Daniel Urieli's Ph.D Dissertation:
Autonomous Trading in Modern Electricity Markets
This is the home page of Daniel Urieli's Ph.D dissertation, Autonomous Trading in Modern Electricity Markets.
The dissertation focuses on the research question:
How should an autonomous broker agent act to maximize its utility by trading
in time-constrained, modern electricity markets?
The dissertation's contributions are fully implemented in the TacTex
power trading agent, whose binaries and source code are freely available (see below).
Dissertation Document and Slides
The dissertation document is available in the UT AI lab webpage and in the University of Texas Libraries. The dissertation's defense slides are available here.
TacTex's Binaries:
The TacTex agent binaries that played in the Power TAC finals can be found here:
TacTex 2013
TacTex 2014
TacTex 2015
Note: Each of these binaries works with a corresponding version of the Power
TAC server that was used in the finals in the same year. For details on getting
and running these server versions see http://www.powertac.org
TacTex's Source Code:
TacTex's complete source code is now in GitHub. See:
https://github.com/LARG/TacTex
TacTex's Home Page:
TacTex's home page is:
https://www.cs.utexas.edu/~TacTex
Dissertation's Abstract
The smart grid is an electricity grid augmented with digital technologies that automate the management of electricity delivery. The smart grid is envisioned to be a main enabler of sustainable, clean, efficient, reliable, and secure energy supply. One of the milestones in the smart grid vision will be programs for customers to participate in electricity markets through demand-side management and distributed generation; electricity markets will (directly or indirectly) incentivize customers to adapt their demand to supply conditions, which in turn will help to utilize intermittent energy resources such as from solar and wind, and to reduce peak-demand.
Since wholesale electricity markets are not designed for individual participation, retail brokers could represent customer populations in the wholesale market, and make profit while contributing to the electricity grid's stability and reducing customer costs. A retail broker will need to operate continually and make real-time decisions in a complex, dynamic environment. Therefore, it will benefit from employing an autonomous broker agent. With this motivation in mind, this dissertation makes five main contributions to the areas of artificial intelligence, smart grids, and electricity markets.
First, this dissertation formalizes the problem of autonomous trading by a retail broker in modern electricity markets. Since the trading problem is intractable to solve exactly, this formalization provides a guideline for approximate solutions.
Second, this dissertation introduces a general algorithm for autonomous trading in modern electricity markets, named LATTE (Lookahead-policy for Autonomous Time-constrained Trading of Electricity). LATTE is a general framework that can be instantiated in different ways that tailor it to specific setups.
Third, this dissertation contributes fully implemented and operational autonomous broker agents, each using a different instantiation of LATTE. These agents were successful in international competitions and controlled experiments and can serve as benchmarks for future research in this domain. Detailed descriptions of the agents' behaviors as well as their source code are included in this dissertation.
Fourth, this dissertation contributes extensive empirical analysis which validates the effectiveness of LATTE in different competition levels under a variety of environmental conditions, shedding light on the main reasons for its success by examining the importance of its constituent components.
Fifth, this dissertation examines the impact of Time-Of-Use (TOU) tariffs in competitive electricity markets through empirical analysis. Time-Of-Use tariffs are proposed for demand-side management both in the literature and in the real-world.
The success of the different instantiations of LATTE demonstrates its generality in the context of electricity markets. Ultimately, this dissertation demonstrates that an autonomous broker can act effectively in modern electricity markets by executing an efficient lookahead policy that optimizes its predicted utility, and by doing so the broker can benefit itself, its customers, and the economy.