中国电力 ›› 2021, Vol. 54 ›› Issue (9): 9-16.DOI: 10.11930/j.issn.1004-9649.202006081

• 电网 • 上一篇    下一篇

基于改进纵横交叉算法的电网最优潮流计算

曾琮1, 黄强2, 陈德1, 郑晓莹1, 孟安波1   

  1. 1. 广东工业大学 自动化学院,广东 广州 510006;
    2. 广东电网有限责任公司韶关供电局,广东 韶关 512026
  • 收稿日期:2020-06-03 修回日期:2020-12-03 出版日期:2021-09-05 发布日期:2021-09-14
  • 作者简介:曾琮(1997-),男,硕士研究生,从事人工智能算法在电网优化调度中的应用研究,E-mail:maszeac1997@163.com;孟安波(1971-),男,通信作者,博士,教授,从事电力系统自动化、系统分析与集成等研究,E-mail:menganbo@vip.sina.com
  • 基金资助:
    国家自然科学基金资助项目(面向大规模电网优化调度的纵横交叉群智能优化方法研究,61876040)

Optimal Power Flow Calculation with Improved Crisscross Optimization Algorithm

ZENG Cong1, HUANG Qiang2, CHEN De1, ZHENG Xiaoying1, MENG Anbo1   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Shaoguan Power Supply of Guangdong Power Grid Co., Ltd., Shaoguan 512026, China
  • Received:2020-06-03 Revised:2020-12-03 Online:2021-09-05 Published:2021-09-14
  • Supported by:
    This work is supported by National Natural Science Foundation of China (Research on Crisscross Swarm Intelligent Optimization Method for Large - Scale Optimal Scheduling of Power System, No.61876040)

摘要: 纵横交叉算法(crisscross optimization algorithm, CSO)已应用于解决电网中的多种复杂问题并取得了较好的效果。在CSO算法基础上,提出了一种快速收敛的改进纵横交叉算法(faster crisscross optimization algorithm, FCSO)求解最优潮流问题。该改进算法在原有的双交叉算子的基础上提出了一个全新的算子——中心交叉算子,此算子与横向交叉算子以一定的规律交替进行,种群中每个个体依次与当前最优个体执行交叉操作后再执行竞争算子,有选择地向当前全局最优个体靠拢,以提高单次搜索的质量,加速收敛。在IEEE118节点系统上的仿真结果表明,CSO较同类的群智能优化算法有着收敛精度高、稳定性强的特点,而FCSO能在不损失收敛精度的条件下显著加快收敛速度,大幅缩短寻优时间,为纵横交叉算法应用于实际电网实时调控领域提供了更多的可能。

关键词: 群智能优化算法, 运行优化, 快速收敛纵横交叉算法, 最优潮流计算, 中心交叉算子

Abstract: Crisscross optimization algorithm (CSO) has been applied to solve many complex problems in power system and remarkable results have been achieved. On this basis, a faster crisscross optimization algorithm (FCSO) is proposed to solve the optimal power flow problem. In this algorithm, a new operator—the central crossover operator is proposed based on the original double-crossover operator, which alternates with the crossover operator in a certain pattern; each individual in the population performs a crossover operation with the current optimal individual in turn, and then executes a competition operator to selectively move closer to the global optimal individual, thus improving the quality of each iteration and accelerating convergence. The simulation results in an IEEE-118 bus system show that, compared to other swarm intelligence optimization algorithms, the CSO has the advantage of high convergence accuracy and strong stability, while the FCSO can significantly improve the convergence speed and greatly shorten the consuming time without losing the convergence accuracy, which provides more possibilities for applying the CSO to the real-time control of the power grid.

Key words: swarm intelligent optimization algorithm, operation optimization, faster crisscross optimization algorithm, optimal power flow, central crossover operator