Wind Energy is rapidly growing into one of the most popular energy options for power generation. However, the unpredictability of wind necessitates the employment of a distribution function when describing the wind environment. In the depiction of wind speeds, the two significant parameters of the Weibull distribution, such as the scale parameter and the shape parameter are often utilized. Therefore, it is essential to choose the most appropriate approach for estimating these parameters. Furthermore, the uncertainty in delegated power from variable wind speeds makes system management challenging. In this paper, the Weibull distribution parameters were determined using the Pelican Optimization Algorithm (POA). POA has gained extensive popularity among the research community and is currently used to address a wide range of optimization problems. However, the exploration and exploitation are not properly balanced in this algorithm. To overcome this problem a novel hybrid algorithm namely Mutualism phase based POA (MPOA) is proposed with the help of mutualism phase of Symbiotic Organisms Search (SOS) algorithm. To demonstrate the efficacy and robustness MPOA is applied on multi-objective Optimal Power Flow (MO-OPF) problems. For this study IEEE 30 bus standard system and modified IEEE 30 bus system consisting of two wind farms are taken. The purpose of this work is to examine the impact of changing the Weibull parameters, reverse and penalty cost coefficients on the overall generation cost while considering all the security aspects.

中文翻译：

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基于互利共生和精英策略的 Pelican 优化算法求解多目标随机最优潮流问题

风能正在迅速发展成为最受欢迎的发电能源选择之一。然而，风的不可预测性使得在描述风环境时需要使用分布函数。在风速的描述中，经常利用威布尔分布的两个重要参数，即尺度参数和形状参数。因此，有必要选择最合适的方法来估计这些参数。此外，风速变化带来的委托电力的不确定性使得系统管理具有挑战性。在本文中，使用鹈鹕优化算法（POA）确定威布尔分布参数。 POA 在研究界获得了广泛的欢迎，目前用于解决各种优化问题。然而，该算法中的探索和利用并没有得到适当的平衡。为了克服这一问题，借助共生生物搜索（SOS）算法的共生相，提出了一种新的混合算法，即基于共生相的POA（MPOA）。为了证明 MPOA 的有效性和鲁棒性，将其应用于多目标最优功率流 (MO-OPF) 问题。本研究采用了 IEEE 30 总线标准系统和由两个风电场组成的改进的 IEEE 30 总线系统。这项工作的目的是在考虑所有安全方面的同时，研究改变威布尔参数、反向和惩罚成本系数对总体发电成本的影响。