Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SC42

We build a new integrated Dynamic Regional Food, Energy, Water Systems (DR- FEWS) modeling framework that incorporates key economic elements and environmental linkages. The framework represents the regional production of food and energy services that use land, water, energy resources and that depend on farmer behavior and watershed dynamics. It accounts for environmental impacts from regional production and the effects of exogenous changes in uncertain national and global conditions (e.g., trade war, climate change) on the regional FEWS. We apply the model to the Great Lakes region to simulate its regional FEWS dynamics over time. 3 - Climate Policy under Cooperation and Competition between Regions with Spatial Heat Transport Yongyang Cai, Ohio State University, Columbus, OH, United States, William Brock, Anastasios Xepapadeas, Kenneth Judd We build a novel stochastic dynamic regional integrated assessment model (IAM) of the climate and economic system including a number of important climate science elements that are missingin most IAMs. These elements are spatial heat transport from the Equator to the Poles, sea level rise, permafrost thaw and tipping points. We study optimal policies under cooperation and various degrees of competition between regions. Our results suggest that when the elements of climate science which are accounted for in this paper are ignored, important policy variables such as the social cost of carbon and adaptation could be seriously biased. n SC44 North Bldg 227C Algorithms for Power Systems Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Richard Zhang, UC Berkeley, Berkeley, CA, 94709, United States Co-Chair: Somayeh Sojoudi, University of California, Berkeley, Berkeley, CA, 94703, United States 1 - ARPA-E Grid Optimization Competition Kory W. Hedman, Program Director, Advanced Research Projects Agency-Energy, Washington, DC, United States This talk will give background information regarding the Advanced Research Projects Agency-Energy (ARPA-E) and related efforts in the areas of energy systems and operations research. The goal of ARPA-E’s Grid Optimization (GO) Competition is to challenge the research and industrial communities to develop transformational and disruptive methods for solving power system optimization problems, including the security-constrained ac optimal power flow (SCOPF), a non-convex optimization problem. More information can be found at: https://gocompetition.energy.gov/. 2 - Efficient Computational Methods for Optimal Power Flow, State Estimation and Topology Error Detection Javad Lavaei, University of California, Berkeley, 4141 Etcheverry Hall, Berkeley, CA, 94720, United States Power optimization problems are nonlinear and subject to modeling errors. In this talk, we develop efficient mathematical techniques based on both conic optimization and local search algorithms to solve fundamental power operational problems with guarantees. We test our techniques on various real-world systems. 3 - Sparse Semidefinite Programs with Near-linear Time Complexity Richard Zhang, UC Berkeley, 621 Sutardja Dai Hall, University of California, Berkeley, CA, 94709, United States Some of the strongest polynomial-time relaxations to NP-hard problems are semidefinite programs (SDPs), but their high solution complexities limits their use. Given that SDP relaxations are often sparse, a technique known as chordal conversion can sometimes reduce complexity substantially. In this paper, we describe a modification of chordal conversion that allows any general-purpose interior-point method to solve a certain class of sparse SDPs with a guaranteed complexity of O(n^1.5) time and O(n) memory. To illustrate the use of this technique, we solve the AC optimal power flow relaxation (ACOPF) on power system models with up to n = 13659 nodes in 5 minutes, using SeDuMi on a standard laptop. 4 - Statistical Analysis of PMU Data Daniel Bienstock, Columbia University, Dept of IEOR, 342 Mudd, New York, NY, 10027, United States We describe ongoing work using PMU data obtained through an industrial collaboration, focusing on structural analysis. Another facet of the research involves fast estimation of principal components of covariance matrices (i.e., streaming PCA). This is joint work with Mauro Escobar and Apurv Shukla (Columbia) and Michael Chertkov (LANL).

n SC42 North Bldg 227A Agent Based Simulation in Healthcare Sponsored: Simulation Sponsored Session Chair: Chao Meng, Valdosta State University 1 - Data Analytics Integrated Simulation for Older Adults Healthcare Utilization Modeling and Evaluation under Multi-type Settings Xuxue Sun, University of South Florida, 4202 E. Fowler Ave., ENB 118, Tampa, FL, 33620, United States, Chao Meng, Nan Kong, Hongdao Meng, Kathryn Hyer, Mingyang Li To meet the ever-increasing healthcare demand of America’s older adults with the limited and costly healthcare resources, there is a pressing need to investigate how a heterogeneous population of older adults will influence the utilization of different healthcare settings, such as acute care and long-term care facilities. In this work, advanced Bayesian data analytics is integrated with the simulation techniques to jointly model and evaluate both hospital and nursing home healthcare utilization. Real case studies based on Florida’s Medicare and Medicaid claims data are studied to demonstrate the effectiveness of the proposed framework. 2 - Multi-agent Based Hybrid Adaptive Training System for Functional Endoscopic Sinus Surgery (FESS) Saurabh Jain, University of Arizona, Tucson, AZ, 85719, United States, Seunghan Lee, Samuel Barber, Eugene Chang, Young-Jun Son Multi-Agent Simulation has been widely adopted for training programs. In Otolaryngology, FESS benefits from simulation due to steep learning curves to decrease operative risks and time. We propose a multi-agent task planning framework with adaptive feedback based on real-time evaluation of trainee’s surgical proficiency in a virtual environment. This system encapsulates cost- effectiveness, high-fidelity and optimal computation guided by real-time responses. 3 - Hospital Supply Chain Network Design using Agent Based Simulation Sojung Kim, Texas A&M University-Commerce, P.O. Box 3011, Engineering & Technology, Commerce, TX, 75429-3011, United States, Karli Thornton This study aims at developing a hospital supply chain using agent-based simulation (ABS) to minimize the total operational cost. Since ABS includes multiple agents making decisions based on their perception processes, it can accurately model a realistic healthcare supply chain environment. Three agents such as consumers, distribution centers, and hospitals are developed under AnyLogic« ABS software with a healthcare supply chain data in Montana, USA. In addition, OptQuest« in AnyLogic is used as an optimization engine to find the appropriate locations of distribution centers in a hospital supply chain network. n SC43 North Bldg 227B Advances in Integrated Assessment Modeling Emerging Topic: Energy and Climate Emerging Topic Session Chair: Yongyang Cai, Ohio State University, OH, United States 1 - Using Integrated Models to Value the Use of Bulk Energy Storage for Reducing CO2 Emissions from Regional Electricity Systems Jeffrey M. Bielicki, Ohio State University, Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, United States, Jonathan D. Ogland-Hand, Yaoping Wang, Benjamin M. Adams, Thomas A. Buscheck, Martin O. Saar Bulk energy storage (BES) can reduce CO2 emissions by increasing the utilization of variable wind and solar electricity capacity. BES could thus have value to reducing system-wide CO2 emissions. We integrated process-level modeling with a systems-level optimization model to estimate this value for CO2-Geothermal Bulk Energy Storage, Compressed Air Energy Storage, and Pumped Hydro Energy Storage. Our results suggest that the value of BES to reducing CO2 emissions can exceed the operating costs of BES, but BES deployment does not always reduce CO2 emissions: the dispatch order, the net load, and the BES technology all influence how BES changes system-wide CO2 emissions. 2 - DRFEWS: A Dynamic Regional Integrated Framework of Food, Energy and Water Systems Shaohui Tang, The Ohio State University, 2120 Fyffe Rd., Columbus, OH, 43210, United States, Yongyang Cai, Brian Cultice, Yaoping Wang, Jeffrey Bielicki, Randall Alan, Ian Sheldon, Elena Irwin

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