Wind/PV Allocation Applying Whale Optimization Algorithm Versus System Uncertainties

With the rapid growth of integrating renewable energy resources as distributed generators; such as Wind and PV systems, optimal Wind/PV allocation became a crucial objective to focus on pursuing. This should be smartly attained through efficient optimization techniques to minimize losses and improve the voltage profile. One of the most effective techniques is the Whale Optimization Algorithm (WOA). This study aims to investigate the effect of integrating distributed energy resources on a distribution network considering the load, wind, and photovoltaic systems uncertainties with respect to power loss and voltage index improvement. The investigation is demonstrated on the IEEE 69 bus system using the Whale Optimization Algorithm and then compared to the Genetic Algorithm (GA). Initially, the wind turbine and PV cases are represented to optimize the total power loss and the voltage index separately and then compared to the Genetic Algorithm. Finally, the wind and PV are integrated. The results showed that integrating PV sources using the WOA reduces power loss by almost 10% and improves voltage index by almost 35% better than Wind. The conversion time required by the GA to reach the optimum location is 1.5 times the required time by the WOA. The probabilistic power loss achieved using the WOA is reduced by 30% compared to GA at the same number of iterations. Also, integrating 2 distributed generators (DGs) of PV sources reduces power loss and improves the voltage index better than Wind by almost 40% and 50% respectively. Also, integrating 2 DGs (Wind & PV) sources is more effective than 2 DGs (Wind or PV) in terms of power loss reduction and voltage index improvement.