Due to the strong fluctuations of renewable energies, times with feed-in peaks in the electric distribution grids occur as well as times with too little amounts of energy. The balance between supply and consumption is a central challenge for future grid topologies.
Consumer-based load management uses the storage capacity of devices to postpone their electric power consumption to off-peak hours. In this context, autonomous load management is a special approach, which is based on one-way communication. Thereby, a pseudo-cost function is used to harness the storage potential of the devices.
The idea is to optimize switching times of electric consumers directly onsite based on their physical models. Compared to central approaches, higher robustness, higher time resolution of data measurement and higher model accuracies can be achieved due to local data acquisition, model parametrization and control. Furthermore, protection of privacy can be improved.
Our research center energy develops autonomous algorithms and prototypes to shift loads of thermal energy storages like hot water boilers and cold storages as well as electrochemical storages like stationary battery systems and electric vehicles.
