ABSTRACT

For planning the economic operation and control of an existing electricity grid and its future extension planning, the optimal power flow (OPF) results are essential. In this paper, a hybrid algorithm is proposed, which is based on a sine-cosine mutation operator and a modified Jaya (SCM-MJ) algorithm to solve the OPF problems. The efficacy of the SCM-MJ algorithm is primarily evaluated using thirteen (unimodal and multimodal) mathematical benchmark functions. Later, the SCM-MJ algorithm is applied on the Algerian 59-bus system and IEEE 118-bus test system to handle the OPF problems. The SCM-MJ has successfully achieved a minimum value of an objective function over several runs than other modern meta-heuristic optimization approaches in all the thirteen mathematical benchmark functions as well as in OPF case studies. For example, in the Algerian 59-bus system, the quadratic fuel cost obtained by SCM-MJ is 1688 $/hour, with a saving of 13.12% of the initial fuel cost. This benefit increases further with the size of the system. The SCM-MJ algorithm has provided high-quality solutions for mathematical benchmark functions and OPF problems quickly and efficiently. The comparison of numerical outcomes demonstrates that the suggested SCM-MJ algorithm dominates over other approaches for solving the OPF problem.

摘要

对于规划现有电网的经济运行和控制及其未来的扩展规划，最优潮流（OPF）结果是必不可少的。本文提出了一种基于正余弦变异算子和改进的Jaya (SCM-MJ)算法的混合算法来解决OPF问题。SCM-MJ 算法的功效主要使用十三个（单峰和多峰）数学基准函数进行评估。后来，SCM-MJ算法应用于阿尔及利亚59总线系统和IEEE 118总线测试系统，处理OPF问题。在所有 13 个数学基准函数以及 OPF 案例研究中，SCM-MJ 与其他现代元启发式优化方法相比，在多次运行中成功实现了目标函数的最小值。例如，在阿尔及利亚59路公交车系统中，SCM-MJ获得的二次燃油成本为1688美元/小时，节省了13.12%的初始燃油成本。这种好处随着系统的规模而进一步增加。SCM-MJ算法快速高效地为数学基准函数和OPF问题提供了高质量的解决方案。数值结果的比较表明，建议的 SCM-MJ 算法优于其他解决 OPF 问题的方法。