A pinboard by
Laurel Dunn

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.


A spatial-temporal vulnerability assessment to support the building of community resilience against power outage impacts

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

An optimal integrated planning method for supporting growing penetration of electric vehicles in distribution systems

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

Privacy in Information-Rich Intelligent Infrastructure

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