Dear Colleagues,

With the current grid infrastructure and an increasing percentage of renewable energy generation, there will be days that during certain hours (e.g., around noon with a lot of PV and wind production) not all renewable energy generated in certain parts of the power grid can be transported to other regions and therefore has to be curtailed. On the other hand, it is also expected that the need for electricity will grow in the future due to an increasing electrification of heating and transport. Large quantities of E-vehicles and heat pumps enlarge variability and lead to higher peak load concentrations. This again may increase the need for costly grid capacity investments.

Optimization and control

To avoid or reduce the need for grid investments, especially in distribution grids, it is essential to exploit the flexibility available in the grid, e.g., by controlling/optimizing the charging of E-vehicles, time-shiftable appliances (e.g., washing machines, air-conditioners, freezers, heat pumps), and storage assets. Such an optimization of energy streams is often called demand side management (DSM) and has the goal to reach a certain objective for the consumption of electricity within a distribution grid.

The objective may, for example, be market-driven, technology-driven (e.g., avoiding violation of grid restrictions) or maximize self-consumption. On a longer term, with a much higher penetration of renewable generation, it will become even more important to optimize the power profiles of parts of the power grid not only within a day but also over days or weeks, since otherwise, the resulting imbalances will ask for a large amount of central reserve capacity (predominantly based on fossil fuels and dimensioned for the largest peak and hence operating with a low efficiency). For this, different forms of (decentralized) energy storage assets for short-term as well as long-term storage are needed. These storage assets need to be controlled/optimized as well.

Potential topics include but are not limited to:

• Control and optimization of smart electricity grids
• Control and optimization of local heat networks
• Control and optimization of storage
• Energy-autonomous regions
• Control to support energy markets
• Demand side management
• Agent-based modeling of smart grids
• Distributed algorithms

Prof. Gerard J.M. Smit
Guest Editor

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• control and optimization algorithms
• smart energy systems
• control of storage
• distributed algorithms
• energy markets
• privacy and security in smart grids
• resilience
• communication technologies for smart grids