PhD Candidate, UC Berkeley
Examines whether electric vehicles are a cost-effective source of backup power during power outages.
Electric power systems have always played a major role in supporting technology advancements that have transformed modern societies. Historic examples include illumination and refrigeration, more recent examples include data centers and electric heating. Power systems have become more reliable to support these loads. U.S. utilities spend billions of dollars each year to maintain and improve the reliability of the grid. However, power outages still happen, and climate change could make power outages more frequent and more severe.
We have lots of options for making the grid more reliable or for reducing the implications of power outages. Utilities can invest in more robust infrastructure or reduce exposure to the elements by putting infrastructure underground. Grid customers can invest in backup generators or energy storage systems that allow them to continue to power important loads (like data centers, or even a refrigerator) even without power from the grid. These investments are really expensive, and all of these stakeholders can make more informed investment decisions by looking at data on past power outages, or by making predictions of future power outages.
My research looks at how various stakeholders (e.g., utilities, grid customers, and regulators) can leverage data to optimize the investments they make to reduce the impacts of power outages.
Abstract: Incident data about disruptions to the electric power grid provide useful information that can be used as inputs into risk management policies in the energy sector for disruptions from a variety of origins, including terrorist attacks. This article uses data from the Disturbance Analysis Working Group (DAWG) database, which is maintained by the North American Electric Reliability Council (NERC), to look at incidents over time in the United States and Canada for the period 1990-2004. Negative binomial regression, logistic regression, and weighted least squares regression are used to gain a better understanding of how these disturbances varied over time and by season during this period, and to analyze how characteristics such as number of customers lost and outage duration are related to different characteristics of the outages. The results of the models can be used as inputs to construct various scenarios to estimate potential outcomes of electric power outages, encompassing the risks, consequences, and costs of such outages.
Pub.: 21 Jul '07, Pinned: 28 Jun '17
Abstract: 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.
Pub.: 25 Oct '13, Pinned: 28 Jun '17
Abstract: Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two-step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two-step outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.
Pub.: 25 Oct '16, Pinned: 28 Jun '17
Abstract: Publication date: Available online 3 January 2017 Source:Technological Forecasting and Social Change Author(s): Thomas Münzberg, Marcus Wiens, Frank Schultmann Power outages are among the most serious Critical Infrastructure (CI) disruptions and require effective disaster management with collaboration of affected CI providers and disaster management authorities. To support building community resilience, we introduce a vulnerability assessment which allows an enhanced spatial-temporal understanding of initial power outage impacts. Using the assessment enables planers to better identify which and when CIs become vulnerable and how important they are in comparison to other CIs before the overall crisis situation escalates and unmanageable cascading effects occur. The assessment addresses the initial phase of a power outage and corresponding early measures of local risk and crisis management organizations according to the German disaster management system. The assessment is an indicator-based approach which is extended to consider time-depending effects through time-referenced demand and the depletion of Coping Capacity Resources (CCR). The estimation of the relevance of CIs regarding the provision of vital services and products is addressed by a modified Delphi method. In addition, an expert survey was conducted to shed light on the evaluation of coping resources. In this paper, we describe the components of the assessment and propose different aggregation approaches which each enhances the understanding of spatial-temporal impacts of a power outage, and, hence, increases the forecasting capability for disaster management authorities. For demonstration purposes, the assessment is implemented for the case of the city of Mannheim, Germany.
Pub.: 06 Jan '17, Pinned: 28 Jun '17
Abstract: This paper proposes a multi-year expansion planning method for enabling distribution systems to support growing penetrations of plug-in electric vehicles. As distinct from the existing studies, the temporal characteristics of charging loads and their reliability impacts are especially focused in our work. To achieve this, a novel dual-stage optimization framework is developed. The proposed method considers the capacity reinforcement of distribution systems in conjunction with their operation decisions and coordinates them under the same frame so as to minimize the total system costs for accommodating electric vehicles. The uncertainties associated with renewable energy generation, charging behaviors, and conventional load demand are represented by multiple probabilistic scenarios. To fully reveal the impacts of electric vehicle integration, both uncontrolled and coordinated charging schemes are considered in our analysis. Furthermore, as charging loads bring about extra demand to the grid, the reliability criteria is also taken into account in the proposed model. Using a heuristic algorithm combined with reliability analysis, the optimal solution for the concerned problem can be determined, which involves the best timing, locations, and capacities for installation of distributed generation units and network components. The effectiveness of the proposed framework is examined based on a 38-bus test system and the obtained results verify the performance of the approach.
Pub.: 06 Mar '17, Pinned: 28 Jun '17
Abstract: Intelligent infrastructure will critically rely on the dense instrumentation of sensors and actuators that constantly transmit streaming data to cloud-based analytics for real-time monitoring. For example, driverless cars communicate real-time location and other data to companies like Google, which aggregate regional data in order to provide real-time traffic maps. Such traffic maps can be extremely useful to the driver (for optimal travel routing), as well as to city transportation administrators for real-time accident response that can have an impact on traffic capacity. Intelligent infrastructure monitoring compromises the privacy of drivers who continuously share their location to cloud aggregators, with unpredictable consequences. Without a framework for protecting the privacy of the driver's data, drivers may be very conservative about sharing their data with cloud-based analytics that will be responsible for adding the intelligence to intelligent infrastructure. In the energy sector, the Smart Grid revolution relies critically on real-time metering of energy supply and demand with very high granularity. This is turn enables real-time demand response and creates a new energy market that can incorporate unpredictable renewable energy sources while ensuring grid stability and reliability. However, real-time streaming data captured by smart meters contain a lot of private information, such as our home activities or lack of, which can be easily inferred by anyone that has access to the smart meter data, resulting not only in loss of privacy but potentially also putting us at risk.
Pub.: 06 Jun '17, Pinned: 28 Jun '17
Abstract: A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060–15065] argue that it is feasible to provide “low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055”, with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.
Pub.: 19 Jun '17, Pinned: 28 Jun '17