部署由可再生能源供应的分布式发电机(DG)对高效电网运行提出了重大挑战。DG的适当大小和位置,特别是光伏(PV)和风力涡轮机(WT),由于可再生能源的不确定特性,它仍然至关重要。为了克服这些挑战,这项研究探索了一种名为草原犬鼠优化器(PDO)的元启发式技术的增强版本。改良的草原土拨鼠优化器(mPDO)结合了受粘液模子算法(SMA)食物方法启发的新颖探索阶段。提出了mPDO算法,以分析不同的动态负载特性对配电网性能以及基于PV和基于WT的DG的设计的实质性影响。优化问题包含各种操作约束以减轻配电网络中的能量损失。Further,该研究通过采用适当的概率分布来解决与PV和WT功率输出的随机特性相关的不确定性。使用Cec2020基准套装测试函数和严格的统计分析对mPDO算法进行评估,以在考虑不同类型的优化问题的同时从数学上衡量其成功率和功效。开发的mPDO算法用于合并PV和WT单元,单独和同时,进入IEEE69总线配电网络。这是考虑到住宅,商业,工业,和混合时变电压相关负载需求。使用标准基准函数证明了改进算法的有效性,并与原始PDO和其他知名算法进行了比较分析,利用各种统计指标。数值结果强调了负载类型和时变发电在DG规划中的重要影响。此外,mPDO算法击败了替代方案,并在所有检查方案中提高了分布式发电机的技术优势。
Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators\' technical advantages across all examined scenarios.