Consumer Decision Making in Power TAC

Sustainable energy systems of the future will need more than efficient, clean, low-cost, renewable energy sources; they will also need efficient price signals that motivate sustainable energy consumption as well as a better real-time alignment of energy demand and supply. It is known how to build “smart grid" components that can record energy usage in real time and help consumers better manage their energy usage. However, this is only the technical foundation.

Variable energy prices that truly reflect energy scarcity can motivate consumers to shift their loads to minimize cost, and for producers to better dispatch their capacities. This will be critical to the effort to develop a more sustainable energy infrastructure based on increasing proportions of variable-output sources, such as wind and solar power.

The performance of markets depends on economically motivated behavior of the participants, but proposed retail energy markets are too complex for straightforward game-theoretic analysis. Agent-based simulation environments have been used to study the operation of wholesale power markets, but these studies are not able to explore the full range of unanticipated self-interested or destructive behaviors of the participants. Smart grid pilot projects, on the other hand, are limited in their ability to test system dynamics for extreme situations. They also lack the competitiveness of open markets, because a single project consortium typically controls and optimizes the interaction of all parts of the pilot regions. Therefore, we are working on an open, competitive market simulation platform that will address the need for policy guidance based on robust research results on the structure and operation of retail power markets. The Power Trading Agent Competition (see http://www.powertac.org) is an example of a Trading Agent Competition (TAC - see http://www.tradingagents.org for details) applied to electrical power markets (see Figure 1). Power TAC will model the retail energy ecosystem of the future.

Competing research groups will construct trading agents to act as self-interested “brokers" that aggregate energy supply and demand with the intent of earning a profit. Brokers will buy and sell energy through contracts with retail customers (households, small and medium enterprises, owners of electric vehicles), and by trading in a wholesale market that models a real-world market such as the European or North American wholesale power markets. Brokers compete with each other to attract customers by offering tariff contracts to a population of anonymous small household and business customers, and by negotiating individual contracts with larger customers.

The goal of this joint research project is to develop and learn a predictive model for automatically contracting customers to tariffs offered by brokers during the simulation. For that purpose we will design an empirical study that allows us to observe consumers' preferences w.r.t. to different characteristics of tariff models and to build a predictive model based on the collected empirical data. The research is conducted as a Cooperation between AAU and Rotterdam School of Management of Erasmus University.