Applying Point Estimation and Monte Carlo Simulation Methods in Solving Probabilistic Optimal Power Flow Considering Renewable Energy Uncertainties

The increasing penetration of renewable energy results in changing the traditional power system planning and operation tools. As the generated power by the renewable energy resources are probabilistically changed, the certain power system analysis tolls cannot be applied in this case.  Probabilistic optimal power flow is one of the most useful tools regarding the power system analysis in presence the uncertainties. In this paper, Monte Carlo simulation and point estimation methods are used to solve the POPF in the presence of wind and solar sources uncertainties. These methods are simulated on the PEGASE 89–bus European system. The most important novelty of this paper is arising from the comparison detailed studies of point estimation methods with the Monte Carlo simulation method. As the obtained results confirm, the point estimation methods lead to increasing the computing time efficiency in compare to the Monte Carlo simulation method. Also, increasing the number of sampling points in PEMs will be resulted in increasing the accuracy of the obtained results, while the computing time is still lower than the Monte Carlo simulation method.