Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm

The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non-convex engineering optimization issues. The traditional LCA (t-LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t-LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t-LCA based on Weibull flight operator, mutation-based approach, quasi-opposite-based learning and gorilla troops exploitation-based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non-parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable-based (wind turbines + PVs) optimal power flow problem using a modified RER-based IEEE 57-bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57-bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.