MAE Seminar: Uncertainty Management in Gas Pipeline Operations

Abstract: Electricity generation in the United States today relies primarily on natural gas-fired power plants and distributed renewable sources. Gas-fired generators are used to provide base electric load as well as to balance out variable and intermittent production by wind and solar resources. As a result, gas pipeline systems must support quickly changing deliveries and high volumes for which they were not designed, and the increase in net load can cause pipelines to reach their capacity. This has complicated gas pipeline operations and increased gas price volatility and curtailments of gas and electricity service. Today’s gas pipeline managers must meet the simultaneous challenges of coordinating with power grid operations, responding to growing variability in loads, reducing greenhouse gas emissions, and transitioning away from fossil fuels. In this talk, I will review recent trends and challenges for gas pipelines and the bulk power system, and then focus on methods for managing uncertainty in gas pipeline operations. Uncertainty of several different types arises from power grid operations and affects wholesale gas consumption, and various approaches have been developed to model and mitigate these effects. Recent methods can determine the capacity of a natural gas transmission system to service uncertain consumption. The goal is to provide probabilistic guarantees on flow schedules to meet operating requirements and provide given temporary reserves of energy in the form of gas that a pipeline stores. Finally, the blending of hydrogen produced using renewable energy into natural gas pipelines is considered as a means of displacing fossil gas and reducing carbon emissions. I will discuss how such blending affects gas pipeline flow physics, energy capacity and economics, as well as the additional network modeling needed to extend pipeline optimization to this gas mixture.

Bio: Anatoly Zlotnik is a staff scientist in the Theoretical Division at Los Alamos National Laboratory, where he was previously a postdoctoral research associate at the Center for Nonlinear Studies. He has B.S. and M.S. degrees in systems and control engineering from Case Western Reserve University, an M.S. in applied mathematics from the University of Nebraska - Lincoln, and a Ph.D. in systems science and mathematics from Washington University in St. Louis. He has led and contributed to energy systems modeling and analysis projects at LANL and is author or co-author of over 50 peer-reviewed journal and conference publications on optimization and optimal control and applications to energy systems, neural engineering, and magnetic resonance spectroscopy. His research interests are in dynamical systems, optimal control, nonlinear oscillations, network science, and scientific computing, with applications to energy systems and neural engineering.