2016 INFORMS Annual Meeting Program

MC58

INFORMS Nashville – 2016

MC58 Music Row 6- Omni Energy VII Contributed Session Chair: Par Holmberg, Associate Professor, Research Insitute of Industrial Economics (IFN), Grevgatan 34, Stockholm, SE10215, Sweden, par.holmberg@ifn.se 1 - Electricity Resource Capacity Expansion With Distributed Energy Resources: A New MILP Formulation Jesse D Jenkins, PhD Candidate, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States, jessedj@mit.edu Conventional electricity capacity expansion models do not properly consider distributed energy resources (DERs), including distributed generation, storage and demand response. DERs provide locational benefits—e.g. loss mitigation, congestion relief, network capacity deferral—which must be considered along with the costs of different unit scales. In this new MILP formulation, conventional generation and DER investments are made across several transmission zones and distribution voltage levels subject to power flow constraints, network reinforcement costs, losses, and operational constraints capturing reserves, ramp rates, and unit commitment constraints for thermal generators. 2 - Price Projections For Ancillary Services Markets Under Hypothetical Future Scenarios We forecast broad future ancillary service price trends in U.S. power markets by first identifying a set of key parameters that influence prices. Baseline models are then developed and calibrated based on historical data and current regional characteristics. We then utilize AURORAxmp to model hourly dispatch in each region and forecast the impact of changes in these inputs, such as electricity demand, fuel prices, renewable penetration levels, and availability of AS supply. Finally, we identify correlations to broadly project price trends under hypothetical future scenarios, e.g. increased wind penetration, decreased natural gas prices, and increased supply of flexible generation resources. 3 - An Efficient Integer L-shaped Method For A Two-stage Self-healing Power Grid Problem Amir Golshani, PhD Candidate, University of Central Florida, Orlando, FL, United States, amir.golshani@knights.ucf.edu, Wei Sun, Qipeng Zheng When a power system enters an emergency state, the self-healing process is initiated by system operators to bring the system back to its normal condition. This presentation proposes a two-stage self-healing optimization problem with a set of practical constraints containing integer variables in both stages. To solve the proposed problem, the integer L-shaped algorithm together with an efficient optimality cut based on the physical characteristics of power system will be presented. Standard IEEE test system is used to demonstrate the effectiveness of the proposed algorithm and optimality cut. 4 - Toward Cost-efficient And Reliable Unit Commitment Under Uncertainty Hrvoje Pandzic, Faculty of Electrical Engineering and Computing University of Zagreb, Unska 3, Zagreb, 10000, Croatia, hrvoje.pandzic@fer.hr, Yury Dvorkin, Ting Qiu, Yishen Wang, Daniel Kirschen This presentation will describe a new improved interval unit commitment formulation that combines some aspects of stochastic and interval formulations. A systematic and rigorous assessment of the cost and reliability performance of the improved interval, interval, stochastic and robust unit commitment will be demonstrated as well. 5 - Price Instability In Multi-unit Auctions Par Holmberg, Associate Professor, Research Insitute of Industrial Economics (IFN), Grevgatan 34, Stockholm, SE10215, Sweden, par.holmberg@ifn.se, Edward James Anderson We consider a uniform-price procurement auction with indivisible units and private costs. We solve for a Bayesian Nash equilibrium and show that the equilibrium has a price instability in the sense that a minor change in a supplier’s realized cost can result in a drastic change in the market price. The price instability is reduced as the size of indivisible units decreases for a given total production capacity. In the limit, where the size of units approaches zero and costs are almost surely common knowledge, the Bayesian equilibrium converges to a pure-strategy NE without price instability, the Supply Function Equilibrium (SFE). Todd Levin, Energy Systems Engineer, Argonne National Laboratory, 9700 S. Cass Ave, Bldg 362, Lemont, IL, 60439, United States, tlevin@anl.gov, Zhi Zhou

MC59 Cumberland 1- Omni Freight Network Design General Session Chair: James F Campbell, University of Missouri-St Louis, Saint Louis, MO, TBD, United States, campbell@umsl.edu 1 - Strategic Design For Delivery With Drones And Trucks James F Campbell, University of Missouri-St Louis, St. Louis, MO, United States, campbell@umsl.edu, Donald C. Sweeney II, Juan Zhang Our research develops continuous approximation models for the strategic design of drone and hybrid truck-drone delivery systems. We consider aerial and ground-based drones that can be launched from fixed or relocatable facilities, or from trucks. In contrast to discrete VRP-based optimization models, we treat the demand for deliveries as a continuous spatial density over a region. Analytical results and illustrations assess the economic and service performance from using the best mix of drones and trucks, and provide strategic managerial insights. Results show how using drones in conjunction with trucks alters the optimal delivery strategy and can facilitate lower cost and faster deliveries. 2 - A Quantitative Model For Truck Parking And Hours Of Service Regulations Sarah G Nurre, University of Arkansas, 425 W. Louise Street, Fayetteville, AR, 72701, United States, snurre@uark.edu Truck parking and hours-of-service (HOS) regulations are consistently reported as two of the top concerns in the trucking industry. Parking shortages are a function of inadequate capacity and changes to HOS regulations requiring drivers to stop frequently and for longer periods. We develop a network-based optimization model which determines the best times and locations for stopping along a set of truck routes while adhering to system-wide HOS regulations, network, and scheduling constraints. We present the results and insights deduced from experiments run using historical truck route data. MC60 Cumberland 2- Omni Methodological Advances and Empirical Discoveries in Travel and Activity Choice Modeling Sponsored: TSL, Urban Transportation Sponsored Session Chair: Sayeeda B. Ayaz, UMass Amherst, UMass Amherst, Amherst, MA, 01003, United States, sbayaz@engin.umass.edu 1 - Bike Route Choice Modeling Without Choice Sets Of Paths: Estimation, Prediction And Accessibility Measure Maelle Zimmermann, Université de Montreal, maelle.zimmermann@gmail.com We estimate a link-based bike route choice model in a real network which does not require to sample any choice set of paths, similar to the recursive logit (RL) model formulated by Fosgerau (2013). We provide numerical estimation results, and we show the advantages of this approach in the context of prediction by focusing on two applications of the model: i) simulation of bike traffic flows; ii) measuring bike accessibility. Compared to the path-based approach which requires to generate choice sets, the RL model proves to make significant gains in computational time and to avoid paradoxical results discussed in previous works, e.g. in Nassir (2014). 2 - A Random Utility Based Estimation Framework For The Household Activity Pattern Problem Zhiheng Xu, University at Buffalo, Buffalo, NY, United States, zhihengx@buffalo.edu, Jee Eun Kang, Roger Chen We develop an estimation framework for the Household Activity Pattern Problem (HAPP) based on random utility theory. The estimation procedure is based on the realization that travelers’ complex activity-travel pattern decisions form a continuous path in space-time. The proposed framework is comprised of choice set generation, choice set individualization, and multinomial logit estimation procedures. 3 - Looking-ahead Route Choice Behavior Based On Driving Simulator And Pc-based Experiments Sayeeda B. Ayaz, University of Massachusetts Amherst, Marston 139, 130 Natural Resources Rd, Amherst, MA, 01003, United States, sbayaz@engin.umass.edu, Hengliang Tian, Song Gao, Donald Fisher We study drivers’ route choice behavior with real-time traffic information based on driving simulator and PC-based experiments. A looking-ahead route choice refers to a decision taking into account future diversion possibilities at downstream nodes based on real-time information not yet available at the time of

202