Multi-Site Energy Use Management in the Absence of Smart Grids

  • Zeynep Bektas
  • Gülgün Kayakutlu
  • M. Özgür Kayalica
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)

Abstract

Demand-side management (DSM) allows an energy load to be balanced across multiple consumers. Energy consumption fluctuations cause important costs based on the alternating energy price tariffs. DSM creates opportunities for consumers to reduce their energy consumption costs by smoothing the daily load curve. An MINLP model is constructed based on power consumption, which aligns with the production schedules of the industrial units. Then, these feasible schedules are used as an input for a cooperative Bayesian game that is designed to balance the hourly loads. A case study of three factories, where the demand-side manager tries to minimize the instability of purchasing electricity from the general grid through load balancing, is considered.

Notes

Acknowledgements

We would like to express our gratitude to the Hayat Kimya managers, who helped us to find a case study district and provided the necessary data and information to implement our model.

References

  1. Allaz, B., & Vila, J. L. (1993). Cournot competition, forward markets and efficiency. Journal of Economic Theory, 59, 1–16.CrossRefMATHGoogle Scholar
  2. Alvarez, C., Gabaldon, A., & Molina, A. (2004). Assessment and simulation of the responsive demand potential in end user facilities: Application to an university customer. IEEE Transactions on Power Systems, 19(2), 1223–1231.CrossRefGoogle Scholar
  3. Atikol, U. (2013). A simple peak shifting DSM (demand-side management) strategy for residential water heaters. Energy, 62, 435–440.CrossRefGoogle Scholar
  4. Bahrami, S., & Parniani, M. (2014). Game theoretic based charging strategy for plug-in hybrid electric vehicles. IEEE Transactions on Smart Grid, 5(5), 2368–2375.CrossRefGoogle Scholar
  5. Bektas, Z., & Kayalica, M. O. (2015). Energy demand side management in the lack of smart grids. In F. Cucchiella & L. Koh (Eds.), Sustainable future energy technology and supply chains (pp. 157–170). Switzerland: Springer International Publishing.CrossRefGoogle Scholar
  6. Bergaentzle, C., Clastres, C., & Khalfallah, H. (2014). Demand-side management and European environmental and energy goals: An optimal complementary approach. Energy Policy, 67, 858–869.CrossRefGoogle Scholar
  7. Chen, C., Kishore, S., & Snyder, L. V. (2011). An innovative RTP-based residential power scheduling scheme for smart grids. In: IEEE book group authors (Eds.), Proceeding Book of the International Conference on Acoustics, Speech and Signal Processing, (pp. 5956–5959). New York: IEEE.Google Scholar
  8. Chen, H., Li, Y., Louie, R. H. Y., & Vucetic, B. (2014). Autonomous demand side management based on energy consumption scheduling and instantaneous load billing: An aggregative game approach. IEEE Transactions on Smart Grid, 5(4), 1744–1754.CrossRefGoogle Scholar
  9. Doya, K., Ishii, S., Pouget, A., & Rao, R. P. N. (2011). Bayesian brain: Probabilistic approaches to neural coding. Boston: MIT Press.MATHGoogle Scholar
  10. He, Y., Wang, B., Li, D., Du, M., Huang, K., & Xia, T. (2015). China’s electricity transmission and distribution tariff mechanism based on sustainable development. International Journal of Electrical Power & Energy Systems, 64, 902–910.CrossRefGoogle Scholar
  11. Lampropoulos, I., Kling, W. L., Ribeiro, P. F., Berg van den, J. (2013). History of demand side management and classification of demand response control schemes. In: IEEE book group authors (Eds.), Proceeding Book of the Power and Energy Society General Meeting. New York: IEEE.Google Scholar
  12. Lasaulce, S., & Tembine, H. (2011). Game theory and learning for wireless networks. London: Elsevier.Google Scholar
  13. Lopez, M. A., de la Torre, S., Martin, S., & Aguado, J. A. (2015). Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. International Journal of Electrical Power & Energy Systems, 64, 689–698.CrossRefGoogle Scholar
  14. Mangiatordi, F., Pallotti, E., & Del Vecchio, P. (2013). A non-cooperative game theoretic approach for energy management in MV grid. In: IEEE Book Group Authors (Eds.), Proceeding book of the 13th international conference on environment and electrical engineering (pp. 266–271). New York: IEEE.Google Scholar
  15. Marzband, M., Sumper, A., Dominguez-Garcia, J. L., & Gumara-Ferret, R. (2013a). Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP. Energy Conversion and Management, 76, 314–322.Google Scholar
  16. Marzband, M., Sumper, A., Ruiz-Alvarez, A., Dominguez-Garcia, J. L., & Tomoiaga, B. (2013b). Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets. Applied Energy, 106, 365–376.Google Scholar
  17. Marzband, M., Ghadimi, M., Sumper, A., & Dominguez-Garcia, J. L. (2014). Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Applied Energy, 128, 164–174.Google Scholar
  18. Marzband, M., Azarinejadian, F., Savaghebi, M., & Guerrero, J. M. (2015a). An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with markov chain. IEEE Systems Journal, 99, 1–11.Google Scholar
  19. Marzband, M., Parhizi, N., & Adabi, J. (2015b). Optimal energy management for stand-alone microgrids based on multi-period imperialist competition algorithm considering uncertainties: Experimental validation. International Transactions on Electrical Energy Systems.Google Scholar
  20. Marzband, M., Parhizi, N., Savaghebi, M., & Guerrero, J. M. (2015c). Distributed smart decision-making for a multimicrogrid system based on a hierarchical interactive architecture. IEEE Transactions on Energy Conversion, 99, 1–12.Google Scholar
  21. Marzband, M., Yousefnejad, E., Sumper, A., & Dominguez-Garcia, J. L. (2016). Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. Electrical Power and Energy Systems, 75, 265–274.CrossRefGoogle Scholar
  22. Mohsenian-Rad, A. H., Wong, V. W. S., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 1(3), 320–331.CrossRefGoogle Scholar
  23. National Rates. Republic of Turkey Energy Market Regulatory Authority. (2015). http://www.epdk.org.tr/index.php/elektrik-piyasasi/tarifeler?id=133. Accessed July 27, 2015.
  24. Nwulu, N. I., & Xia, X. (2015). Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Conversion and Management, 89, 963–974.CrossRefGoogle Scholar
  25. Sheikhi, A., Rayati, M., Bahrami, S., Ranjbar, A. M., & Sattari, S. (2015). A cloud computing framework on demand side management game in smart energy hubs. International Journal of Electrical Power & Energy Systems, 64, 1007–1016.CrossRefGoogle Scholar
  26. Su, W., & Huang, A. Q. (2014). A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers. Applied Energy, 119, 341–350.CrossRefGoogle Scholar
  27. Tembine, H. (2016). Mean-field-type optimization for demand-supply management under operational constraints in smart grid. Energy Systems, 7, 333–356.CrossRefGoogle Scholar
  28. Winston, W. L. (2004). Operations research: Applications and algorithms. Canada: Thomson.MATHGoogle Scholar
  29. Wu, C., Mohsenian-Rad, H., Huang, J., & Wang, A. Y. (2011). Demand side management for wind power integration in microgrid using dynamic potential game theory. In: IEEE Book Group Authors (Eds.), Proceeding book of the IEEE international workshop on smart grid communications and networks (pp. 1199–1204). New York: IEEE.Google Scholar
  30. Yang, P., Tang, G., & Nehorai, A. (2012). Optimal time-of-use electricity pricing using game theory. In: IEEE Book Group Authors (Eds.), Proceeding book of the international conference on acoustics, speech and signal processing (pp. 3081–3084). New York: IEEE.Google Scholar
  31. Zhao, L., Liang, R., Zhang, J., Ma, L., & Zhao, T. (2014). A new method for building energy consumption statistics evaluation: Ratio of real energy consumption expense to energy consumption. Energy Systems, 5, 627–642.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zeynep Bektas
    • 1
  • Gülgün Kayakutlu
    • 2
  • M. Özgür Kayalica
    • 3
  1. 1.Department of Industrial EngineeringIstanbul UniversityAvcilarTurkey
  2. 2.Energy InstituteIstanbul Technical UniversityMackaTurkey
  3. 3.Faculty of ManagementIstanbul Technical UniversityMackaTurkey

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