Probabilistic Load Flow Considering Load and Wind Power Uncertainties using Modified Point Estimation Method

Nowadays, renewable energy sources (REs) are increasingly integrated into electrical power networks. Among many REs, wind energy has emerged as a prominent source of electricity. However, rising wind power penetration has increased the system's net generation variability. Consequently, the ability to monitor and simulate the behavior of wind power generation (WPG) in detail is critical. Furthermore, the wind speed or wind power output of different wind farms can be highly interdependent and may not follow Normal distribution. This study proposes a probabilistic load flow (PLF) technique for modeling normally distributed loads and non-normally distributed WPG based on the modified point estimation method (PEM). This modification allows modeling dependent input random variables as a function of many independent ones using the Nataf transformation. By utilizing the findings of the Monte-Carlo method as a reference, the usefulness of the suggested technique is tested by conducting case studies on a 24-bus equivalent system of the Indian Southern region power grid. Simulation results indicate that the modified PEM can easily handle the correlation and have high processing efficiency.