Skill optimization algorithm for solving optimal power flow problem

This research presents the implementation of a modern meta-heuristic algorithm called the skill optimization algorithm (SOA) to solve the optimal power flow problem (OPF). An IEEE 30-bus transmission system is selected to test the real performance of SOA. The main objective function of the study is to minimize the total fuel cost (TFC) of all thermal units. To clarify the high performance of SOA, a classical meta-heuristic named particle swarm optimization (PSO) is also applied for comparison. All results reached by SOA are compared with those of PSO on different criteria. Particularly, SOA has reached smaller cost than PSO by $1.04, equivalent to 0.13% of PSO’s TFC. Furthermore, SOA has reached a more stable performance by finding better average and maximum TFC over fifty runs. The evaluation of these criteria indicates that SOA completely outperforms PSO. Besides, the optimal solution reached by SOA satisfies all considered constraints with zero violation of the dependent variables. Therefore, SOA is highly suggested to handle the OPF problem.

本研究提出了一种新型元启发式算法——技能优化算法（Skill Optimization Algorithm, SOA），用于求解最优潮流问题（Optimal Power Flow, OPF）。选取IEEE 30节点输电系统作为测试平台，验证SOA的实际性能表现。本研究的核心目标函数为最小化所有火电机组的总燃料成本（Total Fuel Cost, TFC）。为验证SOA的优异性能，同时选取经典元启发式算法粒子群优化（Particle Swarm Optimization, PSO）作为对比算法。基于多维度评价指标，将SOA所得全部结果与PSO的结果进行对比分析。具体而言，SOA所得成本较PSO低1.04美元，相当于PSO总燃料成本的0.13%。此外，在50次重复实验中，SOA获得了更稳定的性能表现，其平均总燃料成本与最大总燃料成本均优于PSO。基于上述指标的评估结果表明，SOA的整体性能全面优于PSO。此外，SOA所得最优解满足全部预设约束条件，从属变量无任何违规情况。因此，推荐将SOA用于求解OPF问题。