Informs Annual Meeting 2017

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INFORMS Houston – 2017

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361E Energy Contributed Session

361F Improving Energy Efficiency of Personal Transportation Sponsored Session

Chair: Jose-Ignacio Munoz-Hernandez, University of Castilla- La Mancha, Ciudad Real, Spain, joseignacio.munoz@uclm.es 1 - Allocating Distributed Generators for Resilient Distribution System under Uncertain Probability Distribution of Natural Disasters Sadra Babaei, Oklahoma State University, 322 Engineering North, Industrial Engineering & Mgmt, Stillwater, OK, 74078, United States, sadra.babaei@okstate.edu A new distributionally robust defender-attacker-defender model is proposed for the planning of line hardening and allocating distributed generators, to hedge against the risk of disruptions caused by natural disasters. We consider the case that the true probability distribution of extreme weather is ambiguous, and minimize the load shedding with the worst-case scenario of weather distribution. Unlike traditional robust models, our approach takes advantage of moment information of the weather distribution that is learned from historical data. We reformulate the proposed model as a tractable two-stage robust optimization and employ a column-and-constraint generation algorithm to solve it. 2 - Decision Criteria for Wave Energy Extraction Characterizing its Temporal and Spatial Variability Francisco Haces Fernandez, Texas A&M.University-Kingsville, Kingsville, TX, United States, fr.haces@gmail.com, Hua Li, David Ramirez Wave energy is one of the most promising renewable energy resources. However, its commercial development has been slow and is facing important challenges. Understanding its high temporal and spatial variability will significantly improve its extraction. This research is able to identify how wave energy behaves as a two temporal estate system, with high and low energy periods. Energy Events have distinctive characteristics, allowing them to be identifiable on calendar periods, by its seasonal frequencies and on energy content. This study provides decision criteria for the installation of wave energy extraction equipment that provides the optimal fit for each location. 3 - Improving the Efficiency and Performance of Genetic Algorithm Based Layout Optimization Method on Irregular Shape Wind Farms Hua Li, Associate Professor, Texas A&M University-Kingsville, 700 University Blvd MSC 191, Kingsville, TX, 78363-8202, United States, hua.li@tamuk.edu, Yan Xu Layout optimization has become a one of the critical tools to help increase the power output or decrease the cost of a wind farm. Most of the existing layout optimization algorithms are based on regular shape wind farms, while most commercial wind farms have irregular shapes. A layout optimization algorithm that can be applied on different irregular shape wind farm with consideration of different commercial wind turbine models and hub heights is needed to help improve the efficiency and performance of commercial wind farms. This study develops and improves a genetic algorithm based wind farm layout optimization method for irregular shape wind farms with real world parameters. 4 - Optimal Use of the Storage Capacity of a Pool of Electrical Vehicles, Connected to a Micro Grid or a Virtual Power Plant Jose-Ignacio Munoz-Hernandez, University of Castilla - La Mancha, Edifico Politecnico - UCLM, Avda Camilo Jose Cela, S/N, Ciudad Real, 13071, Spain, joseignacio.munoz@uclm.es, Pablo Diaz-Cachinero, Javier Contreras Micro grids and Virtual Power Plants consist of several types of generation resources to meet their demand necessities. Electrical vehicles are mobile demands that can plug into the network at different sites but, at the same time, they also can be used as power reservoirs or sources. Due to the increasing penetration of renewable energy sources, electric vehicles, and storage units, the associated uncertainty needs to be properly characterized. This work develops a new approach to optimize the use of this sort of resources in a micro grid and in a virtual power plant.

Chair: Hesham Rakha, PhD, P. Eng., Virginia Tech Transportation Institute, 3500 Transportation Research Plaza,, Blacksburg, VA, 24061, United States, hrakha@vt.edu 1 - A Web Based Personal Driving Assistant using Real Time Data and a Dynamic Programming Model Mohammad Ali Alamdar Yazdi, PhD Student, Auburn University, 516 E. Glenn Ave, Apt 128, Auburn, AL, 36830, United States, mza0052@auburn.edu, Fadel Mounir Megahed, Alexander Vinel This talk presents a web based Eco-Driving Assistance System to improve a vehicle’s miles per gallon (mpg) fuel-economy performance. More specifically, a dynamic programming model is constructed to dynamically optimize the current speed, gear, and route based on forthcoming route conditions (weather, traffic, elevation, etc.) collected from different web APIs. 2 - An Algorithm for Incentive Allocation Problem as a Part of Smart Mobility Solutions Mehrdad Shahabi, University of Michigan, 2350 Hayward St, 205, Ann Arbor, MI, 48109, United States, mshahabi@umich.edu, Yafeng Yin, Chenfeng Xiong, Lei Zhang Recently, there has been a surge of interest in transportation demand management tools involving incentive provision in order to manage the growing congestion problems in transportation networks. With the goal of decreasing the energy consumption on the system level, our focus in this work is to design a framework through which a number of travelers are offered incentives on some travel alternatives different from their original travel option. The results confirm significant savings in energy consumption as a result of changing travel behavior. 3 - Multi-scale and Multi-modal Agent-based Modeling of Network- wide Energy/Fuel use of Travel Incentive Strategies Hesham Rakha, Samuel Reynolds, Pritchard Professor of Engineering, Virginia Tech, Blacksburg, VA, 24060, United States, hrakha@vt.edu, Ahmed Elbery, Filip Dvorak, Matthew Klenk, Jinghui Wang, Ahmed Ghanem The need to objectively and systematically evaluate the network-wide impacts of travel incentive strategies necessitates the development of multi-scale multi- modal agent-based modeling tools. This presentation describes the various building blocks of the proposed modeling tool, namely: (a) traveler behavior modeling; (b) modeling of train dynamics; (c) modeling driver and vehicle longitudinal and lateral movement; (d) modeling of multi-modal energy/fuel consumption (gasoline, electric, and plugin hybrid electric vehicles, CNG buses, and electric trains); (e) integration of microscopic and mesoscopic modeling into a fully-integrated multi-scale multi-modal agent-based modeler. 362A Operations Research in Plant Breeding Invited: Agricultural Analytics Invited Session Chair: Lizhi Wang, Iowa State University, Ames, IA, 50011, United States, lzwang@iastate.edu 1 - The Extended Predicted Cross Value for Genetic Introgression of Multiple Alleles with Multiple Individuals Ye Han, Iowa State University, Black Engineering, Room 0076, Ames, IA, 50014, United States, yeh@iastate.edu, Lizhi Wang, William D. Beavis, John N. Cameron In our recent research, we applied operations research approaches to optimize introgression of multiple alleles from a donor to a recipient genome. We designed a novel metric called the Predicted Cross Value (PCV) for selecting breeding parents. The PCV has been proved to bring in significantly advantages over the existing approaches by simulations. In this research, according to the practical breeding requirements, we extend the PCV idea to introgression process for multiple individuals. The updated metric is referred as PCV for multiple individuals or NPCV. We examine the NPCV in different case studies compared with the PCV or existing approach and demonstrate the results by simulations. TA54

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