Large-scale Load-Altering Attacks (LAAs) against Internet-of-Things (IoT) enabled high-wattage electrical appliances pose a serious threat to power system security and stability. This paper investigates, for the first time, the optimal mitigation strategy from a system perspective against such attacks. In particular, a Cyber-Resilient Economic Dispatch (CRED) concept is proposed and seamlessly integrated with attack detection and identification to form a cyber resiliency enhancement framework. Instead of only relying on local resources, CRED coordinates the frequency droop control gains of Inverter-Based Resources (IBRs) in the system to mitigate the destabilizing effect of LAAs while minimizing the overall operational cost. To achieve this, the LAA-inclusive system frequency dynamics is formulated and the corresponding system stability constraints are explicitly derived based on parametric sensitivities, which are further incorporated into the system scheduling model with minimum error through a novel recursive linearization method. In addition, a distributionally robust approach is proposed to account for the uncertainty associated with system dynamics driven by the LAA detection/parameter estimation errors. The overall performance of the proposed CRED model is demonstrated through extensive simulations in a modified IEEE reliability test system.

Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch

Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each other with the deepening of the network attack and defense game, which may cause the attacker to crack a certain type of defense strategy, resulting in an invalid defense strategy. The failure of the defense strategy reduces the accuracy and guidance value of existing methods. To solve the above problem, we propose a reward value learning mechanism (RLM). By analyzing previous game information, RLM automatically incentives or punishes the attack and defense reward values for the next stage, which reduces the probability of defense strategy failure. RLM is introduced into the dynamic network attack and defense process under incomplete information, and a multistage evolutionary game model with a learning mechanism is constructed. Based on the above model, we design the optimal defense strategy selection algorithm. Experimental results demonstrate that the evolutionary game model with RLM has better results in the value of reward and defense success rate than the evolutionary game model without RLM.

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Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

The invasive deployment of digital technologies in power grids increases their exposure to cyber threats. In this paper, we study a type of load-altering cyberattack that targets peak shaving programs wherein a utility directly controls the loads of domestic electric water heaters. We consider a cyberattack scenario in which an adversary gains access to the advanced metering infrastructure and sends malicious activation commands to a large number of water heaters forcing them to simultaneously switch on in the midst of a peak shaving period, thus causing a sudden, large power demand that could lead to a system-wide blackout. We develop a simulation environment to simulate the loads of a population of electric water heaters under different operating conditions as well as under this type of cyberattack. Then, we propose a cybersecurity-by-design approach to mitigate the risk of such cyberattack. The efficacy of the proposed solution is analyzed using software experiments.

Cyberattacks against Direct Load Control of Residential Electric Water Heaters in Smart Grids

Dynamic load altering attack detection for cyber physical power systems via sliding mode observer

Intelligent attackers can suitably tamper sensor/actuator data at various Smart grid surfaces causing intentional power oscillations, which if left undetected, can lead to voltage disruptions. We develop a novel combination of formal methods and machine learning tools that learns power system dynamics with the objective of generating unsafe yet stealthy false data based attack sequences. We enable the grid with anomaly detectors in a generalized manner so that it is difficult for an attacker to remain undetected. Our methodology, when applied on an IEEE 14 bus power grid model, uncovers stealthy attack vectors even in presence of such detectors.

Youqi Guo

Papers

Reinforcement-learning-based dynamic defense strategy of multistage game against dynamic load altering attack