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Weather-related power outages and electric system resiliency

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Abstract

High winds, especially when combined with precipitation from seasonal storms, can cause damage to electricity utility systems, resulting in service interruptions to large numbers of electricity customers. While most such power outages are caused by damage from trees and tree limbs falling on local electricity distribution lines and poles, major power outages tend to be caused by damage to electricity transmission lines which carry bulk power long distances. Depending on the severity of the storm and resulting impairment, power outages can last a few hours or extend to periods of several days, and have real economic effects. Power outages can impact businesses (primarily through lost orders and damage to perishable goods and inventories), and manufacturers (mainly through downtime and lost production, or equipment damage). Data from various studies lead to cost estimates from storm-related outages to the U.S. economy at between $20 billion and $55 billion annually. Data also suggest the trend of outages from weather-related events is increasing. Suggested solutions for reducing impacts from weather-related outages include improved tree-trimming schedules to keep rights-of-way clear, placing distribution and some transmission lines underground, implementing Smart Grid improvements to enhance power system operations and control, inclusion of more distributed generation, and changing utility maintenance practices and metrics to focus on power system reliability. However, most of these potential solutions come with high costs which must be balanced against the perceived benefits. A number of options exist for Congress to consider which could help reduce stormrelated outages. These range from improving the quality of data on storm-related outages, to a greater strategic investment in the U.S. electricity grid. Congress could empower a federal agency to develop standards for the consistent reporting of power outage data. While responsibility for the reliability of the bulk electric system is under the Federal Energy Regulatory Commission (as per the Energy Policy Act of 2005), no central responsibility exists for the reliability of distribution systems. One possible option could be to bring distribution systems under the Electric Reliability Organization for reliability purposes. Recovery after storm-related outages might be enhanced by a federal role in formalizing the review or coordination of electric utility mutual assistance agreements (MAAs). This would not necessarily mean federal approval of MAAs, but may help in the cooperative coordination of additional federal and state resources, especially in a wide, multi-state weather event. While there has been much discussion of transmission system inadequacies and inefficiencies, many distribution systems are in dire need of upgrades or repairs. The cost of upgrading the U.S. grid to meet future uses is expected to be high, with the American Society of Civil Engineers estimating a need of $673 billion by 2020. While the federal government recently made funding available of almost $16 billion for specific Smart Grid projects and new transmission lines under the American Recovery and Reinvestment Act of 2009, there has not been a comprehensive effort to study the needs, set goals, and provide targeted funding for modernization of the U.S. grid as part of a long-term national energy strategy. Such an effort would also require decisions about the appropriate roles of government and the private sector. Power delivery systems are most vulnerable to storms and extreme weather events. Improving the overall condition and efficiency of the power delivery system can only serve to improve the resiliency of the system, and help hasten recovery from weather-related outages. Ultimately, however, electric utilities are responsible for this infrastructure. They are in the business of selling electricity, and they cannot sell electricity if their power delivery systems are out of service.

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... The already fragile electric critical infrastructure may be under more pressure to meet the balance of supply and demand [4]. Power facilities are mostly operating at their limit states, and the trend of power outages due to extreme weather is likely to continue to increase [15]. For example, in 2008, severe snow and ice storms in southern China caused power outages in 170 cities across 13 provinces [16]. ...
... Power outages caused by strong winds mainly originate from collapsed poles or towers and broken conductors in T & D system. While the transmission system is much less affected due to being more robust, but should not be ignored [15,20]. Mechanical applied stresses from wind loads acting on conductors can compress lines, causing poles or towers to break or fall due to excessive tension at suspension point, conductors to gallop, sag and even break [21]. ...
Article
Given that wind hazards pose a considerable damage to power system in various countries, researchers have developed methods for disaster prevention to diminish the vulnerabilities. This paper attempts to review the failure analysis procedure under the impact of wind hazards, considering the following attributes: wind field generation method, wind-induced electrical component failure model in transmission system, and building outage prediction model from data-driven view in distribution system. First, we compare the calculation of circulation and translation wind speed, with geographical, temporal and uncertainty factors of wind speed considered. Then, we introduce the wind-induced component failure models derived by wind load calculation and fragility curves. The application of the series reliability model in multiple cluster failures is briefly described. Finally, we summarize the applications of data analysis in building outage prediction models. The reviewed literature contains the statistical learning methods and machine learning methods. It is concluded that the frameworks for component-level failure model and user-level outage prediction model are well established. However, most of the studies still develop two models, separately. Future researchers may target at the hybrid application of both models to better provide fault analysis, prediction and emergency in power system under wind hazards.
... states of a reconfigurable network, optimal FVCs, and their overall dynamics XSGi, XIGk states of SG unit i and IG unit k YB, YA admittance matrices before and after NR , 1-3, N objective function and constraints of an optimization problem to design optimal FVCs Q, , , , , 1-5 positive definite matrix for the Lyapunov condition and auxiliary variables for LMI constraints  � ,  congruence transformation matrices I. INTRODUCTION XTREME weather events, such as floods and storms, are increasingly threatening the reliability of distribution power grids. In the United States, the costs of weather-related power outages were estimated to range between approximately $25 billion and $70 billion per year during the period from 2003 to 2012 [1]. Over this period, the annual number of major weather-related outages, which affected at least 50,000 customers, increased from less than 40 to more than 80 [2]. ...
... Over this period, the annual number of major weather-related outages, which affected at least 50,000 customers, increased from less than 40 to more than 80 [2]. Moreover, 90% of outages occurred at the distribution levels [1]- [3]. This emphasizes that improving the resilience of distribution networks (DNs) is of key importance when establishing future smart grids [4], [5]. ...
Preprint
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Network reconfiguration (NR) has attracted much attention due to its ability to convert conventional distribution networks (DNs) into self-healing grids. This paper proposes a new strategy for real-time voltage regulation (VR) in a reconfigurable DN, whereby optimal feedforward control of synchronous and inverter-based distributed generators (DGs) is achieved in coordination with the operation of feeder line switches (SWs). This enables preemptive compensation of upcoming deviations in DN voltages caused by NR-aided load restoration. A robust optimization problem is formulated using a dynamic analytical model of NR to design the feedforward voltage controllers (FVCs) that minimize voltage deviations with respect to the H infinity norm. Errors in the estimates of DG parameters and load demands are reflected in the design of optimal FVCs through polytopic uncertainty modeling, further improving the robustness of the proposed VR strategy. Small-signal analysis and case studies are conducted, demonstrating the effectiveness of the optimal robust FVCs in improving real-time VR when NR is activated for load restoration. The performances of the proposed FVCs are also verified under various operating conditions of a reconfigurable DN, characterized principally by SW operations, network parameter errors, and communication time delays.
... The leading cause of power outages and blackouts in the United States is severe weather in the form of thunderstorms, tornadoes, and hurricanes (Campbell, 2012). The age-related deterioration of power infrastructure in the U.S. has also contributed to an increased incidence of weather-induced power outages (Campbell, 2012). ...
... The leading cause of power outages and blackouts in the United States is severe weather in the form of thunderstorms, tornadoes, and hurricanes (Campbell, 2012). The age-related deterioration of power infrastructure in the U.S. has also contributed to an increased incidence of weather-induced power outages (Campbell, 2012). Over the span of 9 years between 2003 and 2012, about 679 widespread power outages were reportedly attributed to severe weather [(Executive Office of the President, 2013); (Aboshosha and El Damatty, 2014)]. ...
Conference Paper
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Transmission lines systems are important components of the electrical power infrastructure. However, these systems are vulnerable to damage from high wind events such as hurricanes. This study presents the results from a 1:50 scale aeroelastic model of a multi-span transmission lines system subjected to simulated hurricane winds. The system considered in this study consists of three lattice towers, four spans of conductors and two end-frames. The aeroelastic tests were conducted at the NSF NHERI Wall of Wind Experimental Facility (WOW EF) at the Florida International University (FIU). A horizontal distortion scaling technique was used in order to fit the entire model on the WOW turntable. The system was tested at various wind speeds ranging from 35 m/s to 78 m/s (equivalent full-scale speeds) for varying wind directions. A system identification (SID) technique was used to evaluate experimental-based along-wind aerodynamic damping coefficients and compare with their theoretical counterparts. Comparisons were done for two aeroelastic models: (i) a self-supported lattice tower, and (ii) a multi-span transmission lines system. A buffeting analysis was conducted to estimate the response of the conductors and compare it to measured experimental values. The responses of the single lattice tower and the multi-span transmission lines system were also compared.
... HE frequency and duration of widespread power outages have increased in recent years due to rapid growth in the rate of occurrence of extreme weather events, such as storms and hurricanes. In the United States, weather-related power outages affecting at least 50,000 customers were observed less than 10 times in 1993, but more than 120 times in 2011 [1]. Approximately 90% of the weather-related outages occurred at the distribution level [2]. ...
... However, the feedback control loops are activated only after the load demand variation due to NR significantly affects the MG frequency. This has motivated the development of new frequency control strategies, wherein DGs can preemptively compensate for the Young-Jin Kim 1 Feedforward Control of DGs for a Self-healing Microgrid T > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 load variation, given that in general, NR is initiated in a controlled manner. In power grids, the preemptive control has become increasingly feasible, as modeling and parameter estimation techniques have continued to be developed. ...
Preprint
Full-text available
Network reconfiguration (NR) has recently received significant attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the feedforward control of synchronous and inverter-interfaced distributed generators (DGs) is achieved in coordination with the operations of sectionalizing and tie switches (SWs). This enables DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. An analytical dynamic model of a reconfigurable MG is developed to analyze the MG frequency response to NR and hence determine the desired dynamics of the feedforward controllers, with the integration of feedback loops for inertial response emulation and primary and secondary frequency control. A small-signal analysis is conducted to analyze the contribution of the supplementary feedforward control to the MG frequency regulation. Simulation case studies of NR-aided load restoration are also performed. The results of the small-signal analysis and case studies confirm that the proposed strategy is effective for improving the MG frequency regulation under various conditions of load demand, model parameter errors, and communication time delays.
... Overall, since the planning problem involves the modeling of uncertain factors (e.g., fault location, load profile), the 1 Since the decision variables "restored load" or "load curtailment" appear in all OMS strategies, the load variables are omitted in this table. 2 In this paper, "DG" represents fuel-based DG for simplicity. Fig. 7. Comparison between pre-disaster allocation and long-term planning problems. ...
... In this study, the nonlinear constraints are temporarily released and solved by a heuristic search. Therefore, the remaining constraints are linear.2 In this table, network reconfiguration means keeping the whole network connected and served by the upstream grid. ...
Article
Distribution system infrastructures are vulnerable to extreme weather events, such as hurricane, ice coating, flood, and wildfires. Resilience is a measure of the system's ability to prevent the damage during extreme events and to recover the system function after such events. With the economic development, it becomes increasingly important for power utilities to maintain critical loads always in service and to reduce the unserved energy of all loads. If many distribution system equipments are damaged, the utility companies dispatch static or mobile distributed energy resources, reconfigure the network topology in order to restore the islanded sections of the distribution system. In recent years, a large number of studies have been done on operation and planning strategies to enhance the distribution system resilience. This review paper introduces the background of resilient distribution system. Then, it makes a comprehensive summary of the resources for resilience enhancement, the mathematical model of operation and planning algorithms. In particular, the objective function, mathematical formulation, decision variables, and solution algorithm of each study are compared. Finally, the roadmap of resilient distribution system is extracted and the future research direction on this topic is proposed.
... The collapse of overhead power line guyed towers is one of the leading causes of power grid failures, subjecting electricity companies to pay considerable high-value fines [1,2]. In addition, city blackouts resulting from the collapse of cable-stayed structures cause a financial loss in the order of millions of dollars for companies and industries. ...
... where v represents the considered base vector parallel to the cable and v represents the vector g. Thus, we define θ cbx , according to Equation (2), as the angle between the xth-cable and the vector g. ...
Article
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The collapse of overhead power line guyed towers is one of the leading causes of power grid failures, subjecting electricity companies to pay considerable, high-value fines. In this way, the current work proposes a novel and complete framework for the remote monitoring of mechanical stresses in guyed towers. The framework method comprises a mesh network for data forwarding and neural networks to improve the performance of Low-Power and Lossy Networks. The method also considers the use of multiple sensors in the sensor fusion technique. As a result, the risk of collapse of guyed cable towers reduces, due to the remote monitoring and preventive actions promoted by the framework. Furthermore, the proposed method uses multiple input variable fusions, such as accelerometers and tension sensors, to estimate the tower’s displacement. These estimations help address the structural health of the tower against failures (i.e., loosening of the stay cables, displacement, and vibrations) that can cause catastrophic events, such as tower collapse or even cable rupture.
... In the United States, it is estimated by two different studies that the annual cost to the economy of weather related outages is between $18-33 billion [13] and $25-70 billion [5] annually. While a reduction of outages in the electric grid has an opportunity to save billions, vegetation management itself is very expensive. ...
... While this study is focused on the effect of ETT, it is likely that there is a multi-optimization scheme that utilizes various resilience efforts and programs to best optimize system resiliency while balancing budget constraints. Some of these other techniques include reducing the cycle time between when trimming is performed [19,34], removing hazardous trees [43] and re-planting with trees that are smaller at maturity [10], upgrading to rubber-coated wire [10], using low-sag conductors [42], and undergrounding wires [5]. Each technology comes with its own advantages and challenges, and it is up to stakeholders to determine the optimal scheme considering the local power infrastructure, physical environment, budget, and weather and climate conditions. ...
Article
This paper develops a machine learning outage prediction model (OPM) to serve as a simulation framework capable of quantifying the reduction in damages to the distribution electric grid due to vegetation management for storm events. The model covers the Eversource Energy distribution grid territory in Connecticut and uses a random forest model with input variables for vegetation, vegetation management, land cover, drought, elevation, weather and electrical infrastructure to predict outages for each circuit (the operational units of the power distribution network). The model is trained on 165 storms from the years 2005 to 2019. The results show that over the last five years of the study (2015–2019) the annual reduction in trouble spots in the electric grid due to enhanced tree trimming is between 25.7 and 42.5% and there is good matching between increased trouble spot reduction and increased vegetation management. Further, we demonstrate improved outage predictions when including vegetation management data as an input variable, with a 4.1% reduction in mean absolute percentage error in leave-one-storm-out cross-validation. This framework could be used to examine varying vegetation management scenarios and the results should be useful for decision makers such as utilities, municipalities and regulators in optimizing vegetation management and broader grid resilience enhancement plans.
... Different reliability thresholds exist. In Europe, a system interruption not greater than three minutes is considered reliable (Campbell, 2012); while in the USA it should not exceed five minutes. Eskom in South Africa considers interruption of less than one minute as reliable for high voltage networks and less than five minutes for medium voltage (Chatterton, 2014). ...
Article
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Most rural Tanzanians have had no access to electricity. But efforts have been made to remedy this, including an extension of the national grid and the establishment of independent power plants in rural areas. The result is a recordable increase of people with access to electricity; however, the realization of reliable power for both consumers and suppliers has remained a puzzle. This paper out to examine the reliability of rural electricity systems based on consumer measures; to find out determinants for system reliability; and examine how outage incidences exacerbate households’ expenditure on backup fuels. Reliability was assessed through a stepwise approach, where a general system reliability index and trend analysis were used. It was found that system reliability was enhanced because consumers only spent 6–15 days per year without electricity due to outages. These are tolerable outages, given the volatility of the rural system. Further, weather, fire outbreaks in bushes, and lightning, significantly determined system reliability. Nonetheless, despite the reasonable reliability, some outage incidences had dragged consumers into unplanned expenditure on backup fuel. It is recommended that there should be a continuous inspection of the system, and the use of supervisory control and data acquisition device on the distribution line for accurate monitoring is imperative.
... For example, high temperatures from heatwaves will limit the amount of energy that can be transferred [3], lightning strikes cause faults on the lines [4], and the high winds from storms may damage overhead lines [5]. In the U.S., extreme weather events have caused 50% to 60% of the power interruptions [6] and $20 to $55 billion annual economic losses [7]. To mitigate the impacts of extreme weather events on electric infrastructures and power grids, extensive efforts have been devoted toward proposing the concept of resilience. ...
Article
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Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. In this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.
... The results are consistent with the findings of Majiwa (2014) which concluded that there existed negative and significant relationship between electricity equipment vandalism and the financial performance of businesses in Nairobi. The results are also consistent with Campbell (2012) that customers experience power interruptions due to vandalism to electrical equipment. Vandalism to electrical equipment jeopardizes the individuals' safety, the public's safety as well as the safety of employees working in companies. ...
Article
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The importance of power supply to economic development of any nation cannot be overemphasized. Availability and access to reliable electricity supply has a rippling effect on productivity of manufacturing firms. Unreliable power leads to disruptions in production, loss of perishable goods, damage to sensitive equipment and loss of orders. The study examined the relationship between power supply interruptions and manufacturing companies’ financial performance in Machakos County. The study employed descriptive research design. The population for this study constituted the 69manufacturing firms in Machakos County. The study found that scheduled maintenance and financial performance are positively and significantly related. Equipment vandalism and financial performance are negatively and significantly related. It was also established that response time and financial performance are positively and significantly related. Repair costs and financial performance are negatively and significantly related. In conclusion, electricity is necessary for the operation of any manufacturing company and any interference with electricity supply makes manufacturing companies run into seriouslosses. The financial performance of manufacturing companies in Machakos County are always affected by; scheduled maintenance by power utility, vandalism of electrical equipment, the response time by power utility staff to restore power and the cost the companies incur in terms of paying losses as a result of damage caused by power outage. Due to the important nature of electricity in the manufacturing industry, there is need to create clear policy framework protecting manufacturing companies from regular power outage which has an adverse effect on the operations of the manufacturing companies in the country. Scheduled maintenance by should be done in such a way that it does not interfere with the normal operations of manufacturing companies. The government andthe utility company should ensure that there are vigilant groups are formed to safe guard power equipment and power lines against vandalism which has become a major setback to the utility company in its attempt to maintain constant power supply. Keywords: scheduled maintenance, equipment vandalism, response time, interruption repairs, financial performance, manufacturing companies, Machakos County
... In the U.S., weather-induced disruptions to power systems cost around $20-$55 billion in annual economic losses [1]. Among these disruptions, about 90% of outages occur in electricity distribution networks (DNs) [2]. Smart grid technologies such as microgrids powered by Distributed Energy Resources (DERs) permit DNs to provide power to the loads even when the bulk supply from central generation is disrupted [3], [4]. ...
Article
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In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network.
... As virtually all sectors of modern society rely on effective power distribution, widespread outages produce critical social and economic consequences. In the U.S. alone, up to 44% of outages are estimated to be storm-related and produce an estimated $70 billion of annual economic losses [1]. Provided the significant utility company investment in the overhead timber pole-wire power distribution system (PDS), enhanced PDS grid management, such as through strengthened physical infrastructure, has substantial lucrative potential while simultaneously increasing the reliability of power delivery to customers. ...
Article
Power outages caused by severe storms produce enormous economic losses and societal disruptions. Infrastructure hardening for a more resilient power grid can reduce weather-induced outages but necessitates accurate simulations of intervention efficacy. Data-driven models have been developed to forecast outages but struggle with extreme events where data might be limited. Meanwhile, physics-based models can predict failures under strong winds but have limited scope in lower wind ranges. Despite the two models’ complementary benefits, studies investigating their integration have been limited. In the present study, the physical attributes of the infrastructure system are incorporated into data-driven models by coupling structural fragilities of the pole-wire overhead power distribution system with machine learning (ML) techniques applied in an outage prediction model for the northeastern United States. The ML model is used to calibrate the physics-based fragility curves, which are subsequently used to predict outages for high-impact events where empirical data may be limited. The results of this hybrid physics-based and data-driven (HPD) model indicate modeling improvements using hybrid over strictly data-driven approaches for extreme events. Root mean square error improvements of 48% are exhibited for high-impact event outage prediction. The hybrid model was then utilized to counterfactually assess the impacts of grid hardening activities such as pole replacement, pole class upgrade, improved pole chemical treatment, and undergrounding in reducing pole failures over the last fifteen years. The results indicate selected strategies targeted to the oldest 5% of infrastructure could have reduced over 100 (33% of all) pole failures annually across the state of Connecticut.
... Unpredicted and unexpected climate shocks cause energy supply poverty in urban areas where households cannot supply their energy (Jessel et al., 2019;Thomson et al., 2019). Moreover, the economic effects of climate extremes are also significant at different scales, such as impairment of energy systems (Cronin et al., 2018), increased healthcare costs (Gasparrini et al., 2015;Jessel et al., 2019), power outages as well as disturbances in crucial modern days platforms (Campbell, 2013;Kenward & Raja, 2014). ...
Article
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Climate change and urbanization are two major challenges when planning for sustainable energy transition in cities. The common approach for energy demand estimation is using only typical meso-scale weather data in building energy models (BEMs), which underestimates the impacts of extreme climate and microclimate variations. To quantify the impacts of such underestimation on assessing the future energy performance of buildings, this study simulates a high spatiotemporal resolution BEM for two representative residential buildings located in a 600 × 600 m2 urban area in Southeast Sweden while accounting for both climate change and microclimate. Future climate data are synthesized using 13 future climate scenarios over 2010-2099, divided into three 30-year periods, and microclimate data are generated considering the urban morphology of the area. It is revealed that microclimate can cause 17% rise in cooling degree-day (CDD) and 7% reduction in heating degree-day (HDD) on average compared to mesoclimate. Considering typical weather conditions, CDD increases by 45% and HDD decreases by 8% from one 30-year period to another. Differences can become much larger during extreme weather conditions. For example, CDD can increase by 500% in an extreme warm July compared to a typical one. Results also indicate that annual cooling demand becomes four and five times bigger than 2010-2039 in 2040-2069 and 2070-2099, respectively. The daily peak cooling load can increase up to 25% in an extreme warm day when accounting for microclimate. In the absence of cooling systems during extreme warm days, the indoor temperature stays above 26°C continuously over a week and reaches above 29.2°C. Moreover, the annual overheating hours can increase up to 140% in the future. These all indicate that not accounting for influencing climate variations can result in maladaptation or insufficient adaptation of urban areas to climate change.
... Electric failures alone can turn into serious losses whose estimates range from $22 to $135 billion annually [6][7][8]. Moreover, failures might have complex and adverse socioeconomic consequences in communities heavily reliant on the electricity supply [9,10]. ...
Article
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Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of Northern Norway. First, we collected data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploited statistical and machine-learning techniques to predict the occurrence of failures. The classification models achieve good performance, meaning that there is a significant relationship between the collected variables and fault occurrence. Thus, we interpreted the variables that mostly explain the classification results to be the main driving factors of power interruption. Wind speed of gust and local industry activity are found to be the main controlling parameters in explaining the power failure occurrences. The result could provide important information to the distribution system operator for implementing strategies to prevent and mitigate incoming failures.
... While the overall effectiveness of vegetation management has been demonstrated in other works [11][12][13], the possibility of diminishing returns with increasing storm strength exists and has yet to be evaluated. Improving system resilience for all severity of storms, ranging from the more frequent, lower severity events, to extreme, less frequent, events is important because in total, weather-related outages cost the US economy in the range of 18-70 billion US dollars annually [14,15]. To reduce outages from trees across the spectrum of weather from blue-sky days (nonstorm weather) to hurricanes, vegetation management strategies are employed across the industry at the cost of billions of dollars annually [16] and vegetation management is considered one of the largest recurring expenses associated with overhead utility infrastructure in North America [2]. ...
Article
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This paper develops a statistical framework to analyze the effectiveness of vegetation management at reducing power outages during storms of varying severity levels. The framework was applied on the Eversource Energy distribution grid in Connecticut, USA based on 173 rain and wind events from 2005–2020, including Hurricane Irene, Hurricane Sandy, and Tropical Storm Isaias. The data were binned by storm severity (high/low) and vegetation management levels, where a maximum applicable length of vegetation management for each circuit was determined, and the data were divided into four bins based on the actual length of vegetation management performed divided by the maximum applicable value (0–25%, 25–50%, 50–75%, and 75–100%). Then, weather and overhead line length normalized outage statistics were taken for each group. The statistics were used to determine the effectiveness of vegetation management and its dependence on storm severity. The results demonstrate a higher reduction in damages for lower-severity storms, with a reduction in normalized outages between 45.8% and 63.8%. For high-severity events, there is a large increase in effectiveness between the highest level of vegetation management and the two lower levels, with 75–100% vegetation management leading to a 37.3% reduction in trouble spots. Yet, when evaluating system reliability, it is important to look at all storms combined, and the results of this study provide useful information on total annual trouble spots and allow for analysis of how various vegetation management scenarios would impact trouble spots in the electric grid. This framework can also be used to better understand how more rigorous vegetation management standards (applying ETT) help reduce outages at an individual event level. In future work, a similar framework may be used to evaluate other resilience improvements.
... This is a concept that is gaining popularity following the onslaught of hurricanes in the Caribbean and coastal parts of America. Other developments in modeling distributed energy systems include efforts to (a) incorporate electric vehicles [30], (b) smart grids to reduce storm related outages [31] and (c) demonstrate how individual power component faults can be minimized using microgrids [32]. ...
Article
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Resilience of power systems is already a key issue that is getting frequent attention all over the world. It is useful to analyze resilience issues not only for bulk supply, but at all levels including at a customer level. This is because distributed energy resources can play a prominent role in enhancing resilience. Although the literature on planning models, tools and data for bulk supply and distribution systems have expanded in recent years, customer-centric planning, e.g., for an individual household, is yet to receive adequate attention. Although solar PV and battery storage at a household level have been analyzed, how these resources can be optimally combined, together with grid supply, from a resilience perspective is the focus of this study. The study demonstrates how a conceptual framework can be developed to show the trade-off between system costs and resilience including its dimensions such as duration, depth and frequency of service outages. A planning model is developed that incorporates multiple facets of resilience and individual customer preferences. The model considers power system resilience explicitly as a constraint. The model is implemented for a household level case study in Miami, Florida. The results show there are complex trade-offs among different dimensions of resilience. The study demonstrates how combined resilience metrics can be formulated and evaluated using the proposed least-cost planning model at a household level to optimize grid supply together with solar, battery storage and diesel generators. The model allows a planner to directly embed a resilience standard to drive the optimal supply mix. These concepts and the modeling construct can also be applied at other levels of planning, including community level and bulk supply system planning.
... It should be noted that the probability of wire break on long overhead transmission lines in unpopulated areas is increased due to complexity of servicing these lines. Weather conditions in different regions are one of main factors that affect to high-voltage power grid operation, which is described in [14] or [15]. The most problematic are power supply schemes for remote consumers without reserving the high-voltage power grid. ...
Article
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This paper presents simulation-based studies on the 110 kV substations located in the Euro-Arctic region of Russia. Longitudinal and transversal disturbances in the high voltage grid are studied. The influence of the harsh climatic conditions on the rate of occurrence of accidents is shown. The 110-kV section of the Kola power system was considered and the features of this section were analyzed. The simulation model of the investigated section was created in the ATP-EMTP software and a wire break of the overhead transmission line followed by a single phase-to-ground faults of the same phase was simulated. The influence of combined overvoltage on substations electrical equipment is considered. The simulation results show that overvoltages in power grids with one-way power supply can pose a significant threat to insulation of any substation equipment.
... On the other hand, heterogeneous ice nucleation occurs in the atmosphere, a wide range of species (e.g., the extracellular matrix in wood frogs 10 , Antarctic fishes 11,12 ) and technological embodiments (e.g., aviation 13 and infrastructures, including transportation, power, and energy systems [14][15][16] ) and causes outstanding financial burden 17 . Nanoscopic anti-icing surfaces developed to address the icing challenge aim to tune the water-ice transformation at a few nanometer scales. ...
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Water-ice transformation of few nm nanodroplets plays a critical role in nature including climate change, microphysics of clouds, survival mechanism of animals in cold environments, and a broad spectrum of technologies. In most of these scenarios, water-ice transformation occurs in a heterogenous mode where nanodroplets are in contact with another medium. Despite computational efforts, experimental probing of this transformation at few nm scales remains unresolved. Here, we report direct probing of water-ice transformation down to 2 nm scale and the length-scale dependence of transformation temperature through two independent metrologies. The transformation temperature shows a sharp length dependence in nanodroplets smaller than 10 nm and for 2 nm droplet, this temperature falls below the homogenous bulk nucleation limit. Contrary to nucleation on curved rigid solid surfaces, ice formation on soft interfaces (omnipresent in nature) can deform the interface leading to suppression of ice nucleation. For soft interfaces, ice nucleation temperature depends on surface modulus. Considering the interfacial deformation, the findings are in good agreement with predictions of classical nucleation theory. This understanding contributes to a greater knowledge of natural phenomena and rational design of anti-icing systems for aviation, wind energy and infrastructures and even cryopreservation systems.
... Moreover, 80% of these outages occurred as a result of failures at distribution level (EC, 2018), according to the DSO Observatory Project (DSO-OP, 2018 edition). Consequently, climate change related risks could affect the electric grid operation, especially at the distributional level, if operators do not proceed with the selection and proper implementation of effective adaptation measures, policies and strategies (Campbell, 2012). This new reality should also be considered in the setting of COVID-19 pandemic, which can cause yet-to-be-determined long-term challenges for the power utilities, such as uncertainty in demand forecasts, changes in daily load patterns, increased RES penetration and reverse power flow, supply chain disruptions, etc. ...
Conference Paper
As electrification spreads widely in almost every sector of everyday life, the need for energy security through a resilient electricity supply system that will sufficiently cover electric power demand and ensure reliable power transmission and distribution from generation points to consumption centres (i.e. end users) becomes of paramount importance. However, the threats and challenges that network operators have to tackle are intensified due to climate change implications. Snowstorms, floods, heat waves, hurricanes, and wildfires are some of the most common disruptions that are part of the new reality and put electric power systems into severe danger since exposure of infrastructure to such extreme natural phenomena can cause widespread equipment damages and cascading failures leading to blackouts. In contrast to the previous years, the priority of electric utilities has now shifted to making the power grid more resilient and not just technically reliable, so that it will be capable of "absorbing" disruptions without "breaking", while facing an extreme event. This paper reviews the effects of the extreme weather event "Medea" on the Hellenic electricity distribution network along with the disaster management strategy adopted so as to effectively respond to the emergency situation. Through impact analysis and assessment, specific aggravating factors that affect electric grid vulnerability are identified, while indicative adaptation measures, policies and strategies (i.e. system hardening design, operational, etc.) are proposed in order to enhance electric grid resilience against changing climatic patterns, by ensuring network functionality, stability, continuity and integrity.
... • The upstream transmission system normally operates in extreme weather. This assumption is based on the statistical data that 90% of the unscheduled line faults in the U.S. occur in distribution systems [25]. • The faulted line can be located by the fault indicator [17,26]. ...
Article
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In this paper, a post-disaster distribution system repair and restoration (DSRR) strategy is proposed to improve distribution system resilience. The DSRR strategy is formulated as a two-stage optimization. The first stage is a comprehensive co-optimization of repair crew scheduling, dynamic network reconfiguration, and distributed energy resource (DER) dispatch based on the forecast load profile. The goal is to minimize the accumulative operating cost caused by the load reduction payment as well as DER operating cost. In particular, since the number of available repair crews is usually smaller than the number of faulted lines after a disaster event, the DSRR strategy determines the optimal scheduling for repairing faulted lines. The second stage is a re-dispatch of the DER power output and load shedding based on the real-time load demand of each bus. The proposed algorithm is validated by case studies of the IEEE 33-bus and 123-bus test systems. We consider those scenarios in which faults occur in multiple heavy-loaded feeders. The simulation results demonstrate that the DSRR strategy effectively coordinate the repair scheduling, network reconfiguration and load shedding to minimize the operating cost.
... For example, between 2003 and 2012, there were approximately 679 weather-related outages in the United States, each affecting at least 50,000 customers, and 80-90% of these outages were due to the failures occurred in distribution networks [1]. Furthermore, the annual cost of storm-related outages in the United States is about $20-$55 billion [2]. The number and severity of such HILP natural disasters are expected to rise in the coming years due to the increasing global warming. ...
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Due to the accelerated climate change, it is anticipated that the number and severity of natural disasters such as hurricanes, blizzards, and floods will be increased in the coming years. In this regard, this paper presents a distribution system planning model to improve the system resilience against hurricane. A scenario‐based mathematical model is proposed to capture the random nature of weather events. Moreover, a stochastic optimization model is developed to simultaneously harden the distribution lines and place different types of distributed generation (DG) units such as microturbines (MTs), wind turbines (WTs), and photovoltaic cells (PVs). The conditional value at risk (CVaR) is used as a risk index to manage the system risk against different failure scenarios. The problem is formulated as a mixed integer linear programming (MILP) model that can be solved by various commercial solvers. Finally, to illustrate the effectiveness of the proposed model, it is implemented on the IEEE 33 bus system, and various case studies are defined. The results show the effectiveness of our mathematical model in improving the distribution system resiliency and managing the system risk.
... However, the extreme disruptions under natural hazards, which lead to line failure, equipment damage, infrastructure destruction, and so on, would result in large-scale outages and loss of health and wealth. For example, in the US, natural hazards cause about 25 to 70 billion dollars to cost annually [1]. erefore, it is necessary to assess and improve the power system resilience under natural hazards, especially for the microgrid system. ...
Article
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Recently, increasing the number and severity of the natural hazards requires the resilience assessment and enhancement of the power system, especially the microgrid system. The emergency distribution generations have great potential to enhance the resilience of microgrid against blackouts under the emergency environment. This paper investigates the resilience assessment of the microgrid system under natural hazard, where emergency distribution generations are firstly pre-positioned on system nodes and then reconfigured after system damage occurs. A new resilience metric index and an efficient approximation computation method are provided for the resilience assessment of the focused problem. A new pre-position strategy and a new reconfiguration strategy on emergency distribution generations are proposed for the microgrid system emergency restoration under natural hazard. Also, a framework of resilience assessment is provided for problem-solving. The results of extensive experiments on the modified IEEE 30-Bus system and modified IEEE 118-Bus system confirm the effectiveness of the resilience assessment methodology and the superiority of proposed restoration strategies.
... The power grid is a key infrastructure that supports many critical activities and operations in society. Although transmission towers are often designed to withstand strong wind hazards, they have experienced damage and failures during extreme events such as hurricanes, tornados, and downbursts (Campbell and Lowry 2012;Elawady et al. 2017;Elawady and El Damatty 2016). Because the failure of transmission towers can result in considerable economic losses and societal disruptions, analyzing their reliability is critically important. ...
... Despite substantial provincial and federal investments in wildfire agencies across Canada, several catastrophic fires in the WHI have occurred in recent decades (Hope et al., 2016;Stocks & Martell, 2016). The interaction of wildfire with public infrastructure, such as power grids, can have particularly disruptive outcomes, causing widespread power outages that affect public health and economic productivity (Campbell & Lowry, 2012;Klinger et al., 2014). Given these possible severe impacts, managers designing infrastructure in fire-prone areas require a means of assessing wildfire threat that spans the projected lifetime of infrastructure projects. ...
Article
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Infrastructure built in fire‐prone wildland areas often has a high potential of being impacted by wildfire. Managers designing infrastructure in these areas, therefore, require assessments of wildfire threat to quantify the uncertainty of future vegetation and climatic conditions. In this study, we combine wildfire simulation and forest landscape composition modeling to identify areas highly susceptible to wildfire within and surrounding a proposed conservation corridor in Quebec, Canada. In this measure, managers have proposed raising the conductors of a new 735‐kV hydroelectric powerline above the forest canopy within a wildlife connectivity corridor to mitigate impacts to threatened boreal woodland caribou (Rangifer tarandus). Retention of coniferous vegetation, however, can increase the likelihood of an intense wildfire damaging powerline infrastructure. To assess the likelihood of high‐intensity wildfires for the next 100 years, we evaluated three time periods (2020, 2070, 2120), three climate scenarios (observed, RCP 4.5, RCP 8.5), and four vegetation projections (static, no harvest, extensive harvesting, harvesting excluded in preserves). Under present‐day conditions, we found a comparatively low probability of high‐intensity wildfire within the corridor, due to the protective influence of a nearby, poorly regenerated burned area. Wildfire probability will increase into the future, with strong, weather‐induced inflation in the number of annual ignitions and wildfire spread potential. However, a conversion to less‐flammable vegetation triggered by interactions between climate change and disturbance may attenuate this trend. By addressing the range of uncertainty of future conditions, we present a robust strategy to assist decision‐making about long‐term risk management for the proposed conservation measure and powerline.
... Tree contact as a result of vegetation growth is also a major problem in the electrical distribution grid. While tree contact is most prevalent in adverse weather outage events [54,55], there are tree contacts that occur even in normal weather conditions [56]. During the study period, 28% of NWOs in MA were a result of tree contact ( Figure 10). ...
Article
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Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO.
... Failure of transmission towers can result in considerable economic loss and societal and economic disruptions. Although transmission towers are often designed to withstand strong wind hazards, they have experienced damage and failures during extreme events such as hurricanes, tornados and downbursts [44]- [47]. The estimation of the reliability of transmission towers through probabilistic models is needed for risk and resilience analysis and decision-making purposes. ...
Preprint
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In many fields of science and engineering, models with different fidelities are available. Physical experiments or detailed simulations that accurately capture the behavior of the system are regarded as high-fidelity models with low model uncertainty, however, they are expensive to run. On the other hand, simplified physical experiments or numerical models are seen as low-fidelity models that are cheaper to evaluate. Although low-fidelity models are often not suitable for direct use in reliability analysis due to their low accuracy, they can offer information about the trend of the high-fidelity model thus providing the opportunity to explore the design space at a low cost. This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA). Contrary to selecting training points and information sources in two separate stages as done in state-of-the-art mfEGRA method, the proposed approach finds the optimal training point and information source simultaneously using the novel collective learning function (CLF). CLF is able to assess the global impact of a candidate training point from an information source and it accommodates any learning function that satisfies a certain profile. In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems. The performance of the proposed method is demonstrated by three mathematical examples and one engineering problem concerning the wind reliability of transmission towers. It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.
... And by that time, the storm has greatly weakened and had been moving out from Massachusetts. According to a number of recent outage reports [9,11,14,20], under the impact of extreme weather, these customer outages were majorly caused by damages to distribution systems. Much of the transmission and distribution networks, across the United States, and particularly in the Midwest and Eastern regions, are still above ground, leaving them vulnerable to the effects of extreme weather [20]. ...
Preprint
In recent years, extreme weather events frequently cause large-scale power outages, affecting millions of customers for extended duration. Resilience, the capability of withstanding, adapting to, and recovering from a large-scale disruption, has becomes a top priority for power sector, in addition to economics and sustainability. However, a good understanding on the power grid resilience is still lacking, as most approaches still either stay on the conceptual level, yielding no actionable results, or focus on a particular technical issue, revealing little insights on the system level. In this study, we take a quantitative approach to understanding power system resilience by directly exploring real power outage data. We first give a qualitative analysis on power system resilience and large-scale power outage process, identifying key elements and developing conceptual models to describe grid resilience. Then we propose a spatio-temporal random process model, with parameters representing the identified resilience capabilities and interdependence between service areas. We perform analyse using our model on a set of large-scale customer-level quarter-hourly historical power outage data and corresponding weather records from three major service territories on the east-coast of the United States under normal daily operations and three major extreme weather events. It has shown that weather only directly cause a small portion of power outages, and the peak of power outages usually lag the weather events. Planning vulnerability and excessively accumulation of weather effects play a key role in causing sustained local outages to the power system in a short time. The local outages caused by weather events will later propagate to broader regions through the power grid, which subsequently lead to a much larger number of non-local power outages.
... In the worst-case, this could reduce annual GDP growth by up to 1% (Wade and Jennings, 2015). The transportation departments (Venner and Zamurs, 2012) and power industries (Campbell and Lowry, 2012) are expected to be the most heavily impacted by rising maintenance costs, although very few data exist on these topics. ...
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Grid resilience and reliability are pivotal in the transition to low and zero carbon energy systems. Tree-trimming operations (TTOs) have become a pivotal tool for increasing the resilience power grids, especially in highly forested regions. Building on recent literature, we aim at assessing the temporal and spatial extents of the benefits that TTOs produce on the grid from three perspectives: the frequency, extent, and duration of outages. We use a unique dataset provided by Eversource Energy, New England's largest utility company, with outage events from 2009 to 2015. We employ spatial econometrics to investigate both the legacy and spatial extent of TTOs. Our results show TTOs benefits occur for all three metrics for at least 4 years, and benefits spillover to up to 2 km throughout the treated areas, with significant spatial spillovers across the state greater than direct effects. Implications lead to supporting TTOs as part of the hardening policies for utility companies, especially as home-based activities increase in importance in a post-COVID19 world.
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Over the last decade, it has been recognized that the resiliency of cities and communities relies on the efficient and effective operation of several lifeline infrastructures and services, such as energy, food and water, communications, shelter, sanitation, emergency response, and transportation, to maintain public health and safety during a natural disaster. Surprisingly, some argue that the benefits to the public of incorporating resilience and robustness versus the risks of a major disaster do not match the extra costs. Some experts conclude that building resilience and robustness into a community is valuable in theory, but in practice, resilience and robustness are abstract and very costly. This highlights the challenges community leaders and stakeholders have in communicating resilience needs and developing efficient and cost-effective solutions.
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The past several decades have witnessed an increasing number of natural and manmade hazards with a dramatic impact on the normal operations of the society. The occurrences of these hazards manifest a growing trend of uncertainty. Assessing the performance of systems under such hazards is a salient concern of researchers and practitioners. The notion of ‘resilience’ has been proposed and popularised to characterise system performance deterioration and restoration due to different hazards and threats. Substantial effort has been devoted to quantify and describe resilience from different perspectives. However, there is no generic metric for assessing the resilience of different systems under different hazards. This paper provides a review of existing approaches that quantitatively assess resilience, along with their applicable scenarios and limitations. New general and generic resilience metrics for systems with multimodal performance are proposed. Opportunities for multi-hazard resilience modelling and enhancements are presented.
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n many fields of science and engineering, models with different fidelities are available. Physical experiments or detailed simulations that accurately capture the behavior of the system are regarded as high-fidelity models with low model uncertainty; however, they are expensive to run. On the other hand, simplified physical experiments or numerical models are seen as low-fidelity models that are cheaper to evaluate. Although low-fidelity models are often not suitable for direct use in reliability analysis due to their low accuracy, they can offer information about the trend of the high-fidelity model thus providing the opportunity to explore the design space at a low cost. This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA). Contrary to selecting training points and information sources in two separate stages as done in state- of-the-art mfEGRA method, the proposed approach finds the optimal training point and information source simultaneously using the novel collective learning function (CLF). CLF is able to assess the global impact of a candidate training point from an information source and it accommodates any learning function that satisfies a certain profile. In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems. The performance of the proposed method is demonstrated by three mathematical examples and one engineering problem concerning the wind reliability of transmission towers. It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.
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Every year power outages affect millions of people. In the US, historical data show that between 2000 and 2016, 75% of outages (in terms of duration) were caused by severe weather events. The National Association of Regulatory Commissioners has recently emphasized the importance of building electricity sector resilience in order to ensure long-term reliability and economic benefits for stakeholders. This study established a disaster impact assessment model to estimate economic losses due to severe weather–induced power outages in the US in terms of the nation’s gross domestic product (GDP). Economic losses were estimated the using extended version of Leontief’s input–output model with historical data from the US Bureau of Economic Analysis between 1997 and 2019. The study showed an estimated GDP loss of $11.6 billion (in 2019 values) due to 1% inoperability in the utility sector. These results can be used to (1) provide a range of investment and (2) justify the need for investment in long-term resilience planning in the utility sector. Furthermore, the results can be used to identify industries that are vulnerable due to inoperability in the utility sector.
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Extreme weather events such as tropical cyclones (TC) are considered the most significant weather events causing severe damage to the power distribution grid. However, few or no studies have been performed at the perspectives of the power system although it is necessary, but a relevant study has recently received attention in South Korea. Accordingly, the motivation of this paper addresses the need to present a study of the impact of TCs, focusing on the analysis and the estimation of TC-induced damage. The study uses three sets of historical data: TC data, power distribution grid damage data, and local weather data from 2008 to 2018. Using these data, first, this paper provides an overview of the damage characteristics to understand power system damage. TC, local weather, and TC-induced damage data are analyzed to find their correlated characteristic. Then, TCs are classified by their track patterns associated with the damage characteristic. The importance of TC track is proved by the significance test and comparing the damages by TC tracks. Furthermore, the underlying key hazard variables (i.e., TC and local weather variables) associated with TC-induced damages are identified. Lastly, this paper proposes a multi-stage damage estimation methodology to evaluate the intensity of TC-induced damages using identified major TCs and local weather variables as well as TC tracking information. Therefore, this study will provide useful insights to assess TC-induced damages from the power system perspective in South Korea.
Preprint
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In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed-integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.
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Transmission lines systems are important components of the electrical power infrastructure. However, these systems are vulnerable to damage from high wind events such as hurricanes. This study presents the results from a 1:50 scale aeroelastic model of a multi span transmission lines system subjected to simulated hurricane winds. The transmission lines system considered in this study consists of three lattice towers, four spans of conductors and two end frames. The aeroelastic tests were conducted at the NSF NHERI Wall of Wind Experimental Facility (WOW EF) at the Florida International University (FIU). A horizontal distortion scaling technique was used in order to fit the entire model on the WOW turntable. The system was tested at various wind speeds ranging from 35 m/s to 78 m/s (equivalent full scale speeds) for varying wind directions. A system identification (SID) technique was used to evaluate experimental based along wind aerodynamic damping coefficients and compare with their theoretical counterparts. Comparisons were done for two aeroelastic models: (i) a self supported lattice tower, and (ii) a multi span transmission lines system. A buffeting analysis was conducted to estimate the response of the conductors and compare it to measured experimental values. The responses of the single lattice tower and the multi span transmission lines system were compared. The coupling effects seem to drastically change the aerodynamic damping of the system, compared to the single lattice tower case. The estimation of the drag forces on the conductors are in good agreement with their experimental counterparts. The incorporation of the change in turbulence intensity along the height of the towers appears to better estimate the response of the transmission tower, in comparison with previous methods which assumed constant turbulence intensity. Dynamic amplification factors and gust effect factors were computed, and comparisons were made with code specific values. The resonance contribution is shown to reach a maximum of 18% and 30% of the peak response of the stand alone tower and entire system, respectively.
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Power systems are undergoing transformations in order to address the intermittent power challenges due to the strong growth of renewable generating units, decrease vulnerability to failures and improve their resilience in order to provide service that is more reliable. In this context, one of the main concerns is related to the potential consequences of failures caused by rare and difficult-to-predict adverse weather events. The growing increase in severe weather events makes the assessment of such events increasingly important to prepare the system for these disasters. In this work, we originally propose to combine failure rate models due to adverse weather with an optimum power flow-based simulation system. The model is solved by a custom Monte Carlo scheme to quantify the impact of weather events on the reliability of the power grid. The approach is demonstrated with reference to the IEEE-RTS network test case.
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Recently, many investigations have been studied on the effects of the uncommon extreme events like hurricanes in electrical distribution grids. These events leads to damage to distribution grid equipments, and they are cause widespread blackouts. This paper presents short-term resilience enhancement of the residential sections against hurricane at day-ahead. The lines outage in the electrical distribution grid is implemented as stochastic modeling by hurricane. The resilience enhancement is done in the multiple energy systems such as integrated gas, heat and electrical. The three-stage multi-objective functions optimization is proposed for resilience-oriented enhancement. The proposed strategy for resilience-oriented enhancement including installation of the mobile generators (MGs), implementation of the demand side management (DSM) strategy and local generation (LG) in each stage. In first stage optimization, minimizing the installation costs of the MGs is modeled subject to lines outage. The DSM strategy is modeled in second stage optimization as minimization of the electrical demand deviation than optimal demand level at day-ahead. Then, minimization of the 1) operation cost and 2) loss of power supply probability (LPSP) index are considered in third stage optimization. In addition to, installation of the MGs and DSM in first and second stages; LG by electrical storage systems (ESSs) in third stage optimization is taken into accounted. The effectiveness of the proposed strategies is confirmed on the IEEE 33-bus distribution grid as three case studies by numerical simulation. The obtained results for operation cost and LPSP index in first case are $1,098,833.4 and 0.11 kW, respectively. In second case, LPSP index is minimized by %52.2 than first case. Finally, operation cost and LPSP in third case and with participation of the ESS are reduced by %52.2 and %0.78 in comparison to second case, respectively.
Conference Paper
The drag coefficients and gust response factors of overhead lattice transmission towers are the main aerodynamic characteristics of such structures that help determine the equivalent static wind loads. ASCE Manual No. 7 recommends values for the global drag coefficients of generic lattice frames based on their solidity ratio. Moreover, ASCE Manual No. 74 suggests gust response factor for transmission towers in terms of the height of the structure. However, the solidity ratio and the height of lattice transmission towers do not encapsulate the variation in aerodynamic properties along the sections of these structures. Therefore, there is a need for assessing local drag coefficients and gust response factors of transmission towers to provide more reliable estimates of wind-induced loads. To address these issues, in this study, the drag coefficients and gust response factors of a transmission tower are estimated using wind tunnel experiments and a Kalman filtering method. For this purpose, along-wind and crosswind responses are obtained from a series of aeroelastic wind tests, conducted at the National Science Foundation (NSF) Natural Hazard Engineering Research Infrastructure (NHERI) Wall of Wind (WOW) Experimental Facility (EF) at Florida International University (FIU). The tests were carried out on a scaled high-voltage lattice transmission tower model under simulated wind speeds and wind directions. The findings of this study indicate that the existing equations in ASCE Manual No. 7 and Manual No. 74 for square trussed towers may result in an underestimation of wind loads on lattice towers by 12% and 13% for drag coefficients and gust response factors, respectively.
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