Abstract
Solving optimization problems with multiple uncertainties has always been a challenging task in different scopes of science. While different approaches have been developed to take advantage of the stochastic space of the problem, these methods are intensively dependent of the probabilistic information of various variables which are not always available. Relying on the severity of the failure, information-gap decision theory (IGDT) is a robust optimization approach which is entirely autonomous from probabilistic information. In this model, a forecasted amount is presumed for each uncertain variable, and the sensitivity of objective functions is analyzed according to the deviation of each of these uncertain parameters from their forecasted value. In this method, two main types of uncertainty set models including energy-bound model and envelope-bound model are handled. In this chapter, these principles and fundamentals of IGDT are described.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ben-Haim, Y. (2006). Info-gap decision theory: Decisions under severe uncertainty. San Diego: Academic.
Rezaei, N., Ahmadi, A., Khazali, A., & Guerrero, J. M. (2018). Energy and frequency hierarchical management system using information gap decision theory for islanded microgrids. IEEE Transactions on Industrial Electronics, 65, 7921–7932.
Khazali, A., Rezaei, N., Ahmadi, A., & Hredzak, B. (2018). Information gap decision theory based preventive/corrective voltage control for smart power systems with high wind penetration. IEEE Transactions on Industrial Informatics, 14(10), 4385–4394.
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.
Bryan, B. S., & Thompson, C. J. (2007). Managing credit risk with info-gap uncertainty. The Journal of Risk Finance, 8(1), 24–34.
Stranlund, J. K., & Ben-Haim, Y. (2008). Price-based vs. quantity-based environmental regulation under Knightian uncertainty: An info-gap robust satisficing perspective. Journal of Environmental Management, 87(3), 443–449.
Yakov, B. H. (2005). Value-at-risk with info-gap uncertainty. The Journal of Risk Finance, 6(5), 388–403.
Ben-Haim, Y., & Laufer, A. (1998). Robust reliability of projects with activity-duration uncertainty. Journal of Construction Engineering and Management, 124(2), 125–132.
Meir, T., & Ben-Asher, J. Z. (2005). Modeling and analysis of integration processes for engineering systems. Systems Engineering, 8(1), 62–77.
Regev, S., Shtub, A., & Ben-Haim, Y. (2006). Managing project risks as knowledge gaps. Project Management Quarterly, 37(5), 17.
Carmel, Y., & Ben-Haim, Y. (2005). Info-gap robust-satisficing model of foraging behavior: Do foragers optimize or satisfice? The American Naturalist, 166(5), 633–641.
Dehghan, S., Kazemi, A., & Amjady, N. Multi-objective robust transmission expansion planning using information-gap decision theory and augmented ε-constraint method. IET Generation, Transmission & Distribution, 8(5), 828–840. Available: http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2013.0427
Ahmadi, A., Mavalizadeh, H., Zobaa, A. F., & Shayanfar, H. A. Reliability-based model for generation and transmission expansion planning. IET Generation, Transmission & Distribution, 11(2), 504–511. Available: http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2016.1058
Shafiee, S., Zareipour, H., Knight, A. M., Amjady, N., & Mohammadi-Ivatloo, B. (2017). Risk-constrained bidding and offering strategy for a merchant compressed air energy storage plant. IEEE Transactions on Power Systems, 32(2), 946–957.
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.
Rabiee, A., Nikkhah, S., Soroudi, A., & Hooshmand, E. Information gap decision theory for voltage stability constrained OPF considering the uncertainty of multiple wind farms. IET Renewable Power Generation, 11(5), 585–592. Available: http://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2016.0509
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.
Sniedovich, M. (2007). The art and science of modeling decision-making under severe uncertainty. Mathematical Modeling; Severe Uncertainty; Maximin; Worst-Case Analysis; Robust Optimization; Info-gap, 1, 26.
Moilanen, A., Teeffelen, A. J. A. V., Ben-Haim, Y., & Ferrier, S. (2009). How much compensation is enough? A framework for incorporating uncertainty and time discounting when calculating offset ratios for impacted habitat. Restoration Ecology, 17(4), 470–478.
Soroudi, A., & Amraee, T. (2013). Decision making under uncertainty in energy systems: State of the art. Renewable and Sustainable Energy Reviews, 28, 376–384.
Mohammadi-Ivatloo, B., Zareipour, H., Amjady, N., & Ehsan, M. (2013). Application of information-gap decision theory to risk-constrained self-scheduling of GenCos. IEEE Transactions on Power Systems, 28(2), 1093–1102.
Moilanen, A., & Wintle, B. A. (2006). Uncertainty analysis favours selection of spatially aggregated reserve networks. Biological Conservation, 129(3), 427–434.
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.
Moilanen, A., et al. (2006). Planning for robust reserve networks using uncertainty analysis. Ecological Modelling, 199(1), 115–124.
Nicholson, E., & Possingham, H. P. (2007). Making conservation decisions under uncertainty for the persistence of multiple species. Ecological Applications, 17(1), 251–265.
Moilanen, A., Wintle, B. A., Elith, J., & Burgman, M. (2006). Uncertainty analysis for regional-scale reserve selection. Conservation Biology, 20(6), 1688–1697.
McDonald-Madden, E., Baxter, P. W. J., & Possingham, H. P. (2008). Making robust decisions for conservation with restricted money and knowledge. Journal of Applied Ecology, 45(6), 1630–1638.
Ghahary, K., Abdollahi, A., Rashidinejad, M., & Alizadeh, M. I. (2018). Optimal reserve market clearing considering uncertain demand response using information gap decision theory. International Journal of Electrical Power & Energy Systems, 101, 213–222.
Rezaei, N., Ahmadi, A., Khazali, A., & Aghaei, J. (2018). Multi-objective risk-constrained optimal bidding strategy of smart microgrids: An IGDT-based normal boundary intersection approach. IEEE Transactions on Industrial Informatics, 1–1. https://doi.org/10.1109/TII.2018.2850533
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.
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.
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.
Ahmadi, A., Nezhad, A. E., & Hredzak, B. (2018). Security-constrained unit commitment in presence of lithium-ion battery storage units using information-gap decision theory. IEEE Transactions on Industrial Informatics, 99, 1–1.
Jabari, F., Nojavan, S., Mohammadi-Ivatloo, B., Ghaebi, H., & Mehrjerdi, H. (2018). Risk-constrained scheduling of solar Stirling engine based industrial continuous heat treatment furnace. Applied Thermal Engineering, 128, 940–955.
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.
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.
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.
Mavalizadeh, H., Ahmadi, A., Gandoman, F. H., Siano, P., & Shayanfar, H. A. (2017). Multiobjective robust power system expansion planning considering generation units retirement. IEEE Systems Journal, 12(3), 2664–2675.
Nojavan, S., Zare, K., & Mohammadi-Ivatloo, B. (2017). Information gap decision theory-based risk-constrained bidding strategy of price-taker GenCo in joint energy and reserve markets. Electric Power Components and Systems, 45(1), 49–62.
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.
Charwand, M., Ahmadi, A., Sharaf Adel, M., Gitizadeh, M., & Esmaeel Nezhad, A. (2015). Robust hydrothermal scheduling under load uncertainty using information gap decision theory. International Transactions on Electrical Energy Systems, 26(3), 464–485.
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.
Schweppe, F. C. (1973). Uncertain dynamic systems. Englewood Cliffs, N. J.: Prentice Hall.
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.
Murphy, C., Soroudi, A., & Keane, A. (2016). Information gap decision theory-based congestion and voltage management in the presence of uncertain wind power. IEEE Transactions on Sustainable Energy, 7(2), 841–849.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rezaei, N., Ahmadi, A., Nezhad, A.E., Khazali, A. (2019). Information-Gap Decision Theory: Principles and Fundamentals. 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_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-04296-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04295-0
Online ISBN: 978-3-030-04296-7
eBook Packages: EngineeringEngineering (R0)