No abstract

As a result of the renewed interest for the distributed power generation (DG); meanly because of the constraints on the traditional power generation besides the great development in the DG technologies, increasing amounts of DG are being used. To accommodate this new type of generation, the existing network should be utilized and developed in an optimal manner. This paper presents an optimal proposed approach to determine the optimal sitting and sizing of DG with multi-system constraints to achieve a single or multi-objectives using genetic algorism (GA). The Linear Programming (LP) is used not only to confirm the optimization results obtained by GA but also to investigate the influences of varying ratings and locations of DG on the objective functions. The methodology is implemented and tested on a real section of the West Delta subtransmission network, as a part of Egypt network. Results are presented, demonstrating that the proper sitting and sizing of DG are important to improve the voltage profile, increase the spinning reserve, reduce the power flow in critical lines and reduce the system power losses.

The recent development of phasor measurement units (PMUs), enhancing synchronization and high time-resolution of measurements, allows the creation of dynamic snapshots and makes network real-time observability possible. A proposed analysis of power system observability and the rules of PMUs placement with the aim of linear static state estimation (SE) of power system networks are presented in this paper. According to the Power System Analysis Toolbox (PSAT), MATLAB toolbox, several algorithms of PMUs allocations as well as their differences and relations are introduced such as a graph-theoretic procedure based on Depth First Search, Simulated Annealing Method, Minimum Spanning Tree Method, and others. These different methods are used to benchmark the real networks with different scales, topologies and voltage levels in Egypt besides the standard test systems of IEEE 14-bus and IEEE 30-bus. The obtained results indicate the effectiveness of the applied methods of optimal PMUs placement for power system observability with the aim of linear static SE as well as the further requirements for optimal PMUs placement, which are not provided by any of the different methods for future work. By comparing the studied methods, we found that, it could not be said that there is an algorithm is optimal for all cases.