The increasing amount of renewable energy sources in the electrical energy system leads to an increasing number of converter-based generators connected to the electrical power grid. Other than conventional power plants that are often connected to the grid via synchronous generators, converter-based generators do not provide mechanical inertia intrinsically. Therefore, ensuring frequency stability in the electrical power grid might become even more difficult in the future. With the concept of synthetic inertia, the converter-based generators partially imitate the behavior of conventional generators. By implementing such a concept in converters, they are capable of contributing to frequency stability as well. This paper compares two strategies to realize synthetic inertia by modeling converter-based generators in MATLAB/SIMULINK and simulating their behavior in a small Microgrid. The results prove that any kind of realization of synthetic inertia helps to improve frequency stability. Each of the two investigated strategies may have their scope of application in a future electrical energy system.

With the continuous maturity of tidal current power generation technology, the scientific requirements for testing and evaluation are also increasing. In the process of analyzing the measured power data of tidal current power generation, the selection of sampling interval has a significant impact on the accuracy of power fluctuation analysis. Choosing an appropriate sampling interval can reduce data processing and improve calculation speed while ensuring the accuracy of test data. Based on the measured power data of tidal current power generation in a certain sea area of China during rising and falling tides, this paper calculated and analyzed the power fluctuation of multiple sampling intervals through discrete index analysis and first-order difference component analysis. And based on the entropy weight method, obtained the regularity of power fluctuation with sampling interval, calculated the comprehensive fluctuation coefficient to determine the maximum sampling interval, and proposed a recommended sampling interval of 60 seconds. This has important practical significance for scientifically testing tidal current power generation.

Integrating distributed generation into power grids creates various technical challenges for network operation. Volatility of renewable energy resources may increase the probability of blackouts. In order to restore networks fast, securely and reliably after blackouts, within the research project RestoreGrid4RES’s network restoration strategies, the related issues caused by distributed generation are investigated and novel methods to face those challenges are developed. This paper focuses on (i) algorithms to identify possible restoration paths, (ii) key performance indicators for the assessment of grid restoration options and (iii) an evaluation of the results for network restoration strategies.