The project aims to make recommendations about the implementation of control, communications, and business schemes for enabling thermal loads to provide ancillary services in the form of control reserves for the Swiss power grid. Ancillary services provide a fast-reacting compensation for a power shortage or surplus in the transmission grid.
Thermal loads such as building HVAC systems and household appliances have an inherent thermal storage capacity, which provides flexibility in their power consumption without compromising their original purpose. Hence, one can envision effective demand response schemes exploiting these thermal loads to balance the power grid locally, reducing transmission congestions, improving ancillary service market operations, and reducing power peaks. Most importantly, this facilitates the integration of renewable energy sources, which critically rely on ancillary services today. Heatreserves is the first external project using the ehub platform after its launch.
In Switzerland and many other European countries, the future energy system will rely heavily on renewable energy. This will cause an important reengineering of this part of the electrical infrastructure. Therefore, a massive penetration of distributed power sources and distributed storage devices calls for a new layout and system design of the urban energy system.
The results of our studies will form the basis for the planning such systems and grids. Both the design (planning) of the energy system and the operation will be considered.
The developed microgrid framework consists of independent resource and grid agents communicating with each other. The goal is that the grid operates in a safe state as it can determine the load on its lines and the resources can operate flexibly and independently. This cooperation based, distributed control scheme has a cycle time of 100ms leading to fast corrections form optimal trajectories. With this scheme one can also operate in islanding mode with the ability to connect and disconnect whole districts to the distribution grid.
SAlt, LIthium-ion and SuperCapacitors Storages in the Distribution Grid (SALISC)
Decentral batteries or district sized battery installations provide a layer of flexibility to the distribution grid and its stakeholder. In SALISC Empa investigates in the design and operational stages of battery usages to determine their profitability in 2018 and in 2025. Multiple storage technologies, sizes, locations and control schemes are analyzed according the general conditions of a distribution grid of Glattwerke AG acting as DCO. The most promising solutions are implemented on the ehub platform and its storage technologies to exemplary validate the performance of the found solutions.
Especially the effect of Molten Salt (NaNiCl2) and Lithium Ion (NMC-G) Storages is investigated. The impact of additional of Super Capacitors shall highlight the significance of this technology to a storage setup in a distribution grid.
Optimized Local Control Scheme for Active Distribution Grids Using Machine Learning Techniques
The decentralized control scheme based on Machine Learning (ML) technique proposed in  is applied to the NEST microgrid, allocated in the EMPA Laboratory in Dübendorf. However, due to the low voltage magnitudes over the grid, in order to have consistent results, the battery is forced to operate into a high injection regime, increasing the local voltages and allowing to adopt the Reactive Power Control (RPC) as active measure to achieve network-wide optimal operation. In this approach, the online optimization problem is performed in two stages. First, a day-ahead optimization problem for the BESS is implemented with the objective to impose high injections during noon hours and guarantee a secure operation. Second, a centralized, OPF-based scheme is used to generate a sequence of optimal DER setpoints accounting for BESS injections. Finally, the local DER controllers are developed as explained in  and applied in the real-time operation.
 F. Bellizio, S. Karagiannopoulos, P. Aristidou, and G. Hug, "Optimized local control schemes for distribution grids using machine learning techniques," IEEE Power and Energy Society General Meeting, 2018.