Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SA45

the system model. To solve the modeling problem, we propose a support vector regression (SVR) approach to reveal the mapping rules between different variables and recover useful variables based on physical understanding and data mining. We illustrate the advantages of using the SVR model over traditional regression method which finds line parameters in distribution grids. 2 - Real-time Prediction of the Duration of Distribution System Outages Baosen Zhang, University of Washington, Seattle, WA, United States, Aaron Jaech, Mari Ostendorf, Daniel Kirschen This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors. The initial duration prediction is made based on environmental factors, and it is updated based on incoming field reports using natural language processing to automatically analyze the text. Experiments using 15 years of outage records show good initial results and improved performance leveraging text. Case studies show that the language processing identifies phrases that point to outage causes and repair steps. 3 - Data-driven Learning Methods for Detecting and Mitigating Load Redistribution Attacks Lalitha Sankar, Arizona State University, 551 E. Tyler Mall, Tempe, AZ, 85281, United States The electric power grid is a critical cyber-physical infrastructure that is vulnerable to data injection attacks. We present data-driven detection techniques against a wide class of cyberattacks that maliciously redistribute loads by modifying measurements including nearest neighbor, SVM, and neural networks. The detectors are both trained and tested using publicly available PJM zonal load data. Mapping the dataset to the IEEE 30-bus system, the efficacy of the detectors, designed in a semi-supervised manner with labeled non-anomalous historical data, is tested with both attacked and non-anomalous data. We show that all three detectors designed are very accurate. Power System Resilient Design and Optimization Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Seyedamirabbas Mousavian, Clarkson University, Potsdam, NY, 13699-5790, United States 1 - An Accurate Charging Model of Battery Energy Storage Hrvoje Pandzic, University of Zagreb, Unska 3, Zagreb, 10000, Croatia Battery storage is becoming an important part of modern power systems and its operation model needs to be integrated in the market clearing and investment models. However, models that commonly represent the operation of large-scale battery energy storage used in scientific literature are inaccurate. In case energy storage is unable to absorb or deliver scheduled quantities, market players might suffer monetary losses and power system operation might be jeopardized. In this work, an accurate battery charging model is formulated, which closely reflects the actual battery charging constraints. 2 - Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach Yury Dvorkin, New York University, Metrotech Center, Fay Street, Brooklyn, NY, 11201, United States We propose a two-stage optimization model that optimizes investments in transportable ES units, i.e. those that can be moved using regular transportation routes, in the first stage and can re-route them in the second stage to form dynamic microgrids and to avoid the expected load shedding caused by natural disasters. We show that this model cannot be solved efficiently with off-the-shelf solvers, even in relatively small instances, and therefore apply a progressive hedging algorithm. 3 - Robust Estimation of Reactive Power for an Active Distribution System Zhengshuo Li, Southern Methodist University, Dallas, TX, United States, Jianhui Wang, Hongbin Sun, Feng Qiu, Qinglai Guo Reactive power potential (RPP) of an active power system (DPS) is the range between the maximal inductive and capacitive reactive power the DPS can reliably provide to transmission networks. This paper proposes a robust RPP estimation method based on two-stage robust optimization, where the RPP is pre- estimated in the first stage and its robust feasibility for every instance of the uncertainty in the DPS is checked in the second stage. The column-and-constraint generation algorithm is adopted to solve this model in finite iterations. Case studies confirms that this robust method excels in yielding a completely reliable RPP. n SA45 North Bldg 228A

n SA43 North Bldg 227B The Future of Energy: A Systems Perspective Emerging Topic: Energy and Climate Emerging Topic Session Chair: Benjamin D. Leibowicz, University of Texas-Austin, Austin, TX, 78712-1591, United States 1 - U.S. Energy Infrastructure of the Future: Electricity Capacity Planning through 2050 Gopika G. Jayadev, University of Texas-Austin, Austin, TX, United States, Benjamin D. Leibowicz, Erhan Kutanoglu We develop an integrated mixed-integer programming framework that optimizes long-term capacity investments and operational schedules for energy supply and end-use technologies. Our methodology extends an existing energy-economic modeling framework. Our framework simultaneously optimizes generation investments, the locations of these investments as well as the transmission network linking the generation facilities to different demand regions. We analyze our model for the US electricity market and present our findings. 2 - A Stochastic Model of Socio-technical Regime Transitions Featuring the Impact of Policy Decisions Max Brozynski, University of Texas, Austin, 1240 Barton Hills Dr, #107, Austin, TX, 76109, United States The study of energy transitions has enjoyed much attention recently due to the scientific community’s focus on stabilizing greenhouse gas emissions. But, while historical perspectives on how energy systems transition from one regime to another are informative, a formal mathematical theory of energy system transitions is needed to bridge the gap between observing past experiences and predicting future behavior. In this paper, we present a model of socio-technical regime transitions formulated as a Markov decision process. This stochastic model captures key features of such transitions, including economies of scale, path dependence, consumer choice, and the impacts of policy decisions. 3 - Grid-integrated Renewable Energy Resource Assessments: Wind and Solar Supply Curves for China Michael R. Davidson, Harvard Kennedy School, One Brattle St., 356H, Cambridge, MA, 02138, United States Renewable energy resource assessments are important for long-term power systems planning but typically ignore or downplay operational details. In this study, building on earlier wind-focused work, we present an efficient algorithm for creating a combined solar and wind “supply curve for China building on fine geographic and hourly resolution data. We compare operational outcomes with traditional economic dispatch optimizations. 4 - Improved Energy Systems Planning and Decision-making using Integrated Assessment Research Zarrar Khan, Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500, College Park, MD, 20740, United States, Gokul Iyer This talk will discuss recent forays in integrated assessment research to improve representations of spatial, temporal, and process detail in representations of the energy sector. The talk will present applications of a U.S. focused version of the Global Change Assessment Model (GCAM-USA) to highlight the value of detailed modeling of the U.S. energy sector within the context of broader interactions of the energy sector with the economy, agriculture, land-use, climate, and water systems. n SA44 North Bldg 227C Joint Session ENRE/Practice Curated: Data Analytics for Power Systems Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Ming Jin, UC Berkeley, Berkeley, CA, 94720, United States Co-Chair: Javad Lavaei, University of California, UC, Berkeley, CA, United States 1 - Data Driven Power Flow Analysis in Distribution Grids with Incomplete System Information Yang Weng, Arizona State University, 551 E. Tyler Mall, ERC 563, Engineering Research Center (ERC), Tempe, AZ, 85281, United States, Jiafan Yu, Ram Rajagopal The increasing integration of distributed energy resources calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of

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