港口大规模冷箱负荷群用电的一致性分层优化调度方法
Consensus Based Hierarchical Optimization Scheduling Method for Large-scale Reefer Loads in Ports
杨莉 1黄文焘 1余墨多 1邰能灵 1李然 1谭恩荣 2邵思语3
作者信息
- 1. 上海市智能船舶综合电力系统工程技术研究中心(上海交通大学),上海市 闵行区 200240
- 2. 山东港口日照港集团有限公司,山东省 日照市 276826
- 3. 中国船舶及海洋工程设计研究院,上海市 黄浦区 200011
- 折叠
摘要
为解决港口大量冷藏集装箱负荷群优化调度面临的优化效果与计算效率难题,该文提出冷箱集群分层迭代调度架构及多智体制冷效率一致性优化策略.建立考虑热动态过程的冷箱负荷用电模型,并根据用电特性将冷箱聚类为集群,降低冷箱控制维度与信息交互量级.建立冷箱动态电价与集群用电功率迭代优化的预调度模型,提出冷箱制冷效率主从一致性的功率动态分配算法,冷箱个体根据电价、温度、制冷限值主动响应预调度策略,实现大规模冷箱自趋优运行和负荷功率有序转移.以日照港为算例,所提方法可将用电成本降低12.5%,计算效率提升4倍,优化结果与全局优化的偏差仅为0.5%,实现了大规模冷箱群高效优化.
Abstract
To improve the optimization effect and efficiency of large-scale reefer-loads scheduling in ports,this paper proposes a hierarchical iterative scheduling architecture and the multi-agent refrigerating efficiency consensus optimization strategy for reefer clusters.The reefer power model considering the thermodynamic process is established,and the reefers are clustered according to power characteristics,which reduces the control dimensions and the information interactions.Then the pre-scheduling model for the iterative optimization of dynamic price and reefer cluster consumption is established.A leader-follower refrigerating efficiency consensus algorithm for power dynamic allocation is proposed to make each reefer actively respond to the pre-scheduling strategy according to the electricity price,temperature,and cooling limits.It realizes self-optimizing operation of massive reefers and orderly load transfer.Finally,taking Rizhao Port as an example,the proposed method can reduce the electricity cost by 12.5%and increase the computational efficiency by 4 times.The deviation of the optimization results from the global optimization is reduced to 0.5%.It realizes efficient optimization of large-scale reefer loads.
关键词
分层优化调度/制冷效率一致性/计算效率/冷箱集群/动态电价Key words
hierarchical optimal scheduling/refrigerating efficiency consensus/computational efficiency/cluster of reefers/dynamic pricing引用本文复制引用
基金项目
国家重点研发计划(2019YFE0102900)
上海市教委科研创新重大项目(2019YFE0102900)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据