Power hardware-in-the-loop (PHiL) simulations provide a powerful environment in the critical process of testing new components and controllers. In this work, we aim to explain the impact of time delays in a PHiL setup and recommend how to consider them in different investigations. The general concept of PHiL, with its necessary components, is explained and the benefits compared to pure simulation and implemented field tests are presented. An example for a flexible PHiL environment is shown in form of the Power Hardware-in-the-Loop Simulation Laboratory (PHiLsLab) at TU Hamburg. In the PHiLsLab, different hardware components are used as the simulator to provide a grid interface via an amplifier system, a real-time simulator by OPAL-RT, a programmable logic controller by Bachmann, and an M-DUINO microcontroller. Benefits and limitations of the different simulators are shown using case examples of conducted investigations. Essentially, all platforms prove to be appropriate and sufficiently powerful simulators, if the time constants and complexity of the investigated case fit the simulator performance. The communication interfaces used between simulator and amplifier system differ in communication speed and delay; therefore, they have to be considered to determine the level of dynamic interactions between the simulated rest of system and the hardware under test.

This work focuses on calculating the amount of control reserve, which can be provided by a pool of renewable power plants on the next day. The power forecast of wind and solar power plants depends on the weather forecast, which always contains errors. A merger of individual plants at different locations is advantageous in order to reduce the overall forecast error. Still, the amount of control reserve needs to be determined with a high level of reliability. For the calculation, a probabilistic approach based on historical and current weather data is chosen. In order to model the spatial dependencies of the forecast errors between individual plants, R‐vine copulas are used. In the copula theory, R‐vine copulas provide high accuracy in modelling the dependency of stochastic variables. The methodology is validated and compared to three alternative approaches with a use case of 32 wind and solar plants. The calculated amount of control reserve provided and the achieved reliability proves to be superior to alternative approaches. Additionally, the required reliability level is varied to investigate the impact on the amount of control reserve, which can be offered with the pool.

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