2018-02-01, Wache, Holger, Künzli, Michael, Schulz, Nicola, Bichsel, Jürg, Hall, Monika

The increasing number of volatile energy sources, such as solar power plants, challenges the power network operators, the energy brokers as well as the electricity market actors. In this work, a multi-agent based approach will be introduced that allows multiple households to trade flexibilities, on top of the usual selling of produced energy to the paying consumer. Flexibility trading allows different optimisations for different actors. They can slightly shift their electricity consumption, e.g. by turning boilers on/off, to optimise the system (e.g. follow a predefined schedule). Simulation results indicate that a flexibility market consisting of only few households can already help to optimise the system.

2015, Lammel, Benjamin, Schulz, Nicola, Bichsel, Jürg, Wache, Holger, Farooq, Abdul, Hoffmann, Caroline, Mettler, Fabian

In this article, the "SmartStability" concept is introduced and first results are shown. The concept is based on the exchange of electrical energy within a network of households that possess temporal flexibilities in consuming or providing energy from or to the network. The exchange is governed by a market-economic negotiation principle between the households. Temporal flexibility is achieved by exploiting thermal capacities of the buildings themselves and those of warm water storages, and by allowing certain temperature bands. Electric and thermal energy forms are coupled by means of heat pumps and electric water boilers. The physical energy exchange takes place via the electrical grid. The behaviour of a SmartStability network has been simulated, based on physical models of the energetic resources within each network unit, and by interlinking the individual units to form the entire SmartStability network within a multi-agent environment. Goal of several simulation scenarios was the adaptation of the time-dependent power consumption profile of the network to a given schedule. Networks consisting of 5 to 100 houses have been simulated. The simulation results show that deviations from schedule can be reduced by approx. 50% by the market-economics-based self-optimization and the resulting intelligent operation of resources. By additionally using battery storages, the deviation from schedule can be further significantly reduced.