The new surge of interest towards mass integration of Electric Vehicles (EVs) in distribution smart grids can expose the high-voltage grid to instability conditions, for instance, through cyber threats initiated from the residential or public EV Supply Equipment (EVSE). This paper (i) investigates the impact of switching attacks on EV charging infrastructure and their impacts on the inter-area stability of the transmission grid, and (ii) proposes a two-stage detection and mitigation technique for those attacks. Initially, we demonstrate that leveraging the existing vulnerabilities in charging stations' cyberspace and the topology of the grid, an adversary can switch the injected or absorbed power of EVs with inter-area frequency and cause a blackout by destabilizing the angular speed of the grid's generators. Then, a Back Propagation Neural Network (BPNN) scheme is designed and hosted at the central management system (CMS) of a public EVSE network. Using this BPNN scheme, the switching attacks are accurately detected by analyzing the features of charging/discharging requests. Moreover, the detected attacks are mitigated by delaying or discarding the request execution. Finally, to cope with the conditions where the residential chargers are under-attack, or when the BPNN fails to provide accurate detection, a wide area H ^∞ controller is designed to keep the angular speed of the synchronous generators within the acceptable limits. The effectiveness of the proposed techniques is evaluated using two-area Kundur and 5-area Australian grids.

An adequate charging infrastructure advocates to ameliorate the range anxiety to propel the disparaged electric vehicle (EV) market. But, the high initial installation cost, requirement of suitable places and the anticipated immense load on the grid during peak times hinder to elongate the charging station network, especially in urban areas. Fortunately, the bidirectional energy transferring capability between vehicles (i.e., V2V) may act as an auxiliary solution to charge an EV at any place and at any time without leaning on a stationary charging infrastructure. In this work, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. A provider intends to maximize the served number of EVs using its limited number of charging trucks, when an EV should be considered as served only if it would be fully charged during its declared charging window. All charging requests are assumed to be received by an agent which provisions a route and schedule for each charging truck and all trucks should return to the depot after serving EVs. We formulate this combinatorially hard problem as an integer linear program (ILP) to maximize the number of served EVs by determining the optimal trajectory of each truck. Owing to its complexity, we present a solution methodology by decomposing the problem using Dantzig-Wolfe decomposition approach; we divide the problem into one master problem and a set of pricing problems (one for each EV) and achieve the solution iteratively. Though the solution achieved from the decomposition might not be optimal, it is faster to be applicable in practice. We also compare the performance with two heuristic algorithms and report on the collected results.

The Precision Time Protocol (PTP) is the favorable mechanism to provide unified and precise time at the substation level in the smart grid. PTP was shown to be vulnerable to cyberattacks targeting its components and synchronization services. In this paper, we capitalize on the theory and outcome of our previous work to contribute a more complete solution that addresses PTP cyber security. We propose to close the PTP loop through an extension that introduces new functionality and messages. This extension covers the PTP attack surface and enables the detection of attacks on PTP time synchronization. We formally model and verify the proposed extension using UPPAAL model checker. In addition, we validate the proposed extension using Omnet++ simulation. The evaluation demonstrates that our approach preserves PTP functionality, and successfully detects cyber attacks targeting PTP components in a timely manner.

In this work, we investigate that the abundance of Electric Vehicles (EVs) can be exploited to target the stability of the power grid. Through a cyber attack that compromises a lot of available EVs and their charging infrastructure, we present a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid. The threat model as well as linearized state-space representation of the grid are formulated to illustrate possible consequences of the attack. Two variations of switching attack are considered, namely, switching of EV charging and discharging power into the grid. Moreover, two possible attack strategies are also considered (i) using an insider to reveal the accurate system parameters and (ii) using reconnaissance activities in the absence of the grid parameters. In the former strategy, the system equations are used to compute the required knowledge to launch the attack. However, a stealthy system identification technique, which is tailored based on Eigenvalue Realization Algorithm (ERA), is proposed in latter strategy to calculate the required data for attack execution. The two-area Kundur, 39-Bus New England, and the Australian 5-area power grids are used to demonstrate the attack strategies and their consequences. The collected results demonstrate that by manipulation of EV charging stations and launching a coordinated switching attack to those portions of load, inter-area oscillations can be initiated. Finally, to protect the grid from this anticipated attack, a Support Vector Machine (SVM) based framework is proposed to detect and eliminate this attack even before being executed.

Critical infrastructure, such as the smart grid, is vulnerable to failures and attacks. The complex nature of these systems embeds hidden vulnerabilities that threaten their functionality when exploited. In this paper, we perform a vulnerability analysis of the smart grid based on the power flow dynamics and in the presence of the essential communication network. Our analysis identifies a small number of power lines and communication links that can trigger a cascading failure and result in a blackout when removed. We quantify the failure effect in the form of fractional loss in the served load. Moreover, we formulate a mathematical model to present both components of the smart grid and their interdependency. A scalable algorithm is introduced to analyze the output of the model. We evaluate the proposed model and algorithm on the IEEE 14, 30, 57 and 300 Bus systems and associated communication networks, and report on the collected results.

Security metric deployment is of immense importance for power utilities especially in future smart grids, which are being actively pursued in smart cities initiatives around the world. Through these metrics, utilities attain real-time awareness of the grid’s security posture. In this paper, we propose a contingency analysis based security evaluation framework for smart grid systems -CASeS. Based on the power flow equations of the grid, communication network characteristics, and concurrent power contingencies, CASeS leverages Markovian Decison Processes (MDP) to quantify the smart grid security. The security index delivered through CASeS emerges as a novel solution for concurrent power contingency consideration, and allows for the preparation of corrective actions to address the quantified grid criticality level. We evaluate CASeS on the standard IEEE 14-Bus and 39-Bus systems. The collected results demonstrate the usefulness of CASeS in quantifying the security of those systems.