基于改进的禁忌搜索算法在有序用电中的应用
Application of Improved Tabu Search Algorithm Based on Orderly Power Consumption
王烁 1王卓城 1杜江帆 1段凤熙 2黄惠倩 2蔡伟鸿2
作者信息
- 1. 广东电网有限责任公司汕头供电局,广东 汕头 515041
- 2. 汕头大学数学与计算机学院计算机系,广东 汕头 515063
- 折叠
摘要
国家电力系统负荷与日俱增,用户用电数据海量增长,需要高性能优化算法来解决此类复杂的有序用电问题.为了填补有序用电系统调度中的负荷缺口,提高供电效能并合理选择用户,本文提出了一种改进的禁忌搜索算法来求解电力供需平衡的多目标优化调度问题.使用融入了莱维飞行的野狗优化算法对禁忌搜索算法中的初始化阶段进行优化,得到一种改进的禁忌搜索算法.该方案增强了原始算法的搜索能力,加快了算法的收敛速度,提供了一种更优的解决方案.最后通过消融实验以及与5个经典的启发式算法进行对比实验来验证所提出的算法的性能.
Abstract
High-performance optimization algorithms are needed to handle the complicated issues of orderly electricity consumption due to the growing load on the national power system and the vast growth of consumer electricity consumption data.In order to tackle the multi-objective optimal scheduling problem of balancing power supply and demand,an enhanced taboo search method is proposed.This algorithm will help fill the load gap in the scheduling of an orderly power system,increase the efficiency of the power supply,and rationally pick consumers.To improve the taboo search algorithm,the startup phase is optimized using the wild dog optimization technique that incorporates Levy flying.This plan improves the original algorithm's search capabilities,hastens algorithm convergence,and offers a superior result.Finally,comparison studies with five traditional heuristic algorithms and ablation experiments are used to demonstrate how well the novel approach performs.
关键词
禁忌搜索算法/野狗优化算法/有序用电/多目标优化调度Key words
Tabu search algorithm/Dingo optimization algorithm/ordered power consumption/multi-objective optimal scheduling引用本文复制引用
基金项目
广东省科技计划项目(2016B010124012)
广东省科技计划项目(2016B090920095)
出版年
2024
NETL
NSTL
维普
万方数据