2015 Informs Annual Meeting

TC57

INFORMS Philadelphia – 2015

TC57 57-Room 109B, CC Long-Term Electric Power System Planning Models Sponsor: ENRE – Energy I – Electricity Sponsored Session Chair: Ramteen Sioshansi, Associate Professor, The Ohio State University, Integrated Systems Engineering, 1971 Neil Avenue, Columbus, OH, 43210, United States of America, sioshansi.1@osu.edu 1 - Optimizing Storage Operations in Longer-term Power System Models Sonja Wogrin, Comillas Pontifical University, Calle Alberto Aguilera 23, Madrid, Spain, sonja.wogrin@comillas.edu, David Galbally, Javier Reneses In a rapidly changing power system the proper characterization of storage behavior becomes an increasingly important issue. We propose a new formulation to capture storage behavior in medium- and long-term power system models that use a load duration curve. In such models the chronology among individual hours is lost; our approach addresses related shortcomings and is able to accurately replicate hourly evolution of storage levels while keeping computational time tractable. 2 - Multi-stage Investment in Renewable Energies via Linear Decision Rules Maria Ruth Dominguez Martin, Universidad de Castilla - La Mancha, Av. Carlos III, s/n, Toledo, Spain, Ruth.Dominguez@uclm.es, Miguel Carrion, Antonio Conejo Investment in generating capacity involves high uncertainty. In the real world, these decisions are usually made in several stages. We propose a multi-stage investment model to transform a thermal-based power system into a renewable- dominated one. We consider the uncertainty of the demand growth and the investment costs, as well as the variability of the renewable power production throughout the year. 3 - Impact of Unit Commitment Constraints on Generation Expansion Decisions under Wind Uncertainty Jalal Kazempour, Technical University of Denmark, Department Most of available generation expansion decision-making tools in the literature neglect a number of unit commitment (UC) constraints, e.g., on/off commitment status, ramping limits and minimum production level of thermal units. This study analyzes the impact of those constraints on generation expansion decisions in power systems with significant wind power integration. To this end, a two-stage stochastic programming problem is proposed and solved using a decomposition technique. 4 - Stochastic Generation and Transmission Investment Planning Model Yixian Liu, The Ohio State University, 210 Baker Systems Bldg., 1971 Neil Ave., Columbus, OH, 43210, United States of America, liu.2441@osu.edu, Ramteen Sioshansi Increasing electricity demand makes it necessary to expand the capacity of the current electrical grid. We propose a multi-stage stochastic linear programming model to seek an optimal investment plan in generators, storage devices and transmission lines in a long time horizon. Multiple uncertainties are considered involving both investment and dispatch decisions. The model is large in scale and may take excessive computational time to be solved. Decomposition methods will be applied to solve it. of Electrical Engineering, Kgs. Lyngby, 2800, Denmark, seykaz@elektro.dtu.dk, Amin Nasri, Antonio Conejo

drought risk assessment model is constructed from the thermoelectric power sector’s operational perspective. 2 - Portfolio Approach for Optimal Rooftop Solar Arrays Selection for Distributed Generation Olufemi Omitaomu, Senior Research Scientist, Oak Ridge National Laboratory, 1 Bethel Valley Road, MS-6017, Oak Ridge, TN, 37831, United States of America, omitaomuoa@ornl.gov, Xueping Li We present a portfolio selection approach that consider thousands of buildings with different solar energy potential and that are being considered for utility-scale distributed power generation. Our approach uses Markowitz mean-variance portfolio selection model to select suitable rooftops by identifying a combination of buildings that will maximize solar energy outputs and minimize system variability. Our approach is implemented using some real data-sets.

TC59 59-Room 110B, CC Impacts of Climate Change Sponsor: ENRE – Environment II – Forestry Sponsored Session

Chair: Chris Lauer, Oregon State, Portland, OR, United States of America, cjlauer@gmail.com 1 - Planning Forest Harvesting under Climate Change: A Stochastic Optimization Model

Jordi Garcia, Researcher, Instituto Superior de Agronomia, Universidade de Lisboa, Portugal, Tapada da Ajuda., Lisbon, Portugal, jordigarcia@isa.ulisboa.pt, Andres Weintraub, Cristobal Pais, Joanna Bachmatiuk

In this paper we consider a medium term forest planning problem in the presence of uncertainty due to climate change. For each time period the forest planner must decide which areas to cut in order to maximize expected net profit. A multistage stochastic model using 32 climate scenarios was developed and solved to determine optimal harvesting decisions under uncertainty. The stochastic solutions were compared to the solution of a deterministic model where an average climate scenario was used. 2 - Will Climate Change Induced Effects Cause Harm to Value Chains of the Bio-based Industry? Peter Rauch, Pd, BOKU, Feistmantelstrasse 4, Wien, 1180, Austria, peter.rauch@boku.ac.at Uncertainty increasingly affects ecosystems and storms and bark beetle infestations are the main causes of forest damage. Risks and their impacts on value chains of the bio-based industry are evaluated by a System Dynamics model of the Austrian wood supply including a stochastic simulation of risk agents. Results provide insights on probabilistic future wood supply security for sawlogs, pulpwood resp. energy wood and reveal a contra-intuitive system effect for the climate change scenario. 3 - Multiobjective Optimization to Study the Impact of Climate Change on the Joint Provision of Ecosystem Services Nick Kullman, Masters Student, University of Washington, 360 Bloedel Hall, Seattle, WA, United States of America, nick.kullman@gmail.com, Sventlana Kushch, Sandor Toth Climate change has been shown to alter the provision of forest ecosystem services such as carbon sequestration and wildfire mitigation. Less understood is how climate change will alter the tradeoffs among ecosystem services acquired simultaneously. We present a scenario-based multi-objective mathematical programming method to study these changes on the joint provision of ecosystem services in the Deschutes National Forest. 4 - Incorporating Acclimation and Feedback into Reserve Selection During Climate Change Austin Phillips, University of Washington, Seattle, WA, United States of America, austinjphillips90@gmail.com, Sandor Toth, Robert Haight Climate change threatens many species, and conservation in such a dynamic setting is challenging. We developed a mixed integer reserve selection model that pairs population dynamics and sequential selection in a nonlinear feedback loop. The model accounts for species’ ability to acclimate, as well as disperse, to track suitable conditions. We explore optimal management strategies to facilitate species’ survival as they disperse and acclimate in response to warming.

TC58 58-Room 110A, CC Electricity and System Resilience Sponsor: ENRE – Energy I – Electricity Sponsored Session

Chair: Valerie Thomas, Professor, Georgia Institute of Technology, 755 Ferst Drive, NW, Atlanta, GA, United States of America, valerie.thomas@isye.gatech.edu 1 - Stochastic Model for Assessment of Thermoelectric Power Generation Drought Risk under Climate Change Royce Francis, George Washington University, 800 22nd St. NW B1850, Washington, DC, 21212, United States of America, seed@gwu.edu, Behailu Bekera The objective of this article is to propose a stochastic method for analyzing drought risk to the thermoelectric power generation infrastructure sector due to its heavy reliance on freshwater availability. In particular, this article proposes a thermoelectric drought characterization framework from which a stochastic

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