Cost Saving and Ancillary Service Provisioning in Green Mobile Networks

  • Muhammad Ali
  • Michela Meo
  • Daniela RengaEmail author
Part of the Internet of Things book series (ITTCC)


Mobile Network Operators (MNOs) are facing huge operational costs, due to the staggering increase of mobile traffic and to substantial bandwidth reliability requirements needed to enable the services of Smart Urban Ecosystems. With the purpose of reducing the cost due to power supply, dynamic load adaptation techniques are often implemented in Mobile Networks, in order to save energy when the traffic demand is low. Moreover, renewable energy (RE) sources are commonly introduced to power base stations, further contributing to decrease the operational expenditures. Finally, in a Demand Response context, the Smart Grid (SG) may actively ask its customers to dynamically adapt their consumption, by means of monetary incentives. The MNO is interested in improving the interaction with the SG, since mutual benefits can be obtained: cost reduction for the MNO and ancillary service provisioning from the SG side. We investigate via simulation a mobile access network where WiFi offloading techniques are combined with a properly designed energy management strategy, in order to reduce the load and better satisfy the SG requests. The impact of WiFi offloading is analyzed in different scenarios, including those envisioning the use of RE to power base stations (BSs) and/or the application of Resource on Demand (RoD) strategies, that activate or deactivate BSs based on traffic demand. Real data about traffic, RE production and SG requests are adopted. WiFi offloading results effective both in improving the probability of providing ancillary services and in reducing operational costs in any scenario, even when no RE is available. Furthermore, its impact is even more significant than the application of RoD strategies. Positive revenues are also possible for the MNO when RE are used, even when photovoltaic panels with relatively small capacity are installed.


  1. 1.
    Cisco, in Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2016–2021 White Paper, Feb 2017Google Scholar
  2. 2.
    H. Al Haj Hassan, L. Nuaymi, A. Pelov, Renewable energy in cellular networks: A survey, in 2013 IEEE Online Conference on Green Communications (GreenCom), Oct 2013, pp. 1–7Google Scholar
  3. 3.
    S. Buzzi et al., A survey of energy-efficient techniques for 5G networks and challenges ahead. IEEE J. Sel. Areas Commun. 34(4), 697–709 (2016)CrossRefGoogle Scholar
  4. 4.
    J. Wu et al., Energy-efficient base-stations sleep-mode techniques in green cellular networks: a survey. IEEE Commun. Surv. Tutor. 17(2), 803–826 (Secondquarter 2015)Google Scholar
  5. 5.
    M. Dalmasso, M. Meo, D. Renga, Radio resource management for improving energy self-sufficiency of green mobile networks. Perform. Eval. Rev. 44(2), 82–87 (2016)CrossRefGoogle Scholar
  6. 6.
    M. Deruyck, Reducing the impact of solar energy shortages on the wireless access network powered by a PV panel system and the power grid, in IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications—(PIMRC): Mobile and Wireless Networks, Valencia, Spain, Sept 2016, p. 2016Google Scholar
  7. 7.
    H. Ghazzai et al., Green networking in cellular hetnets: a unified radio resource management framework with base station ON/OFF switching. IEEE Trans. Veh. Technol. PP(99), 1–1 (2016)Google Scholar
  8. 8.
    S. Zhou, J. Gong, Z. Niu, Sleep control for base stations powered by heterogeneous energy sources, in 2013 International Conference on ICT Convergence (ICTC) Oct 2013, pp. 666–670Google Scholar
  9. 9.
    Y. He et al., OnWiFi offloading in heterogeneous networks: various incentives and trade-off strategies, in IEEE Communications Surveys Tutorials 18.4 (Fourthquarter 2016), pp. 2345–2385Google Scholar
  10. 10.
    F. Rebecchi et al., Data offloading techniques in cellular networks: a survey. IEEE Commun. Surv. Tutor. 17(2), 580–603 (Secondquarter 2015)Google Scholar
  11. 11.
    H.A.H. Hassan et al., Renewable energy usage in the context of energy- efficient mobile network, in 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), May 2015, pp. 1–7Google Scholar
  12. 12.
    D. Renga et al., Improving the interaction of a green mobile network with the smart grid, in 2017 IEEE International Conference on Communications (ICC), May 2017, pp. 1–7Google Scholar
  13. 13.
    A. Malik, J. Ravishankar, A review of demand response techniques in smart grids, in 2016 IEEE Electrical Power and Energy Conference (EPEC), Oct 2016, pp. 1–6Google Scholar
  14. 14.
    RTE-France, (Reseau de transport d’electricite), (2015)
  15. 15.
    M. Ali, M. Meo, D. Renga. WiFi offloading for enhanced interaction with the Smart Grid in green mobile networks, in 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), May 2017, pp. 233–238Google Scholar
  16. 16.
    M.A. Imran et al., Energy efficiency analysis of the reference systems, areas of improvements and target breakdown (Technical Report, ICT-EARTH deliverable, 2011)Google Scholar
  17. 17.
  18. 18.
    K. Lee et al., Mobile data offloading: how much can WiFi deliver? IEEE/ACM Trans. Netw. 21(2), 536–550 (2013)Google Scholar
  19. 19.
    E. Bulut, B.K. Szymanski, WiFi Access Point Deployment for Efficient Mobile Data Offloading, in Proceedings of the First ACM International Workshop on Practical Issues and Applications in Next Generation Wireless Networks. PINGEN ’12 (ACM, Istanbul, Turkey, 2012), pp. 45–50. ISBN: 978-1-4503-1531-9Google Scholar
  20. 20.
    E.M.R. Oliveira, A. Carneiro, Routine-based network deployment, in, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Apr 2014, pp. 183–184Google Scholar
  21. 21.
    A. P. Dobos. PVWatts Version 5 Manual, Sept 2014Google Scholar
  22. 22.
    Shounan Hua et al., Application of valve-regulated lead-acid batteries for storage of solar electricity in stand-alone photovoltaic systems in the northwest areas of China. J. Power Sour. 158(2), 1178–1185 (2006)CrossRefGoogle Scholar
  23. 23.
    J. W. Stevens, G.P. Corey, A study of lead-acid battery efficiency near top-of-charge and the impact on PV system design, in 1996 Conference Record of the Twenty Fifth IEEE Photovoltaic Specialists Conference, May 1996, pp. 1485–1488Google Scholar
  24. 24.
    H. Al-Sheikh, N. Moubayed, Health status and diagnosis of batteries in renewable energy systems: an overview, in 2012 International Conference and Exposition on Electrical and Power Engineering (EPE), Oct 2012, pp. 922–927Google Scholar
  25. 25.
    M. Jafari et al., Technical issues of sizing Lead-Acid batteries for application in residential renewable energy systems, in, 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS), Nov 2015, pp. 1–6Google Scholar
  26. 26.
    H. Gharavi, R. Ghafurian, IEEE recommended practice for sizing lead-acid batteries for stand-alone photovoltaic (PV) systems IEEE Std 1013–2007. Proc. IEEE. 99(6), 917–921 (2011)Google Scholar
  27. 27.
    D.P. Jenkins, J. Fletcher, D. Kane, Lifetime prediction and sizing of lead-acid batteries for microgeneration storage applications. IET Renew. Power Gener. 2(3), 191–200 (2008)CrossRefGoogle Scholar
  28. 28.
    M. Meo et al., Dimensioning the power supply of a LTE macro BS connected to a PV panel and the power grid, in 2015 IEEE International Conference on Communications (ICC), June 2015, pp. 178–184Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Elettronica e TelecomunicazioniPolitecnico di TorinoTurinItaly

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