5G Mobile Cellular Networks: Enabling Distributed State Estimation for Smart Grids

With the transition toward 5G, mobile cellular networks are evolving into a powerful platform for ubiquitous large-scale information acquisition, communication, storage, and processing. 5G will provide suitable services for mission-critical and real-time applications such as the ones envisioned in future smart grids. In this work, we show how the emerging 5G mobile cellular network, with its evolution of machine-type communications and the concept of mobile edge computing, provides an adequate environment for distributed monitoring and control tasks in smart grids. In particular, we present in detail how smart grids could benefit from advanced distributed state estimation methods placed within the 5G environment. We present an overview of emerging distributed state estimation solutions, focusing on those based on distributed optimization and probabilistic graphical models, and investigate their integration as part of the future 5G smart grid services.

[1]  Lei Du,et al.  A Scalable and Flexible Radio Access Network Architecture for Fifth Generation Mobile Networks , 2016, IEEE Communications Magazine.

[2]  Markus Rupp,et al.  A comparison between one-way delays in operating HSPA and LTE networks , 2012, 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt).

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

[4]  Tarik Taleb,et al.  Machine-type communications: current status and future perspectives toward 5G systems , 2015, IEEE Communications Magazine.

[5]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[6]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[7]  Hao Xu,et al.  An overview of 3GPP enhancements on machine to machine communications , 2016, IEEE Communications Magazine.

[8]  Carles Antón-Haro,et al.  Multiarea state estimation with legacy and synchronized measurements , 2016, 2016 IEEE International Conference on Communications (ICC).

[9]  Henning Wiemann,et al.  The LTE link-layer design , 2009, IEEE Communications Magazine.

[10]  Mirsad Cosovic,et al.  Distributed Gauss-Newton method for AC state estimation: A belief propagation approach , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[11]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.

[12]  Georgios B. Giannakis,et al.  Power System Nonlinear State Estimation Using Distributed Semidefinite Programming , 2014, IEEE Journal of Selected Topics in Signal Processing.

[13]  Jing Huang,et al.  State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid , 2012, IEEE Signal Processing Magazine.

[14]  Tao Yang,et al.  A Belief Propagation Based Power Distribution System State Estimator , 2011, IEEE Computational Intelligence Magazine.

[15]  E. Caro,et al.  Decentralized State Estimation and Bad Measurement Identification: An Efficient Lagrangian Relaxation Approach , 2011, IEEE Transactions on Power Systems.

[16]  Antonio Gómez Expósito,et al.  A Multilevel State Estimation Paradigm for Smart Grids , 2011, Proceedings of the IEEE.

[17]  Riccardo Trivisonno,et al.  SDN‐based 5G mobile networks: architecture, functions, procedures and backward compatibility , 2015, Trans. Emerg. Telecommun. Technol..