Samy Faddel

Physical-Model-Checking to Detect Switching-Related Attacks in Power Systems

Recent public disclosures on attacks targeting the power industry showed that savvy attackers are now capable of occulting themselves from conventional rule-based network intrusion detection systems (IDS), bringing about serious threats. In order to leverage the work of rule-based IDS, this paper presents an artificially intelligent physical-model-checking intrusion detection framework capable of detecting tampered-with control commands from control centers of power grids. Unlike the work presented in the literature, the work in this paper utilizes artificial intelligence (AI) to learn the load flow characteristics of the power system and benefits from the fast responses of the AI to decode and understand contents of network packets. The output of the AI is processed through an expert system to verify that incoming control commands do not violate the physical system operational constraints and do not put the power system in an insecure state. The proposed content-aware IDS is tested in simulation on a 14-bus IEEE benchmark system. Experimental verification on a small power system, with an IEC 61850 network architecture is also carried out. The results showed the accuracy of the proposed framework in successfully detecting malicious and/or erroneous control commands.

Bi-layer multi-objective optimal allocation and sizing of electric vehicle parking garage

The anticipated increase in Electric vehicles (EVs) adoption necessitates the need for electrified transportation infrastructure to charge these vehicles. Although the EV parking garage can represent a good investment opportunity, it brings more challenges to the distribution system operator. Therefore, the allocation and sizing of the parking garage should be carefully planned. The planning process should take into consideration the economic aspects of the investor as well as the technical aspects of the distribution system. In this paper, a bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size an EV parking garage. The optimization formulation tries to maximize the profits of the EV parking garage investor as well as minimizing the losses and voltage deviations for the distribution system operator. Dealing with these contradicting objectives simultaneously will results in a set of Pareto solutions. The results showed the different trade-offs that might be induced while dealing with these contradictory objectives. A decision-making criterion based on statistics was used to decide on the optimal location and size of the parking garage. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location was also performed.

Automated distributed electric vehicle controller for residential demand side management

Electric vehicles (EVs) are gaining more interest recently due to the various challenges and opportunities they can provide to the utility operator. For electric utilities that have demand side management (DSM) program, EVs can represent a burden or a help depending on the control strategy and the DSM program. In this paper, an automated fuzzy-based controller is proposed to successfully integrate and coordinate the EVs charging in a decentralized and fair way. The controller takes into consideration the owner requirements, the voltage at the point of connection and the pricing signal coming from the utility. The controller is tested under different DSM programs that exist in the literature and a new DSM program that is compatible with the current infrastructure is proposed to accommodate EVs as prosumers. The controller performance is validated through Matlab simulations. The results showed that the controller can successfully coordinate the charging of EVs in a fair manner, avoid under-voltages and shave the system peaks without any rebound effect.