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Distribution grid reliability and resilience has become a major topic of concern for utilities and their regulators. In particular, with the increase in severity of extreme events, utilities are considering major investments in distribution grid assets to mitigate the damage of highly impactful outages. Communicating the overall economic and risk-mitigation benefits of these investments to regulators is an important element of the approval process. Today, industry reliability and resilience planning practices are based largely on methods that do not take explicit account of risk. This paper proposes a practical method for identifying optimal combinations of investments in new line segments and storage devices while considering the balance between the risk associated with high impact low probability events and the reliability related to routine failures. We show that this method can be scaled to address large scale networks and demonstrate its benefits using a Target Feeder from the Commonwealth Edison Reliability Program., Comment: in IEEE Transactions on Power Systems, 2023

Managing the stability of today's electric power systems is based on decades of experience with the physical properties and control responses of large synchronous generators. Today's electric power systems are rapidly transitioning toward having an increasing proportion of generation from nontraditional sources, such as wind and solar (among others), as well as energy storage devices, such as batteries. In addition to the variable nature of many renewable generation sources (because of the weather-driven nature of their fuel supply), these newer sources vary in size - from residential-scale rooftop systems to utility-scale power plants - and they are interconnected throughout the electric grid, both from within the distribution system and directly to the high-voltage transmission system. Most important for our purposes, many of these new resources are connected to the power system through power electronic inverters. Collectively, we refer to these sources as inverter-based resources.

The costs that power interruptions impose on customers and society have emerged as essential considerations for decision-making about power system reliability and resilience. While the direct costs of localized and relatively short-duration power interruptions are well-understood, little is known about the full impact of widespread and long-duration power interruptions, especially the indirect costs and related economy-wide impacts of these events. As a result, the costs of widespread and long-duration power interruptions are generally not or only incompletely considered in utility planning activities. This paper describes a new approach for estimating the economic costs of widespread and long-duration power interruptions. This method involves using survey responses from utility customers to calibrate a regional economic model that can estimate both the direct and indirect costs of these events. Including better estimates of these costs will enhance both the comprehensiveness and completeness of the considerations relied on to support utility planning decisions, especially on grid-hardening strategies and other capital-intensive investments in electricity sector resilience.

Modern grid monitoring equipment enables utilities to collect detailed records of power interruptions. These data are aggregated to compute publicly reported metrics describing high-level characteristics of grid performance. The current work explores the depth of insights that can be gained from public data, and the implications of losing visibility into heterogeneity in grid performance through aggregation. We present an exploratory analysis examining three years of high-resolution power interruption data collected by archiving information posted in real-time on the public-facing website of a utility in the Western United States. We report on the size, frequency and duration of individual power interruptions, and on spatio-temporal variability in aggregate reliability metrics. Our results show that metrics of grid performance can vary spatially and temporally by orders of magnitude, revealing heterogeneity that is not evidenced in publicly reported metrics. We show that limited access to granular information presents a substantive barrier to conducting detailed policy analysis, and discuss how more widespread data access could help to answer questions that remain unanswered in the literature to date. Given open questions about whether grid performance is adequate to support societal needs, we recommend establishing pathways to make high-resolution power interruption data available to support policy research.

Circulating autoantibodies (auto-Abs) neutralizing high concentrations (10 ng/ml; in plasma diluted 1:10) of IFN-α and/or IFN-ω are found in about 10% of patients with critical COVID-19 (coronavirus disease 2019) pneumonia but not in individuals with asymptomatic infections. We detect auto-Abs neutralizing 100-fold lower, more physiological, concentrations of IFN-α and/or IFN-ω (100 pg/ml; in 1:10 dilutions of plasma) in 13.6% of 3595 patients with critical COVID-19, including 21% of 374 patients >80 years, and 6.5% of 522 patients with severe COVID-19. These antibodies are also detected in 18% of the 1124 deceased patients (aged 20 days to 99 years; mean: 70 years). Moreover, another 1.3% of patients with critical COVID-19 and 0.9% of the deceased patients have auto-Abs neutralizing high concentrations of IFN-β. We also show, in a sample of 34,159 uninfected individuals from the general population, that auto-Abs neutralizing high concentrations of IFN-α and/or IFN-ω are present in 0.18% of individuals between 18 and 69 years, 1.1% between 70 and 79 years, and 3.4% >80 years. Moreover, the proportion of individuals carrying auto-Abs neutralizing lower concentrations is greater in a subsample of 10,778 uninfected individuals: 1% of individuals 80 years. By contrast, auto-Abs neutralizing IFN-β do not become more frequent with age. Auto-Abs neutralizing type I IFNs predate SARS-CoV-2 infection and sharply increase in prevalence after the age of 70 years. They account for about 20% of both critical COVID-19 cases in the over 80s and total fatal COVID-19 cases. © 2021 The Authors, some rights reserved.

Author(s): Sanstad, Alan H; Zhu, Qianru; Leibowicz, Benjamin; Larsen, Peter H; Eto, Joseph H | Abstract: The risks of long-duration, widespread interruptions (LDWIs) in electrical power are a concern of U.S. regulators, the electric power industry, and stakeholders. Those responsible for making decisions about increasingly costly investments to prevent such interruptions and to facilitate rapid recovery when they do occur need relevant information on which to base those decisions. Although a number of studies have examined the physical and engineering impacts of extreme weather and other precipitating events on the bulk power system, decision makers evaluating investments in preventive strategies need information on the costs of past power interruptions and the benefits of preventing them in the future. This paper contributes to addressing this need by offering six case studies that detail the economics, at the level of the utility service territory, of power interruptions caused by extreme weather and lasting from a few days to several weeks. These intermediate-duration interruptions have been, and will continue to be, the most common type of major electric power disruption. They are longer than the short-term disruptions addressed in utility reliability planning, but not as long or widespread geographically as the national-scale interruptions with durations of many weeks, months, or longer that have been examined in some recent studies. Through case studies, we are able to address the following five questions that have important policy and long-term planning implications for reducing power system vulnerabilities: 1. How do utilities assess the costs of system damage caused by extreme weather and the costs of recovering from this damage? 2. How do utilities estimate customer costs of past power interruptions? 3. How do utilities or others estimate the costs and benefits of investments to reduce power system vulnerabilities to future extreme weather events? 4. How do utilities and regulators use the concept of resilience in economic assessments of extreme weather impacts and the value of preventive investments? 5. How do regulatory processes influence utilities’ economic analysis related to power interruptions? Finally, the case studies reveal a number of areas in which further research could be beneficial to utilities and regulators dealing with risks associated with LDWIs.

Author(s): Faris, Anthony; Kosterev, Dmitry; Eto, Joseph H; Chassin, Dave | Abstract: In 2015, NERC’s reliability standards were revised to require the use of dynamic load models in transmission planning studies. To comply with the standards, planners must use load models that explicitly represent the dynamic behavior of the different constituents of load at each load bus within their transmission planning models. The most important of these constituents are motor-driven and power electronics-based loads. Collectively, these representations are known as composite load models. In anticipation of the compliance date for the new standards, NERC’s Load Modeling Task Force (LMTF), in 2019, initiated a field test of composite load models involving the regional reliability planning entities. In support of the field test, DOE and BPA researchers developed region-specific composite load models that could be assigned to each non-industrial load bus in the planning models for each of the North American interconnections. Separate models were developed for each hour of a summer peak day, a winter peak day, and a spring light-load day. This report is the technical documentation for the load composition analysis that was conducted to develop these non-industrial composite load models.

There has been a limited amount of peer-reviewed literature on long-term trends in electricity reliability including the underlying factors that impact reliability across the United States. In this analysis, we considered up to 16 years of data from 203 U S. utilities—representing about 70% of electricity sales. Annual frequency of interruptions for an average customer—at the regional and U.S. national-level—has generally decreased over this timeframe. But we do not find that there is a statistically significant trend in the annual duration of interruptions for an average customer. We find that more explicit measures of severe weather are correlated with reliability. We are able to explain 7% and 16% of past variation in the reliability metrics system average interruption duration and frequency indices, respectively, is due to severe weather—a significant improvement over earlier studies. We find that current year spending by utilities is correlated with worse reliability and that cumulative spending over the preceding three years is correlated with better reliability. Finally, we demonstrate that using a statistical instrument to represent the annual frequency of interruptions for an average customer can greatly improve analysis of trends in the annual duration of interruptions for an average customer.

© 2018 Elsevier Ltd Electricity reliability and resiliency have become a topic of heightened interest in recent years in the United States. As utilities, regulators, and policymakers determine how to achieve optimal levels of electricity reliability while considering how best to prepare for future disruptions in power, the related issue of how much it costs when customers lose power remains a largely unanswered question. In 2006, Lawrence Berkeley National Laboratory developed an end-use based framework that estimates the cost of power interruptions in the U.S that has served as a foundational paper using the best available, yet far from perfect, information at that time. Since then, an abundance of work has been done to improve the quality and availability of information that now allow us to make a much more robust assessment of the cost of power interruptions to U.S. customers. In this work, we find that the total U.S. cost of sustained power interruptions is $44 billion per year (2015-$) −25% more than the $26 billion per year in 2002-$ (or $35 billion per year in 2015-$) estimated in our 2006 study.

Inverters using phase-locked loops for control depend on voltages generated by synchronous machines to operate. This might be problematic if much of the conventional generation fleet is displaced by inverters. To solve this problem, grid-forming control for inverters has been proposed as being capable of autonomously regulating grid voltages and frequency. Presently, the performance of bulk power systems with massive penetration of grid-forming inverters has not been thoroughly studied as to elucidate benefits. Hence, this paper presents inverter models with two grid-forming strategies: virtual oscillator control and droop control. The two models are specifically developed to be used in positive-sequence simulation packages and have been implemented in PSLF. The implementations are used to study the performance of bulk power grids incorporating inverters with gridforming capability. Specifically, simulations are conducted on a modified IEEE 39-bus test system and the microWECC test system with varying levels of synchronous and inverter-based generation. The dynamic performance of the tested systems with gridforming inverters during contingency events is better than cases with only synchronous generation.

This study examines the relationship between annual changes in electricity reliability reported by a large cross-section of U.S. electricity distribution utilities over a period of 13 years and a broad set of potential explanatory variables, including weather and utility characteristics. We find statistically significant correlations between the average number of power interruptions experienced annually and above average wind speeds, precipitation, lightning strikes, and a measure of population density: customers per line mile. We also find significant relationships between the average number of minutes of power interruptions experienced and above average wind speeds, precipitation, cooling degree-days, and one strategy used to mitigate the impacts of severe weather: the amount of underground transmission and distribution line miles. Perhaps most importantly, we find a significant time trend of increasing annual average number of minutes of power interruptions over time—especially when interruptions associated with extreme weather are included. The research method described in this analysis can provide a basis for future efforts to project long-term trends in reliability and the associated benefits of strategies to improve grid resiliency to severe weather—both in the U.S. and abroad.

For a microgrid with a mix of distributed energy resources (DERs), major challenges on its survivability are found in the islanded condition. In particular, a sudden loss of generation or a large and fluctuating load could force the microgrid to operate near its capacity limits. Such a situation can cause a cascading collapse of the mixed-source microgrid, even when the load demand is within the system's power rating. This condition was observed during several tests carried out at the Consortium for Electric Reliability Technology Solutions Microgrid Test Bed. This paper analyzes the root causes behind the collapse. It highlights that the capacity of a low-inertia system to support load changes is contributed by faster responding DERs initially. Therefore, the microgrid is particularly susceptible if the faster responding DERs do not have adequate reserve margin. Two control methods are evaluated for providing safeguards to these DERs and prevent the system collapse.

The problem of whether, where, when, and what types of transmission facilities to build in terms of minimizing costs and maximizing net economic benefits has been a challenge for the power industry from the beginning-ever since Thomas Edison debated whether to create longer dc distribution lines (with their high losses) or build new power stations in expanding his urban markets. Today?s planning decisions are far more complex, as grids cover the continent and new transmission, generation, and demand-side technologies emerge.

© The Institution of Engineering and Technology 2018 This study reports on the results from efforts by the Institute of Electric and Electronics Engineers (IEEE) Distribution Reliability Working Group (DRWG) and the U.S. Energy Information Administration (EIA) to improve the usefulness of reliability metrics by developing and then applying consistent, yet distinct measures of the continuity of supply based on the portion of the electric power system from which power interruptions originate: the lower voltage distribution system versus the high-voltage bulk power system. The modified metrics better support reliability planning in the US because they separately measure the effectiveness of actions to improve reliability made by the two distinct groups of firms (and their regulators or oversight bodies) that are responsible for planning and operating each portion of the US electric power system. The authors then present for the first time quantitative information on the reliability of each portion of the US electric power system. When reliability is measured using the system average interruption duration index and the system average interruption frequency index, they find that the distribution system accounts for at least 94 and 92%, respectively, of all interruptions. They also find that these relationships have been stable over the recent past.

Author(s): Larsen, P; Kristina S. H., Kristina SH; Eto, Joseph; Sanstad, Alan | Abstract: The costs to electric utility customers from short-term, limited geographic-scale power disruptions have been studied for many years. However, there is increasing interest among regulators, policy-makers, and utilities in developing and implementing methods for analyzing interruptions that are of longer duration (days, weeks, or longer) and of a larger geographic scope (entire metropolitan areas or regions which may extend across multiple service territories). A 2017 report from the U.S. National Academies of Sciences, Engineering, and Medicine indicated the importance of “develop[ing] comprehensive studies to assess the value to customers of improved reliability and resilience…during large-area, long-duration blackouts.” As a contribution toward fulfilling this recommendation, the U.S. Department of Energy’s (DOE) Office of Electricity’s Transmission Permitting and Technical Assistance Division, in partnership with Lawrence Berkeley National Laboratory (LBNL), convened a workshop to identify research needs and discuss potential avenues for methodological advances in the economics of widespread, long-duration power interruptions. The workshop was organized around six key themes in the economics of widespread, long-duration interruptions. LBNL conducted a competitive solicitation for white papers on these themes, and the authors selected were leading experts in academia, industry, and the non-profit sector. They were tasked with assessing the state of knowledge on particular topics, and identifying research needs and promising directions to expand it. These topics included definitions of resilience and reliability; regional economic modeling approaches; uncertainty quantification; data challenges and opportunities; contingent valuation survey techniques; and reduced-form analytical tools for assessing the impacts of power interruptions of this scale. The authors of the six white papers presented their findings at the workshop, titled “Frontiers in the Economics of Widespread, Long-Duration Power Interruptions,” and held in Washington, D.C., on March 6, 2018. Each paper was the subject of its own session, with the presentations followed by comments by assigned discussants, and in turn a general discussion of the papers, their findings, and the issues they raised. In addition to the authors, discussants, and LBNL and DOE staff, the workshop participants comprised government, industry, and academic thought leaders from across the country.

More accurate information is needed for utilities, regulators, and policymakers in assessing the reliability of the U.S. electric power system. This work focuses on recent efforts by the Institute of Electric and Electronics Engineers (IEEE) Distribution Reliability Working Group (DRWG) and the U.S. Energy Information Administration (EIA) to assess reliability performance of distribution systems separately from transmission systems. When reliability is measured using the system average interruption duration index (SAIDI), we find the distribution system is responsible for 95% or more of all interruptions. When reliability is measured using the system average interruption frequency index (SAIFI), we find the distribution system is responsible for 91% or more of all interruptions. Major events do not appear to change these results. Based on information collected by IEEE, we find that these trends have been fairly stable in recent years. We hope this work facilitates dialogue among utilities and their regulatory agencies and state or local government that ensures further enhancements or improvements can be informed by more detailed reliability metric information. We believe this work informs discussion regarding how much and in what part of the power system to invest in when assessing improvements in electric reliability.

© 2017 Elsevier Ltd This analysis integrates regional models of power system reliability, output from atmosphere-ocean general circulation models, and results from the Interruption Cost Estimate (ICE) Calculator to project long-run costs to electric utility customers from power interruptions under different future severe weather and electricity system scenarios. We discuss the challenges when attempting to model long-run costs to utility customers including the use of imperfect metrics to measure severe weather. Despite these challenges, initial findings show that discounted cumulative customer costs, through the middle of the century, could range from $1.5-$3.4 trillion ($2015) without aggressive undergrounding of the power system and increased utility operations and maintenance (O&M) spending and $1.5-$2.5 trillion with aggressive undergrounding and increased spending. By the end of the century, cumulative customer costs could range from $1.9-$5.6 trillion (without aggressive undergrounding and increased spending) and $2.0-$3.6 trillion (with aggressive undergrounding and increased spending). We find that, in some scenarios, aggressive undergrounding of distribution lines and increased O&M spending is not always cost-effective. We conclude by identifying important topics for follow-on research, which have the potential to improve the cost estimates of this model.

This paper presents novel analyses of high-resolution wind power and electric system load time series data. We use a discrete wavelet transform to resolve the data into independent time series of step changes (deltas) at different time scales, and present a variety of statistical metrics as a function of the time scale. We show that the probability distribution for wind power deltas is not Gaussian, has an exponential shape near the center and is well fit by a power-law in the tails. We provide a physical interpretation for the observed power-law behavior, and discuss the potential significance for modeling studies, prediction of extreme events, and the extrapolation of statistical characteristics to higher wind penetration levels. Several metrics are presented to quantify the degree of auto-correlation in the wind data, and of the correlations between wind and load. We show that the shape of the autocorrelation function is the same at different time scales, a property of self-similar statistical processes that is consistent with the observed power-law behavior.

The Office of Electricity Delivery and Energy Reliability of the U.S. Department of Energy (DOE), the Bonneville Power Administration (BPA), and industry and academic collaborators have leveraged resources to develop a new, cost-effective method for validating power plant models using synchrophasor data.

© 1969-2012 IEEE. IEEE Standard 1366 offers a method for segmenting reliability performance data to isolate the effects of major events from the underlying year-to-year trends in reliability. Recent analysis by the IEEE Distribution Reliability Working Group (DRWG) has found that reliability performance of some utilities differs from the expectations that helped guide the development of the Standard 1366 method. This paper proposes quantitative metrics to evaluate the performance of the Standard 1366 method in identifying major events and in reducing year-to-year variability in utility reliability. The metrics are applied to a large sample of utility-reported reliability data to assess performance of the method with alternative specifications that have been considered by the DRWG. We find that none of the alternatives perform uniformly 'better' than the current Standard 1366 method. That is, none of the modifications uniformly lowers the year-to-year variability in System Average Interruption Duration Index without major events. Instead, for any given alternative, while it may lower the value of this metric for some utilities, it also increases it for other utilities (sometimes dramatically). Thus, we illustrate some of the trade-offs that must be considered in using the Standard 1366 method and highlight the usefulness of the metrics we have proposed in conducting these evaluations.

End-use load composition is rapidly changing, particularly towards electronically-coupled or inverter-based load. In particularly, the proliferation of electric vehicles (EV) is expected to continue as consumer prices decrease and charging capability expands. The electric power grid has experienced disruptive technologies since its inception, and it is imperative to study potential future impacts in order to prepare for them if they do occur. This paper highlights fundamental load response of EV chargers, and proposes a set of requirements that are deemed “grid-friendly” to the overall stability and control of the bulk power system at the interconnection-level. Loads exhibiting at least constant current characteristic (constant impedance are very much desirable if possible) with ride-through and reconnection capability will ensure long-term reliability of the grid. The goal is to outline these grid-level requirements in an effort to collaborate with the manufacturing community for sustained grid support from the end-use loads.

This paper focuses on testing the dynamic behavior of single-phase air conditioner motors on distribution power networks. The primary goal is to study the phenomenon of delayed voltage recovery by applying multiple instances of a custom-built single-phase induction motor model on a given distribution feeder. This model was developed in an Electromagnetic Transients Program (EMTP) simulation environment. The motors were subjected to voltage disturbances seen in feeders experiencing the fault-induced delayed voltage recovery (FIDVR) phenomenon. To study the FIDVR phenomenon, a range of voltage depressions were simulated for predetermined system conditions. This paper describes a point-on-wave model development and simulation study that supports a broader investigation of the effect of air conditioning and similar loads on the recovery of electric utility voltage after faults.

For a microgrid with a mix of distributed energy resources (DERs), major challenges on its survivability are found in the islanded condition. In particular, a sudden loss of generation or a large and fluctuating load could force the microgrid to operate near its capacity limits. Such a situation can cause a cascading collapse of the system, even when the load demand is within the DER's kW rating — as observed during several tests at the Consortium for Electric Reliability Technology Solutions (CERTS) Microgrid Test Bed. This paper analyzes the prime-mover stalling phenomena behind the system collapse. It highlights how the reserve margin of the system is lowered during transient conditions. Furthermore, two control methods are evaluated to resolve the microgrid collapse problem.

Utilities have increasingly been applying customer interruption costs to reliability and resiliency planning, using tools such as the Interruption Cost Estimate (ICE) Calculator. Furthermore, applications of value-based reliability planning can be found in the industry and academic literature for generation, transmission and distribution planning and at the federal policy-making level. With the wealth of guidance documentation and planning examples that is now available, the utility industry is positioned to further expand upon the application of value-based reliability planning by integrating customer interruption costs into outage management systems (OMS) and their associated business processes. This paper summarizes how (and why) to integrate customer interruption costs into OMS and discusses how utilities can act on customer interruption cost information to provide more value to customers.