Roshanak Nateghi

Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.

Predicting Hurricane Power Outages to Support Storm Response Planning

Hurricanes regularly cause widespread and prolonged power outages along the U.S. coastline. These power outages have significant impacts on other infrastructure dependent on electric power and on the population living in the impacted area. Efficient and effective emergency response planning within power utilities, other utilities dependent on electric power, private companies, and local, state, and federal government agencies benefit from accurate estimates of the extent and spatial distribution of power outages in advance of an approaching hurricane. A number of models have been developed for predicting power outages in advance of a hurricane, but these have been specific to a given utility service area, limiting their use to support wider emergency response planning. In this paper, we describe the development of a hurricane power outage prediction model applicable along the full U.S. coastline using only publicly available data, we demonstrate the use of the model for Hurricane Sandy, and we use the model to estimate what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.S. energy infrastructure.

Power outage estimation for tropical cyclones: Improved accuracy with simpler models

In this article, we discuss an outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.

Simulation of tropical cyclone impacts to the U.S. power system under climate change scenarios

The links between climate change and tropical cyclone behavior are frequently studied but still uncertain. This uncertainty makes planning for climate change a difficult task. Here we focus on one area of climate-related risk: the impact of tropical cyclones on United States power systems, and we evaluate this risk through the simulation of impacts to the power system under 12 plausible scenarios in which climate change may affect tropical cyclone intensity, frequency, and location. We use a sensitivity analysis based approached grounded in the literature rather than directly simulating from specific GCM output due to the high degree of uncertainty in both the climate models and the climate-hurricane relationship. We show how changes in tropical cyclone activity influence extreme wind speeds, probability of power outages, and the proportion of people without power. While climate change and its impacts are often discussed globally, this work provides information at a much more local scale. The sensitivity of an individual area can be assessed, and the information presented here can be incorporated into planning and mitigation strategies for power systems faced with an uncertain future in a changing climate.

Statistical analysis of the effectiveness of seawalls and coastal forests in mitigating tsunami impacts in iwate and miyagi prefectures

The Pacific coast of the Tohoku region of Japan experiences repeated tsunamis, with the most recent events having occurred in 1896, 1933, 1960, and 2011. These events have caused large loss of life and damage throughout the coastal region. There is uncertainty about the degree to which seawalls reduce deaths and building damage during tsunamis in Japan. On the one hand they provide physical protection against tsunamis as long as they are not overtopped and do not fail. On the other hand, the presence of a seawall may induce a false sense of security, encouraging additional development behind the seawall and reducing evacuation rates during an event. We analyze municipality-level and sub-municipality-level data on the impacts of the 1896, 1933, 1960, and 2011 tsunamis, finding that seawalls larger than 5 m in height generally have served a protective role in these past events, reducing both death rates and the damage rates of residential buildings. However, seawalls smaller than 5 m in height appear to have encouraged development in vulnerable areas and exacerbated damage. We also find that the extent of flooding is a critical factor in estimating both death rates and building damage rates, suggesting that additional measures, such as multiple lines of defense and elevating topography, may have significant benefits in reducing the impacts of tsunamis. Moreover, the area of coastal forests was found to be inversely related to death and destruction rates, indicating that forests either mitigated the impacts of these tsunamis, or displaced development that would otherwise have been damaged.

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months.

Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards

The U.S. federal government regulates the reliability of bulk power systems, while the reliability of power distribution systems is regulated at a state level. In this article, we review the history of regulating electric service reliability and study the existing reliability metrics, indices, and standards for power transmission and distribution networks. We assess the foundations of the reliability standards and metrics, discuss how they are applied to outages caused by large exogenous disturbances such as natural disasters, and investigate whether the standards adequately internalize the impacts of these events. Our reflections shed light on how existing standards conceptualize reliability, question the basis for treating large-scale hazard-induced outages differently from normal daily outages, and discuss whether this conceptualization maps well onto customer expectations. We show that the risk indices for transmission systems used in regulating power system reliability do not adequately capture the risks that transmission systems are prone to, particularly when it comes to low-probability high-impact events. We also point out several shortcomings associated with the way in which regulators require utilities to calculate and report distribution system reliability indices. We offer several recommendations for improving the conceptualization of reliability metrics and standards. We conclude that while the approaches taken in reliability standards have made considerable advances in enhancing the reliability of power systems and may be logical from a utility perspective during normal operation, existing standards do not provide a sufficient incentive structure for the utilities to adequately ensure high levels of reliability for end-users, particularly during large-scale events.

A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S.

Abstract Severe weather-induced power outages affect millions of people and cost billions of dollars of economic losses each year. The National Association of Regulatory Utility Commissioners have recently highlighted the importance of building electricity sectors resilience, and thereby enhancing service-security and long-term economic benefits. In this paper, we propose a multi-hazard approach to characterize the key predictors of severe weather-induced sustained power outages. We developed a two-stage hybrid risk estimation model, leveraging algorithmic data-mining techniques. We trained our risk models using publicly available information on historical major power outages, socio-economic data, state-level climatological observations, electricity consumption patterns and land-use data. Our results suggest that power outage risk is a function of various factors such as the type of natural hazard, expanse of overhead T&D systems, the extent of state-level rural versus urban areas, and potentially the levels of investments in operations/maintenance activities (e.g., tree-trimming, replacing old equipment, etc.). The proposed framework can help state regulatory commissions make risk-informed resilience investment decisions.

Climate, weather, socio-economic and electricity usage data for the residential and commercial sectors in FL, U.S

This paper presents the data that is used in the article entitled “Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States” (Mukhopadhyay and Nateghi, 2017) [1]. The data described in this paper pertains to the state of Florida (during the period of January 1990 to November 2015). It can be classified into four categories of (i) state-level electricity consumption data; (ii) climate data; (iii) weather data; and (iv) socio-economic data. While, electricity consumption data and climate data are obtained at monthly scale directly from the source, the weather data was initially obtained at daily-level, and then aggregated to monthly level for the purpose of analysis. The time scale of socio-economic data varies from monthly-level to yearly-level. This dataset can be used to analyze the influence of climate and weather on the electricity demand as described in Mukhopadhyay and Nateghi (2017) [1].

A Comparison of Top-Down Statistical Models With Bottom-Up Methods for Power System Reliability Estimation in High Wind Events

The North American electric power grid is considered to be the largest and most complex technical system in the world. Reliability of this heterogeneous and highly interconnected systems is paramount since our national security, digital economy, and transportation and water systems require robust operation of the electric power system. The focus of this paper is on comparing top-down statistical approaches with bottom-up engineering models that are used in estimating the reliability of power systems during hurricanes and high wind events. It then gives a synthesized overview of the advantages and disadvantages of each approach and highlights areas in which additional research is needed.