Roman Schwalbe

Controlling active low voltage distribution grids with minimum efforts on costs and engineering

Intelligent control approaches for smart low voltage (LV) grids have to meet the future requirements caused by increasing penetration of decentralized generation (DG) from renewable sources and new network participators like electric vehicles. These technical requirements can be solved, but especially in LV networks it is necessary to consider the cost benefit ratio. This paper will therefore focus on active control approaches investigated within the research project “DG DemoNet - Smart LV Grid”. By using an existing Smart Grid metering infrastructure and pursuing the long-term objective of a “plug&automate” solution, the mentioned challenges should be solved with acceptable costs regarding investment, maintenance and operation. In this paper the proposed control concepts for the transformers on-load-tap-changer (OLTC) are in detail discussed along with first experiences from simulations of some of these concepts.

Steady-state co-simulation with PowerFactory

Power system analysis applications like PowerFactory make it possible to investigate research questions within a dedicated domain specific environment. With the increasing complexity in cyber-physical systems the need for coupling models or systems for simulation becomes eminent. By utilizing and extending existing interfacing mechanisms the pros and cons for different coupling approaches under different simulation time scales (steady state, transient) are compared. The tight coupling using steady-state simulation together with external simulators have a significant increase in performance and usability. This paper shows the different possibilities of coupling a power system simulation application, namely PowerFactory, with other continuous and discrete event models and simulators. Selected examples for co-simulation applications are discussed.

Interfacing PowerFactory: Co-simulation, Real-Time Simulation and Controller Hardware-in-the-Loop Applications

Various possibilities exist to interface the power system model and simulation engine of PowerFactory. These interfaces open applications for co-simulation, real-time simulation for controller hardware-in-the-loop (HIL) set-ups or automation and control of simulation. This chapter gives a comprehensive overview of the various interfaces PowerFactory provides—namely to MATLAB, external C++ functions, OPC, RCOM, API and DGS—as well as typical applications for their use. An example for connecting to an external controller for automated voltage control of an on load tap changer using common TCP/IP connections is elaborated in detail.

DG-demonet smart LV grid - increasing hosting capacity of LV grids by extended planning and voltage control

This paper describes the impact of different voltage control strategies on low voltage grids with a high share of decentralized generation. These control strategies use the on-load-tap-change capability of new MV/LV-transformers as well as the capability of new PV inverters and other flexible devices to control active and reactive power. The control strategies were operated in field tests in three Austrian LV grids. The demonstrated impact of these control strategies on voltage band usage is faced with an extended planning approach that uses all available measurement data from the grid for a more accurate prediction and optimization of grid voltages.

Increasing PV hosting capacity in LV grids with a probabilistic planning approach

Enabling a high hosting capacity of PV infeed in LV grids can be a great challenge for distribution system operators. To assure 100% PV infeed at any time the maximal PV infeed with the minimal load situation is used in conventional network planning. When looking at real grid situations, this worst case scenario only occurs rarely. This paper describes a probabilistic planning approach that increases the hosting capacity of PV while an active power curtailment, realized with PV inverters P(U) control, is accepted for short time periods. As a consequence, this probabilistic planning approach improves the assessment of the available network capacity for PV.