Abstract Cascading failures on techno-socio-economic systems can have dramatic and catastrophic implications in society. A damage caused by a cascading failure, such as age power blackout, is complex to predict, understand, prevent and mitigate as such complex phenomena are usually a result of an interplay between structural and functional nonlinear dynamics. Therefore, systematic and generic measurements of network reliability and repairability against cascading failures is of a paramount importance to build a more sustainable and resilient society. This paper contributes a probabilistic framework for measuring network reliability and repairability against cascading failures. In contrast to related work, the framework is designed on the basis that network reliability is multifaceted and therefore a single metric cannot adequately characterize it. The concept of ‘repairability envelope’ is introduced that illustrates trajectories of performance improvement and trade-offs for countermeasures against cascading failures. The framework is illustrated via four model-independent and application-independent metrics that characterize the topological damage, the network spread of the cascading failure, the evolution of its propagation, the correlation of different cascading failure outbreaks and other aspects by using probability density functions and cumulative distribution functions. The applicability of the framework is experimentally evaluated in a theoretical model of damage spread and an empirical one of power cascading failures. It is shown that the reliability and repairability in two systems of a totally different nature undergoing cascading failures can be better understood by the same generic measurements of the proposed framework.
The introduction of ICT in techno-socio-economic systems, such as Smart Grids, traffic management, food supply chains and others, transforms the role of simulation as a scientific method for studying these complex systems. The scientific focus and challenge in simulations move from understanding system complexity to actually prototyping online and distributed regulatory mechanisms for supporting system operations. Existing simulation tools are not designed to address the challenges of this new reality, however, simulation is all about capturing reality at an adequate level of detail. This paper fills this gap by introducing a Java-based distributed simulation framework for inter-connected and inter-dependent techno-socio-economic system: SFINA, the Simulation Framework for Intelligent Network Adaptations. Three layers outline the design approach of SFINA: (i) integration of domain knowledge and dynamics that govern various techno-socio-economic systems, (ii) system modeling with dynamic flow networks represented by temporal directed weighted graphs and (iii) simulation of generic regulation models, policies and mechanisms applicable in several domains. SFINA aims at minimizing the fragmentation and discrepancies between different simulation communities by allowing the interoperability of SFINA with several other existing domain backends. The coupling of three such backends with SFINA is illustrated in the domain of Smart Grids and disaster mitigation. It is shown that the same model of cascading failures in Smart Grids is developed once and evaluated with both MATPOWER and InterPSS backends without changing a single line of application code. Similarly, application code developed in SFINA is reused for the evaluation of mitigation strategies in a backend that simulates the flows of a disaster spread. Results provide a proof-of-concept for the high modularity and reconfigurability of SFINA and puts the foundations of a new generation of simulation tools that prototype and validate online decentralized regulation in techno-socioeconomic systems.
The introduction of active devices in Smart Grids, such as smart transformers, powered by intelligent software and networking capabilities, brings paramount opportunities for online automated control and regulation. However, online mitigation of disruptive events such as cascading failures, is challenging. Local intelligence by itself cannot tackle such complex collective phenomena with domino effects. Collective intelligence coordinating rapid mitigation actions is required. This paper introduces analytical results from which two optimization strategies for self-repairable Smart Grids are derived. These strategies build a coordination mechanism for smart transformers that runs in three healing modes and performs collective decision-making of the phase angles in the lines of a transmission system to improve reliability under disruptive events, i.e. line failures causing cascading failures. Experimental evaluation using self-repairability envelopes in different case networks, AC power flows and varying number of smart transformers confirms that the higher the number of smart transformers participating in the coordination, the higher the reliability and the capability of a network to self-repair.
Evangelos Pournaras, Mark Ballandies, Dinesh Acharya, Manish Thapa and Ben-Elias Brandt, Prototyping Self-managed Interdependent Networks – Self-healing Synergies against Cascading Failures, in the Proceedings of the 13th International Symposium on Software Engineering for Adaptive and Self-managing Systems-SEAMS-2018, Gothenburg, Sweden, May 2018
The interconnection of networks between several techno-socio-economic sectors such as energy, transport, and communication, questions the manageability and resilience of the digital society. System interdependencies alter the fundamental dynamics that govern isolated systems, which can unexpectedly trigger catastrophic instabilities such as cascading failures. This paper envisions a general- purpose, yet simple prototyping of self-management software systems that can turn system interdependencies from a cause of in- stability to an opportunity for higher resilience. Such prototyping proves to be challenging given the highly interdisciplinary scope of interdependent networks. Different system dynamics and organizational constraints such as the distributed nature of interdependent networks or the autonomy and authority of system operators over their controlled infrastructure perplex the design for a general prototyping approach, which earlier work has not yet addressed. This paper contributes such a modular design solution implemented as an open source software extension of SFINA, the Simulation Framework for Intelligent Network Adaptations. The applicability of the software artifact is demonstrated with the introduction of a novel self-healing mechanism for interdependent power networks, which optimizes power ow exchanges between a damaged and a healer network to mitigate power cascading failures. Results show a significant decrease in the damage spread by self-healing synergies, while the degree of interconnectivity between the power networks indicates a tradeo between links survivability and load served. The contributions of this paper aspire to bring closer several research communities working on modeling and simulation of different domains with an economic and societal impact on the resilience of real-world interdependent networks.