Techno-economic multi-objective reactive power planning in integrated wind power system with improving voltage stability

Electrical power systems encounter a variety of challenges due to load growth and technological improvement; reactive power planning (RPP) and improvement of voltage stability (VS) are the two most significant ones. The integration of wind farms (WFs) into power networks has many advantages in terms of operation cost and emissions. But leads to voltage instability if it is not optimally sized, placed, and coordinated with VAR sources. In this study, a probabilistic multi-objective RPP framework is proposed for power systems with high penetration of WFs. A novel wind turbine model that can dynamically control reactive power is suggested based on the capability curve of a double-fed induction generator. A new bi-level optimization technique is introduced to address the problem considering the uncertainties of loads and wind power. Multi-objective genetic algorithm is employed at the upper level to optimally allocate VAR sources, and select the optimal locations of WFs to improve VS and decrease VAR sources' costs. While at the lower level, the overall operation cost is minimized. A fuzzy min-max method is modified to find the optimum compromise solution. The results show that the proposed technique is effective in improving system VS and operation costs.

Automated summary

In this study, a probabilistic multi-objective reactive power planning (RPP) framework is proposed for power systems with high penetration of wind farms (WFs). A novel wind turbine model that can dynamically control reactive power is suggested based on the capability curve of a double-fed induction generator. A new bi-level optimization technique is introduced to address the uncertainties of loads and wind power, resulting in improved voltage stability (VS) and decreased VAR sources' costs.