Open AccessProceedings Article
TacTex'13: a champion adaptive power trading agent
Daniel Urieli,Peter Stone +1 more
- pp 465-471
TLDR
The complex decision-making problem that Tac Tex'13 faces is formalized, and its solution is approximate in TacTex'13's constituent components, as well as the success of the complete agent through analysis of competition results.Abstract:
Sustainable energy systems of the future will no longer be able to rely on the current paradigm that energy supply follows demand. Many of the renewable energy resources do not produce power on demand, and therefore there is a need for new market structures that motivate sustainable behaviors by participants. The Power Trading Agent Competition (Power TAC) is a new annual competition that focuses on the design and operation of future retail power markets, specifically in smart grid environments with renewable energy production, smart metering, and autonomous agents acting on behalf of customers and retailers. It uses a rich, open-source simulation platform that is based on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making, as well as the robustness of proposed market designs. This paper introduces TACTEX'13, the champion agent from the inaugural competition in 2013. TACTEX'13 learns and adapts to the environment in which it operates, by heavily relying on reinforcement learning and prediction methods. This paper describes the constituent components of TACTEX'13 and examines its success through analysis of competition results and subsequent controlled experiments.read more
Citations
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Summarizing agent strategies
TL;DR: A conceptual framework for strategy summarization is proposed, which is envisioning as a collaborative process that involves both agents and people, and possible testbeds that could be used to evaluate progress in research on strategy summaryization are suggested.
Proceedings ArticleDOI
A Winner Agent in a Smart Grid Simulation Platform
Serkan Özdemir,Rainer Unland +1 more
TL;DR: The trading strategies of AgentUDE, which won the Power Trading Agent Competition 2014 Final using adaptive trading strategies, are detailed and the tournament data is analyzed from the winner agent's point of view.
Proceedings ArticleDOI
Winning in Retail Market Games: Relative Profit and Logit Demand
TL;DR: This work examines retailers that maximize their relative profit, which is the (absolute) profit relative to the average profit of the other retailers, and shows that the relative Profit, as a function of the own price, has a unique local maximum.
Posted Content
Bidding in Smart Grid PDAs: Theory, Analysis and Strategy (Extended Version).
TL;DR: In this article, a single unit single-shot double auction with a certain clearing price and payment rule, referred to as ACPR, was analyzed, and the best response for a bidder with complete information was derived.
Book ChapterDOI
Aiming for Half Gets You to the Top: Winning PowerTAC 2020
TL;DR: In this article, the authors present a 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.
References
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Journal ArticleDOI
Power TAC: A competitive economic simulation of the smart grid
TL;DR: The Power Trading Agent Competition (Power TAC) as mentioned in this paper is a rich competitive simulation of future retail power markets, which can help to understand the dynamics of customer and retailer decision-making and the robustness of market designs.
Proceedings ArticleDOI
Strategic sequential bidding in auctions using dynamic programming
Gerald Tesauro,Jonathan Bredin +1 more
TL;DR: A general framework in which real-time Dynamic Programming (DP) can be used to formulate agent bidding strategies in a broad class of auctions characterized by sequential bidding and continuous clearing, and suggests that this algorithm may offer the best performance of any published CDA bidding strategy.
Journal ArticleDOI
A reinforcement learning approach to autonomous decision-making in smart electricity markets
TL;DR: This work proposes a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies.
Journal ArticleDOI
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions
TL;DR: A new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label.
Proceedings ArticleDOI
Strategy learning for autonomous agents in smart grid markets
Prashant Reddy,Manuela Veloso +1 more
TL;DR: The learning of pricing strategies for an autonomous Broker Agent to profitably participate in a Tariff Market is investigated using Markov Decision Processes (MDPs) and reinforcement learning and results show the learned strategy performing vastly better than a random strategy and significantly better than two other non-learning strategies.