Smart Grid Economics: Policy Guidance Through Competitive Simulation

Sustainable energy systems of the future will need more than efficient, clean, low-cost, renewable energy sources. They will also need market structures that motivate sustainable behaviors on the part of households, businesses, and investors. We know how to build "smart grid"' components that can assist energy consumers to manage their consumption, but without pricing policies that motivate consumers to install and use these new tools, they will be ineffective at maximizing utilization of renewable energy sources while minimizing dependence on non-renewable energy. Unfortunately, serious market breakdowns such as the California energy crisis in 2000 have made policy makers justifiably wary of setting up new retail energy markets.

The design of retail energy markets depends heavily on economically-motivated behavior of the participants, but proposed retail markets are too complex for straightforward game-theory analysis.
Agent-based simulation environments have been used to study the operation of wholesale power markets, but these studies are not effective in ensuring against market breakdowns due to unanticipated self-interested or destructive behaviors of the participants. Therefore, Wolf Ketter and his colleagues are working on developing an open, competitive simulation approach that will address the need for policy guidance based on robust research results on the structure and operation of retail power markets. These results in turn will help policy makers to create institutions that produce the intended effects on energy production and consumption.

Over the years, such competitions have been important catalysts for progress in Artificial Intelligence. Prior to beginning work on the Power TAC (www.powertac.org) scenario, Wolf Ketter has been involved in the Trading Agent Competition for Supply Chain Management (TAC SCM) for several years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets.
Since its introduction in 2003, the competition has attracted over 150 entries and brought together researchers from AI and beyond in 75 competing teams from 25 different countries.

The talk will describe these two scenarios and some of the conceptual and computational challenges that must be overcome to design and build agents that can operate successfully in such environments. It will also address a few of the ways in which this research might begin to influence industry practice in the real world.