Enhancing Smart Grids via Advanced Metering Infrastructure and Fog Computing Fusion

The smart grid is a new generation of the power grid that incorporates advanced features such as distributed energy resources, two-way communication and situation awareness. It is not just energy that is exchanged between consumers and producers but information. An efficient and robust smart grid requires efficient and robust communication and computation infrastructure to carry and process the associated data. We provide an overview of the possibilities that fog computing offer for smart grids. In our investigation, the pillars of fog computing, such as decentralization, resiliency, scalability and mobility, offer a perfect match for the decentralized smart grid. Fog computing nodes, capable of communication and coordination, incorporated in smart meters, will provide distributed control, communication and computation. Thus, enhancing reliability, resiliency and scalability of the smart grid as more and more distributed energy resources (DERs) are added to the grid.

[1]  Nadeem Javaid,et al.  Cloud–Fog–Based Smart Grid Model for Efficient Resource Management , 2018, Sustainability.

[2]  Andrea Varga,et al.  The role of the Smart meters in the energy management systems , 2012 .

[3]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[4]  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 .

[5]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[6]  Mohammad Saad Alam,et al.  Fog Computing in IoT Aided Smart Grid Transition- Requirements, Prospects, Status Quos and Challenges , 2018, ArXiv.

[7]  Jing Liao,et al.  Non-intrusive appliance load monitoring using low-resolution smart meter data , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[8]  Suat Özdemir,et al.  A fog computing based smart grid model , 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC).

[9]  Julian de Hoog,et al.  Interconnecting Fog computing and microgrids for greening IoT , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[10]  Nadeem Javaid,et al.  Integration of Cloud-Fog Based Environment with Smart Grid , 2018, INCoS.

[11]  Marimuthu Palaniswami,et al.  PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[12]  Krzysztof Gajowniczek,et al.  Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data , 2015 .

[13]  P. G. V. Naranjo,et al.  Big Data Over SmartGrid-A Fog Computing Perspective , 2016 .

[14]  KokSheik Wong,et al.  An anomaly detection framework for identifying energy theft and defective meters in smart grids , 2018, International Journal of Electrical Power & Energy Systems.

[15]  Fei Hu,et al.  Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter , 2014, IEEE Transactions on Control of Network Systems.

[16]  Rajkumar Buyya,et al.  Fog Computing: A Taxonomy, Survey and Future Directions , 2016, Internet of Everything.

[17]  Nadeem Javaid,et al.  Cloud-Fog Based Smart Grid Paradigm for Effective Resource Distribution , 2018, NBiS.

[18]  Hannu Tenhunen,et al.  Communication and Security Technologies for Smart Grid , 2017, Int. J. Embed. Real Time Commun. Syst..

[19]  KokSheik Wong,et al.  Detection of energy theft and defective smart meters in smart grids using linear regression , 2017 .

[20]  Rabindra K. Barik,et al.  FogGrid: Leveraging Fog Computing for Enhanced Smart Grid Network , 2017, 2017 14th IEEE India Council International Conference (INDICON).

[21]  Jianhua Li,et al.  Fog Computing-Enabled Secure Demand Response for Internet of Energy Against Collusion Attacks Using Consensus and ACE , 2018, IEEE Access.

[22]  Walmir Freitas,et al.  Fault location in distribution systems based on smart feeder meters , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.