| Abstract |
The energy sector will undergo fundamental changes over the next ten
years. Prices for fossil energy resources are continuously
increasing, there is an urgent need to reduce CO2 emissions, and the
United States and European Union are strongly motivated to become
more independent from foreign energy imports. These factors will
lead to installation of large numbers of distributed renewable
energy generators, which are often intermittent in nature. This
trend conflicts with the current power grid control infrastructure
and strategies, where a few centralized control centers manage a
limited number of large power plants such that their output meets
energy demands in real time. As the proportion of distributed and
intermittent generation capacity increases, this task becomes much
harder, especially as the local and regional distribution grids
where renewable energy generators are usually installed are
currently virtually unmanaged, lack real time metering and are not
built to cope with power flow inversions (yet).
All this is about to change, and so the control strategies must be
adapted accordingly. While the hierarchical command-and-control
approach served well in a world with a few large scale generation
facilities and many small consumers, a more flexible, decentralized,
and self-organizing control infrastructure will have to be developed
that can be actively managed to balance both the large grid as a
whole, as well as the many lower voltage sub-grids.
We propose a competitive simulation testbed to stimulate research
and development of agents that help manage these tasks. Participants
will develop intelligent agents that are responsible to level energy
supply from generators with energy demand from consumers. The
competition is designed to closely model reality, by bootstrapping
the simulation environment with real historic load, generation, and
weather data. The simulation environment will provide a low-risk
platform that combines simulated markets and real-world data to
develop solutions that can be applied to build the self-organizing
intelligent energy grid of the future. |