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Introduction to Information Gap Decision Theory Method

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Abstract

Nowadays, variable nature of electrical demand and uncertain behavior of renewable energy resources cause large power systems to operate at their stability boundaries. Hence, occurrence of a contingency may cause an interconnected electricity grid to be faced with cascaded outages, loss of dynamic stability, and a widespread blackout. In recent years, various methods have been presented by scholars to model uncertainties associated with energy market prices, electricity demand, and renewable energy resources. Information gap decision theory (IGDT) is a practical strategy with no need to probability distribution function of uncertain parameter (which is used in probabilistic approaches such as chance-constrained and stochastic programming methods) and membership functions employed in fuzzy algorithms. Hence, this chapter presents a comprehensive review on application of IGDT in power system studies. Moreover, a mathematical framework is provided to model the uncertain parameter using IGDT.

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References

  1. Jabari, F., et al. (2016). Optimal short-term scheduling of a novel tri-generation system in the presence of demand response programs and battery storage system. Energy Conversion and Management, 122, 95–108.

    Article  Google Scholar 

  2. Jabari, F., et al. (2018). Design and performance investigation of a biogas fueled combined cooling and power generation system. Energy Conversion and Management, 169, 371–382.

    Article  Google Scholar 

  3. Jabari, F., Nojavan, S., & Mohammadi Ivatloo, B. (2016). Designing and optimizing a novel advanced adiabatic compressed air energy storage and air source heat pump based μ-combined cooling, heating and power system. Energy, 116, 64–77.

    Article  Google Scholar 

  4. Rabiee, A., Nikkhah, S., & Soroudi, A. (2018). Information gap decision theory to deal with long-term wind energy planning considering voltage stability. Energy, 147, 451–463.

    Article  Google Scholar 

  5. Najafi-Ghalelou, A., Nojavan, S., & Zare, K. (2018). Robust thermal and electrical management of smart home using information gap decision theory. Applied Thermal Engineering, 132, 221–232.

    Article  Google Scholar 

  6. Nojavan, S., Majidi, M., & Zare, K. (2017). Performance improvement of a battery/PV/fuel cell/grid hybrid energy system considering load uncertainty modeling using IGDT. Energy Conversion and Management, 147, 29–39.

    Article  Google Scholar 

  7. Nikoobakht, A., & Aghaei, J. (2017). IGDT-based robust optimal utilisation of wind power generation using coordinated flexibility resources. IET Renewable Power Generation, 11(2), 264–277.

    Article  Google Scholar 

  8. Soroudi, A., & Ehsan, M. (2013). IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty. IEEE Transactions on Smart Grid, 4(2), 886–895.

    Article  Google Scholar 

  9. Ahmadigorji, M., Amjady, N., & Dehghan, S. (2018). A robust model for multiyear distribution network reinforcement planning based on information-gap decision theory. IEEE Transactions on Power Systems, 33(2), 1339–1351.

    Article  Google Scholar 

  10. Dehghan, S., Kazemi, A., & Amjady, N. (2014). Multi-objective robust transmission expansion planning using information-gap decision theory and augmented ɛ-constraint method. IET Generation, Transmission & Distribution, 8, 828–840.

    Article  Google Scholar 

  11. Rabiee, A., Soroudi, A., & Keane, A. (2015). Information gap decision theory based OPF with HVDC connected wind farms. IEEE Transactions on Power Systems, 30(6), 3396–3406.

    Article  Google Scholar 

  12. Chen, K., et al. (2015). Robust restoration decision-making model for distribution networks based on information gap decision theory. IEEE Transactions on Smart Grid, 6(2), 587–597.

    Article  Google Scholar 

  13. Mohammadi-Ivatloo, B., et al. (2013). Application of information-gap decision theory to risk-constrained self-scheduling of GenCos. IEEE Transactions on Power Systems, 28(2), 1093–1102.

    Article  Google Scholar 

  14. Mathuria, P., & Bhakar, R. (2014). Info-gap approach to manage GenCo’s trading portfolio with uncertain market returns. IEEE Transactions on Power Systems, 29(6), 2916–2925.

    Article  Google Scholar 

  15. Ke, D., et al. (2018). Application of information gap decision theory to the design of robust wide-area power system stabilizers considering uncertainties of wind power. IEEE Transactions on Sustainable Energy, 9(2), 805–817.

    Article  Google Scholar 

  16. Cao, X., Wang, J., & Zeng, B. (2018). A chance constrained information-gap decision model for multi-period microgrid planning. IEEE Transactions on Power Systems, 33(3), 2684–2695.

    Article  Google Scholar 

  17. Rabiee, A., et al. (2017). Information gap decision theory for voltage stability constrained OPF considering the uncertainty of multiple wind farms. IET Renewable Power Generation, 11(5), 585–592.

    Article  Google Scholar 

  18. Zhao, T., Zhang, J., & Wang, P. (2016). Flexible active distribution system management considering interaction with transmission networks using information-gap decision theory. CSEE Journal of Power and Energy Systems, 2(4), 76–86.

    Article  Google Scholar 

  19. Najafi-Ghalelou, A., Nojavan, S., & Zare, K. (2018). Heating and power hub models for robust performance of smart building using information gap decision theory. International Journal of Electrical Power & Energy Systems, 98, 23–35.

    Article  Google Scholar 

  20. Najafi-Ghalelou, A., Nojavan, S., & Zare, K. (2018). Information gap decision theory-based risk-constrained scheduling of smart home energy consumption in the presence of solar thermal storage system. Solar Energy, 163, 271–287.

    Article  Google Scholar 

  21. Moradi-Dalvand, M., et al. (2015). Self-scheduling of a wind producer based on information gap decision theory. Energy, 81, 588–600.

    Article  Google Scholar 

  22. Soroudi, A., Rabiee, A., & Keane, A. (2017). Information gap decision theory approach to deal with wind power uncertainty in unit commitment. Electric Power Systems Research, 145, 137–148.

    Article  Google Scholar 

  23. Jabari, F., et al. (2018). Risk-constrained scheduling of solar Stirling engine based industrial continuous heat treatment furnace. Applied Thermal Engineering, 128, 940–955.

    Article  Google Scholar 

  24. Charwand, M., & Moshavash, Z. (2014). Midterm decision-making framework for an electricity retailer based on information gap decision theory. International Journal of Electrical Power & Energy Systems, 63, 185–195.

    Article  Google Scholar 

  25. Nojavan, S., Majidi, M., & Zare, K. (2017). Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program. International Journal of Hydrogen Energy, 42(16), 11857–11867.

    Article  Google Scholar 

  26. Yokomizo, H., et al. (2013). Setting the most robust effluent level under severe uncertainty: Application of information-gap decision theory to chemical management. Chemosphere, 93(10), 2224–2229.

    Article  Google Scholar 

  27. Aghaei, J., et al. (2017). Optimal robust unit commitment of CHP plants in electricity markets using information gap decision theory. IEEE Transactions on Smart Grid, 8(5), 2296–2304.

    Article  Google Scholar 

  28. Zhao, J., et al. (2017). Risk-based day-ahead scheduling of electric vehicle aggregator using information gap decision theory. IEEE Transactions on Smart Grid, 8(4), 1609–1618.

    Article  Google Scholar 

  29. Bagal, H. A., et al. (2018). Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Solar Energy, 169, 343–352.

    Article  Google Scholar 

  30. Qdr, Q. (2006). Benefits of demand response in electricity markets and recommendations for achieving them. US Department of Energy.

    Google Scholar 

  31. Ghahary, K., et al. (2018). Optimal reserve market clearing considering uncertain demand response using information gap decision theory. International Journal of Electrical Power & Energy Systems, 101, 213–222.

    Article  Google Scholar 

  32. Nojavan, S., Zare, K., & Mohammadi-Ivatloo, B. (2017). Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory. International Journal of Electrical Power & Energy Systems, 93, 156–170.

    Article  Google Scholar 

  33. Nojavan, S., Zare, K., & Ashpazi, M. A. (2015). A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market. International Journal of Electrical Power & Energy Systems, 69, 335–343.

    Article  Google Scholar 

  34. Zare, K., Moghaddam, M. P., & Sheikh-El-Eslami, M. K. (2011). Risk-based electricity procurement for large consumers. IEEE Transactions on Power Systems, 26(4), 1826–1835.

    Article  Google Scholar 

  35. Zare, K., Moghaddam, M. P., & Sheikh El Eslami, M. K. (2010). Electricity procurement for large consumers based on information gap decision theory. Energy Policy, 38(1), 234–242.

    Article  Google Scholar 

  36. Nojavan, S., Ghesmati, H., & Zare, K. (2016). Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electric Power Systems Research, 130, 46–58.

    Article  Google Scholar 

  37. Nojavan, S., & Zare, K. (2013). Risk-based optimal bidding strategy of generation company in day-ahead electricity market using information gap decision theory. International Journal of Electrical Power & Energy Systems, 48, 83–92.

    Article  Google Scholar 

  38. Zare, K., Moghaddam, M. P., & Sheikh El Eslami, M. K. (2010). Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology. Energy, 35(7), 2999–3007.

    Article  Google Scholar 

  39. Alipour, M., Zare, K., & Mohammadi-Ivatloo, B. (2016). Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets. Renewable and Sustainable Energy Reviews, 60, 421–432.

    Article  Google Scholar 

  40. Kazemi, M., Mohammadi-Ivatloo, B., & Ehsan, M. (2015). Risk-constrained strategic bidding of GenCos considering demand response. IEEE Transactions on Power Systems, 30(1), 376–384.

    Article  Google Scholar 

  41. Kazemi, M., Mohammadi-Ivatloo, B., & Ehsan, M. (2014). Risk-based bidding of large electric utilities using information gap decision theory considering demand response. Electric Power Systems Research, 114, 86–92.

    Article  Google Scholar 

  42. Shafiee, S., et al. (2017). Risk-constrained bidding and offering strategy for a merchant compressed air energy storage plant. IEEE Transactions on Power Systems, 32(2), 946–957.

    Google Scholar 

  43. Mathuria, P., & Bhakar, R. (2015). GenCo’s integrated trading decision making to manage multimarket uncertainties. IEEE Power & Energy Society General Meeting. (pp. 1–1).

    Google Scholar 

  44. Ben-Haim, Y. (2006). Chapter 2: Uncertainty. In Y. Ben-Haim (Ed.), Info-gap decision theory (2nd ed., pp. 9–36). Oxford: Academic.

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Farkhondeh Jabari, Behnam Mohammadi-ivatloo & Mohammad-Bagher Bannae-Sharifian

  2. Department of Mechanical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

    Hadi Ghaebi

Authors
  1. Farkhondeh Jabari
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  2. Behnam Mohammadi-ivatloo
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  3. Hadi Ghaebi
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  4. Mohammad-Bagher Bannae-Sharifian
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Corresponding author

Correspondence to Farkhondeh Jabari .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Behnam Mohammadi-ivatloo

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Morteza Nazari-Heris

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Jabari, F., Mohammadi-ivatloo, B., Ghaebi, H., Bannae-Sharifian, MB. (2019). Introduction to Information Gap Decision Theory Method. In: Mohammadi-ivatloo, B., Nazari-Heris, M. (eds) Robust Optimal Planning and Operation of Electrical Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-04296-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-04296-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04295-0

  • Online ISBN: 978-3-030-04296-7

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