Mobile Smart Grids: Exploiting the TV White Space in Urban Scenarios

Due to its attractive characteristics, the TV white space (TVWS) spectrum is considered the ideal candidate to enable the deployment of smart grid networks (SGNs) via cognitive radio paradigm. However, the intermittent availability of the TVWS spectrum as well as its scarcity in urban scenarios could compromise the tight smart grid requirements in terms of reliability, latency, and data rate. This degradation could be even more severe when mobile grid nodes, e.g., electric vehicles, are considered. Stemming from this, we first develop an analytical framework to account for the mobility in SG scenarios. Then, we design a switching procedure based on the use of two different bands: TVWS spectrum and Industrial, Scientific and Medical (ISM) spectrum. The switching procedure selects, among the available spectrum bands, the one maximizing the achievable throughput at an arbitrary SGN. Such a procedure accounts for the presence of interfering SGNs on the TVWS spectrum through both their traffic and mobility patterns. By wisely using both the ISM and the TVWS spectrum, the proposed switching procedure is able to: 1) increase the achievable data rate, and to 2) reduce the outage event rate, improving the reliability and the latency of the smart grid communications. Moreover, we show the performance of the proposed switching procedure depends largely on the time devoted to sense. Hence, the proper setting of such a parameter is critical for the performance of any SGN. For this, we derive an optimization criterion maximizing the throughput under the constraint of bounding the outage rate. The theoretical analysis is validated through extensive numerical simulations.

[1]  Angela Sara Cacciapuoti,et al.  Interference Analysis for Secondary Coexistence in TV White Space , 2015, IEEE Communications Letters.

[2]  Angela Sara Cacciapuoti,et al.  On the Achievable Throughput Over TVWS Sensor Networks , 2016, Sensors.

[3]  C. T. Rim,et al.  Dynamics Characterization of the Inductive Power Transfer System for Online Electric Vehicles by Laplace Phasor Transform , 2013, IEEE Transactions on Power Electronics.

[4]  Anant Sahai,et al.  Allowing sensing as a supplement: An approach to the weakly-localized whitespace device problem , 2014, 2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN).

[5]  Jiming Chen,et al.  Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid , 2013, IEEE Transactions on Smart Grid.

[6]  Mohsen Guizani,et al.  Cognitive radio based hierarchical communications infrastructure for smart grid , 2011, IEEE Network.

[7]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[8]  Angela Sara Cacciapuoti,et al.  Database access strategy for TV White Space cognitive radio networks , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking Workshops (SECON Workshops).

[9]  Luigi Paura,et al.  Sensing-time optimization in cognitive radio enabling Smart Grid , 2014, 2014 Euro Med Telco Conference (EMTC).

[10]  Özgür B. Akan,et al.  Opportunistic reliability for cognitive radio sensor actor networks in smart grid , 2016, Ad Hoc Networks.

[11]  Andreas Achtzehn,et al.  TV White Space in Europe , 2012, IEEE Transactions on Mobile Computing.

[12]  Young Dae Ko,et al.  The Optimal System Design of the Online Electric Vehicle Utilizing Wireless Power Transmission Technology , 2013, IEEE Transactions on Intelligent Transportation Systems.

[13]  Ian F. Akyildiz,et al.  Optimal Primary-User Mobility Aware Spectrum Sensing Design for Cognitive Radio Networks , 2013, IEEE Journal on Selected Areas in Communications.

[14]  Hsiao-Hwa Chen,et al.  Smart Grid Communication: Its Challenges and Opportunities , 2013, IEEE Transactions on Smart Grid.

[15]  Luigi Paura,et al.  Optimal Strategy Design for Enabling the Coexistence of Heterogeneous Networks in TV White Space , 2016, IEEE Transactions on Vehicular Technology.

[16]  Yan Zhang,et al.  Guest Editorial: Smart Grid Communications Systems , 2014, IEEE Syst. J..

[17]  Young Dae Ko,et al.  Optimal design of the wireless charging electric vehicle , 2012, 2012 IEEE International Electric Vehicle Conference.

[18]  Xuemin Shen,et al.  Spatial and temporal online charging/discharging coordination for mobile PEVs , 2015, IEEE Wireless Communications.

[19]  Luigi Paura,et al.  On the Probabilistic Deployment of Smart Grid Networks in TV White Space , 2016, Sensors.

[20]  Vigna Kumaran Ramachandaramurthy,et al.  Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques , 2016 .

[21]  Luigi Paura,et al.  Cooperative Spectrum Sensing Techniques with Temporal Dispersive Reporting Channels , 2011, IEEE Transactions on Wireless Communications.

[22]  Luigi Paura,et al.  On Spectrum Sensing Optimal Design in Spatial–Temporal Domain for Cognitive Radio Networks , 2016, IEEE Transactions on Vehicular Technology.

[23]  Ping Zhang,et al.  Joint Spatial and Temporal Spectrum Sharing for Demand Response Management in Cognitive Radio Enabled Smart Grid , 2014, IEEE Transactions on Smart Grid.

[24]  Angela Sara Cacciapuoti,et al.  Spectrum Sensing in small-scale networks: Dealing with multiple mobile PUs , 2015, Ad Hoc Networks.

[25]  Martin Reisslein,et al.  Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols , 2016, IEEE Communications Surveys & Tutorials.

[26]  Luigi Paura,et al.  Enabling Smart Grid via TV White Space Cognitive Radio , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[27]  Ian F. Akyildiz,et al.  Cooperative spectrum sensing in cognitive radio networks: A survey , 2011, Phys. Commun..

[28]  Jin-Woo Jung,et al.  Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration , 2014 .