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Austin Back Matter 61-415 T ECHNICAL S ESSIONS Sunday, 8:00am - 9:30am How to Navigate the Technical Sessions ■ SA01 There are three primary resources to help you C - Ballroom D1, Level 4 understand and navigate the Technical Sessions: Simulation Modeling in Energy Markets Sponsor: Energy, Natural Resources and the Environment/ Energy • This Technical Session listing, which provides the Sponsored Session most detailed information. The listing is presented chronologically by day/time, showing each session Chair: Augusto Ruperez Micola, Assistant Professor, Universitat Pompeu Fabra, Barcelona, Spain, [email protected] and the papers/abstracts/authors within each 1 - A Decentralized Mechanism for Market Coupling session. Alfredo Garcìa, Associate Professor, University of Virginia, 151 • The Session Chair, Author, and Session indices Engineer’s Way, P.O. Box 400747, Charlottesville, VA, United States provide cross-reference assistance (pages 456-496). of America, [email protected], Mingyi Hong, Himanshu Gupta Several electricity markets in Europe have been integrated through a procedure • The Track Schedule is on pages 50-57. This is an known as “market coupling” which combines electricity trading with the allocation overview of the tracks (general topic areas) and of cross-border transmission capacity. We analyze a decentralized mechanism for market coupling in which TSOs procure cross-border transmission capacity on when/where they are scheduled. behalf of their own markets while maintaining control of internal trading. Quickest Way to Find Your Own Session 2 - A Multi-agent Energy Trading Competition Wolfgang Ketter, Assistant Professor, Erasmus University, Rotterdam Use the Author Index (pages 460-485) — the session School of Management, Department of Decision and Information code for your presentation(s) will be shown along with Science, Rotterdam, Netherlands, [email protected], John Collins, the track number. You can also refer to the full session Carsten Block listing for the room location of your session(s). We present the design of an open,competitive simulation approach that will produce robust research results on the structure and operation of retail power markets as well as on automating market interaction by means of competitively tested and benchmarked agents. These results will yield policy guidance that can significantly reduce the risk of instituting competitive energy markets at the retail level. The Session Codes 3 - Test of LSE’s Strategic Behavior in a Two Settlement Market Huan Zhao, Iowa State University, 419 S Walnut Avenue, Ames, IA, 50010, United States of America, [email protected], Abhishek Somani Track number. Coordinates with the room locations shown in the In most of the restructured power markets, a centralized power pool is operated in SB01 both day-ahead and real-time market. This day-ahead market is supposed to Track Schedule. Room locations are increase the system reliability. In reality, strategic LSE could utilize this two also indicated in the listing for each settlement system to reduce its procurement cost. In this study, we plan to utilize an session. agent-based model AMES to test LSE’s bidding strategy between the two markets. This work includes the modification of DCOPF and change of LSE’s learning Time Block. Matches the time algorithm. The day of blocks shown in the Track 4 - Wind Power, Load Volatility and Spot Electricity Prices the week Schedule. Augusto Ruperez Micola, Assistant Professor, Universitat Pompeu Fabra, Barcelona, Spain, [email protected], Albert Banal-Estanol Time Blocks We analyse whether supply side load volatility influences prices with a focus on the effects of wind power generation on electricity markets. We address three inter- Sunday - Tuesday related questions: Does wind volatility have an influence on spot electricity prices? Do demand volumes influence the results? Does asset ownership influence them? A - 8:00am – 9:30am We model the market using simulations in which agents behave following B - 11:00am - 12:30pm parametrisations of the Experience-Weighted Attractions (EWA) algorithm. C - 1:30pm - 3:00pm D - 4:30pm - 6:00pm ■ SA02 Wednesday C - Ballroom D2, Level 4 A - 8:00am – 9:30am Real Options in the Energy Sector B - 11:00am - 12:30pm Sponsor: Energy, Natural Resources and the Environment/ Energy C - 1:30pm – 3:00pm Sponsored Session D - 3:30pm - 5:00pm Chair: Afzal Siddiqui, University College London, Department of Statistical Science, London, United Kingdom, [email protected] 1 - Capacity Switching Options Under Rivalry and Uncertainty Room Locations/Tracks Ryuta Takashima, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino-shi, Chiba, 275-0016, Japan, All tracks and technical sessions will be held in the [email protected], Afzal Siddiqui Austin Convention Center and the Hilton Austin. Room Generators can invest in either a small or a large plant. The former project offers the numbers are shown on the Track Schedule and in the subsequent option to upgrade capacity. We contrast the direct and sequential investment strategies for a monopolist in order to extract the option value of technical session listing. flexibility. Next, we allow for a symmetric duopoly that can again adopt capacity in either direct or sequential manners. The impact of competition on a typical generator’s value is obtained under both direct and sequential strategies. 61 SA03 INFORMS Austin – 2010 2 - Valuation of CCS-ready Coal-fired Power Plants: A Multi- 2 - Design of Robust Hedging Strategies for U.S. Electric dimensional Real Options Approach Sector Planning Reinhard Madlener, Professor, RWTH Aachen University, Mathieustr. Joe DeCarolis, Assistant Professor, North Carolina State University, 6, Aachen, 52074, Germany, [email protected], 2501 Stinson Drive, Campus Box 7908, Raleigh, NC, 27695, United Wilko Rohlfs States of America, [email protected], Kevin Hunter, The economic valuation of a CCS-ready coal power plant is strongly influenced by Sarat Sreepathi the time between the investment and the CCS retrofit. To determine the optimal This talk describes a multi-stage stochastic optimization of the U.S. electric sector time delay we develop a real options model with a multi-dimensional optimal under a climate policy. The model optimizes the retirement, installation, and threshold value that incorporates uncertainty in the price of fuel input, CO2, utilization of generating capacity over the next 40 years. Future CO2 targets are electricity, capture, transport and storage (CTS), and investment cost. modeled as stage-wise uncertain parameters. To test the robustness of the resultant 3 - Duopolistic Competition Under Risk Aversion and Uncertainty hedging strategy, a technique called modeling to generate alternatives is discussed. Afzal Siddiqui, University College London, Department of Statistical 3 - Optimal Design of Biofuel Production System and Resource Science, London, United Kingdom, [email protected], Allocation: A California Case Study Michail Chronopoulos, Bert De Reyck Chien-Wei Chen, University of California-Davis, 5000 Orchard Park A monopolist typically defers entry into an industry as both price uncertainty and Cir 7612, Davis, CA, 95616, United States of America, the level of relative risk aversion increase. The former attribute is present in most [email protected] energy industries, while the latter may be relevant for reasons of market A well designed biofuel production system may alleviate greenhouse gas emission incompleteness or the presence of technical uncertainty. By contrast, it has been and energy security issues. An important question is how to maintain a low-cost shown that the presence of a rival hastens entry under risk neutrality. Here, we and low-risk biofuel supply system under future uncertainties such as demand, examine how duopolistic competition affects the entry decisions of risk-averse supply, and technologies. A two-stage stochastic programming model is developed investors. for an entire biofuel pathway. To overcome the computational challenges, an effective decomposition method based on progressive hedge (PH) method is implemented. ■ SA03 4 - The Role of Storage Facility Design in Disruption Management of C - Ballroom D3, Level 4 Biofuel Supply Chains Yongxi Huang, University of California-Davis, One Shields Avenue, Underground Mining Applications Davis, CA, 95616, United States of America, [email protected], Sponsor: Energy, Natural Resources and the Environment/ Mining Yueyue Fan Sponsored Session A biofuel supply chain consists of various components that are interdependent of Chair: Alexandra Newman, Associate Professor, Colorado School of each other. A crucial question is how to improve the reliability of the biofuel system against potential disruptions. A stochastic mixed-integer programming model that Mines, Room 319, Engineering Hall, Golden, CO, 80401, United States of integrates feedstock seasonality, geographic variation, and demand fluctuation is America, [email protected] developed, aiming at minimizing the total expected cost of the entire biofuel supply 1 - Long-term Extraction and Backfill Scheduling in a Complex chain. Progressive Hedging method was used to solve the stochastic model. Underground Mine Donal O’Sullivan, PhD Student, Colorado School of Mines, Division of Economics and Business, 816 15th Street, Golden, CO, 80401, ■ SA05 United States of America, [email protected], Alexandra Newman C - Ballroom D5, Level 4 We present an integer programming model to
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