Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system's efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid's availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids' digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid's digitization is still ongoing there is a general problem of a lack of data on the grid's status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop a framework to simulate a behavior of distribution networks in the presence of faults, and fluctuating generation and load that, alone or combined, may cause disturbances. With the framework we generate a large set of data that we use to develop and evaluate a voltage-sag disturbance predictor. To quantify how prediction and proactive mitigation of disturbances enhance availability we create an availability model of a proactive management. The model is generic and may be applied to evaluate the effect of proactive management on availability in other types of systems, and adapted for quantifying other types of properties as well. Also, we design a metric and a method for optimizing failure prediction to maximize availability with proactive approach. In our conclusion, the level of availability improvement with proactive approach is comparable to the one when using high-reliability and costly components. Following the results of the case study conducted for a 14-bus ADN, grid's availability may be improved by up to an order of magnitude if disturbances are managed proactively instead of reactively. The main results and contributions may be summarized as follows: (i) Taxonomy of faults in Smart Grid has been developed; (ii) Methodology and methods for proactive management of disturbances have been proposed; (iii) Model to quantify availability with proactive management has been developed; (iv) Simulation and fault-injection framework has been designed and implemented to generate disturbance-related data; (v) In the scope of a case study, a voltage-sag predictor, based on machine- learning classification algorithms, has been designed and the effect of proactive disturbance management on downtime and availability has been quantified

Quantitative dependability and interdependency models for large-scale cyber-physical systems

Cyber-physical systems link cyber infrastructure with physical processes through an integrated network of physical components, sensors, actuators, and computers that are interconnected by communication links. Modern critical infrastructures such as smart grids, intelligent water distribution networks, and intelligent transportation systems are prominent examples of cyber-physical systems. Developed countries are entirely reliant on these critical infrastructures, hence the need for rigorous assessment of the trustworthiness of these systems. The objective of this research is quantitative modeling of dependability attributes -- including reliability and survivability -- of cyber-physical systems, with domain-specific case studies on smart grids and intelligent water distribution networks. To this end, we make the following research contributions: i) quantifying, in terms of loss of reliability and survivability, the effect of introducing computing and communication technologies; and ii) identifying and quantifying interdependencies in cyber-physical systems and investigating their effect on fault propagation paths and degradation of dependability attributes. Our proposed approach relies on observation of system behavior in response to disruptive events. We utilize a Markovian technique to formalize a unified reliability model. For survivability evaluation, we capture temporal changes to a service index chosen to represent the extent of functionality retained. In modeling of interdependency, we apply correlation and causation analyses to identify links and use graph-theoretical metrics for quantifying them. The metrics and models we propose can be instrumental in guiding investments in fortification of and failure mitigation for critical infrastructures. To verify the success of our proposed approach in meeting these goals, we introduce a failure prediction tool capable of identifying system components that are prone to failure as a result of a specific disruptive event. Our prediction tool can enable timely preventative actions and mitigate the consequences of accidental failures and malicious attacks --Abstract, page iii

Dependability analysis and recovery support for smart grids

The increasing scale and complexity of power grids exacerbate concerns about failure propagation. A single contingency, such as outage of a transmission line due to overload or weather-related damage, can cause cascading failures that manifest as blackouts. One objective of smart grids is to reduce the likelihood of cascading failure through the use of power electronics devices that can prevent, isolate, and mitigate the effects of faults. Given that these devices are themselves prone to failure, we seek to quantify the effects of their use on dependability attributes of smart grid. This thesis articulates analytical methods for analyzing two dependability attributes - reliability and survivability - and proposes a recovery strategy that limits service degradation. Reliability captures the probability of system-level failure; Survivability describes degraded operation in the presence of a fault. System condition and service capacity are selected as measures of degradation. Both reliability and survivability are evaluated using N-1 contingency analysis. Importance analysis is used to determine a recovery strategy that maintains the highest survivability in the course of the recovery process. The proposed methods are illustrated by application to the IEEE 9-bus test system, a simple model system that allows for clear articulation of the process. Simulation is used to capture the effect of faults in both physical components of the power grid and the cyber infrastructure that differentiates it as a smart grid --Abstract, page iii

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

Petri Nets for Smart Grids: The Story So Far

Since the energy domain is in a transformative shift towards sustainability, the integration of new technologies and smart systems into traditional power grids has emerged. As an effective approach, Petri Nets (PN) have been applied to model and analyze the complex dynamics in Smart Grid (SG) environments. However, we are currently missing an overview of types of PNs applied to different areas and problems related to SGs. Therefore, this paper proposes four fundamental research questions related to the application areas of PNs in SGs, PNs types, aspects modelled by PNs in the identified areas, and the validation methods in the evaluation. The answers to the research questions are derived from a comprehensive and interdisciplinary literature analysis. The results capture a valuable overview of PNs applications in the global energy landscape and can offer indications for future research directions

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Needs and Challenges Concerning Cyber-Risk Assessment in the Cyber-Physical Smart Grid

Cyber-risk assessment methods are used by energy companies to manage security risks in smart grids. However, current standards, methods and tools do not adequately provide the support needed in practice and the industry is struggling to adopt and carry out cyber-risk assessments. The contribution of this paper is twofold. First, we interview six companies from the energy sector to better understand their needs and challenges. Based on the interviews, we identify seven success criteria cyber-risk assessment methods for the energy sector need to fulfill to provide adequate support. Second, we present the methods CORAS, VAF, TM-STRIDE, and DA-SAN and evaluate the extent to which they fulfill the identified success criteria. Based on the evaluation, we provide lessons learned in terms of gaps that need to be addressed in general to improve cyber-risk assessment in the context of smart grids. Our results indicate the need for the following improvements: 1) ease of use and comprehensible m ethods, 2) support to determine whether a method is a good match for a given context, 3) adequate preparation to conduct cyber-risk assessment, 4) manage complexity, 5) adequate support for risk estimation, 6) support for trustworthiness and uncertainty handling, and 7) support for maintaining risk assessments.acceptedVersio