A multi-objective probabilistic approach for smart voltage control in wind-energy integrated networks considering correlated parameters

Renewable and sustainable energies such as wind power generation are increasingly integrated into electric grids due to their various technical, environmental, and economic advantages. However, these resources impose more uncertainties on the power system in addition to the uncertainty of load demands. These uncertainties and correlations among them confront operation decisions with serious challenges.In this paper, a new multi-objective probabilistic approach is proposed for smart voltage control of wind energy-integrated systems through tap adjustment of power transformers and voltage regulation of generators. The objectives include improving voltage stability margin, voltage profile improvement, and power loss reduction in the presence of wind and demand uncertainties. A data clustering algorithm is employed to handle the uncertainty of the random input variables, and a Cholesky decomposition method is used for modeling the correlation between them. The proposed multi-objective optimization is solved using NSGA-II. The power system has been modeled in DIgSILENT-Powerfactory linked with MATLAB, where the NSGA-II is programmed. The IEEE 14-bus system has been employed to evaluate the performance of the conducted approach. The results demonstrate the effectiveness of the proposed approach in decreasing the losses, increasing the voltage stability margin, and improving the voltage profile of wind-energy-integrated power systems.

Automated summary

In this study, a new multi-objective probabilistic approach is proposed for smart voltage control of wind energy-integrated systems through tap adjustment of power transformers and voltage regulation of generators. By handling the uncertainty of random input variables and modeling their correlation, it is possible to improve the voltage stability margin, voltage profile, and power loss reduction in wind-energy-integrated power systems.