Bottom-up Network Restoration Based on Distributed Generation

The restoration of large electrical power systems after a blackout is often a challenging task. Recently, more and more distributed generations (DGs) are integrated into distribution network. The DGs with different types, such as renewable energy sources (RES) equipped with storage or precise forecasting technology, small hydro-electrical power plants, combined heat and power (CHP) etc., make distribution network much more “active” than before. After power system blackout, an “active” distribution network could reboot immediately by using DGs instead of waiting until transmission network has restored. Since the restored “active” distribution network can be regarded as an additional and controllable power source from the view of transmission network, novel bottom-up restoration operations which have not been seen in traditional restoration plans can be developed.

In this thesis, the proposed bottom-up restoration algorithms consist of four parts. The first part is to determine the black start zones after power system blackout. This algorithm is based on fuzzy classification and calculates the black start zone by considering e.g. generator self-excitation, and over-voltage at end of unload line. The
second and third algorithms which determine the generator startup and load restoration sequence are used to calculate the restoration time and restorable load within each black start zone. The results show that the restoration time can be reduced significantly by proposed algorithms. In the fourth part, the proposed bottomup restoration algorithms are implemented on a real transmission and distribution network in JiangXi province, China. The final results show that DGs in distribution network can improve the entire restoration process in various ways. The results of this thesis demonstrate that the proposed bottom-up network restoration methodologies by using distributed generations (DGs) are faster and more reliable than the traditional network restoration methodology. https://www.upress.uni-kassel.de/katalog/abstract.php?978-3-7376-0710-0