Cloud-Based Optimal Energy Forecasting for Enabling Green Smart Grid Communication

In a smart grid, micro-grids can exchange energy among themselves in order to provide reliable energy service to customers. Therefore, the micro-grids need to exchange their real-time energy status with other micro-grids, which, in turn, maximizes the energy consumption and CO2 emissions to them. In this paper, we propose a cloud-based energy forecasting scheme to minimize the energy consumption and CO2 emission towards enabling a green smart grid communication technology. Additionally, we device an optimal strategy for the proposed cloud-based energy forecasting scheme to minimize the energy consumption furthermore. Numerical results show the effectiveness of the proposed scheme over without cloud-based approach in terms of message overhead, energy consumption, and CO2 emissions of the micro-grids. We see that the proposed scheme can minimize the energy consumption and the CO2 emissions involved in the forecasting process significantly, which supports the green architecture of the smart grid communication technology. Additionally, the message overhead for energy forecasting can also be minimized.

[1]  H. T. Mouftah,et al.  Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues , 2015, IEEE Communications Surveys & Tutorials.

[2]  V. Loumos,et al.  Web-based decision-support system methodology for smart provision of adaptive digital energy services over cloud technologies , 2011, IET Softw..

[3]  Sudip Misra,et al.  D2S: Dynamic Demand Scheduling in Smart Grid Using Optimal Portfolio Selection Strategy , 2015, IEEE Transactions on Smart Grid.

[4]  Kranthimanoj Nagothu,et al.  Persistent Net-AMI for Microgrid Infrastructure Using Cognitive Radio on Cloud Data Centers , 2012, IEEE Systems Journal.

[5]  Tharam S. Dillon,et al.  Identifying prosumer's energy sharing behaviours for forming optimal prosumer-communities , 2011, 2011 International Conference on Cloud and Service Computing.

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

[7]  Sudip Misra,et al.  Cloud Computing Applications for Smart Grid: A Survey , 2015, IEEE Transactions on Parallel and Distributed Systems.

[8]  Amir-Hamed Mohsenian-Rad,et al.  Coordination of Cloud Computing and Smart Power Grids , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[9]  Sudip Misra,et al.  D2P: Distributed Dynamic Pricing Policyin Smart Grid for PHEVs Management , 2015, IEEE Transactions on Parallel and Distributed Systems.

[10]  Sebnem Rusitschka,et al.  Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[11]  Danièle Revel,et al.  IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation , 2011 .

[12]  Mario Gerla,et al.  Energy Service Interface: Accessing to Customer Energy Resources for Smart Grid Interoperation , 2013, IEEE Journal on Selected Areas in Communications.

[13]  Radovan Sernec,et al.  Communication architecture for energy balancing market support on smart grid , 2014, 2014 IEEE International Energy Conference (ENERGYCON).

[14]  Xue Liu,et al.  A Survey on Geographic Load Balancing Based Data Center Power Management in the Smart Grid Environment , 2014, IEEE Communications Surveys & Tutorials.

[15]  Mohammad S. Obaidat,et al.  Energy-efficient smart metering for green smart grid communication , 2014, 2014 IEEE Global Communications Conference.

[16]  H. T. Mouftah,et al.  Reliable overlay topology design for the smart microgrid network , 2011, IEEE Network.

[17]  Katia Obraczka,et al.  Modeling energy consumption in single-hop IEEE 802.11 ad hoc networks , 2004, Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969).