Cascading Failures in Interdependent Infrastructures: An Interdependent Markov-Chain Approach

Many critical infrastructures are interdependent networks in which the behavior of one network impacts those of the others. Despite the fact that interdependencies are essential for the operation of critical infrastructures, such interdependencies can negatively affect the reliability and fuel the cascade of failures within and across the networks. In this paper, a novel interdependent Markov-chain framework is proposed that enables capturing interdependencies between two critical infrastructures with the ultimate goal of predicting their resilience to cascading failures and characterizing the effects of interdependencies on system reliability. The framework is sufficiently general to model cascading failures in any interdependent networks; however, this paper focuses on the electric-cyber infrastructure as an example. Using this framework it is shown that interdependencies among reliable systems, i.e., systems with exponentially distributed failure sizes, can make the individually reliable systems behave unreliably as a whole with power-law failure-size distributions

Stochastic Analysis of Cascading-Failure Dynamics in Power Grids

A scalable and analytically tractable probabilistic model for the cascading failure dynamics in power grids is constructed while retaining key physical attributes and operating characteristics of the power grid. The approach is based upon extracting a reduced abstraction of large-scale power grids using a small number of aggregate state variables while modeling the system dynamics using a continuous-time Markov chain. The aggregate state variables represent critical power-grid attributes, which have been shown, from prior simulation-based and historical-data-based analysis, to strongly influence the cascading behavior. The transition rates among states are formulated in terms of certain parameters that capture grid\u27s operating characteristics comprising loading level, error in transmission-capacity estimation, and constraints in performing load shedding. The model allows the prediction of the evolution of blackout probability in time. Moreover, the asymptotic analysis of the blackout probability enables the calculation of the probability mass function of the blackout size. A key benefit of the model is that it enables the characterization of the severity of cascading failures in terms of the operating characteristics of the power grid.

Optimal Stochastic Day-Ahead Power Management of Hybrid AC-DC Microgrids

Due to the reappearance of DC loads in electrical systems and advanced improvement in energy storage systems (batteries) and environment-friendly properties of photovoltaics as a green energy supply, DC architecture is considered as a new solution for next-generation power distribution systems. Hybrid AC-DC microgrids (MG) can take advantage of DC and AC flows in a smart distribution system. The best strategy for the optimal operation of hybrid MGs is to minimize the converting energy between AC and DC sides such that DC loads are provided by photovoltaics, fuel cells, and the stored energy in batteries and AC loads are satisfied by AC-based sources including wind turbines (WTs) and diesel generators (DEs). Accordingly, this paper aims to scrutinize an optimal green power management strategy for hybrid AC-DC MGs from an economic viewpoint while considering photovoltaics as a prior source for the DC side and wind turbines for the AC side. Moreover, the uncertainties of renewable energy sources (RESs), DC and AC loads, and the correlation among them are investigated using the unscented transformation method

Efficient Interconnectivity Among Networks Under Security Constraint

Interconnectivity among networks is essential for enhancing communication capabilities of networks such as the expansion of geographical range, higher data rate, etc. However, interconnections may initiate vulnerability (e.g., cyber attacks) to a secure network due to introducing gateways and opportunities for security attacks such as malware, which may propagate from the less secure network. In this paper, the interconnectivity among subnetworks is maximized under the constraint of security risk. The dynamics of propagation of security risk is modeled by the evil-rain influence model and the SIR (Susceptible-Infected-Recovered) epidemic model. Through extensive numerical simulations using different network topologies and interconnection patterns, it is shown that the efficiency of interconnectivity increases nonlinearly and vulnerability increases linearly with the number of interconnections among subnetworks. Finally, parametric models are proposed to find the number of interconnections for any given efficiency of interconnectivity and vulnerability of the secure network

Effect of Battery Degradation on the Probabilistic Optimal Operation of Renewable-Based Microgrids

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.</p

Synthesis of telechelic poly(p-benzamide)s

Well-defined telechelic poly(benzamide)s were synthesized by chain-growth polycondensation of phenyl-4-amino benzoate and pentafluorophenyl-4-amino benzoate derivatives with a bifunctional initiator in the presence of LiTMP as base. The polymerization was carried out at −70 °C to prevent self-initiated polymerization. To confirm the control over molecular weight, different defined molecular weight polymers were synthesized and analyzed by GPC. Taking advantage of the labile ester end groups of these poly(benzamide)s, we carried out postpolymerization modifications to introduce different end functional groups such as alkyne, amine, alcohol, alkyl halide, and olefin suitable for different types of postpolymerization reactions. Successful end group modification was confirmed by 1H NMR spectroscopy and isotopically resolved MALDI-ToF mass spectrometry

Survivable Cloud Network Mapping for Disaster Recovery Support

Network virtualization is a key provision for improving the scalability and reliability of cloud computing services. In recent years, various mapping schemes have been developed to reserve VN resources over substrate networks. However, many cloud providers are very concerned about improving service reliability under catastrophic disaster conditions yielding multiple system failures. To address this challenge, this work presents a novel failure region-disjoint VN mapping scheme to improve VN mapping survivability. The problem is first formulated as a mixed integer linear programming problem and then two heuristic solutions are proposed to compute a pair of failure region-disjoint VN mappings. The solution also takes into account mapping costs and load balancing concerns to help improve resource efficiencies. The schemes are then analyzed in detail for a variety of networks and their overall performances compared to some existing survivable VN mapping scheme

Telechelics based on catalytic alternating ring-opening metathesis polymerization

We describe a protocol to synthesize alternating telechelic ROMP copolymers of 7- oxa-norbornene derivatives and cycloalkenes under catalytic conditions. These copolymers were synthesized using Grubbs’ second-generation catalyst. The sterically less hindered backbone double bonds of the resulting alternating copolymers facilitate the chain transfer (secondary metathesis) reactions. In the presence of symmetrical chain transfer agents (CTA), alternating copolymers could be synthesized catalytically. This procedure allows the synthesis of telechelic polymers based on potentially functional 7-oxa-norbornene derivatives under thermodynamic equilibrium conditions. The molar mass of the alternating copolymer was controlled by the monomer to CTA ratio. The end group of the copolymers synthesized in the catalytic manner was addressed by the CTA functionality, yielding telechelic copolymers in excellent yields. 1H NMR spectroscopy, MALDI-ToF mass spectrometry, and SEC analysis confirmed the chemical identity of the alternating telechelic copolymers with excellent control over the molar mass