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. In Power TAC, agents act as retail brokers in a local power distribution region, purchasing power from a wholesale market as well as from local sources, such as homes and businesses with solar panels, and selling power to local customers and into the wholesale market. Retail brokers must solve a supply-chain problem in which the product is infinitely perishable, and supply and demand must be exactly balanced at all times.
Power TAC models the high complexity of contemporary and future energy markets, allowing for large scale experimentation. Main entities are the customers, representing consumers, producers and "pro-sumers" and brokers, who act as intermediary profit maximization parties. Customer models represent households, small and large businesses, multi-residential buildings, wind parks, solar panel owners, electric vehicle owners, etc. Brokers aim at making profit through offering electricity tariffs to customers and trading energy in the wholesale market, while carefully balancing supply and demand.
The current released version of the Power TAC simulation server and sample broker is 1.2.1; see the participant's wiki for instructions on downloading the software and getting started. This is the version that ran the May 2015 competition. Current efforts are focused on adding models and improving the visual interface.
The specification for the 2015 competition is available here.
The Power TAC 2014 tournament finished successfully, see the Power TAC 2014 page for details.
Our Power TAC public wiki is a primary resource for broker developers, and home to the discussions on game design and specification, along with a growing bibliography of background materials on the economics of electric power systems. Information about the the broker development can be found on the Agent page and the server on the Server page.