Figure 3.1. Power system operating states and transitions.
higher vulnerability. The system is in the Emergency state if at least one system inequality constraint is violated. When both, equality and inequality constraints are violated, a transition to In extremis (that is a non-operational state) occurs. Transitions between the states occur due to component failures and restorations. A system is in the Restorative state during a recovery and maintenance.
3.2
Smart Grid Dependability Modeling and Evaluation
With increased utilization and importance of cyber-physical systems in many complex, safety critical, and cross-disciplinary fields, Smart Grid modeling from CPS’es perspective emerged recently as an important research topic resulting in a number of relevant scientific works.
Modeling of the electric power grid as a cyber-based physical system has been proposed in [120]. The work has introduced a novel cyber-based dynamical model whose mathematical description depends on cyber technologies support- ing the physical system. The paper discusses how such a model could be used to enable full observability through a cooperative information exchange among its components. The authors also show how the proposed cyber-physical model could be used to develop interactive protocols between the intelligent electronic devices embedded within the system layers and the network operator. Never- theless, due to a tremendous amount of information that will be produced and collected, novel modeling methods for efficient integration of advanced monitor- ing and control instruments for such cyber-physical systems are needed. These methods should be built as compatible and scalable with the existing SCADA systems and they should also support future industry needs.
43 3.2 Smart Grid Dependability Modeling and Evaluation
Smart Grid as a cyber-physical system but, instead, CPS’es, as individual devices, are modeled as mediators between the physical world and the business aspects of electric power grids. Continuous evolution of embedded and ubiquitous comput- ing technologies is perceived as a driver for decentralization of business decisions by transferring them to computing nodes that are closer to customers.
Utilization of cyber-physical energy systems (CPES’es) for key Smart Grid related challenges such as modeling power systems, energy efficiency, energy resource management, and energy control are studied in[122]. The application of CPES’es for optimal power flow management has been described for a specific use case of a microgrid model. In line with this, some customized solutions such as cyber-physical SCADA for the distributed energy resources management are proposed in[123].
Considering Smart Grid as a cyber-physical system, in[51] interdependencies between electrical and information infrastructure are qualitatively analyzed. A model has been developed to capture the effects of failure propagation from one infrastructure to another, distinguishing between cascading, escalating, common- cause and unrelated failures. Each infrastructure is modeled with a set of states that includes working state, weakened state, partial outage state, failure state, and restauration state. A unified state model of the system is generated combin- ing the states of individual infrastructures. Transitions between the states may be triggered by events from one or another infrastructure and are used to model the interdependencies. Failures of information infrastructure are classified as masked and signaled. Errors of the information infrastructure are classified as active and passive. Active errors are those that directly affect the electric infras- tructure. Passive errors are those that do not affect the electric infrastructure but make electric infrastructure errors undetectable. Malicious attacks are also briefly addressed.
A generic guideline for developing a unified tool for the analysis of reliability of the electric power system, having in mind the interdependencies between the infrastructures, has been proposed in [82]. In the framework both, static and dynamic aspects of the system are considered. A method for structural modeling of the electric system capable to capture high-level elements, such as topology of the system, as well as low-level ones that are associated with basic compo- nents is proposed. The authors differentiate between two types of disruptive events (failures), namely transient or permanent disconnection of a component and transient or permanent overloads. Cyber infrastructure failures are summa- rized as omission failures, time failures, value failures and byzantine failures. Faults are not addressed. The framework also implements a few basic modeling mechanisms.
44 3.2 Smart Grid Dependability Modeling and Evaluation
In[124], a quantitative modeling and analysis of reliability of Smart Grids has been conducted with focus on transmission network. The method relies on the analysis of previous cascading failure scenarios to group grid components into subsystems and reduce the size of the model. The method has been extended in [40] and [94] to analyze reliability of the grid in the presence of interdependent failures. The focus in [94] is on the application of FACTS (Flexible Alternating Current Transmission System) devices that may increase reliability of the grid by controlling power flow in transmission lines. The authors evaluate the effect that failures of FACTS devices have on the grid. Software failures of FACTS devices are analyzed with fault injection and possible failure propagation scenarios are identified. Faults, errors, and failures are not clearly distinguished and terms are occasionally used interchangeably. Four types of software faults with respect to their effect on the power flow are identified and a generic model of the grid reliability is derived.
A few papers focus specifically on dependability of ADNs. Voltage stability in ADNs is studied in[14]. The main conclusion of the work is that current methods are not sufficient to cope with higher penetration of renewable resources. The author also proposes a few novel algorithms for managing a protection system in the presence of distributed generation for improved voltage stability. These schemes are based on proper timing for triggering different protection means that include shunt capacitors, DERs and online tap changers (OLTCs). The author has demonstrated that, with a proper scheme, DERs may generally contribute to grid’s stability.
In [5], the impact of renewable resources, storages, and demand response programs on ADNs, and Smart Grids reliability in general, has been reviewed. The conclusion is that, an ideal mix of Smart Grid resources may lead to its better stability. An architectural blueprint to facilitate design, development and integration of Smart Grid components for ensuring reliability has also been pro- posed.
With proliferation of ICT elements, power grids are becoming more prone to cyber-attacks and cyber-security of Smart Grids is getting more importance and therefore being increasingly researched. The most frequent topics are intrusion prevention, privacy, and confidentiality. A good overview of Smart Grid cyber- security aspects including identification of security requirements, modeling of network vulnerabilities, attack countermeasures, secure communication proto- cols and architectures is given in [125]. The problem of propagation of faults caused by attacks from cyber to physical infrastructure is addressed in[51].