基于改进蚁狮算法的家庭用电优化调度
Optimal Scheduling of Household Electricity Consumption Based on Improved Ant-Lion Algorithm
程江洲 1许辰宇 1鲍刚1
作者信息
- 1. 三峡大学电气与新能源学院,湖北 宜昌 443000
- 折叠
摘要
为了充分挖掘用户侧需求响应能力,平抑用户的负荷波动及降低用户的用电成本,提出了一种基于改进蚁狮优化算法的家庭用电优化调度方法.首先构建了家庭用电优化调度模型,并将峰均比和平均等待时间作为惩罚量引入用电成本得到综合成本函数.其次采用差分进化机制改进了传统的蚁狮算法.最后基于实时电价和临界峰值电价进行仿真,结果表明,改进的蚁狮算法可以有效的降低用户的用电成本,维持用电舒适度,并与粒子群算法、遗传算法和蚁狮算法进行对比,验证了所提算法有较高的稳定性,更强的全局搜索能力,收敛效果更好.
Abstract
In order to fully exploit the demand response capability of the user side,stabilize the user's load fluctu-ation and reduce the user's electricity cost,an optimal scheduling method for household electricity consumption based on the improved ant lion optimization algorithm is proposed.First,an optimal scheduling model for household electric-ity consumption was constructed,and the peak-to-average ratio and average waiting time were introduced into the e-lectricity cost as penalties to obtain a comprehensive cost function.Secondly,the traditional ant lion algorithm was im-proved by using the differential evolution mechanism.Finally,simulation experiments were carried out based on real-time electricity price and critical peak electricity price.The results show that the improved Antlion algorithm can ef-fectively reduce the electricity cost of users and maintain the comfort level of electricity consumption.Compared with particle swarm algorithm,genetic algorithm and antlion algorithm,the proposed algorithm has higher stability,stronger global search ability,and better convergence effect.
关键词
需求响应/惩罚量/综合成本/差分进化/蚁狮算法Key words
Demand response/Penalty amount/Comprehensive cost/Differential evolution/Ant lion algorithm引用本文复制引用
基金项目
国家自然科学基金面上项目(61876097)
湖北省科技计划项目技术创新专项重大项目(2016AAA040)
出版年
2024
NETL
NSTL
维普
万方数据