A fast branch & bound method for the open-shop problem based on a disjunctive graph formulation of the problem is developed. Computational results show that the method yields excellent results. Some benchmark problems from the literature were solved to optimality for the first time.

Various Demand Side Management (DSM) approaches have been developed the last couple of years to avoid costly grid upgrades. However, evaluation of these DSM methodologies is usually restricted to a use-case specific example, making comparison between different DSM approaches hard. This paper presents a novel, open source, load profile generator to evaluate and compare DSM approaches. In addition to static load profiles for both active and reactive power, it also provides flexibility information for various classes of controllable domestic devices. Load profiles and flexibility information are generated using a simple behavioural simulation. The output data uses 1 minute intervals and incorporates device measurements. The generated profiles are in sound with the measurement data obtained in a field test on both the household level and aggregated neighbourhood level. The same dynamics, such as unbalanced loading and rapid power consumption fluctuations, are observed in the generated model.

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An increase in the number of volatile renewables in the electricity grid enhances the imbalance of supply and demand. One promising candidate to solve this problem is to improve the energy storage. The Ecovat system is a new seasonal thermal energy storage system currently under development. In this paper, an integer linear programming model is developed to describe the behaviour and potential of this system. Furthermore, it is compared with a previously developed model, which is simplifying the behaviour of the Ecovat system much more, but is much less computationally expensive. It is shown that the new approach performs significantly better for several cases. For controlling a real Ecovat system in the future we may incorporate a number of improvements identified by our comparison analysis into the previously developed approach, which may help increase the quality of the obtained results without increasing the computational effort too much.

In this paper, we present an algorithm for run-time allocation of hardware resources to software applications. We define the sub-problem of run-time spatial mapping and demonstrate our concept for streaming applications on heterogeneous MPSoCs. The underlying algorithm and the methods used therein are implemented and their use is demonstrated with an illustrative example.