Investigation of learning strategies for the SPOT broker in power TAC

Moinul Morshed Porag Chowdhury, Russell Y. Folk, Ferdinando Fioretto, Christopher Kiekintveld, William Yeoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The Power TAC simulation emphasizes the strategic problems that broker agents face in managing the economics of a smart grid. The brokers must make trades in multiple markets and, to be successful, brokers must make many good predictions about future supply, demand, and prices in the wholesale and tariff markets. In this paper, we investigate the feasibility of using learning strategies to improve the performance of our broker, SPOT. Specifically, we investigate the use of decision trees and neural networks to predict the clearing price in the wholesale market and the use of reinforcement learning to learn good strategies for pricing our tariffs in the tariff market. Our preliminary results show that our learning strategies are promising ways to improve the performance of the agent for future competitions.

Original languageEnglish (US)
Title of host publicationAgent-Mediated Electronic Commerce
Subtitle of host publicationDesigning Trading Strategies and Mechanisms for Electronic Markets - International Workshop on Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, AMEC/TADA 2016
EditorsIoannis A. Vetsikas, Esther David, Sofia Ceppi, Chen Hajaj, Valentin Robu
PublisherSpringer Verlag
Pages96-111
Number of pages16
ISBN (Print)9783319542287
DOIs
StatePublished - 2017
EventInternational Workshop on Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, AMEC/TADA 2016 - New York, United States
Duration: Jul 10 2016Jul 10 2016

Publication series

NameLecture Notes in Business Information Processing
Volume271
ISSN (Print)1865-1348

Conference

ConferenceInternational Workshop on Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, AMEC/TADA 2016
CountryUnited States
CityNew York
Period7/10/167/10/16

Keywords

  • Artificial Intelligence
  • Game theory
  • Machine learning
  • Multi agent system
  • Smart grid

ASJC Scopus subject areas

  • Management Information Systems
  • Control and Systems Engineering
  • Business and International Management
  • Information Systems
  • Modeling and Simulation
  • Information Systems and Management

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  • Cite this

    Chowdhury, M. M. P., Folk, R. Y., Fioretto, F., Kiekintveld, C., & Yeoh, W. (2017). Investigation of learning strategies for the SPOT broker in power TAC. In I. A. Vetsikas, E. David, S. Ceppi, C. Hajaj, & V. Robu (Eds.), Agent-Mediated Electronic Commerce: Designing Trading Strategies and Mechanisms for Electronic Markets - International Workshop on Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, AMEC/TADA 2016 (pp. 96-111). (Lecture Notes in Business Information Processing; Vol. 271). Springer Verlag. https://doi.org/10.1007/978-3-319-54229-4_7