Energy Procurement via Hybrid IGDT-Stochastic Approach

  • Ehsan Zargin
  • Raouf Morsali Asl


By using information gap decision theory (IGDT), the positive and negative aspects of the uncertainty can be analyzed. However, only one uncertain parameter can be modeled by the IGDT. In this chapter, a novel hybrid IGDT-stochastic approach is introduced to model different uncertainties in the system and utilized benefits of the IGDT and stochastic programming methods. In the power procurement problem of the large consumer, uncertainty of load demand and power output of renewable sources are modeled by a set of scenarios, and power price is modeled using the IGDT method to evaluate the positive and negative aspects of power price in the market. It should be noted that the scenarios of load demand and solar irradiation are generated by normal distribution, and the Weibull distribution is used to generate the wind speed scenarios. The application of the hybrid IGDT-stochastic method is analyzed providing numerical studies.


Hybrid IGDT-stochastic approach Large consumer Power procurement Uncertainty of renewable energy sources The Weibull distribution 


  1. 1.
    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
  2. 2.
    S. Nojavan, H. Ghesmati, K. Zare, Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electr. Power Syst. Res. 130, 46–58 (2016)CrossRefGoogle Scholar
  3. 3.
    N. Ghadimi, M.H. Firouz, Short-term management of hydro-power systems based on uncertainty model in electricity markets. J. Power Technol. 95(4), 265–272Google Scholar
  4. 4.
    S. Nojavan, K. Zare, M.A. Ashpazi, A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market. Int. J. Electr. Power Energy Syst. 69, 335–343 (2015)CrossRefGoogle Scholar
  5. 5.
    K. Zare, M.P. Moghaddam, M.K. Sheikh El Eslami, Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology. Energy 35(7), 2999–3007 (2010)CrossRefGoogle Scholar
  6. 6.
    M. Tarafdar Hagh, H. Ebrahimian, N. Ghadimi, Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Front. Energy 9(1), 75–90 (2015)CrossRefGoogle Scholar
  7. 7.
    A. Najafi-Ghalelou, S. Nojavan, K. Zare, Information gap decision theory-based risk-constrained scheduling of smart home energy consumption in the presence of solar thermal storage system. Sol. Energy 163, 271–287 (2018)CrossRefGoogle Scholar
  8. 8.
    A. Kazemi, S. Dehghan, N. Amjady, Multi-objective robust transmission expansion planning using information-gap decision theory and augmented ɛ-constraint method. IET Gener. Transm. Distrib. 8(5), 828–840 (2014)CrossRefGoogle Scholar
  9. 9.
    S. Nojavan, K. Zare, M.R. Feyzi, Optimal bidding strategy of generation station in power market using information gap decision theory (IGDT). Electr. Power Syst. Res. 96, 56–63 (2013)CrossRefGoogle Scholar
  10. 10.
    J. Zhao, C. Wan, Z. Xu, J. Wang, Risk-based day-ahead scheduling of electric vehicle aggregator using information gap decision theory. IEEE Trans. Smart Grid 8(4), 1609–1618 (2017)CrossRefGoogle Scholar
  11. 11.
    S. Nojavan, K. Zare, B. Mohammadi-Ivatloo, Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory. Int. J. Electr. Power Energy Syst. 93, 156–170 (2017)CrossRefGoogle Scholar
  12. 12.
    A. Dolatabadi, M. Jadidbonab, B. Mohammadi-ivatloo, Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach. IEEE Trans. Sustain. Energy, 1–1 (2018)Google Scholar
  13. 13.
    A. Mehdizadeh, N. Taghizadegan, J. Salehi, Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management. Appl. Energy 211, 617–630 (2018)CrossRefGoogle Scholar
  14. 14.
    S. Nojavan, K. Zare, B. Mohammadi-Ivatloo, Selling price determination by electricity retailer in the smart grid under demand side management in the presence of the electrolyser and fuel cell as hydrogen storage system. Int. J. Hydrog. Energy 42(5), 3294–3308 (2017)CrossRefGoogle Scholar
  15. 15.
    A. Najafi-Ghalelou, S. Nojavan, K. Zare, Heating and power hub models for robust performance of smart building using information gap decision theory. Int. J. Electr. Power Energy Syst. 98, 23–35 (2018)CrossRefGoogle Scholar
  16. 16.
    Y. Ben-Haim, Y. Ben-Haim, Info-gap decision theory: decisions under severe uncertainty (Academic, West Bengal, 2006)zbMATHGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ehsan Zargin
    • 1
  • Raouf Morsali Asl
    • 1
  1. 1.Young Researchers and Elite Club, Ardabil BranchIslamic Azad UniversityArdabilIran

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