An MDP-Based Winning Approach to Autonomous Power Trading: Formalization and Empirical Analysis (2016)
With the efforts of moving to sustainable and reliable energy supply, electricity markets are undergoing far-reaching changes. Due to the high-cost of failure in the real-world, it is important to test new market structures in simulation. This is the focus of the Power Trading Agent Competition (Power TAC), which proposes autonomous electricity broker agents as a means for stabilizing the electricity grid. This paper focuses on the question: how should an autonomous electricity broker agent act in competitive electricity markets to maximize its profit. We formalize the electricity trading problem as a continuous, high-dimensional Markov Decision Process (MDP), which is computationally intractable to solve. Our formalization provides a guideline for approximating the MDP's solution, and for extending existing solutions. We show that a previously champion broker can be viewed as approximating the solution using a lookahead policy. We present TacTex'15, which improves upon this previous approximation and achieves state-of-the-art performance in competitions and controlled experiments. Using thousands of experiments against 2015 finalist brokers, we analyze TacTex'15's performance and the reasons for its success. We find that lookahead policies can be effective, but their performance can be sensitive to errors in the transition function prediction, specifically demand-prediction.
In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2016.

Peter Stone Faculty pstone [at] cs utexas edu
Daniel Urieli Ph.D. Alumni urieli [at] cs utexas edu