A novel improved Frilled Lizard algorithm for solving the optimal planning problem of renewable energy sources within distribution grids under uncertainties

In this research, a new improved frilled lizard optimization algorithm is designed and proposed to address the optimal planning problem of renewable energy sources-based distributed generation within radial distribution grids. The primary objective is to minimize power losses, elevate the voltage profile, and improve voltage stability, formulated as a multi-objective optimization problem. The planning process of renewable energy sources in the radial distribution grid is a complex problem that requires accounting for uncertainties in renewable energy sources' power output and load demand fluctuations. To this end, we have developed an appropriate probability model to estimate the stochastic power generation from renewable energy sources based on hourly seasonal data, including wind speed, solar irradiance, and ambient temperature collected over a specified time frame and location. The developed improved frilled lizard optimization technique incorporates three distinct strategies: fitness distance balance, quasi-opposite-based learning, and Cauchy mutation, to enhance its searching capabilities and avoid falling into local optimal traps. The suggested improved frilled lizard optimization is successfully applied to identify the optimal locations and rating capacities of solar photovoltaic strings and wind turbines, as well as the power factor of wind turbines. The effectiveness of the presented approach is demonstrated using the IEEE 85-bus distribution grid. To further assess its feasibility and robustness, a performance comparison is conducted against other recent effective algorithms, including the grey wolf optimizer, the black-winged kite algorithm, and the original frilled lizard optimization. Results show that the proposed improved frilled lizard optimization technique significantly reduces annual average power losses by 70.25%, decreases voltage deviation by 78.51%, and improves the voltage stability index by 20.79%, outperforming other methods.