TY - GEN
T1 - Aiming for Half Gets You to the Top
T2 - 18th European Conference on Multi-Agent Systems, EUMAS 2021
AU - Orfanoudakis, Stavros
AU - Kontos, Stefanos
AU - Akasiadis, Charilaos
AU - Chalkiadakis, Georgios
PY - 2021
Y1 - 2021
N2 - The PowerTAC competition provides a multi-agent simulation platform for electricity markets, in which intelligent agents acting as electricity brokers compete with each other aiming to maximize their profits. Typically, the gains of agents increase as the number of their customers rises, but in parallel, costs also increase as a result of higher transmission fees that need to be paid by the electricity broker. Thus, agents that aim to take over a disproportionately high share of the market, often end up with losses due to being obliged to pay huge transmission capacity fees. In this paper, we present a novel trading strategy that, based on this observation, aims to balance gains against costs; and was utilized by the champion of the PowerTAC-2020 tournament, TUC-TAC. The approach also incorporates a wholesale market strategy that employs Monte Carlo Tree Search to determine TUC-TAC’s best course of action when participating in the market’s double auctions. The strategy is improved by making effective use of a forecasting module that seeks to predict upcoming peaks in demand, since in such intervals incurred costs significantly increase. A post-tournament analysis is also included in this paper, to help draw important lessons regarding the strengths and weaknesses of the various strategies used in the PowerTAC-2020 competition.
AB - The PowerTAC competition provides a multi-agent simulation platform for electricity markets, in which intelligent agents acting as electricity brokers compete with each other aiming to maximize their profits. Typically, the gains of agents increase as the number of their customers rises, but in parallel, costs also increase as a result of higher transmission fees that need to be paid by the electricity broker. Thus, agents that aim to take over a disproportionately high share of the market, often end up with losses due to being obliged to pay huge transmission capacity fees. In this paper, we present a novel trading strategy that, based on this observation, aims to balance gains against costs; and was utilized by the champion of the PowerTAC-2020 tournament, TUC-TAC. The approach also incorporates a wholesale market strategy that employs Monte Carlo Tree Search to determine TUC-TAC’s best course of action when participating in the market’s double auctions. The strategy is improved by making effective use of a forecasting module that seeks to predict upcoming peaks in demand, since in such intervals incurred costs significantly increase. A post-tournament analysis is also included in this paper, to help draw important lessons regarding the strengths and weaknesses of the various strategies used in the PowerTAC-2020 competition.
KW - Bidding strategies
KW - Electricity brokers
KW - Trading agents
UR - http://www.scopus.com/inward/record.url?scp=85113336409&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-82254-5_9
DO - 10.1007/978-3-030-82254-5_9
M3 - Conference contribution
AN - SCOPUS:85113336409
SN - 9783030822538
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 144
EP - 159
BT - Multi-Agent Systems - 18th European Conference, EUMAS 2021, Revised Selected Papers
A2 - Rosenfeld, Ariel
A2 - Talmon, Nimrod
PB - Springer
Y2 - 28 June 2021 through 29 June 2021
ER -