Mousavian, Amir (Clarkson University)

Dr. Amir Mousavian
Clarkson University 
8 Clarkson Avenue
Potsdam, New York 13699

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Risk Mitigation Model for Cyber-Attacks to PMU Networks

The power grid is becoming more dependent on information and communication technologies. Complex networks of advanced sensors such as phasor measurement units (PMUs) are used to collect real time data to improve the observability of the power system. Recent studies have shown that the power grid has significant cyber vulnerabilities which could increase when PMUs are used extensively. Therefore, recognizing and responding to vulnerabilities are critical to the security of the power grid. This research proposes a risk mitigation model for optimal response to cyber-attacks to PMU networks. We model the optimal response action as a mixed integer linear programming (MILP) problem to prevent propagation of the cyber-attacks and maintain the observability of the power system.

Appropriate audience: Faculty/Academics

A Risk-Based Optimization Model for Electric Vehicle Infrastructure Response to Cyber Attacks

Security of the smart grid is at risk when the vulnerabilities of the electric vehicle (EV) infrastructure is not addressed properly. As vehicles are becoming smarter and connected, their risk of being compromised is increasing. On various occasions and venues, hacking into smart or autonomous vehicles have been shown to be possible. For most of the time, a compromised vehicle poses a threat to the driver and other vehicles. On the other hand, when the vehicle is electric, the attack may spread to the power grid infrastructure starting from the EV supply equipment (EVSE) all the way up to the utility systems. Traditional isolation-based protection schemes do not work well in smart grid since electricity services have availability constraints and few of the components have physical backups. In this paper, we propose a mixed integer linear programming model that jointly optimizes security risk and equipment availability in the interdependent power and EV infrastructure. We adopt an epidemic attack model to mimic malware propagation. We assume malware spreads during EV charging when an EV is charged from an infected EVSE and then travels and recharges at another EVSE. In addition, it spreads through the communication network of EVSEs. The proposed response model aims to isolate a subset of compromised and likely compromised EVSEs. The response model minimizes the risk of attack propagation while providing a satisfactory level of equipment availability to supply demand. Our analysis shows the theoretical and practical bounds for the proposed response model in smart grid in the face of attacks to the EV infrastructure.

Appropriate audience: Faculty/Academics

Equilibria in Investment and Spot Electricity Markets: A Conjectural-Variations Approach

We study generation-capacity planning in an oligopolistic restructured electricity market setting, taking a conjectural-variations approach. We do this through a two-stage model that captures an initial investment followed by equilibria in a series of spot electricity markets, in which firms make production decisions. Although we model the generating firms as quantity setters, we do not model a Nash-Cournot equilibrium. Instead, we assume that the firms endogenize the impacts of their production decisions on rivals through reaction parameters, giving a conjectural-variations model of the spot-market equilibrium. Equilibrium conditions in each spot market, as a function of the investment decisions, are derived. This allows characterizing an equilibrium in the investment stage. The proposed model allows the derivation of analytical expressions that characterize such multi-stage equilibria. This proposed model can be used to gain insights on the outcomes and characteristics of inves  tment decisions in an imperfectly competitive market setting. Such insights may allow policymakers to understand the efficiency implications of oligopolistic market structures.

Appropriate audience: Faculty/Academics

Education & Background

  • Ph.D. Industrial and Systems Engineering, Auburn University 
  • Masters of Industrial and Systems Engineering, Auburn University
  • M.B.A., Malek Ashtar University of Technology

I am an Associate Professor of Engineering and Management in the David D. Reh School of Business with a joint courtesy appointment as an Associate Professor of Electrical and Computer Engineering at Clarkson University. I earned my B.S. in Industrial Engineering from Sharif University of Technology, Tehran, Iran in 2007. After my MBA degree in 2010, I received Master's and Ph.D. in Industrial and Systems Engineering in 2012 and 2014 from Auburn University, Auburn, AL.

My research interests are focused on the applications of Operations Research and Statistics on the operations of cyber-physical systems with an emphasis on the cyber-physical security of smart power grids and Electric Transportation Systems. In particular, my research interests are shaped by the emerging trend towards autonomous power grids where centralized power systems are replaced by distributed power grids. The paradigm of future power systems involves the interface of all electrical supplies, including conventional power plants and new add-ons. New technology advancements such as phasor measurement units, smart meters, energy storage systems, and electrical vehicles changed the supply chain of electricity drastically. The upgraded supply chain of electricity opened the prospect of achieving completely autonomous operation of power systems while exponentially raising concerns over the security and resiliency of the power grid. My research agenda focuses on the question of how to leverage these technology advancements to continue the trend of improving the supply chain of electricity while satisfying and exceeding the requirements on the resiliency and security of smart grid operations.