Peter Stone's Selected Publications

Classified by TopicClassified by Publication TypeSorted by DateSorted by First Author Last NameClassified by Funding Source


Autonomous Electricity Trading using Time-Of-Use Tariffs in a Competitive Market

Daniel Urieli and Peter Stone. Autonomous Electricity Trading using Time-Of-Use Tariffs in a Competitive Market. In Proceedings of the 30th Conference on Artificial Intelligence (AAAI 2016), February 2016.

Download

[PDF]1.2MB  

Abstract

This paper studies the impact of Time-Of-Use (TOU) tariffs in a competitive electricity market place. Specifically, it focuses on the question of how should an autonomous broker agent optimize TOU tariffs in a competitive retail market, and what is the impact of such tariffs on the economy. We formalize the problem of TOU tariff optimization and propose an algorithm for approximating its solution. We extensively experiment with our algorithm in a large-scale, detailed electricity retail markets simulation of the Power Trading Agent Competition (Power TAC) and: 1) find that our algorithm results in 15% peak-demand reduction, 2) find that its peak-flattening results in greater profit and/or profit-share for the broker and allows it to win against the 1st and 2nd place brokers from the Power TAC 2014 finals, and 3) analyze several economic implications of using TOU tariffs in competitive retail markets.

BibTeX Entry

@InProceedings{AAAI16-urieli,
  author = {Daniel Urieli and Peter Stone},
  title = "{Autonomous Electricity Trading using {T}ime-{O}f-{U}se Tariffs in a Competitive Market}",
  booktitle = {Proceedings of the 30th Conference on Artificial Intelligence (AAAI 2016)},
  location = {Phoenix, AZ, USA},
  month = {February},
  year = {2016},
  abstract = {
    This paper studies the impact of Time-Of-Use (TOU) tariffs in a competitive
    electricity market place. Specifically, it focuses on the question of how
    should an autonomous broker agent optimize TOU tariffs in a competitive
    retail market, and what is the impact of such tariffs on the economy. We
    formalize the problem of TOU tariff optimization and propose an algorithm
    for approximating its solution. We extensively experiment with our
    algorithm in a large-scale, detailed electricity retail markets simulation
    of the Power Trading Agent Competition (Power TAC) and: 1) find that our
    algorithm results in 15% peak-demand reduction, 2) find that its
    peak-flattening results in greater profit and/or profit-share for the
    broker and allows it to win against the 1st and 2nd place brokers from the
    Power TAC 2014 finals, and 3) analyze several economic implications of
    using TOU tariffs in competitive retail markets.
  },
} 

Generated by bib2html.pl (written by Patrick Riley ) on Sun Aug 20, 2017 16:27:48