{'fraction_non_alphanumeric': 0.01696165191740413, 'fraction_numerical': 0.0014749262536873156, 'mean_word_length': 4.654166666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An application of grey wolf optimizer for optimal power flow of wind integrated power systems

In this paper, the problem of optimal power flow (OPF) is solved in a system integrated with wind farms with the aim of reducing the cost of power production and the reduction of power grid loss by applying the Grey Wolf Optimization (GWO) Algorithm. The variable nature of wind farm output is modeled using two additional cost components corresponding to the states of under estimation and over estimation, where the available power is higher and lower than the scheduled output, respectively. On one side, in the case where there is lower power regards to the planned power, a penalty is added to the cost function. On the other side, if the produced power would be more than the planned power, an additional cost would be added to the cost function because of not buying the overall power of the wind farms. A recently introduced optimization method known as Grey Wolf Optimization Algorithm is employed in this article. The problem of OPF based on proposed approach has been applied on a modified version of IEEE 30-bus test system. The results of this study are compared with the results of Genetic Algorithm (GA). The results show the superiority of the proposed method, both in the convergence speed as well as the final result comparing to other method.