In this paper, we propose two metrics, i.e., the optimal repair time and the resilience reduction worth, to measure the criticality of the components of a network system from the perspective of their ...contribution to system resilience. Specifically, the two metrics quantify: 1) the priority with which a failed component should be repaired and re-installed into the network and 2) the potential loss in the optimal system resilience due to a time delay in the recovery of a failed component, respectively. Given the stochastic nature of disruptive events on infrastructure networks, a Monte Carlo-based method is proposed to generate probability distributions of the two metrics for all of the components of the network; then, a stochastic ranking approach based on the Copeland's pairwise aggregation is used to rank components importance. Numerical results are obtained for the IEEE 30-bus test network and a comparison is made with three classical centrality measures.
•We derive optimal pre-hazard plans for interdependent systems resilience.•Failure uncertainty is accounted for by a two-stage adaptive robust optimization.•Uncertainty set is modeled based on ...components’ failure and repair probabilities.•The resulting trilevel model is solved by a nested decomposition algorithm.•Managerial insights are drawn from a specific case study.
This paper proposes a novel adaptive robust optimization (ARO)-based mathematical framework for resilience enhancement of interdependent critical infrastructure (CI) systems against natural hazards (NHs). In this framework, the potential impacts of a specific NH on an infrastructure are firstly evaluated, in terms of failure and recovery probabilities of system components; these are, then, fed into a two-stage ARO model to determine the optimal planning of resilience strategies under limited investment budget, anticipating the most-likely worst realization of the uncertainty of component failures under the NH. For its exact solution, a decomposition method based on simultaneous column-and-constraint generation is adopted. The approach is applied to a case study concerning the resilience of interdependent power and gas networks subject to (simulated) wind storms. The numerical results demonstrate the effectiveness of the proposed framework for the optimization of the resilience of interdependent CIs under hazardous events; this provides a valuable tool for making informed pre-hazard preparation decisions. The value of a coordinated pre-hazard planning that takes into account CI interdependencies is also highlighted.
•Propose a stochastic programming for infrastructure restoration under uncertainty.•A multi-mode component repair model of higher practicality is considered.•Propose a tailored Benders decomposition ...to effectively solve the model.•Show the added value of the stochastic model against its deterministic counterpart.
The planning of post-disruption restoration in critical infrastructure systems often relies on deterministic assumptions such as complete information on resources and known duration of the repair tasks. In fact, the uncertainties faced by restoration activities, e.g. stemming from subjective estimates of resources and costs, are rarely considered. Thus, the solutions obtained by a deterministic approach may be suboptimal or even infeasible under specific realizations of the uncertainties. To bridge this gap, this paper investigates the effects of uncertain repair time and resources on the post-disruption restoration of critical infrastructure. Two-stage stochastic optimization provides insights for prioritizing the intensity and time allocation of the repair activities with the objective of maximizing system resilience. The inherent stochasticity is represented via a set of scenarios capturing specific realizations of repair activity durations and available resources, and their probabilities. A multi-mode restoration model is proposed that offers more flexibility than its single-mode counterpart. The restoration framework is applied to the reduced British electric power system and the results demonstrate the added value of using the stochastic model as opposed to the deterministic model. Particularly, the benefits of the proposed stochastic method increase as the uncertainty associated with the restoration process grows. Finally, decision-making under uncertainty largely impacts the optimum repair modes and schedule.
The increasing penetration of grid-enabled electric vehicles (EVs) renders road networks (RNs) and power networks (PNs) increasingly interdependent for normal operation. For this reason, recently few ...studies have started to investigate the vulnerability of a highly coupled traffic-power system in the presence of disruptive events. Actually, however, only very few of these studies have considered the impact of EVs on the interdependent traffic-power system during restoration from a disruptive event. In an attempt to fill this gap, in this study, we investigate the restoration planning of both independent RNs and PNs, and interdependent traffic-power systems. A mixed integer program model is formulated to provide optimal reconfiguration and operational solutions for post-disruption traffic-power systems recovery. The objective of the model is to minimize the total cost incurred by system performance loss, which is quantified by the cumulative unmet traffic demand for RNs and load shedding cost for PNs. Several reconfiguration strategies are considered, including links reversing in RNs and line switching in PNs, to optimize system resilience. In the proposed model, the integrated problem of system optimal dynamic traffic assignment and optimal power flow is solved to derive the optimal traffic-power flow. RNs and PNs are coupled through the coordinately allocated spatio-temporal charging demand of EVs. A partial highway network in North Carolina (NC), USA, and a modified IEEE-14 bus system are used to illustrate the application of the model. The numerical results obtained show the added value of coordinately planning restoration for traffic-power systems and the effects of different levels of EV penetration.
•Emergency response of traffic-power systems coupled via grid-enabled electric vehicles.•Explicitly model the interdependency between the two coupled systems.•Originally formulate reconfiguration strategies for the coupled networks.•Coordinately planning the emergency response restores performance more effectively.
With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for ...the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.
This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical ...infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.
Tumor-associated macrophages (TAMs) constitute a large population of glioblastoma and facilitate tumor growth and invasion of tumor cells, but the underlying mechanism remains undefined. In this ...study, we demonstrate that chemokine (C-C motif) ligand 8 (CCL8) is highly expressed by TAMs and contributes to pseudopodia formation by GBM cells. The presence of CCL8 in the glioma microenvironment promotes progression of tumor cells. Moreover, CCL8 induces invasion and stem-like traits of GBM cells, and CCR1 and CCR5 are the main receptors that mediate CCL8-induced biological behavior. Finally, CCL8 dramatically activates ERK1/2 phosphorylation in GBM cells, and blocking TAM-secreted CCL8 by neutralized antibody significantly decreases invasion of glioma cells. Taken together, our data reveal that CCL8 is a TAM-associated factor to mediate invasion and stemness of GBM, and targeting CCL8 may provide an insight strategy for GBM treatment.
In plants, submergence from flooding causes hypoxia, which impairs energy production and affects plant growth, productivity, and survival. In Arabidopsis, hypoxia induces nuclear localization of the ...group VII ethylene‐responsive transcription factor RELATED TO AP2.12 (RAP2.12), following its dissociation from the plasma membrane‐anchored ACYL‐COA BINDING PROTEIN1 (ACBP1) and ACBP2. Here, we show that polyunsaturated linolenoyl‐CoA (18:3‐CoA) regulates RAP2.12 release from the plasma membrane. Submergence caused a significant increase in 18:3‐CoA, but a significant decrease in 18:0‐, 18:1‐, and 18:2‐CoA. Application of 18:3‐CoA promoted nuclear accumulation of the green fluorescent protein (GFP) fusions RAP2.12‐GFP, HYPOXIA‐RESPONSIVE ERF1‐GFP, and RAP2.3‐GFP, and enhanced transcript levels of hypoxia‐responsive genes. Plants with decreased ACBP1 and ACBP2 (acbp1 ACBP2‐RNAi, produced by ACBP2 RNA interference in the acbp1 mutant) had reduced tolerance to hypoxia and impaired 18:3‐CoA‐induced expression of hypoxia‐related genes. In knockout mutants and overexpression lines of LONG‐CHAIN ACYL‐COA SYNTHASE2 (LACS2) and FATTY ACID DESATURASE 3 (FAD3), the acyl‐CoA pool size and 18:3‐CoA levels were closely related to ERF‐VII‐mediated signaling and hypoxia tolerance. These findings demonstrate that polyunsaturation of long‐chain acyl‐CoAs functions as important mechanism in the regulation of plant hypoxia signaling, by modulating ACBP–ERF‐VII dynamics.
BES1 is a key transcription factor that antagonizes JA‐activated plant defense responses by directly suppressing the expression of defensing and GS biosynthesis genes in Arabidopsis.
•Propose a hierarchical model for identifying resilience enhancement strategies.•Adopt a control-based framework to model the transient system behaviors under disruptions.•Propose a multi-objective ...optimization model to find the optimal RES combinations.•Provide a unified framework for analyzing the tradeoffs of different RES options.
Resilience is becoming a key concept for risk assessment and safety management of interdependent critical infrastructures (ICIs). This work proposes a resilience enhancement framework for ICIs. With reference to the accidental event, ex-ante and ex-post solutions for enhancing system resilience are analysed and included into a hierarchical model of resilience enhancement strategies (RES). To provide specific resilience enhancement solutions for ICIs, we integrate the hierarchical model with a model predictive control-based dynamic model of ICI system operation. The relationships between the solutions implemented and their impacts on the system parameters are discussed. A multi-objective optimization (MOO) problem is defined, with the objectives of simultaneously minimizing RES cost and maximizing ICIs resilience. The fast non-dominated sorting genetic algorithm NSGA-II is used to solve the MOO problem. For exemplification, a case study is considered, involving interdependent natural gas network and electric power grid. The results show that the resilience enhancement framework is effective in finding optimal RESs for given ICIs.
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