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Renewable Energy Assisted Sustainable and Environment Friendly Energy Cooperation in Cellular Networks

  • Faran Ahmed
  • Muhammad NaeemEmail author
  • Waleed Ejaz
  • Muhammad Iqbal
  • Alagan Anpalagan
Article

Abstract

In this paper, an energy cost minimization framework is presented for a green cellular network. The proposed novel energy cooperation scheme ensures optimal energy cooperation among grid-connected green cellular base stations (BSs). The framework is both economical and environment-friendly where the energy is saved by cutting down on the grid energy and sharing surplus green energy among the BSs. The intended scenario requires knowledge of harvested energy as well as traffic awareness to determine energy demand of a BS and inter-BS connectivity for incorporating energy transfer. A realistic utility function is developed to minimize energy cost under various constraints which entail energy borrowing from neighboring BSs (offering their surplus energy which is cheaper than grid and diesel generator), thereby reducing the overall energy cost of the network. The proposed framework for energy cooperation has bilinear non-convex structure. We use McCormick envelopes to convexify the optimization problem and transform the bilinear non-convex optimization into a linear optimization problem. The numerical results verify the effectiveness of the proposed traffic aware sustainable and environmental friendly BS operation through energy cooperation.

Keywords

Energy cooperation Green cellular network Renewable energy 

Notes

References

  1. 1.
    Steinacher, M., & Joos, F. (2015). Earth system responses to cumulative carbon emissions. Earth, 12, 9839–9877.Google Scholar
  2. 2.
    Sacuta, N., Young, A., & Worth, K. (2015). International energy agency greenhouse gas Weyburn–Midale \({\rm CO}_2\) monitoring and storage project. Technical Report, Petroleum Technology Research Centre Incorporated.Google Scholar
  3. 3.
    Ospina, A. V., & Heeks, R. (2010). Unveiling the links between icts and climate change in developing countries: A scoping study. International Development Research Centre, 2, 2009–2010.Google Scholar
  4. 4.
    Sun, X., Zhang, Q., Medina, M. A., & Liao, S. (2015). Performance of a free-air cooling system for telecommunications base stations using phase change materials (PCMs): In-situ tests. Applied Energy, 147, 325–334.CrossRefGoogle Scholar
  5. 5.
    Yang, T.-J., Zhang, Y.-J., Tang, S., & Zhang, J. (2016). How to assess and manage energy performance of numerous telecommunication base stations: Evidence in China. Applied Energy, 164, 436–445.CrossRefGoogle Scholar
  6. 6.
    Kaldellis, J. (2010). Optimum hybrid photovoltaic-based solution for remote telecommunication stations. Renewable Energy, 35(10), 2307–2315.CrossRefGoogle Scholar
  7. 7.
    Chan, C. A., Gygax, A. F., Leckie, C., Wong, E., Nirmalathas, A., & Hinton, K. (2016). Telecommunications energy and greenhouse gas emissions management for future network growth. Applied Energy, 166, 174–185.CrossRefGoogle Scholar
  8. 8.
    Sabater, P., Pol, A. M., Ganzo, A. I., Colom, J. O., Mihovska, A., & Prasad, R. (2014). Net zero emissions in radio base stations operating at different conditions. In 2014 4th international conference on Wireless communications, vehicular technology, information theory and aerospace & electronic systems (VITAE). IEEE (pp. 1–5).Google Scholar
  9. 9.
    Spagnuolo, A., Petraglia, A., Vetromile, C., Formosi, R., & Lubritto, C. (2015). Monitoring and optimization of energy consumption of base transceiver stations. Energy, 81, 286–293.CrossRefGoogle Scholar
  10. 10.
    Petraglia, A., Spagnuolo, A., Vetromile, C., D’Onofrio, A., & Lubritto, C. (2015). Heat flows and energetic behavior of a telecommunication radio base station. Energy, 89, 75–83.CrossRefGoogle Scholar
  11. 11.
    G. eSustainability Initiative et al. (2008). SMART 2020: Enabling the low carbon economy in the information age. Climate Group.Google Scholar
  12. 12.
    Fehske, A., Fettweis, G., Malmodin, J., & Biczok, G. (2011). The global footprint of mobile communications: The ecological and economic perspective. Communications Magazine, IEEE, 49(8), 55–62.CrossRefGoogle Scholar
  13. 13.
    Etoh, M., Ohya, T., & Nakayama, Y. (2008). Energy consumption issues on mobile network systems. In IEEE international symposium on applications and the internet (pp. 365–368).Google Scholar
  14. 14.
    Chen, T., Yang, Y., Zhang, H., Kim, H., & Horneman, K. (2011). Network energy saving technologies for green wireless access networks. Wireless Communications, IEEE, 18(5), 30–38.CrossRefGoogle Scholar
  15. 15.
    Bian, S., Wang, X., & Congiatu, M. (2013). An off-grid base station powered by sun wind, and water. In Proceedings of INTELEC (pp. 1–5).Google Scholar
  16. 16.
    Chamola, V., & Sikdar, B. (2015). A multistate markov model for dimensioning solar powered cellular base stations. IEEE Transactions on Sustainable Energy, 6(4), 1650–1652.CrossRefGoogle Scholar
  17. 17.
    Grangeat, C., Grandamy, G., & Wauquiez, F. (2010). A solution to dynamically decrease power consumption of wireless base stations and power them with alternative energies. In IEEE international telecommunications energy conference (pp. 1–4).Google Scholar
  18. 18.
    Nema, P., Rangnekar, S., & Nema, R. (2010). Pre-feasibility study of PV-solar/wind hybrid energy system for GSM type mobile telephony base station in central India. In IEEE international conference on computer and automation engineering (Vol. 5, pp. 152–156).Google Scholar
  19. 19.
    Leithon, J., Sun, S., & Lim, T. J. (2013). Energy management strategies for base stations powered by the smart grid. In IEEE GLOBECOM (pp. 2635–2640).Google Scholar
  20. 20.
    Ahmed, F., Naeem, M., Iqbal, M., & Anpalagan, A. (2016). Renewable energy assisted base station collaboration as micro grid. In Proceeding of IEEE electrical power and energy conference (pp. 2542–2547). Ottawa: IEEE.Google Scholar
  21. 21.
    Deshmukh, M., & Deshmukh, S. (2008). Modeling of hybrid renewable energy systems. Renewable and Sustainable Energy Reviews, 12(1), 235–249.CrossRefGoogle Scholar
  22. 22.
    Fang, X., Misra, S., Xue, G., & Yang, D. (2012). Smart grid: The new and improved power grid: A survey. Communications Surveys & Tutorials, 14(4), 944–980.CrossRefGoogle Scholar
  23. 23.
    Xu, J., Duan, L., & Zhang, R. (2015). Cost-aware green cellular networks with energy and communication cooperation. IEEE Communications Magazine, 53(5), 257–263.CrossRefGoogle Scholar
  24. 24.
    Leithon, J., Lim, T. J., & Sun, S. (2014). Energy exchange among base stations in a cellular network through the smart grid. In In IEEE ICC (pp. 4036–4041).Google Scholar
  25. 25.
    Reyhanian, N., Shah-Mansouri, V., Maham, B., & Yuen, C. (2015). Renewable energy distribution in cooperative cellular networks with energy harvesting. In IEEE PIMRC (pp. 1617–1621).Google Scholar
  26. 26.
    Chia, Y.-K., Sun, S., & Zhang, R. (2013). Energy cooperation in cellular networks with renewable powered base stations. In In Proceeding of IEEE WCNC (pp. 2542–2547).Google Scholar
  27. 27.
    Guo, Y., Xu, J., Duan, L., & Zhang, R. (2014). Joint energy and spectrum cooperation for cellular communication systems. IEEE Transactions on Communications, 62(10), 3678–3691.CrossRefGoogle Scholar
  28. 28.
    Hu, C., Zhang, X., Zhou, S., & Niu, Z. (2013). Utility optimal scheduling in energy cooperation networks powered by renewable energy. In APCC (pp. 403–408).Google Scholar
  29. 29.
    Guo, Z., Lim, T. J., & Motani, M. (2013). Department of Electrical and Computer Engineering, National University of Singapore, Singapore. In In Proceeding of IEEE GlobalSIP (pp. 349–352).Google Scholar
  30. 30.
    Ahmed, F., Naeem, M., Ejaz, W., Iqbal, M., Anpalagan, A., & Kim, H. S. (2018). Renewable energy assisted traffic aware cellular base station energy cooperation. Energies, 11(1), 99.CrossRefGoogle Scholar
  31. 31.
    Mclaughlin, S., Grant, P. M., Thompson, J. S., Haas, H., Laurenson, D. I., Khirallah, C., et al. (2011). Techniques for improving cellular radio base station energy efficiency. IEEE Wireless Communications, 18(5), 10–17.CrossRefGoogle Scholar
  32. 32.
    Mitsos, A., Chachuat, B., & Barton, P. I. (2009). Mccormick-based relaxations of algorithms. SIAM Journal on Optimization, 20(2), 573–601.MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Luenberger, D. G., & Ye, Y. (2016). Linear and nonlinear programming. New York: Springer.CrossRefzbMATHGoogle Scholar
  34. 34.
    Mehrotra, S. (1992). On the implementation of a primal–dual interior point method. SIAM Journal on Optimization, 2(4), 575–601.MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  36. 36.
  37. 37.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.COMSATS University IslamabadWah CantonmentPakistan
  2. 2.Department of Applied Science and EngineeringThompson Rivers UniversityKamloopsCanada
  3. 3.Department of Electrical and Computer EngineeringRyerson UniversityTorontoCanada

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