Power TAC models the high complexity of contemporary and future retail electricity markets, allowing for large-scale experimentation.
Retail electricity trading markets were designed long ago, when power was produced almost exclusively using fossil fuels. Back then, balancing supply and demand of electricity and predicting market behavior was relatively easy. Then came the dawn of renewable energy.
Renewable energy is great – it’s cheap and it’s clean. But the production output of renewables is hard to predict. Wind doesn’t always blow on cue and the sun doesn’t always shine according to forecast. That means the more renewable energy in the energy market mix, the bigger the 'intermittency' problem.
And as more electricity consumers become producers as well – we call them 'prosumers' – the once straightforward supply and demand balancing act becomes breathtakingly complex.
In 2009, we designed an open-source platform to support an artificial intelligence-driven Power Trading Agent Competition – Power TAC – to explore how digital information systems could integrate sustainable energy into existing electricity markets in a profitable way.
Power TAC models the high complexity of contemporary and future electricity markets, allowing for large scale experimentation. Autonomous machine-learning trading agents, or 'brokers', act as intermediary profit maximization parties between the market and 'customers', who represent consumers, producers and prosumers. 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 electricity in the wholesale market, while carefully balancing supply and demand.
With each annual tournament, the models become more sophisticated, the platform more flexible and the results more enlightening.
Now we want to share this platform with your organization – scroll down to see what Power TAC could mean for you.
From predicting EV charging behavior in a new business model ...
The Power TAC platform can model the complex interaction between market players who have the capability to charge at home, at work or on the way as well as businesses that will use their fleets for electricity market arbitrage as well as for transport. Predict the performance of your business model even in rapidly changing markets.
... to forecasting industrial electricity cooperative performance
The advent of autonomous information systems management coupled with the steadily decreasing cost of investing in renewable energy production makes the potential for workable, profitable industrial electricity cooperatives intriguing. Forecast the value of a cooperative in your locality using the Power TAC platform.
... to simulating policy change outcomes in a complex environment.
Renewable energy policy changes regularly produce unintended consequences. The infamous Enron scandal, which destabilized the California electricity market, originated in part in a policy designed with an unnoticed flaw. Use the Power TAC platform to prevent unintended consequences and notice design flaws by simulating market behavior.