Increasing the penetration of Renewable Energy Sources (RES) into power systems created challenges and difficulties in the management of power flow since RES have variable power production based on their sources, such as Wind Turbines (WT), which depend on the wind speed. This article used Optimal Power Flow (OPF) to reduce these difficulties and to explain how the OPF can manage the power flow over the system, taking different cases of WT power production based on the different wind speeds. It also used Fixable AC Transmission (FACT) devices such as Thyristor-Controlled Series Compensators (TCSC) to add features to the controllability of the power system. The OPF is a non-linear optimization problem. To solve this problem, the artificial intelligence optimization technique is used. Particle Swarm Optimization (PSO) has been used in the OPF problem in this article. The Objective Functions O.F. discussed here are losses (MW), Voltage Deviation VD (p.u.), and thermal generation fuel Cost ($/h). This article used the wind turbine bus magnitude voltage and the reactance of TCSC as a control variable in OPF. To test this approach, the IEEE 30 bus system is used.
Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. The Optimal Power Flow (OPF) has been widely used for both the operation and planning of a power system. The OPF is also suited for deregulated environment. Four different objective functions are considered in this study: (1) Improvement voltage profile (2) minimization of active power loss (3) maximum capacity of conductors (4) maximization of reliability level. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Imperialist competitive algorithm (ICA). Key words: Distributed generation, distribution network planning, multi-objective optimization, and Imperialist competitive algorithm.