Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

WD45

output and consider the temporal change of wind power production between the two periods of installation, which shows quite consistent results. 3 - Update on NREL Work in UQ for Loads Analysis Katherine Dykes, NREL, Golden, CO, United States This presentation will provide an update on NREL work using statistical methods applied to wind turbine extreme and fatigue loads analysis. Loads analysis is a cumbersome part of wind turbine design and analysis with significant uncertainty; advanced statistical methods can help improve the accuracy and computational efficiency of this process. n WD45 North Bldg 228A Electrical Markets Contributed Session Chair: G ray Kara, Norwegian University of Science and Technology, H°gskoleringen 1, Trondheim, 7491, Norway 1 - Long- and Short-term Uncertainties in the Capacity Expansion Problem with Renewables and Electric Vehicles Miguel Carrion, Universidad de Castilla-La Mancha, Campus Tecnologico Fabrica de Armas, Toledo, 45071, Spain, Ruth Dominguez, Rafael Zárate-Miñano This paper solves a coordinated generation and storage expansion problem considering long- and short-term uncertainties. We assume that the power system operator is able to control the charging processes of those electric vehicles that are willing to get involved in the power system operation in exchange for a financial reimbursement. The day-ahead energy and reserve capacity markets are explicitly considered in this capacity expansion problem. The resulting stochastic mixed- integer linear problem is solved using Benders decomposition. The proposed formulation is tested on a realistic case study based on an actual isolated power system in Spain. 2 - An Enhanced Argonne Least Cost Electricity Analysis Framework: Impact of High Penetration of Electrified Vehicles Jonghwan Kwon, Argonne National Laboratory, Lemont, IL, United States, Zhi Zhou This study investigates the impact of electrified vehicles with fast charging technology on electricity grid operations, market efficiency, and emission. An enhanced Argonne Least Cost Electricity Analysis Framework (ALEAF) will be used to analyze the impact of fast charging station deployment and status profile in Chicago urban region, which will be provided by a transportation system simulation tool (POLARIS). The modeling framework includes electric grid and market operation models, formulated into multi-stage mixed integer programming problems. This study will allow the industry to understand economic viability of infrastructure and energy implications of vehicle electrification. 3 - The Value of Flexibility in Electricity Markets from Gas Turbine Upgrades: A Stochastic Mixed Integer Programming Approach Sourabh Dalvi, Pennsylvania State University, Leonhard Building, University Park, PA, 16802, United States, Mort Webster The expected increase in generation from renewables and the consequent increased variability in net load has led to calls for more flexible resources in the generation mix. One potential source of flexibility is from performance improvements to existing gas turbines, including higher maximum output, lower minimum output, faster ramping, and less time and lower cost for startups. We apply a stochastic multi-stage mixed integer linear program to model the power system of Public Service of New Mexico, and demonstrate the relative value of different upgrades to the system and to the unit owner. 4 - Production Intermittence in Spot Electricity Markets Arthur Thomas, PhD Student, IFP Energies Nouvelles, 4 avenue de Bois-Préau, Rueil-Malmaison, 92852, France University of Nantes, Chemin de la Censive du Tertre-Batiment Erdre, Nantes, 44322, France, Albert Banal-Estañol, Olivier Massol, Augusto Rupirez Micola This paper analyses the influence of production intermittence on spot markets. We use both game theory and an adaptation of the Camerer and Ho (1999) behavioural model. Controlling for costs, we find that intermittent technologies yield lower prices when incumbents have individual market power, but are higher when they do not have it. This happens both when firms are risk-neutral and risk-averse, and also under different intermittence and ownership configurations. Replacing high-cost assets with low-cost ones results in higher prices than when they are left to co-exist. The findings have implications for, among others, wholesale electricity markets in which wind power is increasingly important.

n WD43 North Bldg 227B Climate Impacts on the Electric Power Sector Emerging Topic: Energy and Climate Emerging Topic Session Chair: Delavane Diaz, Electric Power Research Institute, Washington, DC, 20005, United States Co-Chair: John Bistline, Electric Power Research Institute, CA, United States 1 - Climate Change Vulnerabilities of New York State’s Future Electric System Delavane Diaz, Electric Power Research Institute, Washington, DC, 20005, United States This paper estimates potential impacts of future climate conditions on the NY electricity system through 2050 using EPRI’s US-REGEN model. Specifically, we evaluate future climate changes as characterized in the NY ClimAID assessment through the following climate impact pathways: temperature impacts on of thermal generation and transmission efficiency, water availability for hydro generation and cooling, and temperature impacts on electricity demand. 2 - An Integrated Approach to Climate Impacts on Power Sector Using GCAM Mohamad Hejazi, Research Scientist, Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, 20740, United States, Zarrar Khan, Gokul Iyer, Marshall Wise, Pralit Patel, Sonny Kim Energy, water, and land systems are increasingly interconnected. In this study, we use the Global Change Assessment Model (GCAM), where interactions between population, economic growth, energy, land, and water resources interact simultaneously in a dynamically evolving system, to investigate the direct and indirect effects of climate impacts on the electric sector under different scenarios. 3 - Does Water Scarcity Shift the Electricity Generation Mix toward Fossil Fuels? Empirical Evidence from the United States Jonathan Eyer, USC, Casey Wichman Using an econometric model of plant-level electricity generation between 2001 and 2012, we estimate the effect of water scarcity on the US electricity mix. We find that hydroelectric generation decreases substantially in response to drought, and the replacement fuel varies by region. We quantify the substantial social costs associated with the increased carbon emissions. n WD44 North Bldg 227C Joint Session ENRE/Practice Curated: Data Science in Energy Systems Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Hoon Hwangbo, Texas A&M University, College Station, TX, United States Co-Chair: Eunshin Byon, University of Michigan, Ann Arbor, MI, 48109, United States 1 - Variance Reduction Method for Wind Turbine Extreme Load Estimation Qiyun Pan, University of Michigan, Department of IOE, Ann Arbor, MI, 48109-2117, United States, Eunshin Byon, Henry Lam This study develops a computationally efficient variance reduction method for wind turbine extreme load estimation with the stochastic simulation model. We propose an adaptive method that iteratively refines the input sampling density so that sampling efforts can be steered to focus on important input regions. We devise a parameter updating rule to make the sampling density parameter converge to the unknown target extreme load and prove the extreme load estimation uncertainty becomes smaller than that from crude Monte Carlo simulation. 2 - Quantifying the Effect of Vortex Generator Installation on Wind Power Production Hoon Hwangbo, Texas A&M University, College Station, TX, 77840, United States, Yu Ding Vortex generator installation is known to improve wind power production, and how much to improve is a fundamental managerial question to be addressed. Quantifying the effect of the installation is, though, quite challenging due to the presence of multiple sources of variation causing difference in power output between pre- and post-installation periods. For more accurate quantification, we use a machine learning model to control for some environmental effects in power

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