Scientific Seminar “Multi-timescale simulation and coordinated risk optimization of cascading outages in power systems”.
Speaker: Rui Yao (Tsinghua University, China)
When: October 1, 2015. 11 a.m. – 12 noon
Abstract. Many blackouts in electric power grids throughout the world are caused by cascading outages, which often involve complex processes in various timescales. The multi-timescale nature of cascading outages makes conventional quasi-static simulation methods inaccurate in characterizing actual evolution of outages. Here a multi-timescale cascading outage simulation model using a quasi-dynamic (QD) method is proposed, which realizes reasonable simulation of dynamics in distinct timescales. This model also provides time information of cascading outage process and thus has better practicality. As for the control and optimization of cascading outages, a risk coordinating optimization (RCO) model will be introduced. This optimization model considers dependencies among outages and its objective is well founded with clear physical meaning.
Bio. Rui Yao received the B.S. degree (with distinction) in 2007 in electrical engineering at Tsinghua University, Beijing, China, where he is currently pursuing Ph.D. degree in electrical engineering. Rui is currently a research assistant at the Department of Electrical Engineering, Tsinghua University. He was also a visiting research assistant at the University of Tennessee, Knoxville (UTK), US from Sept. 2014 to Apr. 2015. Rui’s research interests include modeling, risk assessment and mitigation of blackouts, voltage stability in power systems, etc.
Energy Systems Scientific Seminar “Dependencies and Opportunities in Joint Optimization and Control of Power and Natural Gas Transmission Systems”.
Author: Michael (Misha) Chertkov (Adjunct Professor of Skoltech Energy System Center)
When: October 5, 2015. 2-3.30 p.m.
Abstract. Misha Chertkov: “In this presentation, which can be viewed as a lecture within the “Physics and Engineering of Energy” Skoltech course or as a stand-alone research talk, I will describe opportunities but also challenges emerging in view of new dependencies between power and natural gas regional, national, and international systems.
The availability of natural gas and the need for cleaner electric power have prompted widespread installation of gas-fired power plants and caused electric power systems to depend heavily on reliable gas supplies. The use of gas generators for base load and reserve generation causes high intra-day variability in withdrawals from high pressure gas transmission systems, which leads to gas price fluctuations and supply disruptions that affect electric generator dispatch
and threaten the security of power and gas systems. The new situation sets up new problems for optimization dispatch schedule and gas compressor protocols which needs to be compared with the status quo solutions. Some early work on this emerging subject will be discussed.
I will also give a broader overview, aimed at young professionals who are interested to enter this interdisciplinary field, of variety of questions, methods and solutions from physics (of electric and gas flows), statistics, applied mathematics, optimization, control, machine learning and other theoretical engineering disciplines which are expected to play significant role in this exciting area of applied research.”
Bio. Dr. Chertkov’s areas of interest include statistical and mathematical physics applied to energy and communication networks, machine learning, control theory, information theory, computer science, fluid mechanics and optics. Dr. Chertkov received his Ph.D. in physics from the Weizmann Institute of Science in 1996, and his M.Sc. in physics from Novosibirsk State University in 1990. After his Ph.D., Dr. Chertkov spent three years at Princeton University as a R.H. Dicke Fellow in the Department of Physics. He joined Los Alamos National Lab in 1999, initially as a J.R. Oppenheimer Fellow in the Theoretical Division. He is now a technical staff member in the same division. Dr. Chertkov has published more than 150 papers in these research areas. He is an editor of the Journal of Statistical Mechanics (JSTAT), associate editor of IEEE Transactions on Control of Network Systems, member of the Editorial Board of Scientific Reports (Nature Group), a fellow of the American Physical Society (APS) and a senior member of IEEE. Dr. Chertkov is also an Adjunct Professor of the Energy Systems Center at Skoltech (Moscow).
Energy Systems Scientific Seminar “Optimal Temporal Resource Allocation Problems in Renewable Energy”.
Speaker: Bismark Singh (University of Texas at Austin)
When: October 5, 2015. 4-5.30 p.m.
Abstract. This talk consists of two optimization problems in renewable energy.
First, we study the problem of finding an optimal schedule for running a generator coupled with a wind farm to meet a promised electrical load, with the aim of maximizing revenue. The goal is to design a so-called dispatchable system, which can provide firm energy to a day-ahead or an intra-day market. Highly reliable dispatch can be modeled using joint-chance constraints, but their non-convex nature poses significant computational challenges. We study integer programming formulations of these models motivated by extended variable approaches proposed in literature. As a case study, we present results based on the wind power data from Texas, USA.
Second, we consider a pumped storage for hydro-electric power generation. By pumping water to a higher elevation when energy prices are low, and releasing water to generate electricity via a hydro turbine when prices are high, the aim is to create revenue. We formulate a stochastic dynamic program to maximize expected revenue under a stochastic model for energy prices under the assumption that we can determine the pumping-and-generating schedule. However, in reality,
a pumped-storage unit submits bids for energy-generation and energy purchase to a market. We develop a bidding strategy that will allow us to track the desired generate-pump schedule. Thus, we solve a model that yields an optimal block-bidding policy in the sense of tracking the desired stochastic generate-pump policy.
Bio. Bismark Singh is a PhD candidate in Operations Research(OR) at The University of Texas (UT) at Austin, USA. He received his Masters in OR from UT Austin in 2013, and his Bachelors in chemical engineering from the Indian Institute of Technology (IIT) Delhi in 2011.
Bismark’s research interests include applications of optimization to public health and renewable energy. On the public health side, he is involved in developing decision support tools for The Department of State Health Services, Texas. The tools are available at http://flu.tacc.utexas.edu. On the renewable energy side, he is working on wind energy and pumped-hydro storage problems.
In addition to UT Austin, Bismark has help research positions at University of Nottingham (England), Indian Institute of Science Education and Research (India), York University (Canada), and Perm State University (Russia). For more information about him, please visit: https://www.linkedin.com/pub/bismark-singh/45/14b/a9.