Robust Energy Procurement Under Real-Time Pricing

  • Alireza Rezvani


In order to reduce power consumption and operating cost of power generation units, demand response programs can be considered as an effective and essential tool. As a key component of demand response, real-time pricing (RTP) encourages consumers in an economical and efficient way. In this chapter, RTP scheme is implemented to manage peak load for the power procurement problem of a large consumer. Some part of the required power of the large consumer procured from the pool market and power price in the market has uncertainty. So, the robust optimization method is used to model the uncertainty in the system.

The results of using RTP are presented for a case study, and obtained results are analyzed and compared with deterministic and time-of-use demand response program which are detained in Chaps.  4 and  8, respectively.


Real-time pricing Demand response programs Robust energy procurement Power price uncertainty Robust optimization approach 


  1. 1.
    H. Wang, H. Fang, X. Yu, S. Liang, How real time pricing modifies Chinese households’ electricity consumption. J. Clean. Prod. 178, 776–790 (2018)CrossRefGoogle Scholar
  2. 2.
    N. Ghadimi, A. Afkousi-Paqaleh, A. Emamhosseini, A PSO-based fuzzy long-term multi-objective optimization approach for placement and parameter setting of UPFC. Arab. J. Sci. Eng. 39(4), 2953–2963 (2014)CrossRefGoogle Scholar
  3. 3.
    N. Ghadimi, MDE with considered different load scenarios for solving optimal location and sizing of shunt capacitors. Natl. Acad. Sci. Lett. 37(5), 447–450 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    F. Kühnlenz, P.H.J. Nardelli, S. Karhinen, R. Svento, Implementing flexible demand: real-time price vs. market integration. Energy 149, 550–565 (2018)CrossRefGoogle Scholar
  5. 5.
    S. Favuzza et al., Real-time pricing for aggregates energy resources in the Italian energy market. Energy 87, 251–258 (2015)CrossRefGoogle Scholar
  6. 6.
    N. Nezamoddini, Y. Wang, Real-time electricity pricing for industrial customers: survey and case studies in the United States. Appl. Energy 195, 1023–1037 (2017)CrossRefGoogle Scholar
  7. 7.
    Y. Dai, Y. Gao, H. Gao, H. Zhu, Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers. Neurocomputing 260, 149–156 (2017)CrossRefGoogle Scholar
  8. 8.
    H. Anand, R. Ramasubbu, A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources. Renew. Energy 127, 779–789 (2018)CrossRefGoogle Scholar
  9. 9.
    T. Niknam, A. Kavousifard, S. Tabatabaei, J. Aghaei, Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks. J. Power Sources 196(20), 8881–8896 (2011)CrossRefGoogle Scholar
  10. 10.
    H.A. Aalami, S. Nojavan, Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation. IET Gener. Transm. Distrib. 10(1), 107–114 (2016)CrossRefGoogle Scholar
  11. 11.
    S. Nojavan, H. allah Aalami, Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program. Energy Convers. Manag. 103, 1008–1018 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alireza Rezvani
    • 1
    • 2
  1. 1.Young Researchers and Elite Club, Saveh Branch, Islamic Azad UniversitySavehIran
  2. 2.Iran Water and Power Resources Development Company (IWPCO)TehranIran

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