The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back‐up unit (storage). Uncertain solar irradiance and wind speed are simulated via log‐normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed‐rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.

中文翻译：

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通过差分进化粒子群算法优化可控风光能系统的电力系统最优潮流

在确定的极限条件下，要对电力系统的规划和运行进行最佳规划，以优化规划现代电力系统中各种来源产生的能量。近来，随着电能需求的增加，导致了世界上总人口的增加，人们的需求的增加，技术的进步以及生态系统和全球气候变化。满足这种能源需求的最重要的解决方法之一被认为是利用电力系统中的可再生能源（RES）。随着RES的使用，电力系统的结构变得更加复杂。通过将RES集成到现代电力系统中，计划和运营问题产生的最佳潮流（OPF）已转变为难题。本文提出了可控可再生资源渗透率高的电力系统的OPF问题。这些类型的源具有备用单元（存储），因此能够注入确定的功率。分别通过对数正态分布和瑞利概率分布模拟不确定的太阳辐照度和风速。提出的具有可控可再生资源的OPF问题通过差分进化粒子群优化（DEEPSO）算法解决。与其他算法（包括蛾群算法，回溯搜索算法和差分搜索算法）相比，在各种测试系统上进行的仿真说明了DEEPSO的有效性和效率。此外，Wilcoxon符号秩检验用于显示DEEPSO算法的至高性，有效性和鲁棒性。