Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MD46

3 - Impacts of Proactive Expansion Planning Under Multiple Equilibria on Generation Expansion Decisions Enzo E. Sauma, Pontificia Universidad Catolica de Chile, Ave. Vicuna Mackenna 4860, Santiago, Chile, David Pozo, Javier Contreras We use a proactive three-level equilibrium model for power transmission and generation expansion to study the potential impacts of proactive expansion planning on generation expansion decisions. In particular, we show that proactive transmission expansion decisions may lead to suboptimal solutions when the generation expansion equilibrium problem have multiple solutions (i.e., leading to higher total costs and lower social welfare). The resulting formulation is stated as a mathematical program subject to an equilibrium problem with equilibrium constraints (EPEC). To deal with this problem, we also propose an approach to Anthony Papavasiliou, Universite Catholique de Louvain, Center for Operations Research and Econometri, Voie du Roman Pays 34, Louvain la Neuve, 1348, Belgium Scarcity pricing can potentially remunerate flexibility within the context of an energy-only market design. A crucial aspect of scarcity pricing is the back- propagation of real-time scarcity signals to earlier forward markets. The successful back-propagation of scarcity signals hinges on a variety of specific short-term electricity market design choices, including (i) the trading of reserve capacity in real time, (ii) virtual trading, and (iii) the timing of the clearing of reserve capacity in day-ahead markets. In this presentation we propose a family of stochastic equilibrium models for addressing how each of these market design choices affects the back-propagation of scarcity signals. n MD46 North Bldg 228B Joint Session ENRE/Practice Curated: Energy Modeling: Open Source, Applications and New Developments Sponsored: Energy, Natural Res & the Environment/Energy Sponsored Session Chair: Denis Lavigne, PhD, Royal Military College St-Jean, 15, rue Jacques-Cartier Nord, St-Jean-sur-Richelieu, QC, J3B 8R8, Canada 1 - Representing the Demand Side in Energy System Optimization Models Benjamin D. Leibowicz, Assistant Professor, University of Texas- Austin, ETC 5.128D, 204 E. Dean Keeton St. C2200, Austin, TX, 78712-1591, United States Energy system optimization models have traditionally focused on supply-side technology investment and operation decisions. They often neglect demand-side dynamics related to end-use technology choices and demand levels because they are determined by myriad actors making individual decisions. This presentation outlines methodologies for representing the demand side in energy system optimization models, with OSeMOSYS formulations of transportation and buildings as examples. 2 - Storage End Effects and the Value of Stored Energy Taco Niet, British Columbia Institute of Technology, 3700 Willingdon Avenue, Burnaby, BC, V5G 3H2, Canada High temporal resolution modelling of energy systems often requires modelling a number of sub-periods, with the end condition of one sub-period being used to seed the next. When storage is modeled a challenge is to keep the model from draining the stored energy at the end of each sub-period. A common approach is to model extra-long sub-periods and to discard this end effect, increasing computational complexity. We evaluate the alternative of assigning a monetary value to the stored energy at the end of each sub-period using the OSeMOSYS energy system model. We find that assigning a monetary value to storage is an effective method to reduce the impact of end effects when modelling storage. 3 - Osemosys.org and the Global Climate-land-energy-water Model: An Integrated Resource Assessment Tool Supporting Sustainable Pathways for the Energy System Mark Howells, KTH Royal institute of Technology, Brinellvagen 68, Stockholm, 10044, Sweden, NA, Agnese Beltramo, Constantinos Taliotis The Open Source Energy Modelling System (OSeMOSYS) was used recently to perform integrated resource assessment analysis. In these applications, the modelling framework has been enhanced to represent interlinkages in between natural resources and identify possible Climate, Land, Energy and Water strategies (CLEWs) towards more sustainable development pathways for the energy system. In this context, the Global Least-cost User-friendly CLEWs Open Source Explorative (GLUCOSE) model is presented as an example of the developed methodology. It will provide an overview of the resource constraint the environment is facing at the global level and which might affect the energy system in the long run. derive tractable EPEC solutions with global optimality guaranteed. 4 - Market Design Considerations for Scarcity Pricing

4 - An Overview of Past, Present and Future GHG Emissions and Objectives for Canada Leading to Open-source Energy Modeling Denis Lavigne, Professor, Royal Military College Saint-Jean, 29, rue Louis-Frechette, Saint-Jean-sur-Richelieu, QC, J2W 1E9, Canada This talk presents an overview of past, present and future GHG emissions and objectives for Canada. The discussion also includes emissions intensities and provincial figures through the years. A parallel history of some particular bottom- up energy modeling tools is presented. It leads to the opportunity to use an open-source modeling framework such as OSeMOSYS to model cities and provinces of Canada. Examples of such existing work is presented. 5 - Open Source Multi-state Continental Investment Models to Support an Analysis Ecosystem Mark Howells, Royal Institute of Technology (KTH), Stockholm, Sweden, Hauke Henke, Nandi Moksnes, Constantinos Taliotis, Agnese Beltramo Large multi-state electricity generation investment models have been developed. They can be absorbed into teaching programs; extended for special research applications; reduce the time needed to have a functional model and allow for the extraction of sub-models: either single or multi-state. At present such models exist for three regions of the world. These are TEMBA, SAMBA and OSEMBE for Africa, South America and EU-28 respectively. A model for North America are yet to be developed. This paper discusses pertinent aspects of these model bases and lays out challenges to be addressed. n MD47 North Bldg 229A Joint Session Tutorial/Practice Curated: Coalescing Data and Decision Sciences for Analytics Emerging Topic: Practice Curated Track Emerging Topic Session Chair: Lewis Ntaimo, Texas A&M University, 3131 TAMU, College Station, TX, 77843, United States 1 - Coalescing Data and Decision Sciences for Analytics Suvrajeet Sen, University of Southern California, Daniel J. Epstein Dept. of, Industrial and Systems Engineering, Los Angeles, CA, 90089-0193, United States, Yunxiao Deng, Junyi Liu The dream of analytics is to work from common data sources, so that all of its facets (descriptive, predictive, and prescriptive) are supported via a coherent data- driven vision. This vision of analytics is what we refer to as “Integrative Analyticsö. In this tutorial we will cover a variety of OR/MS applications that require specific statistical learning models to be integrated with optimization models. For instance, certain cross-sectional data describing dependence among random variables may lead to regression models with multivariate error terms to be integrated with Stochastic Programming (SP) models. Others may require time series models to be integrated with Stochastic Model Predictive Control (S-MPC). Still other examples lead to particle filtering models providing data for network routing. In essence this tutorial will use these illustrations to motivate a new class of models, which we refer to as Learning Enabled Optimization (LEO) models. As suggested in the title of this tutorial, the applications are derived from integrative analytics. In addition to presenting these examples, the tutorial will cover fundamental concepts for modeling, statistically approximate solution concepts, sampling-based algorithms, and finally, model assessment and selection in the context of LEO models. Given the novelty of this paradigm, we will also outline how instructors may use the material for a graduate course on integrative analytics.

230