Smart grids link various types of energy technologies-such as power electronics, machines, grids, and markets-via communication technology, which leads to a transdisciplinary, multidomain system. Simulation packages for assessing system integration of components typically cover only one subdomain, while simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. This article, part 1 of 2 on this subject, covers fundamental concepts. Part 2 will appear in a future issue of IEEE Electrification Magazine and cover applications.

Understanding smart grid cyber attacks is key for developing appropriate protection and recovery measures. Advanced attacks pursue maximized impact at minimized costs and detectability. This paper conducts risk analysis of combined data integrity and availability attacks against the power system state estimation. We compare the combined attacks with pure integrity attacks -false data injection (FDI) attacks. A security index for vulnerability assessment to these two kinds of attacks is proposed and formulated as a mixed integer linear programming problem. We show that such combined attacks can succeed with fewer resources than FDI attacks. The combined attacks with limited knowledge of the system model also expose advantages in keeping stealth against the bad data detection. Finally, the risk of combined attacks to reliable system operation is evaluated using the results from vulnerability assessment and attack impact analysis. The findings in this paper are validated and supported by a detailed case study.

Smart grids link various types of energy technologies, such as power electronics, machines, grids, and markets, via communication technology, which leads to transdisciplinary, multidomain systems. Simulation packages for assessing the system integration of components typically cover only one subdomain, while greatly simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. The article "Cosimulation of Intelligent Power Systems: Fundamentals, Software Architecture, Numerics, and Coupling," published in the March 2017 issue of this magazine [88], covered the fundamental concepts of this topic, and this follow-up article covers the applied aspects of the subject.

Developing advanced diagnosis tools to detect cyber attacks is the key to security of power systems. It has been shown that multivariate data injection attacks can bypass bad data detectors typically built on static behavior of the systems. In this article, we depart from the existing static viewpoint to develop a diagnosis filter that captures the dynamics signatures of such an intrusion. We introduce a dynamic residual generator approach formulated as robust optimization programs. We investigate two desired features: (i) a non-zero transient and (ii) a non-zero steady-state behavior of the residual generator in the presence of an attack. In (i), the problem is reformulated as a finite but possibly non-convex program. We further develop a linear programming relaxation that improves its scalability and practicality. In (ii), it turns out that the resulting robust program admits an exact convex reformulation, yielding a Nash equilibrium between the attacker and residual generator. This assertion has a stronger implication: the proposed approach is not conservative that the additional knowledge of the attack signal does not improve the diagnosis performance. To validate, we implement the diagnosis filter to detect multivariate attacks on the measurements of the Automatic Generation Control in the IEEE 39-bus system.

Power systems are moving towards hybrid AC/DC grids with the integration of HVDC links, renewable resources and energy storage modules. The load frequency control (LFC) of tomorrow has to consider the complex interactions between these components. Meanwhile, more attention should be paid to cyber security concerns as the LFC loop highly depends on data communications which may be exposed to cyber attacks. In this regard, this article aims to analyze the false data injection (FDI) attacks on the AC/DC interconnected LFC system with inertia emulation and develop advanced diagnosis tools to reveal their occurrence. We build an optimization-based framework for the purpose of vulnerability analysis. Considering the attack impact on frequency stability, it is shown that the multi-area LFC system with parallel AC/DC links and emulated inertia by storage devices is more vulnerable to FDI attacks, compared to the one without inertia emulation and the normal AC system. We then propose a detection approach to detect and isolate each FDI intrusion with a sufficient fast response, and even recover the attack value. In addition to theoretical results, the effectiveness of the proposed method is validated through simulations on the two-area AC/DC interconnected LFC system with inertia emulation capabilities.

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in ‘normal’ operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.