This paper presents a novel approach for an energy control of a DC microgrid. It combines decentralized grid management and energy management. For this purpose, the conventional voltage droop curves are extended to a characteristic diagram with electricity costs as a further dimension. The support points of these characteristic diagrams are then optimized with a particle swarm optimizer. The target criterion of this optimization is a monetary cost function, that takes several effects, such as depth of discharge, on the operating costs into account. The optimized characteristic diagrams are designed more robust by a sensitivity analysis. The proposed method has been tested successfully in simulations and experiment and was always more cost-efficient than the initial characteristics diagram. Index Terms-energy control, DC microgrid, characteristic diagrams, voltage droop control This is the author's version of an article that has been published in the ICIT 2019 proceedings.

A new approach for more energy efficient industrial production processes are smart industrial direct current (DC) microgrids with one or more connections to the alternative current (AC) grid. The advantage of the DC-technology is an easier integration of renewable energies sources and energy storage systems (ESS). Different applications for ESS are possible, for instance an uninterruptible power supply (UPS) for a DC microgrid. Within this paper, a new handling concept for a mains supply failure (e.g. a blackout of the supplying AC grid) with a droop curve control is introduced. In this approach, the droop curve controlling the ESS is adapted, depending on the ESS' state of charge (SoC), which results in a droop curve with a hysteresis. This concept realizes the charging of the ESS only with recuperation energy, that occurs in the DC microgrid during the production process. Thus, all recuperation energy will be kept in the DC microgrid and a transformation of the energy in AC or an energy loss through braking resistors will be avoided. Furthermore, no additional energy is needed to charge the ESS. This increases the energy efficiency of the entire production process. The concept was verified in simulation and validated in experiment and it has shown a DC voltage deviation of less than two percent.

In this paper, a state of charge (SoC) based characteristic diagram control concept for energy storage systems (ESS) within an industrial direct current (DC) microgrid is proposed. The inputs are the SoC of the ESS and the terminal voltage of the DC microgrid. The output is the charge and discharge current for the converter, which connects the ESS with the DC microgrid. An appropriate design concept for the characteristic diagram is then investigated to achieve a flexible control reacting on changing conditions within the DC microgrid. These could be a temporary overload, due to a changing number of grid participants or an additional feed-in through a photovoltaic (PV) system. The characteristic diagram design concept is even applicable without a deep knowledge of the load profile of the DC microgrid. The concept was analyzed and evaluated based on simulations with load profiles from robot cells. The results show that the SoC depends on the current load of the DC microgrid. If the load returns to average, the SoC of the ESS inclines back to the nominal SoC, which is pre-defined by the grid operator. Furthermore, in case of an appropriate design of the characteristic diagram, it can protect the ESS against overcharging or deep discharging.

This paper presents a novel method for designing a decentralized fuzzy controller for DC microgrids, aiming to reduce operating costs. Therefore, a classical fuzzy control is created for each power supplying grid user based on the voltage droop control. The control of the active rectifier is then extended with respect to the electric costs as an additional input. The input membership functions of this controller are in a next step optimized with a genetic algorithm, whereas two different approaches were used: first, only the membership function of the rectifier was optimized, secondly, the energy storage system was added to the optimization. The optimization was performed in terms of minimizing the operative costs of the DC microgrid. This method and its results were in the end compared with the optimized characteristic diagrams presented in [1]. These diagrams were an extension of the voltage droop curves and the optimization was also based on the operating costs of the grid. Results achieved with the new concept are as good as in the previously presented characteristic diagrams approach. In addition optimization time is reduced significantly (up to 50 times) and the definition of membership functions is more handy.

Many consumers in production plants like industrial robots or tool machines perform repetitive movements, which lead to a cyclic load demand. However, these load profiles can usually only be roughly estimated at the planning stage. Hence, a subsequent online adaptation of the energy distribution is useful for cases, such as balancing between the charging and discharging amount of energy storage systems to improve those lifetime and usage. This paper presents a novel method of online adaptation for the load distribution of production processes within industrial direct current (DC) microgrids. The online load profile cycle recognition was used to adapt the energy distribution among the sources and loads in the DC microgrid. These sources can be inverters, rectifiers, energy storage systems or decentralized power supply units, such as photo voltaic systems. The approach consists of three major points, the load profile cycle recognition, the load profile analysis and the online adaptation of the energy distribution. This solution was tested in simulation and in experiment with a test rig, that contains an inverter and an energy storage system. The results show, that the load profile will be recognized latest from the third cycle and that the imbalance between charging and discharging amounts of the energy storage is less than 0.6% for each cycle after adaptation.