Owing to the critical nature of the power grid, coordinated cyber-physical attacks on its critical infrastructure can lead to disastrous human and economic losses. In this paper, a stochastic game-theoretic approach is proposed to analyze the optimal strategies that a power grid defender can adopt to protect the grid against coordinated attacks. First, an optimal load shedding technology is devised to quantify the physical impacts of coordinated attacks. Taking these quantified impacts as input parameters, the interactions between a malicious attacker and the defender are modeled using a resource allocation stochastic game. The game is shown to admit a Nash equilibrium and a novel learning algorithm is introduced to enable the two players to reach such equilibrium strategies while maximizing their respective minimum rewards in a sequence of stages. The convergence of the proposed algorithm to a Nash equilibrium point is proved and its properties are studied. Simulation results of the stochastic game model on the WSCC 9-bus system and the IEEE 118-bus system are contrasted with those of static games, and show that different defense resources owned lead to different defense strategies.Index Terms-Coordinated attacks, optimal load shedding, power grid security, stochastic game theory.

Abstract:In smart cities, advanced metering infrastructure (AMI) of the smart grid facilitates automated metering, control and monitoring of power distribution by employing a wireless network. Due to this wireless nature of communication, there exist potential threats to the data privacy in AMI. Decoding the energy consumption reading, injecting false data/command signals and jamming the networks are some hazardous measures against this technology. Since a smart meter possesses limited memory and computational capability, AMI demands a light, but robust security scheme. In this paper, we propose a localization-based key management system for meter data encryption. Data are encrypted by the key associated with the coordinate of the meter and a random key index. The encryption keys are managed and distributed by a trusted third party (TTP). Localization of the meter is proposed by a method based on received signal strength (RSS) using the maximum likelihood estimator (MLE). The received packets are decrypted at the control center with the key mapped with the key index and the meter's coordinates. Additionally, we propose the k-nearest neighbors (kNN) algorithm for node/meter authentication, capitalizing further on data transmission security. Finally, we evaluate the security strength of a data packet numerically for our method.

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The Advanced Metering Infrastructure (AMI) is a vital element in the current development of the smart grid. AMI technologies provide electric utilities with an effective way of continuous monitoring and remote control of smart grid components. However, owing to its increasing scale and cyber-physical nature, the AMI has been faced with security threats in both cyber and physical domains. This paper provides a comprehensive review of the crucial cyber-physical attacks and counter defense mechanisms in the AMI. First, two attack surfaces are surveyed in the AMI including the communication network and smart meters. The potential cyber-physical attacks are then reviewed for each attack surface. Next, the attack models and their cyber and physical impacts on the smart grid are studied for comparison. Counter defense mechanisms that help mitigate these security threats are discussed. Finally, several mathematical tools which may help in analysis and implementation of security solutions are summarized.

Photovoltaic (PV) systems depend on irradiance, ambient temperature and module temperature. A solar eclipse causes significant changes in these parameters, thereby impacting PV generation profile, performance, and power quality of larger grid where they connect to. This paper presents a case study to evaluate the impacts of the solar eclipse of August 21, 2017 on two real-world grid-tied PV systems (1.4MW and 355kW) in Miami and Daytona, Florida, the feeders they are connected to, and the management areas they belong to. Four types of analyses are conducted to obtain a comprehensive picture of the impacts using 1-minute PV generation data, hourly weather data, real feeder parameters, and daily reliability data. These analyses include: individual PV system performance measurement using power performance index; power quality analysis at the point of interconnection; a study on the operation of voltage regulating devices on the feeders during eclipse peak using an IEEE 8500 test case distribution feeder; and reliability study involving a multilayer perceptron framework for forecasting system reliability of the management areas. Results from this study provide a unique insight into how solar eclipses impact the behavior of PV systems and the grid, which would be of concern to electric utilities in future high penetration scenarios.