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2017 | OriginalPaper | Chapter

Investigation of Learning Strategies for the SPOT Broker in Power TAC

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

Published in: Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets

Publisher: Springer International Publishing

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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.

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previous chapter Now, Later, or Both: A Closed-Form Optimal Decision for a Risk-Averse Buyer
next chapter On the Use of Off-the-Shelf Machine Learning Techniques to Predict Energy Demands of Power TAC Consumers
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Metadata
Title
Investigation of Learning Strategies for the SPOT Broker in Power TAC
Authors
Moinul Morshed Porag Chowdhury
Russell Y. Folk
Ferdinando Fioretto
Christopher Kiekintveld
William Yeoh
Copyright Year
2017
Book
Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets

Print ISBN: 978-3-319-54228-7

Electronic ISBN: 978-3-319-54229-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54229-4_7

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