Robust Unit Commitment Using Information Gap Decision Theory

  • Farkhondeh JabariEmail author
  • Sayyad Nojavan
  • Behnam Mohammadi-ivatloo
  • Hadi Ghaebi
  • Mohammad-Bagher Bannae-Sharifian


Recently, electricity utilization is increasing as a result of population growth. Hence, total fuel consumption of thermal units increases with their inefficient and uneconomical load dispatch. Moreover, uncertainty associated with electricity market prices changes daily profit of market operator. For this purpose, information gap decision theory (IGDT) is implemented on a multi-period unit commitment (UC) problem aiming to maximize total profit obtained from selling electricity to consumers. In this chapter, total revenue achieved from selling energy to customers minus total operational cost of thermal power plants is maximized considering ramp-up and ramp-down rates, minimum up- and downtimes, and production capacity of thermal generation stations in UC problem. Moreover, uncertainty of electricity prices is modeled using IGDT to assess how market operator can make a risk-averse decision at low market prices and obtain higher profit in comparison with base problem, which is solved with same prices. In other words, robustness mode of IGDT enables operator to find a good solution for hour-ahead scheduling of thermal units for underestimated energy rates in a way that total profit will not only be larger than a predefined critical profit but also is more than profit of UC problem, which is solved with same market prices and without application of IGDT strategy. Similarly, opportunistic mode of IGDT makes it possible to maximize profit for overestimated electricity prices so that it will not only be more than a target profit but also is larger than profit of UC problem, which is solved with same prices and without implementation of IGDT. To prove IGDT’s robustness and capability in modeling uncertainties, a ten-unit standard system is discussed in two case studies: case 1, without application of IGDT method, and case 2, with implementation of robustness and opportunistic modes of IGDT. It is found that risk-averse and risk-seeker decisions affect total cost, revenue, and expected profit. Both robust and opportunistic strategies cause more profit than that of obtained from solving UC problem, which is solved with underestimated or overestimated prices and without application of IGDT approach.


Unit commitment Market price uncertainty Information gap decision theory (IGDT) 


  1. 1.
    Anand, H., Narang, N., & Dhillon, J. S. (2018). Profit based unit commitment using hybrid optimization technique. Energy, 148, 701–715.CrossRefGoogle Scholar
  2. 2.
    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.CrossRefGoogle Scholar
  3. 3.
    Liu, G., & Tomsovic, K. (2015). Robust unit commitment considering uncertain demand response. Electric Power Systems Research, 119, 126–137.CrossRefGoogle Scholar
  4. 4.
    Kalantari, A., & Galiana, F. D. (2015). Generalized Sigma approach to unit commitment with uncertain wind power generation. International Journal of Electrical Power & Energy Systems, 65, 367–374.CrossRefGoogle Scholar
  5. 5.
    Philpott, A. B., Craddock, M., & Waterer, H. (2000). Hydro-electric unit commitment subject to uncertain demand. European Journal of Operational Research, 125(2), 410–424.CrossRefGoogle Scholar
  6. 6.
    Dal’ Santo, T., & Costa, A. S. (2016). Hydroelectric unit commitment for power plants composed of distinct groups of generating units. Electric Power Systems Research, 137, 16–25.CrossRefGoogle Scholar
  7. 7.
    Morales-España, G., Lorca, Á., & de Weerdt, M. M. (2018). Robust unit commitment with dispatchable wind power. Electric Power Systems Research, 155, 58–66.CrossRefGoogle Scholar
  8. 8.
    Ming, B., et al. (2018). Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China. Applied Energy, 228, 1341–1352.CrossRefGoogle Scholar
  9. 9.
    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.CrossRefGoogle Scholar
  10. 10.
    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.CrossRefGoogle Scholar
  11. 11.
    Park, H., Jin, Y. G., & Park, J.-K. (2018). Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating. International Journal of Electrical Power & Energy Systems, 102, 211–222.CrossRefGoogle Scholar
  12. 12.
    Durga Hari Kiran, B., & Sailaja Kumari, M. (2016). Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: A probabilistic unit commitment approach. International Journal of Electrical Power & Energy Systems, 81, 114–122.CrossRefGoogle Scholar
  13. 13.
    Soltani, Z., et al. (2018). Integration of smart grid technologies in stochastic multi-objective unit commitment: An economic emission analysis. International Journal of Electrical Power & Energy Systems, 100, 565–590.CrossRefGoogle Scholar
  14. 14.
    Badakhshan, S., Kazemi, M., & Ehsan, M. (2015). Security constrained unit commitment with flexibility in natural gas transmission delivery. Journal of Natural Gas Science and Engineering, 27, 632–640.CrossRefGoogle Scholar
  15. 15.
    Ji, B., et al. (2014). Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration. Energy Conversion and Management, 87, 589–598.CrossRefGoogle Scholar
  16. 16.
    Huang, Y., Zheng, Q. P., & Wang, J. (2014). Two-stage stochastic unit commitment model including non-generation resources with conditional value-at-risk constraints. Electric Power Systems Research, 116, 427–438.CrossRefGoogle Scholar
  17. 17.
    Zhao, C., et al. (2013). Multi-stage robust unit commitment considering wind and demand response uncertainties. IEEE Transactions on Power Systems, 28(3), 2708–2717.CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Esmaeily, A., et al. (2017). Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate. Energy, 122, 182–193.CrossRefGoogle Scholar
  20. 20.
    Dashti, H., et al. (2016). Weekly two-stage robust generation scheduling for hydrothermal power systems. IEEE Transactions on Power Systems, 31(6), 4554–4564.CrossRefGoogle Scholar
  21. 21.
    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.CrossRefGoogle Scholar
  22. 22.
    Ben-Haim, Y. (2006). Chapter 2: Uncertainty. In Y. Ben-Haim (Ed.), Info-gap decision theory (2nd ed., pp. 9–36). Oxford: Academic.CrossRefGoogle Scholar
  23. 23.
    Soroudi, A. (2017). Power system optimization modeling in GAMS. Springer. Cham: Switzerland.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Farkhondeh Jabari
    • 1
    Email author
  • Sayyad Nojavan
    • 2
  • Behnam Mohammadi-ivatloo
    • 1
  • Hadi Ghaebi
    • 3
  • Mohammad-Bagher Bannae-Sharifian
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
  2. 2.Department of Electrical EngineeringUniversity of BonabBonabIran
  3. 3.Department of Mechanical EngineeringUniversity of Mohaghegh ArdabiliArdabilIran

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