Transportation on Land

  • Seyed Mojtaba Poorseyed and Alireza Askarzadeh, 2023, Risk-averse optimal operation of an on-grid photovoltaic/battery/diesel generator hybrid energy system using information gap decision theory, IET Renewable Power Generation, July 2023; pp.1-14, DOI: 10.1049/rpg2.12801. Abstract.
  • Motahareh Mojarad, Mostafa Sedighizadeh, Mohamad Dosaranian-Moghadam, 2021, A two-stage stochastic model based on information gap decision theory method for optimal allocation of intelligent parking lots in distribution systems considering severe uncertainties, Intl. Trans. Electrical Energy Systems, First published: 18 August 2021. Abstract.
  • Jun Liu, Chong Chen, Zhenling Liu, Kittisak Jermsittiparsert, Noradin Ghadimi, 2020, An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles, Journal of Energy Storage, 27 (2020) 101057. Abstract.
  • Parinaz Aliasghari, Behnam Mohammadi-Ivatloo, Mehdi Abapour, 2020, Risk-based scheduling strategy for electric vehicle aggregator using hybrid Stochastic/IGDT approach, Journal of Cleaner Production, vol. 248, 1 March 2020, article 119270. Abstract.
  • Abdollah Ahmadi, Ali Esmaeel Nezhad and Branislav Hredzak, 2019, Security-constrained unit commitment in presence of lithium-ion battery storage units using information-gap decision theory, IEEE Transactions on Industrial Informatics, vol. 15 , Issue: 1 , Jan. 2019, pp.148-157. Abstract.
  • Abdollah Ahmadi, Ali Esmaeel Nezhad, Pierluigi Siano, Branislav Hredzak, Sajeeb Saha, 2019, Information-gap decision theory for robust security-constrained unit commitment of joint renewable energy and gridable vehicles, IEEE Transactions on Industrial Informatics, to appear. Abstract.
  • A. Soroudi, A. Keane, 2015, Risk averse energy hub management considering plug-in electric vehicles using information gap decision theory, in S. Rajakaruna, F. Shahnia, A. Ghosh, eds., Plug in Electric Vehicles in Smart Grids, Springer, Singapore, pp. 107-127,


    The energy hub is defined as the multi-input multi-output energy converter. It usually consists of various converters like thermal generators, combined heat and power (CHP), renewable energies and energy storage devices. The plug-in electric vehicles as energy storage devices can bring various flexibilities to energy hub management problem. These flexibilities include emission reduction, cost reduction, controlling financial risks, mitigating volatility of power output in renewable energy resources, active demand side management and ancillary service provision. In this chapter a comprehensive risk hedging model for energy hub management is proposed. The focus is placed on minimizing both the energy procurement cost and financial risks in energy hub. For controlling the undesired effects of the uncertainties, the Information gap decision theory (IGDT) technique is used as the risk management tool. The proposed model is formulated as a mixed integer linear programming (MILP) problem and solved using General Algebraic Modeling System (GAMS). An illustrative example is analyzed to demonstrate the applicability of the proposed method.

  • Mascareñas, D., Stull, C., Farrar, C., 2012, Development of an info-gap-based path planner to enable nondeterministic low-observability mobile sensor nodes, Proceedings of SPIE – The International Society for Optical Engineering. Vol. 8387, 2012, Article number 838719.


    Mobile sensor nodes are an ideal solution for efficiently collecting measurements for a variety of applications. Mobile sensor nodes offer a particular advantage when measurements must be made in hazardous and/or adversarial environments. When mobile sensor nodes must operate in hostile environments, it would be advantageous for them to be able to avoid undesired interactions with hostile elements. It is also of interest for the mobile sensor node to maintain low-observability in order to avoid detection by hostile elements. Conventional path-planning strategies typically attempt to plan a path by optimizing some performance metric. The problem with this approach in an adversarial environment is that it may be relatively simple for a hostile element to anticipate the mobile sensor node’s actions (i.e. optimal paths are also often predictable paths). Such information could then be leveraged to exploit the mobile sensor node. Furthermore, dynamic adversarial environments are typically characterized by high-uncertainty and highcomplexity that can make synthesizing paths featuring adequate performance very difficult. The goal of this work is to develop a path-planner anchored in info-gap decision theory, capable of generating non-deterministic paths that satisfy predetermined performance requirements in the face of uncertainty surrounding the actions of the hostile element(s) and/or the environment. This type of path-planner will inherently make use of the time-tested security technique of varying paths and changing routines while taking into account the current state estimate of the environment and the uncertainties associated with it.

    © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).


    anti-tamper; anti-theft; cyber-physical security; ground robot; info-gap decision theory; mobile sensor nodes; robotics; unmanned systems

  • David Hambling, 2012, Self-Defense for the Self-Driving Car, Popular Mechanics, Online version.
    Selection from article


    :”One of [David] Mascarenas’s projects makes robots less predictable and reduces their vulnerability to ambush. If you know a driverless delivery truck always goes down the same deserted street at 6:14 am, you can get there first. Mascarenas addressed this using a technique known as info-gap decision theory. This allows the robot to weigh the risk of any particular route with the possible benefits. … Crucially, the process is unpredictable: The machine will not always take the same route twice, and would-be ambushers can’t anticipate where it will be.”