitoring of renewable energy resources in smart grid based on cloud computing framework, analysis of energy management software tools [5] and optimization to support a cognitive network of advance metering

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infrastructure [6], resource optimization [7] has been reported. The cloud-based infrastructure may be deployed for operation and control applications and also to manage the fault detection and alert process. Green cloud computing solutions can not only save energy for the environment but also reduce operational costs [8]. This paper presented a cloud computing model for the following: 1) energy-efficient management of clouds; 2) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations and devices power usage characteristics; and 3) a novel software technology for energy-efficient management of clouds. This Special Section has accepted altogether seven research manuscripts depending on the novelties and problems that they address. Harmon et al. presents a cloud-based communication framework for networked microgrids. The wireless network and cloud infrastructure for bilevel optimization of each individual microgrid and the coordination of networked microgrids are demonstrated. Rayati and Ranjbar present a different aspect of system resilience including optimality, robustness, and flexibility for wind turbine control. The required bandwidth for information exchange related to generation, demand, frequency, and phase angle is considered in the architecture of the cloud. He et al. discusses data management system based on bilinear pairing. In their study, security is improved at the expense of increased computation and communication cost. Bilh et al. present the modeling of average time delay for group of electric vehicles (EVs) using Markov chain theory. The model is validated with NS2 simulator. In another work, Chekired et al. present decentralized cloud computing architecture based on software define networking technology and network function virtualization for real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Lin et al. discuss the data-driven generator coherency identification approach based on spectral clustering algorithm, and silhouette and results are demonstrated on oscillation events from Guangdong power system in China and Western Interconnection power system in North America. Jena et al. presents a hybrid approach using empirical wavelet transform and a random forest for classification of disturbances like line fault, generator outage, and line outage.

Digital Object Identifier 10.1109/TII.2018.2798629 1551-3203 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 14, NO. 3, MARCH 2018

Musleh et al. addresses the time delay effects of the wide area monitoring and control systems (WAMCS) in smart power grids in case of contingences, which may critically affect system stability. The study is performed using an advanced WAMCS testbed where a flexible AC transmission system (FACTS) device is utilized and controlled via a wide-area controller (WAC). Phasor Measurements Units (PMUs) are adopted to collect the real-time measurements for the WAC. In conclusion, cloud computing in smart grid applications has a long way to go before it becomes a success in implementation. We, the Guest Editors, take this opportunity to thank many researchers who have contributed to the success of this Special Section. In addition, we extend our gratitude to the authors of those papers which could not be considered in the Special Section and also to the reviewers for their thoughtful report to bring in quality work. We also thank, Prof. R. Luo, Editor-inChief, and his staff for their guidance in drafting this Special Section. N. KISHOR Faculty of Engineering Sciences University of Agder 4879 Grimstad, Norway M. A. S. MASOUM School of Electrical Engineering, Computer, and Math Science Faculty of Science and Engineering Curtin University Perth, WA 6102, Australia

A. NAYAK Department of Electrical Engineering and Computer Science University of Ottawa Ottawa, ON K1N 6N5, Canada A. SRIVASTAVA Department of Electrical Engineering and Computer Science Washington State University Pullman, WA 99164 USA REFERENCES [1] J. Y. Kim and Y. Kim, “Benefits of cloud computing adoption for smart grid security from security perspective,” J. Supercomput., vol. 72, no. 9, pp. 3522–3534, Sep. 2016. [2] S. Bera, S. Misra, and J. J. P. C. Rodrigues, “Cloud computing applications for smart grid: A survey,” IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 5, pp. 1477–1494, May 2015. [3] M. Yigit, V. C. Gungor, and S. Baktir, “Cloud computing for smart grid applications,” Comput. Netw., vol. 70, no. 9, pp. 312–329, Sep. 2014. [4] X. Jin, Z. He, and Z. Liu, “Multi-agent-based cloud architecture of smart grid,” Energy Procedia, vol. 12, pp. 60–66, 2011. [5] B. Bitzer and E. S. Gebretsadik, “Cloud computing framework for smart grid applications,” in Proc. 48th Int. Univ. Power Eng. Conf., Dublin, Ireland, Sep. 2–5, pp. 1–6, 2013. [6] K. Nagothu, B. Kelley, M. Jamshidi, and A. Rajaee, “Persistent net-AMI for microgrid infrastructure using cognitive radio on cloud data centers,” IEEE Syst. J., vol. 6, no. 1, pp. 4–15, Mar. 2012. [7] X. Fang, D. Yang, and G. Xue, “Evolving smart grid information management cloudward: A cloud optimization perspective,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 111–119, Mar. 2013. [8] R. Buyya, A. Beloglazov, and J. Abawajy, Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges, in Proc. 2010 Int. Conf. Parallel Distrib. Process. Techn. Appl., Las Vegas, NV, USA, Jul. 12–15, pp. 1–12, 2010.

Nand Kishor (SM’12) received the Ph.D. degree from Indian Institute of Technology, Roorkee, India, in 2006. He is currently an Associate Professor with the Department of Engineering Sciences, University of Agder, Grimstad, Norway. From August 2013 to October 2013, he was a Marie Curie Experienced Researcher (Marie Curie Fellow) with the Electrical Engineering Department, Aalto University, Espoo, Finland. He has coedited the book titled Modeling and Dynamic Behaviour of Hydropower Plant (IET, 2017). His research interests includes AI applications in power systems, wireless sensor systems, distributed generation with renewable resources, WAMS, and smart grid technologies. Dr. Kishor serves as Associate Editor for IET Generation, Transmission & Distribution (Feb. 2018-) and IET Renewable Power Generation (Jan. 2018-).

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 14, NO. 3, MARCH 2018

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Mohammad A. S. Masoum received the B.S., M.S., and Ph.D. degrees from the University of Colorado, Boulder, CO, USA, in 1983, 1985, and 1991, respectively. Currently, he is a Professor with the Department of Electrical and Computer Engineering, Curtin University, WA, Australia, and the Program Coordinator at the Centre for Smart Grid and Sustainable Power Systems. His research interests include application of artificial intelligence in smart grids with high penetrations of renewable distributed generations to improve performance, stability, and power quality. He has authored or co-authored more than 300 papers including 120 journal articles. He is the co-author of Power Quality in Power Systems and Electrical Machines (Elsevier, 2008 and 2015) and Power Conversion of Renewable Energy Systems (Springer, 2011 and 2012). Dr. Masoum is an Editor of the IEEE TRANSACTIONS ON SMART GRID, the IEEE POWER ENGINEERING LETTERS, and the Australian Journal of Electrical and Electronic Engineering.

Amiya Nayak received the B.Math. degree in computer science and combinatorics and optimization from the University of Waterloo, Waterloo, ON, Canada, in 1981, and the Ph.D. degree in systems and computer engineering from Carleton University, Ottawa, ON, in 1991. He is currently a Full Professor with the School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON. His research interests include mobile computing, wireless sensor networks, and vehicular ad hoc networks. He has more than 17 years of industrial experience in software engineering, avionics and navigation systems, simulation, and systemlevel performance analysis. Dr. Nayak was on the Editorial Board of several journals, including the IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, the International Journal of Parallel, Emergent and Distributed Systems, the Journal of Sensor and Actuator Networks, and the EURASIP Journal of Wireless Communications and Networking.

Anurag K. Srivastava (SM’08) is an Associate Professor of electric power engineering and the Director of the Smart Grid Demonstration and Research Investigation Laboratory, Energy System Innovation Center at the Washington State University Pullman, WA. He received the Ph.D. degree in electrical engineering from the Illinois Institute of Technology, Chicago, IL, USA, in 2005. In past years, he has worked in different capacities with the R´eseau de transport d´´electricit´e, in France, RWTH Aachen University, in Germany, Idaho National Laboratory, Pacific Northwest National Laboratory, PJM Interconnection, Schweitzer Engineering Laboratory, GE Grid Solutions, Massachusetts Institute of Technology and Mississippi State University, in USA, Indian Institute of Technology Kanpur, in India, and the Asian Institute of Technology, in Thailand. He has authored more than 200 technical publications including a book on Power System Security, and has four pending/awarded patents. His research interest includes data-driven algorithms for power system operation and control. Dr. Srivastava is the Vice Chair of the IEEE Power & Energy Society (PES) PEEC Committee, the Co-chair of the Microgrid Working Group, the Secretary of PES voltage stability working group, the Chair of PES Synchrophasors Applications Working Group, the Past Chair of the the IEEE PES Career Promotion Subcommittee, the Past Chair of the IEEEPES Student Activities Committee, and the Past Vice Chair of the IEEE Synchrophasor Conformity Assessment Program. He is an Associate Editor of the IEEE TRANSACTIONS ON SMART GRID and the IEEE TRANSACTIONS ON POWER SYSTEMS, an Editor of the IET Generation, Transmission and Distribution, and an IEEE Distinguished Lecturer.