Analysis of probabilistic optimal power flow in the power system with the presence of microgrid correlation coefficients

Abstract

AbstractThe uncertainty of microgrid resources in power systems has increased, which implies many challenges for their design and planning. The penetration coefficient of microgrids in power systems, as well as the high uncertainty of these sources, requires an analysis of probabilistic methods. These types of energy sources are inherently uncertain and bring many unknowns to the power system. One of the most important aspects to be analyzed is the distribution of the probabilistic optimal power flow (POPF). This research examines some methods for the distribution of possible loads in power systems, namely the Monte Carlo method (MCM), the two-point estimation method (2PEM), and the three-point estimation method (3PEM). Then, these methods are used to distribute the possible POPF. This work studies the adjusted probability density function (PDF), average, and deviation of losses for each method. Moreover, the appropriate selection of microgrid resources is determined while considering correlated variables. In order to compare the effectiveness of the proposed methods, a 30-bus IEEE standard test system is used in the MATLAB software, showing that 2PEM is more suitable than the others. The results related to microgrids are executed by running P_OPF in buses 5, 7, and 8.According to the results, for bus 8, the possible load distribution value is 52.897 MW, which requires the placement of a wind power plant. For buses 2 and 5, the possible load distribution is less than 10 MW, which is predicted in these buses considering the solar power plant.

Keywords

• POPF
• microgrid
• Monte Carlo
• two/three-point estimation methods
• losses
• correlated