Integration of renewable energy sources (RESs) may increase the risk of power system blackouts because of the uncertainty nature of their output power. In the meantime, RESs have relatively short starting time when compared with non‐black start (BS) generating units. For this reason, RES needs to participate in power system restoration after blackouts. For a complete power system restoration, three stages must be completed. These stages are: generation restoration, transmission system restoration and load pick up. To achieve a faster restoration process, an optimal schedule for the BS units to crank the non‐BS (NBS) units is required with optimal transmission path selection. In addition, to maintain the stability of the system and satisfy the system operational constraints, an optimal load pick‐up sequence is required. In this study, the firefly algorithm (FA) is used to find the optimal final sequence of NBS units restoration, transmission paths and load pick‐up sequence with and without the aid of RES in the system. The objective is to minimise the overall restoration time and the unserved load which maximise the energy capability and improve the sustainability of the system. The proposed algorithm is applied successfully to the IEEE 39‐bus system.

The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.

In this study, a multistage expansion and unit commitment (UC) planning for distribution networks using artificial bee colony (ABC) is presented. A dynamic model for expansion considers installation of new distributed generation units, expansion of pre‐installed generating units’ capacities, rewiring and addition of new load points. The expansion plane is carried out in three stages. In each stage, an hourly‐day‐ahead UC schedule is determined over the course of the stage period. The proposed algorithm uses the ABC to obtain a feasible commitment schedule for the generating units installed. An economical dispatch of these units along with load shedding is then carried out in each stage period to satisfy the system's equality and inequality constraints. The proposed algorithm is applied to different test systems to verify its efficiency.