Giovanni Sansavini

Modeling Interdependent Network Systems for Identifying Cascade-Safe Operating Margins

Infrastructure interdependency stems from the functional and logical relations among individual components in different distributed systems. To characterize the extent to which a contingency affecting an infrastructure is going to weaken, and possibly disrupt, the safe operation of an interconnected system, it is necessary to model the relations established through the connections linking the multiple components of the involved infrastructures. In this work, the modeling of interdependencies among network systems and of their effects on failure propagation is carried out within the simulation framework of a failure cascade process. The sensitivity of the critical loading value (the lower bound of the cascading failure region) and of the average cascade size with respect to the coupling parameters defining the interdependency strength is investigated as a means to arrive at the definition and prescription of cascade-safe operating margins.

Component Criticality in Failure Cascade Processes of Network Systems

In this work, specific indicators are used to characterize the criticality of components in a network system with respect to their contribution to failure cascade processes. A realistic-size network is considered as reference case study. Three different models of cascading failures are analyzed, differing both on the failure load distribution logic and on the cascade triggering event. The criticality indicators are compared to classical measures of topological centrality to identify the one most characteristic of the cascade processes considered.

Vulnerability of Smart Grids With Variable Generation and Consumption: A System of Systems Perspective

This paper looks into the vulnerabilities of the electric power grid and associated communication network, in the face of intermittent power generation and uncertain demand within a complex network framework of analysis of smart grids. The perspective is typical for the system of systems analysis of interdependencies in a critical infrastructure (CI), i.e., the smart grid for electricity distribution. We assess how the integration of the two systems copes with requests to increase power generation due to enhanced power consumption at a load bus. We define adequate measures of vulnerability to identify the most limiting communication time delays. We quantify the probability that a reduction in the functionality of the communication system yields a faulty condition in the electric power grid, and find that a factual indicator to quantify the coupling strength between the two networks is the frequency of load-shedding actions due to excessive communication time delay. We evaluate safety margins with respect to communication specifications, i.e., the data rate of the network, to comply with the safety requirements in the electric power grid. Finally, we find a catastrophic phase transition with respect to this parameter, which affects the safe operation of the CI.

A quantitative method for assessing resilience of interdependent infrastructures

The importance of understanding system resilience and identifying ways to enhance it, especially for interdependent infrastructures our daily life depends on, has been recognized not only by academics, but also by the corporate and public sectors. During recent years, several methods and frameworks have been proposed and developed to explore applicable techniques to assess and analyze system resilience in a comprehensive way. However, they are often tailored to specific disruptive hazards/events, or fail to properly include all the phases such as absorption, adaptation, and recovery. In this paper, a quantitative method for the assessment of the system resilience is proposed. The method consists of two components: an integrated metric for system resilience quantification and a hybrid modeling approach for representing the failure behavior of infrastructure systems. The feasibility and applicability of the proposed method are tested using an electric power supply system as the exemplary infrastructure. Simulation results highlight that the method proves effective in designing, engineering and improving the resilience of infrastructures. Finally, system resilience is proposed as a proxy to quantify the coupling strength between interdependent infrastructures.

AN ANALYTICAL APPROACH TO THE SAFETY OF ROAD NETWORKS

In this paper, recently introduced topological measures of interconnection and efficiency of network systems are applied to the safety analysis of the road transport system of the Province of Piacenza in Italy. The vulnerability of the network is evaluated with respect to the loss of a road link, e.g. due to a car accident, road work or other jamming occurrences. Eventually, the improvement in the global and local safety indicators following the implementation of a road development plan is evaluated.

Optimizing power system investments and resilience against attacks

Abstract This paper studies the combination of capacity expansion and switch installation in electric systems that ensures optimum performance under nominal operations and attacks. The planner–attacker–defender model is adopted to develop decisions that minimize investment and operating costs, and functionality loss after attacks. The model bridges long-term system planning for transmission expansion and short-term switching operations in reaction to attacks. The mixed-integer optimization is solved by decomposition via two-layer cutting plane algorithm. Numerical results on an IEEE system shows that small investments in transmission line switching enhance resilience by responding to disruptions via system reconfiguration. Sensitivity analyses show that transmission planning under the assumption of small-scale attacks provides the most robust strategy, i.e. the minimum-regret planning, if many constraints and limited investment budget affect the planning. On the other hand, the assumption of large-scale attacks provides the most robust strategy if the planning process involves large flexibility and budget.

Building an Integrated Metric for Quantifying the Resilience of Interdependent Infrastructure Systems

Resilience is a dynamic multi-faceted term and complements other terms commonly used in risk analysis, e.g., reliability, availability, vulnerability, etc. The importance of fully understanding system resilience and identifying ways to enhance it, especially for infrastructure systems our daily life depends on, has been recognized not only by researchers, but also by public. During last decade, researchers have proposed different methods and frameworks to quantify/assess system resilience. However, they are tailored to specific disruptive hazards/events, or fail to properly include all the phases such as mitigation, adaptation and recovery. In this paper, an integrated metric for resilience quantification with capabilities of incorporating different performance measures is proposed, which can be used to quantify the performance of interdependent infrastructure systems in a more comprehensive way. The feasibility and applicability of the proposed metric will be tested using an electric power supply system as the exemplary system with the help of advanced modelling and simulation techniques. Furthermore, the discussion related to the effects of interdependencies among systems on their resilience capabilities is also included in this paper.

Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction

Accurate and timely diagnosis of transmission line faults is key for reliable operations of power systems. Existing fault-diagnosis methods rely on expert knowledge or extensive feature extraction, which is also highly dependent on expert knowledge. Additionally, most methods for fault diagnosis of transmission lines require multiple separate subalgorithms for fault classification and location performing each function independently and sequentially. In this research, an integrated framework combining fault classification and location is proposed by applying an innovative machine-learning algorithm: the summation-wavelet extreme learning machine (SW-ELM) that integrates feature extraction in the learning process. As a further contribution, an extension of the SW-ELM, i.e., the summation-Gaussian extreme learning machine (SG-ELM), is proposed and successfully applied to transmission line fault diagnosis. SG-ELM is fully self-learning and does not require ad-hoc feature extraction, making it deployable with minimum expert subjectivity. The developed framework is applied to three transmission-line topologies without any prior parameter tuning or ad-hoc feature extraction. Evaluations on a simulated dataset show that the proposed method can diagnose faults within a single cycle, remain immune to fault resistance and inception angle variation, and deliver high accuracy for both tasks of fault diagnosis: fault type classification and fault location estimation.

Impact of spatio-temporally correlated wind generation on the interdependent operations of gas and electric networks

High penetrations of intermittent renewable energy sources (RES) affect the operations of power plants whose task is the balancing of generation and demand, and may induce critical states in interdependent energy infrastructures. In this contribution, the interdependent electric power and gas transmission networks are assessed under an operational risk perspective for different levels of wind energy integration. This investigation is exemplified with reference to a case study of the gas and electric transmission network of Great Britain (GB). A D-vine copula is developed for producing spatio-temporally correlated wind speed time series. In contrast to multivariate models built with autoregressive techniques or one-parameter multidimensional copulas which are restricted to modelling linear dependence or one type of dependence respectively, vine copulas offer high flexibility in modelling dependence. Due to large penetrations of wind power operational constraint violations in the gas network, e.g. pressure violations or compressor shut-downs, may occur when gas-fired power plants (GFPPs) need to ramp up quickly to compensate correlated fluctuations in wind generation. Results identify that large ramp-down rates of wind generation may cause large energy-not-served (ENS) in the electric network. For high levels of wind energy integration, unfavorable combinations of ramp-up and ramp-down are a realistic starting point of failure cascades leading to high levels of demand-not-served in the electric grid and curtailments and component failures in the gas network. Failure prone components in the gas network are identified.

Multilayer hybrid modeling framework for the performance assessment of interdependent critical infrastructures

Abstract The heterogeneity and tight coupling of modern critical infrastructures make it challenging to create tractable descriptions of their emergent behaviors. Classic analytical methods do not provide adequate insights into system behavior and do not fully capture the complexity of infrastructure interdependencies. Meanwhile, modeling approaches developed to represent the diverse physics and operations of critical infrastructures fail to provide a unifying framework for analyzing performance. This paper attempts to address these challenges by proposing a multilayer hybrid modeling framework that supports the detailed understanding and holistic analysis of critical infrastructure systems. A critical infrastructure is viewed as a combination of integrated subsystems structured in interdependent layers: (i) systems under control; (ii) operational control system; and (iii) human-organizational social system. The systems under control and operational control system constitute the technical components of a critical infrastructure. The human-organizational social system is the non-technical component of a critical infrastructure that captures the human and social factors that influence system performance. The modeling framework is demonstrated using the Swiss electric power supply system, which comprises three interdependent layers: the power grid, a supervisory control and data acquisition (SCADA) system and human operators. The framework can help guide the identification of strategies for designing, maintaining and enhancing the performance of critical infrastructures.