I need a Review for the above topic.
Basically State of art in energy infrastructure resilience and remaining sections which present in the attached document.
I have all the docs from which these can be picked and also an outline document and subtopics which need to be touched upon.
Attaching the docs from which state of the art to be picked, also another word document mentioning the outline(Literature Review-outline)
Literature Review-outline to kept as base file and to be worked on.
Thanks and Regards
|S.No.||Paper Name||Summary||Key topics||Relevence (out of 10)||Look again, if yes, when?|
|1||Resilience-based network component importance measures||Important metric to evaluate recovery, time component, Two measures defined for a network based resilience – adverse impact and positive impact, algorithm to perform stochastic ordering of network, time component included||Network resilience
Component importance measure
|2||Reliability evaluation of linear multi-state consecutively-connected systems constrained by m consecutive and n total gaps||Extends the linear multi-state consecutively-connected system, can we used for ?uid transportation systems, wireless communication systems, sensor systems and logistics systems.||Linear multi-state consecutive-connected
Multi-state connection elements
Universal generating function
Two different types of failures
|5||Only if network modeling is required, and the evaluation is based on distance between the nodes.|
|3||An approach to design interface topologies across interdependent urban infrastructure systems||Approach to design or retro?t interface topologies to minimize cascading failures across urban infrastructure systems. Four types of interface design strategies are formulated based on maximum network component degree, maximum component betweenness, minimum
Euclidean distance across components and component reliability rankings. To compute and compare strategy effectiveness under multiple hazard types, this paper introduces a global annual cascading failure effect (GACFE) metric as well as a GACFE-based cost improvement (GACI) metric.
Interdependent lifeline systems
Cascading failure effect
|7||When working on the cascading effect and modeling piece|
|4||Optimal probabilistic planning of passive harmonic filters in distribution networks with high penetration of photovoltaic generation||Approach based on Genetic Algorithm (GA) and Monte-Carlo Simulation (MCS) for the optimal planning of single-tuned passbe harmonic filter (PHFs) in a distribution network. The objective function minimizes the installation cost and energy losses.||
High photovoltaic penetration
Passive harmonic filter
|4||Can be looked only for methodology for minimizing the objective function|
|5||Stochastic measures of resilience and their application to container terminals||Modeling paradigm for quantifying system resilience, primarily as a function of vulnerability and recoverability. To account for uncertainty, stochastic measures of resilience are introduced, including Time to Total System
Restoration, Time to Full System Service Resilience, and Time to alpha%-resilience.
|6||Resilience improvement planning of power-water distribution systems with multiple microgrids against hurricanes using clean strategies||Stochastic programming aims to minimize the investment cost of resilience improvement strategies and the expected inaccessibility values of loads to power and water under hurricanes.||Microgrids
Resilience improvement planning
Stochastic linear programming
|3||Look for uncertanity parameters|
|7||Framework for analytical quantification of disaster resilience||The recovery process usually depends on available technical and human resources, societal preparedness, public policies and may take different forms, which can be estimated using simplified recovery functions or using more complex organizational and socio-political models.||Disaster resilience
|7||Dimension of resilience, loss function for disaster based scenarios|
|8||Multi-dimensional hurricane resilience assessment of electric power systems||The paper introduces a probabilistic modeling
approach for quantifying the hurricane resilience of contemporary electric power systems. This approach
includes a hurricane hazard model, component fragility models, a power system performance model, and
a system restoration model. These coupled four models enable quantifying hurricane resilience and
estimating economic losses.
|Electric power systems
Probabilistic Resilience Model
|9||Economic resilience to transportation failure: a computable general equilibrium analysis||This study develops and applies a multimodal computable general equilibrium
(CGE) framework to investigate the role of resilience in the economic consequences of
transportation system failures. Vulnerability and economic resilience of different modes of
transportation infrastructure, including air, road, rail, water and local transit, are assessed
using a CGE model that incorporates various resilience tactics including modal substitution, trip conservation, excess capacity, relocation/rerouting, and service recapture. The
linkages between accessibility, vulnerability, and resilience are analyzed.
Computable general equilibrium (CGE) modeling Hurricane Katrina
|10||Optimizing power system investments and resilience against attacks||The plannerattackerdefender model is adopted to develop decisions that minimize investment and operating costs, and functionality loss after attacks. The mixed-integer optimization is solved by decomposition via two-layer cutting plane algorithm.||Electric power network protection
|6||optimal investment planning|
|11||Analyzing driving forces behind changes in energy vulnerability of Spanish electricity generation through a Divisia index-based method||Following logarithmic mean Divisia indexes approach, a new method that enables a complete decomposition of changes in electricity vulnerability into contributions from several drivers||Electricity generation
Energy fossil-fuel dependence
|7||electricity decomposition methods, and time series normalization|
|12||Impacts and implications of climatic extremes for resilience planning of transportation energy: A case study of New York city||Featuring a multi-stage mathematical program to simulate the dependency of travel behavior on fuel availability when the infrastructure of transportation energy is stressed or under attack.||Climate change
Fuel supply chain
|5||Effect of climate|
|13||A holistic framework for building critical infrastructure resilience||This research describes a practical and holistic resilience framework for improving the resilience of CIs taking into account the external agents. The framework is composed of three elements: a set of resilience policies; an influence table that assesses the influence of policies on prevention, absorption and recovery stages; and an implementation methodology that defines the temporal order in which the
policies should be implemented.
|8||Resilience policy relations|
|14||Availability-based engineering resilience metric and its corresponding evaluation methodology||This study proposes a new availability-based engineering resilience metric from the perspective of reliability engineering. Dynamic-Bayesian-network-based evaluation methodology is developed on the basis of the proposed resilience metric.||Resilience
|15||Embodied energy use in Chinas industrial sectors||– A hybrid IO-LCA model was employed to analyze Chinas energy use at sectoral level.
– A case study on Chinas sectoral energy consumption is done.
– Construction and service sectors are actually energy intensive from the supply chain perspectives.
– Upstream and downstream ectoral collaboration along the whole supply chain is necessary.
– Energy conservation policies should be based upon a comprehensive analysis on sectoral energy use.
Production and consumption
|9||Almost similar first half of the research done.|
|16||Resilience of Energy Infrastructure and Services: Modeling, Data Analytics, and Metrics||The focus is on identifying fundamental
challenges and advanced approaches for quantifying resilience. In particular, the first aspect of this problem is how to model large-scale failures, recoveries, and impacts, involving the infrastructure, service providers, customers, and weather. The second aspect is how to identify generic vulnerability in the infrastructure and services through large-scale data analytics. The third aspect is to understand what resilience metrics are needed and how to develop them.
power distribution infrastructure
services to customers
|8||Metric and for lit review|
|17||Resilience analytics with disruption of preferences and lifecycle cost analysis for energy microgrids||Infrastructure resilience
|18||Metrics for energy resilience|
|19||Sustainability of integrated energy systems: A performance-based resilience assessment methodology|
|20||Resilience of Critical Infrastructures: Review and Analysis of Current Approaches|
Systematic literature review on energy infrastructure resilience from a data analytics perspective
Harsh Anand1 and Mohamad Darayi2
1Data Science and Analytics, M.S. Program, 2Assistant Professor of Systems Engineering, School of Graduate Professional Studies, The Pennsylvania State University
Critical infrastructures include any system involved in the production, transportation, communication, and operation of power to consumers for end-use. Energy infrastructures such as electric power, natural gas, and fuel networks are critical systems experiencing revolutionary shifts in all aspects. These systems are highly interconnected within themselves and other critical infrastructure to run the overall economy. With growing dependency, the energy infrastructure networks have become more complex, and their vulnerability under disruption is a prime concern for the smooth functioning of the economy. Each year the U.S. government has been spending millions of dollars to subside the risk of energy infrastructures under natural and human-made hazards such as hurricanes and terrorist attacks, which threats the overall operation of the infrastructure and the economy. It is of utmost importance for the central decision-makers to quantify and extensively evaluate the infrastructures.
To understand the challenges faced by the research community and the open problems in energy infrastructure resilience, we have studied the works of literature in two broad domains systems modeling and analytics. Figure XXX gives an overview of systems modeling and analytical perspectives. The study is conducted for the readers to understand (1) analytical approaches to solve the critical challenges in energy infrastructure resilience and (2) different modeling techniques used to reach the objectives.
As the energy landscape shifts from a traditional grid composed of wires, generators, and growing demand into the grid of the future, all of those aspects of the traditional grid are being redesigned and incorporating new technologies and policies to shift with the world around it. The research is following these shifts, most notably in two distinct ways: sustainability and resilience.
Sustainability, generally, encompasses a longevity of the industry and its ability to adapt to changes. In the energy, it can be looked at through four lenses: environmental, technological, economic, and social. Each of these aspects have become more notable in literature since the implementation of the modern grid decades ago is now aging and society has different focuses for its future.
Renewable resources emerged quickly over the past couple of decades. Climate change has not only put pressure on the planet, but it has also caused consumers to be more proactive in their energy consumption.
Literature aiming to evaluate the environmental sustainability of a SoS includes studies to minimize household carbon emissions, such as the objective of Agusdinata and Dittmar (2009), who consider the impacts of both the supply side and demand side in their household emissions SoS. However, other researchers evaluated environmental sustainability much more broadly. Hadian & Madani (2014) created a framework to evaluate the environmental impact of, carbon emissions, land use, and water use of electric generation fuel types as they show in Figure 2. They also considered the cost of the electric generation to better evaluate the tradeoffs of each resource type. This research was then replicated by Ristic et al (including Madani) in 2019 to evaluate fuel alternatives in the European Union as they seek to decarbonize.
Third, economic sustainability requires the energy industry to be efficient in the cost to consumers, producers, and government bodies. This lens can often be a large hurdle in the process of retrofitting or retiring existing resources, implementing new resources, and continuing to provide power at the least cost, both now and in the future.
Mostly all literature which had a strong focus on economic sustainability also had a strong objective for one other classification of sustainability. Most notably, technological sustainability and economic sustainability were paired together including the works of Allman and Daoutidis (2016), Ristic et al (2019), and Arasteh et al. (2019). However, some research focused more on the sole economic aspects such as Mittal et al (2015), whose framework focused on analyzing market retail structures and Moloney, Fitzgibbon, and McKeogh (2017) whos framework focused on sustaining infrastructure. Their work also classifies in the resilience objective.
Finally, social sustainability is the least intuitive out of the four aspects of sustainability. It focuses on the social benefit of the energy system. In other words, its primary goal is the make the collective society and the people within it better off. Though that is difficult to quantify, the measures of it often contain the other three aspects of sustainability depending on how someone defines the social benefit, including least cost options, increased availability of resources, or policy-making, amongst others.
The social aspect of sustainability was the least common trend in discrete objectives. However, each of the other three aspects can inherently provide social benefit as well. Xiao, Hipel, and Fang (2019)s objectives focused on social sustainability the most in their analysis of the water-food-energy nexus as society needs access those vital resources to survive. Others, including Hadian & Madani (2014) and Ristic et al (2019), acknowledged the societal benefit their research could provide in less direct ways.
Analysis in systems thinking, like many other fields, refers to how the relevant data is interpreted, utilized, evaluated, and predicted in the context of the problem and at any point of the problem. For instance, analytics can be used to determine the inputs to the system-of-systems framework, it can be used to evaluate the outputs of the framework, or it can be used to predict future success, amongst many other applications. Analytics, broadly, are categorized as descriptive, prescriptive, and predictive.
Descriptive, as the name implies, describes the data as it exists or previously existed; its types include mean, variance, correlation, and others. Descriptive analytics can provide valuable insights into the past. This can help people make educated decisions and potentially utilize other analytical techniques. Descriptive analytics is utilized in in most data-based studies to quality check data, verify reasonableness of models, or evaluate results; the SoS literature is no exception.
Almost all literature reviewed for this research contained some aspect of descriptive analytics. This is especially present when the researcher uses predictive techniques as well. The predicting methods must use historical data to learn and the researchers must first describe the data they intend to use to ensure the features are valid and relevant to the predictions. For example, Agusdinata and Dittmar (2009) determined plausible ranges, using descriptive statistics, to simulate observations for their classification and regression tree prediction module.
Predictive analytics uses existing data, mathematical formulations and algorithms to predict the future behavior of data. Predictive analytics has more complexity than descriptive and lends itself to much more in-depth fields of study, such as machine learning. Like many other industries, predictive analytics is becoming more accessible with the large amounts of data as well as readily available tools to analyze the data using robust methods and advanced techniques.
Although the study of predictive analytics has emerged rapidly, its presence is not common in the study of SoS frameworks due to the complexities of both predictive techniques and the SoS itself. However, some researchers utilized the techniques in their studies, though often it was not the main focus of the research. Most notably, forecasting techniques were used to predict external behaviors (Agusdinata and Dittmar, 2009; Mittal et al., 2015; Sianaki and Masoum, 2014); Sianaki and Masoum (2014) have included an entire predictor system which predicts five different elements of the analysis, including demand quantities and timing, energy prices, and the availability of renewable resources.
Prescriptive analytics can utilize the information provided from descriptive and predictive analytics to inform decisions or provide recommendations. Methods of prescriptive analytics include optimizations such as linear programming or stochastic optimization.
Optimizations are very often used to approximate optimal power flows, especially in regards to the energy markets. Therefore, many of the reviewed literature contains some element of prescriptive analytics. Specifically, Zhao et al. (2018) developed a bi-level optimization model which minimizes cost of the SoS subject to various uncertainties. Their study focused on microgrids and their interaction with each other and the larger distribution grid. Similarly, Thacker, Pant and Hall (2017) used a capacity constrained location-allocation optimization algorithm in their framework.
Analysis and Discussion
Contribution of this study
Energy prices and aggregate economic activity: an
Stephen P.A. Brown*, Mine K. Yu¨ cel
Federal Reserve Bank of Dallas, Dallas, TX 75265-5906, USA
Received 21 August 2001; accepted 15 January 2002
In this article, we survey the theory and evidence linking fluctuations in energy prices to those in
aggregate economic activity. We then examine the implications of this research for both monetary
policy and energy policy in response to oil price shocks. The currently available research seems to
provide relatively reliable guidance for monetary policy. Because the precise channels through which
oil price shocks affect economic activity are only partially known, however, research offers less
guidance about how countries should design energy policy should cope with oil price shocks. © 2002
Board of Trustees of the University of Illinois. All rights reserved.
JEL codes: E320 Business Fluctuations; Cycles; Q430 Energy and the Macroeconomy
A considerable body of economic research suggests that oil price fluctuations have figured
prominently in national economic activity since World War II. In fact, rising oil prices
preceded eight of the nine post-WWII recessions. But an acceleration of U.S. economic
activity did not seem to follow the oil price declines that occurred from the early 1980s to
the late 1990s. In addition, rising oil prices seemed to have less of an effect on economic
activity over the past fifteen years than they did in the 35 years following World War II.
Beyond establishing a relationship between oil price movements and aggregate economic
activity, research on the economic response to oil price shocks has gone in several directions.
* Corresponding author.
E-mail address: [email protected] (S.P.A. Brown).
The Quarterly Review of Economics and Finance 42 (2002) 193208
1062-9769/02/$ see front matter © 2002 Board of Trustees of the University of Illinois. All rights reserved.
PII: S 1 0 6 2 – 9 7 6 9 ( 0 2 ) 0 0 1 3 8 – 2
A number of studies have investigated why rising oil prices appear to retard aggregate
economic activity by more than falling oil prices stimulate it. Other studies have investigated
the channels through which oil price shocks are transmitted to economic activity, including
the role of monetary policy. And several have examined the possibility of a weakening
relationship between oil price fluctuations and aggregate economic activity.
In this paper, we survey the theory and evidence linking fluctuations in energy prices to
aggregate economic activity. We then briefly examine the implications of this research for
both monetary policy and energy policy in response to oil price shocks. Research seems to
provide relatively reliable guidance for monetary policy. Because the precise channels
through which oil price shocks affect economic activity are only partially known, however,
research offers less guidance about how energy policy should cope with oil price shocks.
2. Basic theory and evidence
The oil price shock of 1973 and the subsequent recession gave rise to a plethora of studies
analyzing the effects of oil price increases on the economy. The 1973 recession was (at the
time) the longest of the post-World-War-II recessions, and it gave new gravity to the
oil-macroeconomy relationship. The early studies included Pierce and Enzler (1974), Rasche
and Tatom (1977), Mork and Hall (1980), and Darby (1982), all of which documented and
explained the inverse relationship between oil price increases and aggregate economic
Later empirical studiessuch as Gisser and Goodwin (1986) and the Energy Modeling
Forum-7 study documented in Hickman et al. (1987) confirmed the inverse relationship
between oil prices and aggregate economic activity for the United States. Darby (1982),
Burbidge and Harrison (1984), and Bruno and Sachs (1981, 1985) documented similar
oil-price-economy relationships for countries other than the United States. Hamilton (1983)
made a definitive contribution by extending the analysis to show that all but one of the
post-World-War-II recessions were preceded by rising oil prices, and that other business
cycle variables could not account for the recessions.1
In an extensive survey of the empirical literature, Jones and Leiby (1996) find that the
estimated oil price elasticity of GNP in the early studies ranged from ?0.02 to ?0.08, with
the estimates consistently clustered around ?0.05. Tobin (1980) thought the estimated
effects seemed too high to be consistent with a classic supply shock, but Jones and Leiby
(1996) argue that values around ?0.05 are in the ballpark for output elasticities that are
roughly equal to factor shares. After the 1973 oil-price shock, oil s share in GNP was around
Several different channels have been proposed to account for the inverse relationship
between oil price movements and aggregate U.S. economic activity. The most basic is the
classic supply-side effect in which rising oil prices are indicative of the reduced availability
of a basic input to production. Other explanations include income transfers from the
oil-importing nations to the oil-exporting nations, a real balance effect and monetary policy.
Of these explanations, the classic supply-side effect best explains why rising oil prices slows
GDP growth and stimulates inflation.
194 S.P.A. Brown, M.K. Yu¨cel / The Quarterly Review of Economics and Finance 42 (2002) 193208
2.1. A classic supply-side shock
Rising oil prices can be indicative of a classic supply-side shock that reduces potential
output, as in Rasche and Tatom (1977 and 1981), Barro (1984) and Brown and Yu¨cel (1999).
Rising oil prices signal the increased scarcity of energy which is a basic input to production.
Consequently, the growth of output and productivity are slowed. The decline in productivity
growth lessens real wage growth and increases the unemployment rate at which inflation
accelerates. If consumers expect the rise in oil prices to be temporary, or if they expect the
near-term effects on output to be greater than the long-term effects, they will attempt to
smooth out their consumption by saving less or borrowing more which boosts the equilib-
rium real interest rate. With slowing output growth and an increase in the real interest rate,
the demand for real cash balances falls, and for a given rate of growth in the monetary
aggregate, the rate of inflation increases. Therefore, rising oil prices reduce GDP growth and
boost real interest rates and the measured rate of inflation.2
If wages are nominally sticky downward, the reduction in GDP growth will lead to
increased unemployment and a further reduction in GDP growth unless unexpected infla-
tion increases as much as GDP growth falls. The initial reduction in GDP growth is
accompanied by a reduction in labor productivity. Unless real wages fall by as much as the
reduction in labor productivity, firms will lay off workers, which will generate increased
unemployment and further GDP losses. If wages are nominally sticky downward, the only
mechanism through which the necessary wage reduction can occur is through unexpected
inflation that is at least as great as the reduction in GDP growth.3
2.2. Income transfers and aggregate demand
The shift in purchasing power from oil-importing nations to oil-exporting nations that
results from rising oil prices is another avenue through which oil price shocks might affect
economic activity, as emphasized by Fried and Schulze (1975) and Dohner (1981). The shift
in purchasing power reduces consumer demand in the oil-importing nations and increases
consumer demand in the oil-exporting nations, historically by less than the reduction in
consumer demand in the oil-importing nations. On net, world consumer demand for goods
produced in the oil-importing nations is reduced, and the world supply of savings is
increased. The increased supply of savings puts downward pressure on real interest rates
which can partially offset to more than offset the upward pressure on real rates that comes
from consumers in the oil-importing nations attempting to smooth their consumption. The
downward pressure on world interest rates should stimulate investment that offsets the
reduction in consumption and leaves aggregate demand unchanged in the oil-importing
If prices are sticky downward, however, the reduction in consumption spending for goods
produced in oil-importing countries will further reduce GDP growth. The reduction in
consumption spending necessitates a lower price level to yield a new equilibrium. If the price
level cannot fall, consumption spending will fall by more than investment increases.4
Consequently, aggregate demand will fall, further slowing economic growth worldwide, but
Horwich and Weimer (1984) conclude the net effect is smaller than previously suggested.
195S.P.A. Brown, M.K. Yu¨cel / The Quarterly Review of Economics and Finance 42 (2002) 193208
Monetary and/or fiscal policy can be used to stimulate demand sufficiently in the oil-
importing countries that the price reduction is unnecessary to restore equilibrium.
2.3. The real balance effect
As discussed in Mork (1994), the real balance effect was the first explanation of how an
oil price shock affects aggregate economic activity. According to Pierce and Enzler (1974)
an increase in oil prices would lead to increased money demand. The failure of the monetary
authority to meet growing money demand with increased supply would boost interest rates
and retard economic growth.
2.4. The possible role of monetary policy
Although the role of monetary policy was prominent in early explanations of how oil price
shocks affect real economic activity, it was gradually supplanted by real business cycle
theory.5 Nonetheless, an apparent breakdown in the relationship between oil and the econ-
omy during the