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Industrial drive systems are characterized by dynamic peak loads and regenerative braking. These short-term loads lead to oversized supply infrastructure and low conversion efficiencies for prevalent partial loads. Energy storage systems (ESS) can be integrated into the DC link between rectifier and multiple inverters to increase energy efficiency and cover peak loads. To design a controller for the ESS, the underlying controller and process have to be modelled using physical laws and measured data. However, for each application this would mean having to extensively tune and adapt the controller to a specific manufacturing machine to ensure a stable, safe and energy-efficient operation. A self-tuning controller could solve this problem by automatically identifying the process and controller model of the closed-loop system. This paper aims to implement non-invasive closed-loop system identification for retrofitting an energy storage system to a machine tool. The validation of the approach shows that the actual DC link capacity and the controller parameters of the active rectifier can be identified reliably, e.g. with deviations of only 1.5 %.

The future electric grid faces challenges by a rising share of renewable generation. This development increases the number of smaller decentralized power plants and therefore the volatility of energy generation. A technical approach to overcome these challenges is to cluster distributed energy generation and local power demand in microgrids. These can be used to balance power generation and demand locally and in short time frames. The industry relies on an high quality and reliable power supply, which can be ensured in a microgrid architecture, even if the main AC power grid does not meet the requirements. In specific industrial applications prototypes of direct current (DC) microgrids are built. Such a multi terminal architecture is in need for a grid control that adjusts the respective power flow of each device. This study aims on determining the implications for the DC microgrid design process with respect to stability of the control system.

In order to meet the challenges of upcoming decentralized energy systems, local direct current (DC) microgrids (MG) and energy storage systems (ESS) for industrial production sites have been studied. During charging and discharging processes the state of charge (SoC) of an ESS changes. Microgrid control, often based on droop curves, enables intelligent power distribution as well as voltage restoration in multi-infeed topologies and is necessary for taking care of the occurring SoC deviations. This paper compares different SoC control strategies based on droop control. An adaptive state of charge control has been developed using a proportional controller parameter (PCP) for shifting the droop curve on the current axis. In a case study the adaptive SoC control is validated for a droop-controlled industrial DC-microgrid with an ESS used for peak shaving. The performance of the adaptive SoC control is evaluated using quality criteria, e.g. peak load reduction, cut-off frequency and settling time. It is shown that the state of charge can be successfully kept within the desired range over time.

Converter technologies powering drive systems established direct current technology in industrial applications. Recent developments expand the regular Direct Current (DC) link from a multi-axis-drive-systems to an interlinked multiple machine supply system. The extended DC-link allows for the exchange of recuperated energy between different processes and increases the overall system efficiency. The comparison of the extended DC link and the DC microgrids shows that the integration of multiple supply systems, like distributed generation and storage systems, allows for further optimization with respect to reliability, efficiency and robustness. One possible optimization strategy is to reduce dynamic load peaks with a storage system and downsize the grid infeed converter. Several applications for power smoothing can be found in literature, however there is no approach for sizing storage capacity and power for these applications. This paper introduces a systematic approach, which aims for storage sizing based on a load profile of industrial machines.

The industrial energy supply of the production with direct current offers the chances of higher energy efficiency, better integration of generative energy and at the same time robust power supply. In addition to the technological challenges for electrical devices, an intelligent grid management, which regulates the load flow between the participants, is crucial. The distributed grid management supports a scalable, modular network with a hierarchical structure. The paper defines a direct current (DC) grid management by the different roles of every participant. Each role provides special services, needed for a stable grid operation. These services are described and a simulative analysis is carried out. The simulation shows that network stability is ensured and at the same time, the possibility of optimizing the operation management is maintained. The presented results are part of the DC-INDUSTRIE research project, funded by the German government.