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The 2016 Power Trading Agent Competition

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  • Ketter, W.
  • Collins, J.
  • de Weerdt, M.M.

Abstract

This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market posi- tions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. Changes for 2016 are focused on a more realistic cost model for brokers, and are highlighted by change bars in the margins. See Section 7 for details.

Suggested Citation

  • Ketter, W. & Collins, J. & de Weerdt, M.M., 2016. "The 2016 Power Trading Agent Competition," ERIM Report Series Research in Management ERS-2016-002-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  • Handle: RePEc:ems:eureri:79482
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    References listed on IDEAS

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    1. Paul Joskow & Jean Tirole, 2006. "Retail electricity competition," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 799-815, December.
    2. Somani, Abhishek & Tesfatsion, Leigh, 2008. "An Agent-Based Test Bed Study of Wholesale Power Market Performance Measures," ISU General Staff Papers 200801010800001392, Iowa State University, Department of Economics.
    3. Paul Joskow & Jean Tirole, 2006. "Retail electricity competition," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 799-815, December.
    4. Edward Clarke, 1971. "Multipart pricing of public goods," Public Choice, Springer, vol. 11(1), pages 17-33, September.
    5. Möller, Christoph & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "Balancing energy strategies in electricity portfolio management," Energy Economics, Elsevier, vol. 33(1), pages 2-11, January.
    6. Ketter, Wolfgang & Collins, John & Reddy, Prashant, 2013. "Power TAC: A competitive economic simulation of the smart grid," Energy Economics, Elsevier, vol. 39(C), pages 262-270.
    7. Vandezande, Leen & Meeus, Leonardo & Belmans, Ronnie & Saguan, Marcelo & Glachant, Jean-Michel, 2010. "Well-functioning balancing markets: A prerequisite for wind power integration," Energy Policy, Elsevier, vol. 38(7), pages 3146-3154, July.
    8. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
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    10. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
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    Cited by:

    1. Tao Chen & Qais Alsafasfeh & Hajir Pourbabak & Wencong Su, 2017. "The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey," Energies, MDPI, vol. 11(1), pages 1-17, December.
    2. Neda Hajibandeh & Mehdi Ehsan & Soodabeh Soleymani & Miadreza Shafie-khah & João P. S. Catalão, 2017. "The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey," Energies, MDPI, vol. 10(9), pages 1-18, September.

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    More about this item

    Keywords

    autonomous agents; electronic commerce; energy; preferences; portfolio management; power; policy guidance; sustainability; trading agent competiton;
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