The smart grid combines the classical power system with the information technology, leading to a cyber-physical system. In such an environment, the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most of the works on FDI attack detection are based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph’s high-frequency components. By comparing the maximum norm of this outcome with a threshold, we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.

Historically, the power distribution grid was a passive system with limited control capabilities. Due to its increasing digitalization, this paradigm has shifted: the passive architecture of the power system itself, which includes cables, lines, and transformers, is extended by a communication infrastructure to become an active distribution grid. This transformation to an active system results from control capabilities that combine the communication and the physical components of the grid. It aims at optimizing, securing, enhancing, or facilitating the power system operation. The combination of power system, communication, and control capabilities is also referred to as a “smart grid”. A multitude of different architectures exist to realize such integrated systems. They are often labeled with descriptive terms such as “distributed,” “decentralized,” “local,” or “central." However, the actual meaning of these terms varies considerably within the research community.This paper illustrates the conflicting uses of prominent classification terms for the description of smart grid architectures. One source of this inconsistency is that the development of such interconnected systems is not only in the hands of classic power engineering but requires input from neighboring research disciplines such as control theory and automation, information and telecommunication technology, and electronics. This impedes a clear classification of smart grid solutions. Furthermore, this paper proposes a set of well-defined operation architectures specialized for use in power systems. Based on these architectures, this paper defines clear classifiers for the assessment of smart grid solutions. This allows the structural classification and comparison between different smart grid solutions and promotes a mutual understanding between the research disciplines. This paper presents revised parts of Chapters 4.2 and 5.2 of the dissertation of Drayer (Resilient Operation of Distribution Grids with Distributed-Hierarchical Architecture. Energy Management and Power System Operation, vol. 6, 2018).

In this brief announcement we present our ongoing work to localize "false data injection" (FDI) attacks on the system state of modern power systems, better known as smart grids. Because of their exceptional importance for our society and together with the increasing presence of information and telecommunication (ICT) components, these power systems are a vulnerable target for cyber attacks. In our method, we represent the power system as a graph and use a generalized modulation operator that is applied on the states of the system. Our preliminary results indicate that attacked grid states exhibit specific modulation patterns that facilitate the localization of the attacks on the particular buses of the grid. This approach is demonstrated by several case study simulations.

The extensive measurement infrastructure of smart grids is a vulnerable target for cyber attacks aiming at compromising reliable power supply. Thus, the detection of intrusion into the system and the identification of manipulated and false data is a key security capability required for future power systems. In this paper, we apply principal component analysis (PCA), together with a subspace analysis, to detect the presence of such false data injection (FDI) attacks. A key requirement for this method is a database of historical grid states that is used to compute the PCA transformation matrix. Each new grid state is then transformed based on this matrix to calculate its uncorrelated principal components. The presence of FDI attacks leads to a significant increase in the contribution of principal components that span the residual subspace. By comparing this projection against a threshold, the presence of compromised measurements can be detected. This is demonstrated by several case study simulations.

With the transition from the hardware dominated analog power system to a digitized cyber-physical "smart grid", protection from attacks from the cyber domain has become increasingly important. In particular, malicious injection of false data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect a well designed false data injection (FDI) attack, which is based on detailed knowledge of the system topology. The aim of this paper is to overcome this limitation by making use of the inherent graph structure of the grid. Based on approaches developed for signal processing on graphs and on analysis of the spectrum of the graph, the proposed method enables the detection of previously undetectable FDI attacks. The main requirement for the proposed detector is that the estimated grid state is smooth with respect to the underlying weighted graph determined by the admittance matrix, that is, it has a low variation. Then, detection based on analysis of the high frequency components of the graph Fourier transform may be possible, as a function of the underlying topology of the grid. The feasibility of this idea is demonstrated with a case study on the IEEE 14-bus test grid.

New ideas to operate and optimise the distribution grid of the future are regularly proposed by scientists. These new ideas are required as answer to the massive transformations that are currently happening in the distribution grid. One promising path is to move from the classic central to a more decentralised and distributed control. The work in this study highlights the main solutions developed to realise a new decentralised grid operation concept that is compatible with a classic operation scheme. The field test was one of the main demonstrators in the EU-funded project DREAM.

The medium-voltage grid is often planned in loops, but operated in a radial structure to allow a robust protection scheme. This allows the reconfiguration of the grid. This paper focuses on reconfiguration after a fault to re-establish the service, also referred to as self-healing. It includes the localization of faults and finds new grid configurations based on the actual status of the grid. The solving of the reconfiguration problem is nonpolynomial difficult. Finding an acceptable solution to resupply nonfaulted grid parts under tight time limits is difficult. The innovative contribution of this paper is an approach that finds new configurations as local as possible to the faulty area, evolvingly increasing the search space. New configurations are generated based on a heuristic method relying on graph theory. An aggregated fitness function is used to evaluate the solutions. For the realization, a distributed control architecture in the form of an advanced substation automation is proposed.

This paper proposes a classification scheme for the different types of flexibilities that are used in electric grids. This classification scheme, which is called a taxonomy, helps to convey the meaning of different concepts of flexibility in research and industrial projects. It also allows to compare the sources and uses of flexibility in conventional vs. smart grid situations to highlight the evolving nature of the power system.

With the advent of the smart grid, a highly connected and communicative power grid, many new threats have to be considered, and new concepts for the grid to "fail smartly" in the inevitable event of security intrusion are needed. This paper reviews a collection of possible cyber threat scenarios as well as outlines of counter measures to different aspects of the communication infrastructure and components of a future power grid. These scenarios aim to define a test framework to future solutions for resilient distribution grid operation. To allow the evaluation of the effects of possible counter measures to these threats a specialised test environment is presented.

Compared to a centralized grid operation management for the distribution grid, a decentralized agent-based operation has many advantages. Two methods that are part of such a grid operation management are presented. Firstly, a method based on Particle Swarm Optimization ( PSO) is developed. A suitable fitness function is derived to evaluate possible solutions with respect to their multi-dimensional implication for the situation in the grid, i.e. their effect on the voltage and current profile, reactive power flows, power losses, operating costs as well as complexity. Secondly, different local control strategies are evaluated for their potential as a complementary strategy to the above optimization and as an immediate countermeasure to voltage and frequency violations. It can also function as a fallback option in case of emergency. For both methods, simulation results for artificial and real grid data are presented that show their successful application.