Cloud Computing for Smart Grids and Smart Cities


Introduction and motivation

Cloud computing has become mature and pervasive. The main driving force behind cloud computing is to provide on-demand resources from the pool of virtualized resources to tackle elasticity and scalability for large-scale computational tasks. Some widely used cloud-based applications/services include iCloud, YouTube, Dropbox, Gmail, etc. Therefore, it is of interest how cloud computing can be implemented in other emerging areas as well, particularly, smart grids and smart cities.

According to the Energy Information Administration [1], currently 62% of worldwide energy generation comes from coal and gas, 13% from nuclear, 16% from hydro, and less than 4% from other renewables energies. Moreover, worldwide demand of energy is expected to rise by 82% until 2030. By 2040, China’s energy usage will be twice the U.S. level. Most of the power outages (e.g., blackouts on August 14, 2003 in North America, on August 1996 on the west coast, and 2011 in San Diego, etc.) and disturbances occur in the power distribution networks due to several factors, such as cascading failure of power systems, remote console failure, loss of situational awareness-alarm processor failure, inaccurate dynamic simulations of system data, poor practice of Volt/Var and real-time analysis tools, and reliability coordinator role, etc. These observations make it mandatory to transform today’s unidirectional, centralized power system into a bi-directional, distributed, and automated energy value chain, giving rise to the so-called “Smart Grid.” On the other hand, global changes affecting climate, population, urbanization, and advances in urban technology put forward the concept of “Smarter Cities” as a new dimension in urban development.

There are many challenges and opportunities of emerging and future smart grids and smart cities [2, 3], which can be addressed by means of cloud computing. For instance, dynamic energy pricing, i.e., shifting the potential peak demand to a different time when the energy price is lower, consistency of heterogeneous data from different sources like sensors, AMI to SCADA system, real-time massive data streaming, etc. A cloud-based platform will be instrumental in minimizing network complexity and providing cost-effective solutions as well as increasing the utilization of energy. Some previous works investigated the issues related to cloud computing in smart grids and smart cities [4, 5, 6]. However, the presented concepts lack a smart scalable cloud platform to deal with real-time data management and consistency. Smart grid and smart city services/applications may be deployed in various ways, such as in a private cloud, community cloud, or hybrid cloud. For illustration, the deployment of smart grids in a hybrid cloud is shown in Fig. 1.

Deployment of Smart Grid in a Hybrid Cloud Model
Fig. 1. Deployment of Smart Grid in a Hybrid Cloud Model [7].


Research problems

First, it is key to identify the open issues in smart grids and smart cities that can be addressed by using cloud computing. Second, it is important to develop the proliferation of novel cloud-based platforms as the next-generation IT backbone for the realization of smart grids and smart cities.

More specifically, the key issues of smart grids and smart cities are as follows:

  •     Handling enormous amounts of real-time data, i.e., data from heterogeneous sources like end-user metering data, analytic models, phasor measurement units, feeder load data, maps, cameras, electric vehicles, citizens, control systems, instantaneous parameters, etc.
  •     Smart grids and smart cities require more scalable and consistency models, as the data from multiple devices communicate via the SCADA system at the same time. They should receive the same instructions, even if they connect to the SCADA system over different network paths and thus lead to different servers that provide control systems [8]. The trade-offs between consistency and scalability are summarized by using the Consistency Availability and Partitioning (CAP) theorem [9]. However, cloud computing provides weak consistency guarantees [10] to adhere to the CAP theorem. Clearly, in the context of smart grids and smart cities, further investigations on scalability and consistency are needed.

Concerning novel cloud-based platforms for smart grids and smart cities, there exist the following open issues:

  •     Design a smart cloud scheduler in order to improve resource scheduling that provides the reliability.
  •     Develop a real-time based demand/load prediction model at any granularity level in order to optimize energy efficiency.
  •     Realize data consistency in order to maintain scalability and availability of smart grid and smart city services/applications.


Research directions

Starting with a comprehensive state-of-the-art study for smart grids and smart cities, in particular on real-time data management, scalability, and consistency, the project work breakdown is as follows:

Design of smart cloud scheduler

The smart cloud scheduler aggregates all the demands. In order to achieve this, an energy-aware low-latency scheduling mechanism will be developed. The developed smart cloud scheduler takes into consideration historical data, service level agreement, etc.

Design of prediction model

The design of real-time based demand prediction models for smart grids and smart cities is the next step in order to control and optimize the energy usage in real-time. In particular, we will make a comparison between different algorithms while looking for a trade-off between the duration of each simulation and the deviation between simulated and theoretical design objectives.

Perform simulation

We will focus on the experimental realization of the developed schemes, carrying out measurements and comparisons between experimental and simulated results by exploiting open-source cloud platforms, e.g., Eucalyptus.

Optimization and demonstration

Various techniques will be exploited to optimize the performance of the developed algorithms and cloud scheduler. Finally, an experimental test-bed will be set up for smart grid and smart city proof-of-concept demonstrators.

Researchers

Advisor
  •    Prof. Martin Maier

Graduate Student
  •    Bhaskar Prasad Rimal

Publications

  •     B. P. Rimal, D. Pham Van, and M. Maier, “Cloudlet Enhanced Fiber-Wireless Access Networks for Mobile-Edge Computing,” IEEE Transactions on Wireless Communications, vol. 16, no. 6, pp. 3601-3618, Jun. 2017
  •     B. P. Rimal, D. Pham Van, and M. Maier, “Mobile Edge Computing Empowered Fiber-Wireless Access Networks in the 5G Era,” IEEE Communications Magazine, vol. 55, no. 2, pp. 192-200, Feb. 2017
  •     B. P. Rimal and M. Maier, “Workflow Scheduling in Multi-Tenant Cloud Computing Environments,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 1, pp. 290-304, Jan. 2017
  •     D. Pham Van, B. P. Rimal, J. Chen, P. Monti, L. Wosinska, and M. Maier, “Power-Saving Methods for Internet of Things over Converged Fiber-Wireless Access Networks,” IEEE Communications Magazine, vol. 54, no. 11, pp. 166-175, Nov. 2016
  •     D. Pham Van, B. P. Rimal, M. Maier, and L. Valcarenghi, “ECO-FiWi: An Energy Conservation Scheme for Integrated Fiber-Wireless Access Networks,” IEEE Transactions on Wireless Communications, vol. 15, no. 6, pp. 3979-3994, June 2016
  •     M. Maier, M. Chowdhury, B. P. Rimal, and D. Pham Van, “The Tactile Internet: Vision, Recent Progress, and Open Challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 138-145, May 2016
  •     D. Pham Van, B. P. Rimal, M. Maier, and L. Valcarenghi, “Design, Analysis, and Hardware Emulation of a Novel Energy Conservation Scheme for Sensor Enhanced FiWi Networks (ECO-SFiWi),” IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, pp. 1645-1662, May 2016
  •     B. P. Rimal, A. Belgana, and M. Maier, “Game-Theoretic Approach for Energy Trading in Smart Grids,” CRC, "Smart Grid: Networking, Data Management, and Business Models," pp. 387-403, April 2016
  •     B. P. Rimal, D. Pham Van, and M. Maier “Mobile-Edge Computing vs. Centralized Cloud Computing in Fiber-Wireless Access Networks,” Proc., IEEE INFOCOM, Workshop on 5G & Beyond - Enabling Technologies and Applications, San Francisco, CA, USA, April 2016
  •     D. Pham Van, B. P. Rimal, S. Andreev, T. Tirronen, and M. Maier, “Machine-to-Machine Communications over FiWi Enhanced LTE Networks: A Power-Saving Framework and End-to-End Performance,” IEEE/OSA Journal of Lightwave Technology, vol. 34, no. 4, pp. 1062-1071, Feb. 2016
  •     D. Pham Van, B. P. Rimal, and M. Maier “Fiber Optic vs. Wireless Sensors in Energy-Efficient Integrated FiWi Smart Grid Networks: An Energy-Delay and TCO Comparison,” Proc., IEEE INFOCOM, San Francisco, CA, USA, April 2016
  •     S. Mansour, I. Harrabi, M. Maier, and G. Joós, “Co-Simulation Study of Performance Trade-offs between Centralized, Distributed, and Hybrid Adaptive PEV Charging Algorithms,” Computer Networks, vol. 94, pp. 153-165, Dec. 2015
  •     D. Pham Van, B. P. Rimal, and M. Maier “Power-Saving Scheme for PON LTE-A Converged Networks Supporting M2M Communications,” Proc., IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Workshop on Fiber-Wireless (FiWi) Access Networks, pp. 1-5, Montreal, Canada, Oct. 2015
  •     M. Maier and B. P. Rimal, “The Audacity of Fiber-Wireless (FiWi) Networks: Revisited for Clouds and Cloudlets (Invited Paper),” China Communications, Feature Topic on Optical Interconnection Networks for Cloud Data Centers, vol. 12, no. 8, pp. 33-45, Aug. 2015
  •     A. Belgana, B. P. Rimal, and M. Maier, “Open Energy Market Strategies in Microgrids: A Stackelberg Game Approach Based on a Hybrid Multi-Objective Evolutionary Algorithm,” IEEE Transactions on Smart Grid, vol. 6, no. 3, pp. 1243-1252, May 2015
  •     A. Belgana, B. P. Rimal, and M. Maier, “Multi-Objective Pricing Game Among Interconnected Smart Microgrids,” Proc., IEEE Power & Energy Society General Meeting, National Harbor, MD, USA, July 2014.


References

[1]
 Energy Information Administration, http://www.eia.doe.gov.
[2]
 Fang, S. Misra, G. Xue, and D. Yang, “Managing Smart Grid Information in the Cloud: Opportunities, Model, and Applications,” IEEE Network, vol. 26, issue. 4, pp. 32-38, July/August 2012.
[3]
 Naphade, G. Banavar, C. Harrison, J. Paraszczak, and R. Morris, “Smarter Cities and Their Innovation Challenges,” IEEE Computer, vol. 44, issue. 6, pp. 32-39, June 2011.
[4]
 Yamamoto, S. Matsumoto, and M. Nakamura, “Using Cloud Technologies for Large-Scale House Data in Smart City,” Proc. IEEE 4th International Conference on Cloud Computing Technology and Science, pp. 141-148, December 2012.
[5]
 Rusitschka, K. Eger, and C. Gerdes, “Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain,” Proc. First IEEE International Conference on Smart Grid Communications, pp. 438-488, October 2010.
[6]
 Khan and S. L. Kiani, “A Cloud-Based Architecture for Citizen Services in Smart Cities,” Proc. IEEE 5th International Conference on Utility and Cloud Computing, pp. 315-320, November 2012.
[7]
 Primetica, “Enabling the Smart Grid through Cloud Computing,” U.S. Department of Energy Information Management Conference, April 2012.
[8]
 Birman, L. Ganesh, and R. van Renesse, “Running Smart Grid Control Software on Cloud Computing Architectures,” Proc. Computational Needs for the Next Generation Electric Grid, pp. 15-47, U. S. Department of Energy, April 2011.
[9]
 A. Brewer, “Towards robust distributed Systems,” Keynote Presentation, Proc. 19th Annual ACM Symposium on Principles of Distributed Computing, pp. 7, July 2000.
[10]
 Vogels, “Eventually Consistent,” ACM Queue, vol. 6, no. 6, pp. 16-19, October 2008.