Biblio

IEC 61850 is an international standard that is widely used in substation automation systems (SAS) in smart grids. During its development, security was not considered thus leaving SAS vulnerable to attacks from adversaries. IEC 62351 was developed to provide security recommendations for SAS against (distributed) denial-of-service, replay, alteration, spoofing and detection of devices attacks. However, real-time communications, which require protocols such as Generic Object-Oriented Substation Event (GOOSE) to function efficiently, cannot implement these recommendations due to latency constraints. There has been researching that sought to improve the security of GOOSE messages, however, some cannot be practically implemented due to hardware requirements while others are theoretical, even though latency requirements were met. This research investigates the possibility of encrypting GOOSE messages with One- Time Pads (OTP), leveraging the fact that encryption/decryption processes require the random generation of OTPs and modulo addition (XOR), which could be a realistic approach to secure GOOSE while maintaining latency requirements. Results show that GOOSE messages can be encrypted with some future work required.

The cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.

The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.

The smart grid aims to improve the efficiency, reliability and safety of the electric system via modern communication system, it's necessary to utilize cloud computing to process and store the data. In fact, it's a promising paradigm to integrate smart grid into cloud computing. However, access to cloud computing system also brings data security issues. This paper focuses on the protection of user privacy in smart meter system based on data combination privacy and trusted third party. The paper demonstrates the security issues for smart grid communication system and cloud computing respectively, and illustrates the security issues for the integration. And we introduce data chunk storage and chunk relationship confusion to protect user privacy. We also propose a chunk information list system for inserting and searching data.

Despite the benefits offered by smart grids, energy producers, distributors and consumers are increasingly concerned about possible security and privacy threats. These threats typically manifest themselves at runtime as new usage scenarios arise and vulnerabilities are discovered. Adaptive security and privacy promise to address these threats by increasing awareness and automating prevention, detection and recovery from security and privacy requirements' failures at runtime by re-configuring system controls and perhaps even changing requirements. This paper discusses the need for adaptive security and privacy in smart grids by presenting some motivating scenarios. We then outline some research issues that arise in engineering adaptive security. We particularly scrutinize published reports by NIST on smart grid security and privacy as the basis for our discussions.

A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements.

It can get the user's privacy and home energy use information by analyzing the user's electrical load information in smart grid, and this is an area of concern. A rechargeable battery may be used in the home network to protect user's privacy. In this paper, the battery can neither charge nor discharge, and the power of battery is adjustable, at the same time, we model the real user's electrical load information and the battery power information and the recorded electrical power of smart meters which are processed with discrete way. Then we put forward a heuristic algorithm which can make the rate of information leakage less than existing solutions. We use statistical methods to protect user's privacy, the theoretical analysis and the examples show that our solution makes the scene design more reasonable and is more effective than existing solutions to avoid the leakage of the privacy.

Privacy has become a critical topic in the engineering of electric systems. This work proposes an approach for smart-grid-specific privacy requirements engineering by extending previous general privacy requirements engineering frameworks. The proposed extension goes one step further by focusing on privacy in the smart grid. An alignment of smart grid privacy requirements, dependability issues and privacy requirements engineering methods is presented. Starting from this alignment a Threat Tree Analysis is performed to obtain a first set of generic, high level privacy requirements. This set is formulated mostly on the data instead of the information level and provides the basis for further project-specific refinement.

The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and - if feasible - an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid.

Processing smart grid data for analytics purposes brings about a series of privacy-related risks. In order to allow for the most suitable mitigation strategies, reasonable privacy risks need to be addressed by taking into consideration the perspective of each smart grid stakeholder separately. In this context, we use the notion of privacy concerns to reflect potential privacy risks from the perspective of different smart grid stakeholders. Privacy concerns help to derive privacy goals, which we represent using the goals structuring notation. Thus represented goals can more comprehensibly be addressed through technical and non-technical strategies and solutions. The thread of argumentation - from concerns to goals to strategies and solutions - is presented in form of a privacy case, which is analogous to the safety case used in the automotive domain. We provide an exemplar privacy case for the smart grid developed as part of the Aspern Smart City Research project.

This paper proposes a lightweight and privacy-preserving data aggregation scheme for dynamic electricity pricing based billing in smart grids using the concept of single-pass authenticated encryption (AE). Unlike existing literature that only considers static pricing, to the best of our knowledge, this is the first paper to address privacy under dynamic pricing.

Protecting energy consumers's data and privacy is a key factor for the further adoption and diffusion of smart grid technologies and applications. However, current smart grid initiatives and implementations around the globe tend to either focus on the need for technical security to the detriment of privacy or consider privacy as a feature to add after system design. This paper aims to contribute towards filling the gap between this fact and the accepted wisdom that privacy concerns should be addressed as early as possible (preferably when modeling system's requirements). We present a methodological framework for tackling privacy concerns throughout all phases of the smart grid system development process. We describe methods and guiding principles to help smart grid engineers to elicit and analyze privacy threats and requirements from the outset of the system development, and derive the best suitable countermeasures, i.e. privacy enhancing technologies (PETs), accordingly. The paper also provides a summary of modern PETs, and discusses their context of use and contributions with respect to the underlying privacy engineering challenges and the smart grid setting being considered.

A successful Smart Grid system requires purpose-built security architecture which is explicitly designed to protect customer data confidentiality. In addition to the investment on electric power infrastructure for protecting the privacy of Smart Grid-related data, entities need to actively participate in the NIST interoperability framework process; establish policies and oversight structure for the enforcement of cyber security controls of the data through adoption of security best practices, personnel training, cyber vulnerability assessments, and consumer privacy audits.

Cascading failure, which can be triggered by both physical and cyber attacks, is among the most critical threats to the security and resilience of power grids. In current literature, researchers investigate the issue of cascading failure on smart grids mainly from the attacker's perspective. From the perspective of a grid defender or operator, however, it is also an important issue to restore the smart grid suffering from cascading failure back to normal operation as soon as possible. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing of the failed nodes/links in a sequential fashion. Based on a realistic power flow cascading failure model, we exploit a Q-learning approach to develop a practical and effective policy to identify the optimal way of sequential restorations for large-scale smart grids. Simulation results on three power grid test benchmarks demonstrate the learning ability and the effectiveness of the proposed strategy.

Automatic optimal response systems are essential for preserving power system resilience and ensuring faster recovery from emergency under cyber compromise. Numerous research works have developed such response engine for cyber and physical system recovery separately. In this paper, we propose a novel cyber-physical decision support system, SCORE, that computes optimal actions considering pure and hybrid cyber-physical states, using Markov Decision Process (MDP). Such an automatic decision making engine can assist power system operators and network administrators to make a faster response to prevent cascading failures and attack escalation respectively. The hybrid nature of the engine makes the reward and state transition model of the MDP unique. Value iteration and policy iteration techniques are used to compute the optimal actions. Tests are performed on three and five substation power systems to recover from attacks that compromise relays to cause transmission line overflow. The paper also analyses the impact of reward and state transition model on computation. Corresponding results verify the efficacy of the proposed engine.

The United States and European Union have an increasing number of projects that are engaging end-use devices for improved grid capabilities. Areas such as building-to-grid and vehicle-to-grid are simple examples of these advanced capabilities. In this paper, we present an innovative concept study for a ship-to-grid integration. The goal of this study is to simulate a two-way power flow between ship(s) and the grid with GridLAB-D for the port of Kyllini in Greece, where a ship-to-shore interconnection was recently implemented. Extending this further, we explore: (a) the ability of ships to meet their load demand needs, while at berth, by being supplied with energy from the electric grid and thus powering off their diesel engines; and (b) the ability of ships to provide power to critical loads onshore. As a result, the ship-to-grid integration helps (a) mitigate environmental pollutants from the ships' diesel engines and (b) provide resilience to nearby communities during a power disruption due to natural disasters or man-made threats.

Software-defined networking (SDN) allows the smart grid to be centrally controlled and managed by decoupling the control plane from the data plane, but it also expands attack surface for attackers. Existing studies about the security of SDN-enabled smart grid (SDSG) mainly focused on static methods such as access control and identity authentication, which is vulnerable to attackers that carefully probe the system. As the attacks become more variable and complex, there is an urgent need for dynamic defense methods. In this paper, we propose a security function virtualization (SFV) based moving target defense of SDSG which makes the attack surface constantly changing. First, we design a dynamic defense mechanism by migrating virtual security function (VSF) instances as the traffic state changes. The centralized SDN controller is re-designed for global status monitoring and migration management. Moreover, we formalize the VSF instances migration problem as an integer nonlinear programming problem with multiple constraints and design a pre-migration algorithm to prevent VSF instances' resources from being exhausted. Simulation results indicate the feasibility of the proposed scheme.

The intelligent power communication network is closely connected with the power system, and carries the data transmission and intelligent decision in a series of key services in the power system, which is an important guarantee for the smart power service. The self-healing control (SHC) of the distribution network monitors the data of each device and node in the distribution network in real time, simulates and analyzes the data, and predicts the hidden dangers in the normal operation of the distribution network. Control, control strategies such as correcting recovery and troubleshooting when abnormal or fault conditions occur, reducing human intervention, enabling the distribution network to change from abnormal operating state to normal operating state in time, preventing event expansion and reducing the impact of faults on the grid and users.

Distributed Generation Photovoltaic Systems -DGPV - connected to the power distribution grid through electronic inverters can contribute, in an aggregate scenario, to the performance of several power system control functions, notably in self-healing and voltage regulation along a distribution feeder. This paper proposes the use of an optimization method for voltage regulation, focused on reactive power injection control, based on a comprehensive architecture model that coordinates multiple photovoltaic distributed sources to support grid reconfiguration after self-healing action. A sensitivity analysis regarding the performance of voltage regulation, based on a co-simulation of PSCAD and MatLab, shows the effectiveness of using dispersed generation sources to assist grid reconfiguration after disturbances caused by severe faults.

The virtual world does not observe national borders, has no uniform legal system, and does not have a common perception of security and privacy issues. It is however, relatively homogenous in terms of technology.A cyberattack on an energy delivery system can have significant impacts on the availability of a system to perform critical functions as well as the integrity of the system and the confidentiality of sensitive information.

Smart Grids require extensive communication to enable safe and stable energy supply in the age of decentralized and dynamic energy production and consumption. To protect the communication in this critical infrastructure, public authorities mandate smart meter gateways (SMGWs) to be in control of the communication security. To this end, the SMGW intercepts all inbound and outbound communication of its premise, e.g., a factory or smart home, and forwards it on secure channels that the SMGW established itself. However, using the SMGW as proxy, local devices can neither review the security of these remote connections established by the SMGW nor enforce higher security guarantees than established by the all in one configuration of the SMGW which does not allow for use case-specific security settings. We present mechanisms that enable local devices to regain this insight and control over the full connection, i.e., up to the final receiver, while retaining the SMGW's ability to ensure a suitable security level. Our evaluation shows modest computation and transmission overheads for this increased security in the critical smart grid infrastructure.

As the next generation of the power system, smart grid develops towards automated and intellectualized. Along with the benefits brought by smart grids, e.g., improved energy conversion rate, power utilization rate, and power supply quality, are the security challenges. One of the most important issues in smart grids is to ensure reliable communication between the secondary equipment. The state-of-art method to ensure smart grid security is to detect cyber attacks by deep learning. However, due to the small number of negative samples, the performance of the detection system is limited. In this paper, we propose a novel approach that utilizes the Generative Adversarial Network (GAN) to generate abundant negative samples, which helps to improve the performance of the state-of-art detection system. The evaluation results demonstrate that the proposed method can effectively improve the performance of the detection system by 4%.

Security and consistency of smart grids is one of the main issues in the design and maintenance of highly controlled and monitored new power grids. Bad data injection attack could lead to disasters such as power system outage, or huge economical losses. In many attack scenarios, the attacker can come up with new attack strategies that couldn't be detected by the traditional bad data detection methods. Adaptive Partitioning State Estimation (APSE) method [3] has been proposed recently to combat such attacks. In this work, we evaluate and compare with a traditional method. The main idea of APSE is to increase the sensitivity of the chi-square test by partitioning the large grids into small ones and apply the test on each partition individually and repeat this procedure until the faulty node is located. Our simulation findings using MATPOWER program show that the method is not consistent where it is sensitive the systems size and the location of faulty nodes as well.

The technological development of the energy sector also produced complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing which areas of the smart grid system use big data technologies and technologies, big data technologies for detecting smart grid attacks have received attention. Big data analytics can produce efficient solutions and it is especially important to choose which algorithms and metrics to use. For this reason, an application prototype has been proposed that uses a big data method to detect attacks on the smart grid. The algorithm with high accuracy was determined to be 92% for random forests and 87% for decision trees.

Traditional power grid security schemes are being replaced by highly advanced and efficient smart security schemes due to the advancement in grid structure and inclusion of cyber control and monitoring tools. Smart attackers create physical, cyber, or cyber-physical attacks to gain the access of the power system and manipulate/override system status, measurements and commands. In this paper, we formulate the environment for the attacker-defender interaction in the smart power grid. We provide a strategic analysis of the attacker-defender strategic interaction using a game theoretic approach. We apply repeated game to formulate the problem, implement it in the power system, and investigate for optimal strategic behavior in terms of mixed strategies of the players. In order to define the utility or cost function for the game payoffs calculation, generation power is used. Attack-defense budget is also incorporated with the attacker-defender repeated game to reflect a more realistic scenario. The proposed game model is validated using IEEE 39 bus benchmark system. A comparison between the proposed game model and the all monitoring model is provided to validate the observations.