The 2017 Power Trading Agent Competition

46 Pages Posted: 13 Feb 2017  

Wolfgang Ketter

Erasmus Research Institute of Management (ERIM); Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management

John Collins

University of Minnesota - Twin Cities

Mathijs de Weerdt

Delft University of Technology

Date Written: February 13, 2017


This is the specification for the Power Trading Agent Competition for 2017 (Power TAC 2017). 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 positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources.

Changes for 2017 are focused on a more realistic wholesale market, reducing the market power of brokers by making the simulation scenario into a relatively small part of a larger market, and are highlighted by change bars in the margins. See Section 5.3 for details.

Keywords: Autonomous Agents, Electronic Commerce, Energy, Preferences, Portfolio Management, Power, Policy Guidance, Sustainability, Trading Agent Competition

Suggested Citation

Ketter, Wolfgang and Collins, John and Weerdt, Mathijs de, The 2017 Power Trading Agent Competition (February 13, 2017). Available at SSRN: or

Wolfgang Ketter (Contact Author)

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam


Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management ( email )

RSM Erasmus University
PO Box 1738
3000 DR Rotterdam

John Collins

University of Minnesota - Twin Cities ( email )

420 Delaware St. SE
Minneapolis, MN 55455
United States

Mathijs De Weerdt

Delft University of Technology ( email )

Stevinweg 1
Stevinweg 1
Delft, 2628 CN


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