Houssein Essam H, Ismaeel Alaa A K, Said Mokhtar
Faculty of Computers and Information, Minia University, Minia, 61519, Egypt.
Faculty of Computer Studies (FCS), Arab Open University (AOU), 130, Muscat, Oman.
Sci Rep. 2024 Nov 17;14(1):28367. doi: 10.1038/s41598-024-78086-y.
In order to solve the optimal power flow (OPF) problem, a unique algorithm based on a search and rescue method is applied in this study. For the OPF problem under three objective functions, the SAR offers a straightforward and reliable solution. The three objective functions are used to minimize the fuel cost, power loss and voltage deviation as a single objective function. The OPF problem for benchmark test system, including the IEEE-14 bus, IEEE-30 bus, and IEEE-57 bus, are solved by the Search and Rescue algorithm (SAR) under specific objective functions that are determined by the operational and economic performance indices of the power system. To demonstrate the efficacy and possibilities of the SAR algorithm, SAR is contrasted with alternative optimization techniques such as harmony search algorithm, gradient method, adaptive genetic algorithm, biogeography-based optimization, Artificial bee colony, gravitational search algorithm, particle swarm optimization, Jaya algorithm, enhanced genetic algorithm, modified shuffle frog leaping algorithm, practical swarm optimizer, Moth flam optimizer, whale and moth flam optimizer, grey wolf optimizer, cheap optimization algorithm and differential evolution algorithm. The value of minimum power losses based on SAR technique is equal to 0.459733441487247 MW for IEEE-14 bus. The value of minimum total fuel cost based on SAR technique is equal to 8051.12225602148 $/h for IEEE-14 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0357680148269292 for IEEE-14 bus. The value of minimum power losses based on SAR technique is equal to 2.71286428848434 MW for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 798.197578585806 $/h for IEEE-30 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0978069572088536 for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 38017.7691758245 $/h for IEEE-57 bus. The acquired results for the OPF compared to all competitor algorithms in every case of fitness function demonstrate the superiority of the SAR method.
为了解决最优潮流(OPF)问题,本研究应用了一种基于搜索救援方法的独特算法。对于三个目标函数下的OPF问题,搜索救援算法提供了一种直接且可靠的解决方案。这三个目标函数用于将燃料成本、功率损耗和电压偏差最小化,作为单一目标函数。通过搜索救援算法(SAR)在由电力系统运行和经济性能指标确定的特定目标函数下,求解基准测试系统的OPF问题,包括IEEE - 14节点、IEEE - 30节点和IEEE - 57节点。为了证明SAR算法的有效性和可能性,将SAR与其他优化技术进行对比,如和声搜索算法、梯度法、自适应遗传算法、基于生物地理学的优化算法、人工蜂群算法、引力搜索算法、粒子群优化算法、Jaya算法、增强遗传算法、改进的洗牌青蛙跳跃算法、实用群体优化器、蛾火焰优化器、鲸鱼和蛾火焰优化器、灰狼优化器、廉价优化算法和差分进化算法。基于SAR技术,IEEE - 14节点的最小功率损耗值等于0.459733441487247兆瓦。基于SAR技术,IEEE - 14节点的最小总燃料成本值等于8051.12225602148美元/小时。基于SAR技术,IEEE - 14节点的最小电压偏差值等于0.0357680148269292。基于SAR技术,IEEE - 30节点的最小功率损耗值等于2.71286428848434兆瓦。基于SAR技术,IEEE - 30节点的最小总燃料成本值等于798.197578585806美元/小时。基于SAR技术,IEEE - 30节点的最小电压偏差值等于0.0978069572088536。基于SAR技术,IEEE - 57节点的最小总燃料成本值等于38017.7691758245美元/小时。在每个适应度函数情况下,与所有竞争算法相比,OPF的获取结果证明了SAR方法的优越性。