| description abstract | Because today’s power systems encounter so many uncertainties, probabilistic methods, such as probabilistic power flow (PPF), are useful to analyze the systems. One of these methods is Monte-Carlo Simulation (MCS), which has the ability to consider all uncertainties, including renewable energy power production, load, and random outages of components. In this paper, the proposed method is based on MCS and data clustering to improve drawback of MCS, which include high burden of computations. The proposed method cannot only reduce the runtime, but also considers correlation between load and wind power generation (WPG). This correlation would be important to power systems having large-scale wind farms (WFs). The proposed method first was validated by applying it on an IEEE 24-bus reliability test system (RTS). Then the modified version of the method, which can model outages of components, was implemented by using analysis software on a power system that is an actual bulk power system. To demonstrate importance of applying the proposed method, the method was implemented on the system two times. For the first time, the power system was analyzed in the presence of a WF; in the second one, the power system was analyzed such that conventional power plants were replaced with the WF. The results show how much is necessary to apply the probabilistic power flow method on power systems, including WFs. | |
