采用改进北方苍鹰算法的微电网优化调度研究
Research on optimal scheduling of microgrid using improved Northern Goshawk algorithm
陈将宏 1王羲沐 1李伟亮 1李雪莲 1袁腾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
微电网系统通常由多种分布式电源组成,为降低运行成本,常使用智能算法对微电网进行调度.智能算法在求解微电网调度模型时容易陷入局部最优解,导致求解精度差,因此在北方苍鹰算法的基础上,提出了一种混合策略改进的北方苍鹰算法(HNGO),利用反向学习、Metropolies准则以及自适应t分布变异提高求解精度,同时构建了考虑可再生能源出力特性的需求响应模型,使负荷曲线与可再生能源出力曲线更贴近,然后建立日运行成本最低的微电网优化调度模型,并利用HNGO求解.对比仿真结果显示所提算法具有更好的求解精度,且所提需求响应模型能显著降低燃料成本.
Abstract
The microgrid system normally consists of a variety of distributed power sources.To cut the operating cost of the microgrid,intelligent algorithms are often employed to dispatch the microgrid.Intelligent algorithms are prone to fall into local optimal solutions when solving microgrid scheduling models,resulting in poor accuracy.Therefore,based on the Northern Goshawk algorithm,this paper proposes a hybrid strategy improved Northern Goshawk algorithm(HNGO),which uses reverse learning,Metropolies criterion and adaptive T-distribution variation to enhance its accuracy.Meanwhile,a demand response model considering the output characteristics of renewable energy is built,so that the load curve is closer to the output curve of renewable energy.Then,a microgrid optimization scheduling model with the lowest daily operating cost is established,and HNGO is used to find the solution.Our simulation results show the proposed algorithm achieves accuracy,and our proposed demand response model significantly reduces fuel costs.
关键词
北方苍鹰算法/反向学习/模拟退火算法/自适应t分布变异/需求响应Key words
Northern Goshawk algorithm/reverse learning/simulated annealing algorithm/adaptive t distribution variation/demand response引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据