Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SA46

4 - Communication-constrained Expansion Planning for Resilient Distribution Systems Russell Bent, Los Alamos National Laboratory, Lanl, Mail Stop C933, P.O. Box 1663, Los Alamos, NM, 87545, United States, Geunyeong Byeon, Pascal Van Hentenryck, Harsha Nagarajan We discuss the Optimal Resilient Design Problem for Distribution and Communication Systems (ORDPDC). The ORDPDC is formulated as a two-stage stochastic mixed-integer program that captures the physical laws of distribution systems, the communication connectivity of smart grid components, and a set of scenarios which specifies which components are affected by potential disasters. We discuss an exact branch-and-price algorithm for the ORDPDC which features a strong lower bound and a variety of acceleration schemes to address degeneracy. The results demonstrate the significant impact of the network topologies on the expansion plans and costs, and the computational benefits of the approach. n SA46 North Bldg 228B Assessing and Managing Interdependencies of Complex Networks Sponsored: Energy, Natural Res & the Environment/Energy Sponsored Session Chair: Feng Qiu, Argonne National Laboratory, Lemont, IL, 60439, United States Co-Chair: Matteo Spada, Paul Scherrer Institut, Villigen PSI, 5232, Switzerland 1 - Coordinated Operations of Power Grid and Water System with Renewable Integration Daniel Zuniga, Neng Fan. University of Arizona, Tucson, AZ, USA. In this talk, we propose the multi-level robust optimization model for the coordinated operations of power grid and water system with the integration of renewable energy. To solve this complex and large-scale problem, some decomposition-based algorithms will be designed. Some numerical experiments will be performed to validate model and check the efficiency of proposed algorithms. 2 - Resilient Planning of Power Grid and Water System with Renewable Integration Daniel A. Zuniga Vazquez, University of Arizona, Tucson, AZ, United States, Neng Fan In this talk, we propose the multi-level robust optimization model for the coordinated planning of power grid and water system with the integration of renewable energy. To solve this complex and large-scale problem, some decomposition-based algorithms will be designed. Some numerical experiments will be performed to validate model and check the efficiency of proposed algorithms. 3 - Secure Allocation of Power Reserves with Large Renewable Gas-fired generation provides flexibility to the power system for peak-load shaving and reserve allocation. Large penetration of renewables strengthens the gas-electric coupling. Constraints to the operations of the gas transmission system endanger the security of power supply. We assess the impact of gas constraints on the day-ahead electric power and reserve scheduling. The day-ahead scheduling of generator dispatch and reserves is determined via a stochastic, N-1 secure optimization. Minimum pressure constraints update the scheduling. In scarce- wind conditions, reserve planning including gas constraints prevents pressure violations caused by unexpected wind fluctuations. 4 - Robust Co-optimization Planning of Interdependent Electricity and Natural Gas Systems with a Joint N-1 and Probabilistic Reliability Criterion Lei Wu, Clarkson University, 8 Clarkson Avenue, P.O. Box 5720, Potsdam, NY, 13676, United States As the sharp growth of gas-fired power plants and the emergence of Power-to- Gas (PtG) intensify interdependency between electricity and gas systems, it is imperative to co-optimize the two systems for improving overall efficiency. A long-term robust co-optimization planning model is discussed, for minimizing total investment and operation costs. Beside generators, transmission lines, gas suppliers, and pipelines, PtGs and gas compressor stations are also considered as investment candidates to effectively handle wind uncertainty and compensate pressure losses. A joint N-1 and probabilistic reliability criterion to promote economical and reliable planning solutions is also considered. Penetration Under Gas Transmission Constraints Giovanni Sansavini, ETH Zurich, Zurich, Switzerland, Andrea Antenucci

5 - Estimating the Impact of Natural Gas Transmission Pipeline Network Disruptions on Power Generation Charles M. Macal, Argonne National Laboratory, Lemont, IL, United States, Eric Tatara, Jordan Jalving, Victor M. Zavala, Stephen Folga, Guenter Conzelmann Reliable natural gas delivery is critical for operators of gas-fired electric generation plants. Gas pressures and flows at delivery points must be maintained at the required conditions of the plant to ensure continuous operation. We present a method to estimate the impacts of gas pipeline disruptions on power generation using a model of dynamic flow and pressure using the graph-based modeling framework Plasmo.jl. Cases studies are presented to illustrate the model capabilities in an interstate-scale natural gas transmission pipeline that include compressor station power disruptions and pipeline breaks. The model time and space resolution enable us to provide near real-time capabilities. 6 - Risk-based Distributionally Robust Optimal Power Flow with Dynamic Line Rating Rui Gao, Georgia Institute of Technology, 755 Ferst Drive NW, ISyE Main Building, Atlanta, GA, 30332-0205, United States We propose a risk-based data-driven distributionally robust approach to investigating the optimal power flow with dynamic line rating. The risk terms, including penalties for load shedding, wind generation curtailment and line overload, are embedded into the objective function. To robustify the solution, we consider a distributional uncertainty set based on the second order moment, that captures the correlation between wind generation outputs and line ratings, and also the Wasserstein distance, that hedges against data perturbations.We show that the proposed model can be reformulated as a convex conic program. Approximations of the proposed model are suggested, which leads to a significant reduction of the number of the constraints. For practical large-scale test systems, a distributionally robust optimal power flow model with Wasserstein-distance- based distributional uncertainty set and its convex reformulation are also provided. Simulation results on the 5-bus, the IEEE 118-bus and the Polish 2736- bus test systems validate the effectiveness of the proposed models. Change Detection and Prognostics for Transient Real-World Processes Using Streaming Data Emerging Topic Session Chair: Lewis Ntaimo, Texas A&M University, 3131 TAMU, College Station, TX, 77843, United States 1 - Change Detection and Prognostics for Transient Real- WorldProcesses Using Streaming Data Satish Bukkapatnam, Texas A&M University, 3131 TAMU, 4020 Emerging Technologies Building, College Station, TX, 77843, United States, Ashif Sikandar Iquebal Recent advances in sensor arrays and imaging systems have spurred interest in analyzing high-dimensional, streaming time series data from real-world complex systems. These time series data capture the dynamic behaviors and causalities of the underlying processes and provide a computationally efficient means to predict and monitor system state evolution. More pertinently, they can provide the ability to detect incipient and critical changes in a process, which is essential for real- time system integrity assurance. However, effective harnessing of information from these data sources is currently impeded by the mismatch between the key assumption of stationarity underlying most change detection methods and by the nonlinear and nonstationary (transient) dynamics of most real-world processes. The current approaches are slow or simply unable to detect qualitative changes in the behaviors that lead to anomalies. For most real-world systems, the vector field of state dynamics is a nonlinear function of the state variables, i.e., the relationship connecting intrinsic state variables with their autoregressive terms and exogenous variables is nonlinear. Time series emerging from such complex systems exhibit aperiodic (chaotic) patterns even under steady state. Also, since real-world systems often evolve under transient conditions, the signals so obtained tend to exhibit myriad forms of nonstationarity. This tutorial presents a delineation of these diverse transient behaviors, and a review of advancements in change detection and prognostication methods for nonlinear and nonstationary time series. We also provide a comparison of their performances in certain real- world manufacturing and health informatics applications. n SA47 North Bldg 229A

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