1,195 research outputs found
Component Outage Estimation based on Support Vector Machine
Predicting power system component outages in response to an imminent
hurricane plays a major role in preevent planning and post-event recovery of
the power system. An exact prediction of components states, however, is a
challenging task and cannot be easily performed. In this paper, a Support
Vector Machine (SVM) based method is proposed to help estimate the components
states in response to anticipated path and intensity of an imminent hurricane.
Components states are categorized into three classes of damaged, operational,
and uncertain. The damaged components along with the components in uncertain
class are then considered in multiple contingency scenarios of a proposed
Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the
simultaneous outage of multiple components under an N-m-u reliability
criterion. Experimental results on the IEEE 118-bus test system show the merits
and the effectiveness of the proposed SVM classifier and the E-SCUC model in
improving power system resilience in response to extreme events
Resilience assessment and planning in power distribution systems:Past and future considerations
Over the past decade, extreme weather events have significantly increased
worldwide, leading to widespread power outages and blackouts. As these threats
continue to challenge power distribution systems, the importance of mitigating
the impacts of extreme weather events has become paramount. Consequently,
resilience has become crucial for designing and operating power distribution
systems. This work comprehensively explores the current landscape of resilience
evaluation and metrics within the power distribution system domain, reviewing
existing methods and identifying key attributes that define effective
resilience metrics. The challenges encountered during the formulation,
development, and calculation of these metrics are also addressed. Additionally,
this review acknowledges the intricate interdependencies between power
distribution systems and critical infrastructures, including information and
communication technology, transportation, water distribution, and natural gas
networks. It is important to understand these interdependencies and their
impact on power distribution system resilience. Moreover, this work provides an
in-depth analysis of existing research on planning solutions to enhance
distribution system resilience and support power distribution system operators
and planners in developing effective mitigation strategies. These strategies
are crucial for minimizing the adverse impacts of extreme weather events and
fostering overall resilience within power distribution systems.Comment: 27 pages, 7 figures, submitted for review to Renewable and
Sustainable Energy Review
A Parallel Fast-Track Service Restoration Strategy Relying on Sectionalized Interdependent Power-Gas Distribution Systems
In the distribution networks, catastrophic events especially those caused by natural disasters can result in extensive damage that ordinarily needs a wide range of components to be repaired for keeping the lights on. Since the recovery of system is not technically feasible before making compulsory repairs, the predictive scheduling of available repair crews and black start resources not only minimizes the customer downtime but also speeds up the restoration process. To do so, this paper proposes a novel three-stage buildup restoration planning strategy to combine and coordinate repair crew dispatch problem for the interdependent power and natural gas systems with the primary objective of resiliency enhancement. In the proposed model, the system is sectionalized into autonomous subsystems (i.e., microgrid) with multiple energy resources, and then concurrently restored in parallel considering cold load pick-up conditions. Besides, topology refurbishment and intentional microgrid islanding along with energy storages are applied as remedial actions to further improve the resilience of interdependent systems while unpredicted uncertainties are addressed through stochastic/IGDT method. The theoretical and practical implications of the proposed framework push the research frontier of distribution restoration schemes, while its flexibility and generality support application to various extreme weather incidents.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed
The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks
Cyber-Physical Systems (CPS) are increasingly complex and frequently
integrated into modern societies via critical infrastructure systems, products,
and services. Consequently, there is a need for reliable functionality of these
complex systems under various scenarios, from physical failures due to aging,
through to cyber attacks. Indeed, the development of effective strategies to
restore disrupted infrastructure systems continues to be a major challenge.
Hitherto, there have been an increasing number of papers evaluating
cyber-physical infrastructures, yet a comprehensive review focusing on
mathematical modeling and different optimization methods is still lacking.
Thus, this review paper appraises the literature on optimization techniques for
CPS facing disruption, to synthesize key findings on the current methods in
this domain. A total of 108 relevant research papers are reviewed following an
extensive assessment of all major scientific databases. The main mathematical
modeling practices and optimization methods are identified for both
deterministic and stochastic formulations, categorizing them based on the
solution approach (exact, heuristic, meta-heuristic), objective function, and
network size. We also perform keyword clustering and bibliographic coupling
analyses to summarize the current research trends. Future research needs in
terms of the scalability of optimization algorithms are discussed. Overall,
there is a need to shift towards more scalable optimization solution
algorithms, empowered by data-driven methods and machine learning, to provide
reliable decision-support systems for decision-makers and practitioners
Identifying Geographical Interdependency in Critical Infrastructure Systems Using Open Source Geospatial Data in Order to Model Restoration Strategies in the Aftermath of a Large-Scale Disaster
In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium- to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium- to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the ‘geographical interdependency’) of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center’s supply chain in the wake of an extreme event
Resilience-Driven Post-Disruption Restoration of Interdependent Critical Infrastructure Systems Under Uncertainty: Modeling, Risk-Averse Optimization, and Solution Approaches
Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are often overlooked in the CIs resilience literature. Furthermore, CIs are not isolated from each other, but instead, most of them rely on one another for their proper functioning. Hence, the occurrence of a disruption in one CIN could affect other dependent CINs, leading to a more significant adverse impact on communities. Therefore, interdependencies among CINs increase the complexity associated with recovery planning after a disruptive event, making it a more challenging task for decision makers.
Recognizing the inevitability of large-scale disruptions to CIs and their impacts on societies, the research objective of this work is to study the recovery of CINs following a disruptive event. Accordingly, the main contributions of the following two research components are to develop: (i) resilience-based post-disruption stochastic restoration optimization models that respect the spatial nature of CIs, (ii) a general framework for scenario-based stochastic models covering scenario generation, selection, and reduction for resilience applications, (iii) stochastic risk-related cost-based restoration modeling approaches to minimize restoration costs of a system of interdependent critical infrastructure networks (ICINs), (iv) flexible restoration strategies of ICINs under uncertainty, and (v) effective solution approaches to the proposed optimization models.
The first research component considers developing two-stage risk-related stochastic programming models to schedule repair activities for a disrupted CIN to maximize the system resilience. The stochastic models are developed using a scenario-based optimization technique accounting for the uncertainties of the repair time and travel time spent on the underlying transportation network. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk metric is incorporated into the optimization models through the scenario reduction algorithm. The proposed restoration framework is illustrated using the French RTE electric power network.
The second research component studies the restoration problem for a system of ICINs following a disruptive event under uncertainty. A two-stage mean-risk stochastic restoration model is proposed to minimize the total cost associated with ICINs unsatisfied demands, repair tasks, and flow. The model assigns and schedules repair tasks to network-specific work crews with consideration of limited time and resources availability. Additionally, the model features flexible restoration strategies including a multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquakes.
Finally, some other topics are discussed for possible future work
Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations
This comprehensive review paper explores power system resilience, emphasizing
its evolution, comparison with reliability, and conducting a thorough analysis
of the definition and characteristics of resilience. The paper presents the
resilience frameworks and the application of quantitative power system
resilience metrics to assess and quantify resilience. Additionally, it
investigates the relevance of complex network theory in the context of power
system resilience. An integral part of this review involves examining the
incorporation of data-driven techniques in enhancing power system resilience.
This includes the role of data-driven methods in enhancing power system
resilience and predictive analytics. Further, the paper explores the recent
techniques employed for resilience enhancement, which includes planning and
operational techniques. Also, a detailed explanation of microgrid (MG)
deployment, renewable energy integration, and peer-to-peer (P2P) energy trading
in fortifying power systems against disruptions is provided. An analysis of
existing research gaps and challenges is discussed for future directions toward
improvements in power system resilience. Thus, a comprehensive understanding of
power system resilience is provided, which helps in improving the ability of
distribution systems to withstand and recover from extreme events and
disruptions
A two-stage stochastic programming model for electric substation flood mitigation prior to an imminent hurricane
We present a stochastic programming model for informing the deployment of
temporary flood mitigation measures to protect electrical substations prior to
an imminent and uncertain hurricane. The first stage captures the deployment of
a fixed number of mitigation resources, and the second stage captures grid
operation in response to a contingency. The primary objective is to minimize
expected load shed. We develop methods for simulating flooding induced by
extreme rainfall and construct two geographically realistic case studies, one
based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our
model to those case studies, we investigate the effect of the mitigation budget
on the optimal objective value and solutions. Our results highlight the
sensitivity of the optimal mitigation to the budget, a consequence of those
decisions being discrete. We additionally assess the value of having better
mitigation options and the spatial features of the optimal mitigation.Comment: 35 pages, 12 figure
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