EPNES: Security of Supply & Strategic Learning in Restructured Power Markets

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

In the first part of our proposal, we study the long run reliability (or security of supply) of electricity markets under price caps. This (and other forms or market intervention) cast doubts on the market ability to provide new capacity in a socially efficient manner (scale, technology and timing). We propose to develop a dynamic game model of investment. A full characterization of equilibrium investment will shed light on a number of contentious issues in the restructuring debate. Examples include; the possibility of "boom and bust" cycles in equilibrium, a potential technology bias with harmful effects on the environment, and the need to incorporate capacity markets. In the second part of our proposal we study learning algorithms as potentially powerful computational tools for electricity markets. The complexity of electricity markets calls for the incorporation of some form of bounded rationality in the modeling efforts. However, when players use simple, adaptive (possibly sub-optimal) rules, repeated interaction may induce equilibrium outcomes in the long run. Although, many "agent based" simulation models have been advocated for analyzing electricity markets, they lack solid theoretical support on issues such as convergence and/or the nature of equilibrium. In this proposal we will represent competition in electricity markets under certain congestion management protocols, as games with a special structure, i.e. "potential games'. For this class of games, the class of "fictitious play" learning algorithms has been proven to converge with probability one. Computational tests a large-scale model will serve to validate and assess the practicality of the class of strategic learning models proposed.