Abstract: An electrical grid, using physical operating data of the electrical grid a current operating status of the electrical grid is determined by way of a network model of the electrical grid is provided. A first grid component is controlled as a function of a control signal by a regulator with a grid-component-internal control circuit which is adjustable via control parameters. A first component model of the first grid component is imported to determine a dynamic reaction of the first grid component to the control signal depending on the control parameters.

Abstract: A power balance-dependent operating parameter and a surroundings parameter of a first energy source, such as a renewable energy source, are provided. A variable target operating parameter for the first energy source is ascertained dependent on the detected surroundings parameter. The target operating parameter can characterize a first energy source state in which a maximally possible output would be generated under the current given conditions of the surroundings. The operating frequency is then controlled dependent on the detected power balance dependent operating parameter and the ascertained target operating parameter such that a deviation of the operating frequency from a specified frequency is coupled to a deviation of the detected power balance-dependent operating parameter from the ascertained target operating parameter.

Abstract: A method for the computer-assisted configuration of an electrical power grid is provided. Grid forming generating unit nodes are provided in the power grid as first grid nodes and grid-boosting generating unit nodes are optionally provided as second grid nodes. The latter nodes are regenerative energy generation plants such as, for example, photovoltaic systems. In the method, closed-loop control methods and in particular proportional control of the generating unit nodes are set on the basis of a suitable dynamic model of the power grid such that an optimization problem is solved with the aim of optimum operation of the power grid in respect of one or more optimization criteria.

Abstract: A method for computer-aided control of power in an electrical grid is provided. The electrical grid has a redetermined rated frequency and includes network nodes that are interconnected via power supply lines, each of the nodes supplying power to or drawing power from the electrical grid. At least part of the nodes are provided with proportional controllers, for which a reference power and/or a proportionality factor is set, and which control the power that is supplied or drawn by each network node based on the difference between the frequency of the voltage in each network node and the rated frequency of the electrical grid. The reference power and the proportionality factor of at least some of the proportional controllers are determined on the basis of solving an optimization problem. The stability and robustness of the operation of the electrical grid is optimized without the need for carrying out complex simulations.

Abstract: Disclosed is a subnetwork controller (TNR) for a subnetwork (TN) within an interconnected grid (NV), which controller controls power generators (G), subnetworks or loads (L) of the subnetwork in accordance with sensor-captured internal measured variables (yi) and sensor-captured external measured variables as well as external controlled variables (vi) of the subnetwork (TN) in such a way that a dynamic behaviour of the subnetwork (TN) in relation to its adjacent subnetworks corresponds to a defined desired behaviour.

Abstract: For controlling a voltage in a power grid including a PV bus and a PQ bus, a first tolerance range for an active power of the PV bus and a second tolerance range for an active power of the PQ bus are received. A first value for a reactive power of the PQ bus is received. With that, a robust optimization process is run for a set of power flow equations. The first and second tolerance ranges and the first reactive power value are input to the robust optimization process using the first and second tolerance ranges as robust optimization uncertainty. With that, the robust optimization process determines a voltage set point for the PV bus so that the set of power flow equations is fulfilled for the first and second tolerance ranges. According to the determined voltage set point, a voltage at the PV bus is controlled.