aDSM - Active Demand Side Management, 834612

Goal of the project aDSM is the development of hierarchical, scalable systems with distributed intelligence. Household load as well as the demand of electrical vehicles will be used flexible to level the local renewable generation. Load shifting and charge controlling will be used actively and anticipatory based on an optimized renewable generation forecast. If a local balance is not possible, the upper system levels (up to the transmission power grid) or energy storage will intervene coordinated. The aim is to reduce the requirement of storage as well as the network load by the use of aDSM. On the basis of electric vehicles and a PV system a practical demonstration of the aDSM system implementation for a single network node is performed.

Starting point / motivation:

The expansion of the share of renewable energy sources in electricity production and the development of zero-emission vehicles, which are supplied from these sources, is one of the most important tasks for a future climate policy. The integration of both into the existing electrical grid is technically very difficult.

Methodological approach:

The project consortium has access to a wealth of experience from previous projects. From these domestic, mobility, renewable generation and climate data are available. These data will be evaluated in the individual work packages and used to create individual models. National and international inquiries and also discussions with experts are made to improve the modeling systems. The work package "aDSM performance" then combines all models and uses a weighted multi-objective optimization algorithm for the local aDSM system. Network simulations reflect the condition of the entire distribution network. Finally, the local aDSM behavior in a practical implementation of e-vehicles and PV system will be tested.

Content, goals and Expected results / conclusions

The project aDSM has set following main objectives:

·      Determination of available DSM potential of domestics and electric vehicles depend on optimization aim.

·      Development and adaptation of forecasting methods to improve spatial and temporal resolution.

·      Determination of total renewable generation forecast errors for both local and global predictions.

·      Creation of local aDSM model.

·      Grid loads and "storage demand" in the low voltage grid.

·      Verifying the functioning of the aDSM system in the demo implementation.