Informs Annual Meeting 2017

MD80

INFORMS Houston – 2017

2 - Tight Formulation and Pricing in Day-ahead Electricity Markets Bowen Hua, University of Texas at Austin, Austin, TX, United States, bhua@utexas.edu 3 - Combined Cycle Units Modeling and Computational Performance in MISO Day-ahead Market Fengyu Wang, Midcontinent Independent System Operator, 429 Thornberry Dr., Carmel, IN, 46032, United States, fwang@misoenergy.org 4 - Coordination Schemes for the Integration of Transmission and Distribution System Operations Anthony Papavasiliou, Université Catholique de Louvain, Center for Operations Research and Econometri, Voie du Roman Pays 34, Louvain la Neuve, 1348, Belgium, anthony.papavasiliou@uclouvain.be 5 - Hippo - Solving Security Constrained Unit Commitment Problem Feng Pan, Pacific Northwest National Laboratory, 1100 Dexter Ave N, BSRC-379, Seattle, WA, 98109, United States, feng.pan@pnnl.gov 6 - Panelist Robert E. Bixby, Gurobi Optimization, Inc., 3733-1 Westheimer Road, Box 1001, Houston, TX, 77027, United States, bixby@gurobi.com 381C Evolution of the Power Grid Sponsored: Energy, Natural Res & the Environment, Energy Sponsored Session Chair: Erin Baker, Univerxity of Massachusetts-Amherst, Amherst, MA, 01003, United States, edbaker@ecs.umass.edu Co-Chair: Destenie Nock, destenienock5@gmail.com 1 - Long Term Generation and Transmission Co-optimization: Infrastructure Planning to Accommodate Renewables Patrick Maloney, Iowa State University, 2405 Aspen Road, # 210, Ames, IA, 50010, United States, patrickm@iastate.edu, Ping Liu, James McCalley This work investigates deterministic and stochastic long term planning models for the Pacific Northwest using a 300 bus reduced model of a WECC TEPPC dataset. NREL toolkit profiles and resource capacities are used to model realistic wind and PV candidate generation. To compete remote renewable resources against traditional generation near load centers, the model co-optimizes generation and transmission. A major thrust of the tools developed for this work is to enable energy decision makers to explore the decisions that allow them to adapt systems to the future; especially in response to increasing interest in and competiveness from renewable technologies. 2 - Pricing Wind: A Revenue Adequate, Cost Recovering Market Clearing Mechanism to Facilitate Volatile Renewables Golbon Zakeri, University of Auckland, Dept of Eng Science, Private Bag 92019, Auckland, New Zealand, g.zakeri@auckland.ac.nz, Geoffrey Pritchard, Endre Bjorndal, Mette Bjorndal With the increasing penetration of volatile renewable energy into the power grid, it is imperative to ensure sufficient, economical regulation is available to compensate for any excess or short fall of electricity generated from renewables. To this end, we have developed a stochastic programming market mechanism that is an improved version of Pritchard et al’s 2010 model. In this talk we will discuss the properties of our model, that include revenue adequacy in each scenario and expected cost recovery. We have implemented our model on the New Zealand system. Will provide the numerical results that we have obtained thus far. 3 - Effects of Grid-scale Electricity Storage on System Carbon Dioxide Emissions as a Power System Decarbonizes While grid-scale electricity storage may be crucial for decarbonizing the electric power system, studies find it would currently increase net carbon dioxide (CO2) emissions across the U.S. We quantify the effect of storage participating in reserve and/or energy markets on net CO2 emissions as a power system decarbonizes. To do so, we use two optimization models: a capacity expansion model to forecast generator fleet changes and a unit commitment model to quantify net CO2 emissions with and without storage. We preliminarily find that storage reduces net CO2 emissions well before full decarbonization and that storage affects net CO2 emissions more when participating in reserve than in energy markets. MD80 Michael Craig, Carnegie Mellon University, Pittsburgh, PA, United States, mtcraig@andrew.cmu.edu, Paulina Jaramillo, Bri-Mathias Hodge

4 - Evaluation of Generation Portfolios for the New England Power System Destenie Supreece Nock, University of MA-Amherst, 132 Middle Street, Unit 3, Hadley, MA, 01035, United States, dnock@umass.edu, Erin Baker As power systems evolve there has been a shift towards sourcing electricity from more sustainable generation sources. This push combined with the need to ensure the system can reliably supply electricity throughout the year makes the generation capacity planning question more complex. In this paper we propose a method for evaluating a portfolio of generation technologies in terms of their sustainability and a pre-integration metric for reliability. Preference scenarios for sustainability are defined in terms of economical, technological, societal, and environmental factors. 382A Distributional Robust Optimization with Marginals Sponsored: Optimization, Optimization Under Uncertainty Sponsored Session Chair: Karthik Natarajan, Singapore, 439959, Singapore, karthik_natarajan@sutd.edu.sg 1 - Using Marginal Distribution Choice Models in Traffic Equilibrium Selin D. Ahipasaolgu, Assistant Professor, Singapore University of Technology and Design, Singapore, Singapore, ahipasaoglu@sutd.edu.sg, Ugur Arikan, Karthik Natarajan Traffic equilibrium models are fundamental to the analysis of transportation systems. We develop a new user equilibrium model, the MDM-SUE model, that uses the marginal distribution model (MDM) for the underlying route choice. In MDM, the marginal distributions of the path utilities are specified but the joint distribution is not. We show that MDM-SUE exists, is unique under mild assumptions, has a convex optimization formulation, and can be computed efficiently. The MDM-SUE model can be used to recreate logit SUE and weibit SUE. Moreover, the model is flexible as it can capture perception variance scaling at the route level and allows for modeling with skewed and heavy tailed distributions. 2 - Distributionally Robust Markovian Traffic Equilibrium Ugur Arikan, Singapore University of Technology and Design, Singapore, Singapore, ugur_arikan@sutd.edu.sg A class of link based models under a Markovian assumption has been proposed to deal with the drawbacks of route based models in traffic assignment. However, the application has been thus far restricted to the multinomial logit model. We propose a distributionally robust Markovian traffic equilibrium model assuming that marginal distributions of link utilities are known but the joint distribution is unknown. We develop a convex optimization formulation and propose an efficient algorithm to compute equilibrium flows. Our formulation is completely link based, relaxes the assumption of independence and identical distributions, Napat Rujeerapaiboon, Ecole Polytechnique Federale de Lausanne, EPFL.CDM.MTE I.RAO, ODY 1 19 (Odyssea), Station 5, Lausanne, 1015, Switzerland, napat.rujeerapaiboon@epfl.ch, Kilian Schindler, Daniel Kuhn, Wolfram Wiesemann The goal of scenario reduction is to approximate a discrete distribution with another discrete distribution that has fewer atoms. We distinguish continuous scenario reduction, where the new atoms may be chosen freely, and discrete scenario reduction, where the new atoms are chosen from among the existing ones. Using Wasserstein distance to measure proximity between distributions, we analyze the added benefit of continuous over discrete scenario reduction. We also propose polynomial-time constant-factor approximations and exponential-time exact algorithms for both types of scenario reduction. 4 - Revisiting the Marginal Distribution Model for Discrete Optimization: Extensions and Generalizations Louis L. Chen, Massachusetts Institute of Technology, Cambridge, MA, United States, llchen@mit.edu, Karthik Natarajan, David Simchi-Levi Building on previous work which incorporated the Marginal Distribution Model to examine the persistency problem for discrete optimization, we provide extensions in two ways. Firstly, we extend the distributionally robust problem to now incorporate completely arbitrary marginal distributions, and we find a dual optimization problem that not only points the way to tractable cases but also presents a basic multi-marginal transport problem with a story not unlike that found in the assignment game from mathematical economics. Secondly, we investigate the role of the discrete constraint set’s structure in tractability. As well, we consider how the results generalize/unify some other works. MD81 and provides modeling flexibility and computational tractability. 3 - Scenario Reduction Revisited: Fundamental Limits and Guarantees

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