模拟退火算法在新型电力系统负荷侧源网负荷与存储多目标协同调度中的应用

Q2 Energy
Xinming Wang, Huayang Liang, Xiaobo Jia, Shihui Li, Shengyang Kang, Yan Gao
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引用次数: 0

摘要

为提高电网负荷协同调度的适应性,提出了一种基于模拟退火算法的新型电力系统负荷侧电网负荷存储的多目标协同调度方法。采用本地母线传输技术采集新电力系统负荷侧电网负荷储能动态参数。将采集到的负荷动态参数与能量分布状态参数进行融合,提取出电网负荷存储的状态特征。采用模拟退火算法实现了电力系统负荷侧能源网络的负荷特征融合和自适应调度处理,提取了负荷动态参数的频谱特征。采用模拟退火的动态调度方法,实现了能源网络动态负荷的多目标优化。基于模拟退火的协同优化结果,实现了模拟退火算法在新型电力系统负荷与储能多目标协同调度中的优化应用。实验结果表明,经过400次迭代后,所提方法的控制收敛精度达到0.980,明显优于对比方法,并且在调度效率提升、负载调度稳定性、调度时间和能量浪费比等方面表现良好,证明该方法在调度过程中具有良好的多目标集成和较强的优化能力。提高了电力系统的负载均衡调度和自适应控制能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of simulated annealing algorithm in multi-objective cooperative scheduling of load and storage of source network for load side of new power system

To improve the adaptability of grid load collaborative scheduling, a multi-objective collaborative scheduling method based on a simulated annealing algorithm for the load storage of grid loads on the load side of a new power system is proposed. Local bus transmission technology is adopted to collect the dynamic parameters of energy network load energy storage on the load side of the new power system. The collected load dynamic parameters are fused with energy distribution state parameters to extract the state characteristics of energy network load storage. The simulated annealing algorithm is adopted to realize the load characteristics fusion and adaptive scheduling processing of energy network on the load side of the power system, and the spectral characteristics of the load dynamic parameters are extracted. The dynamic scheduling method of simulated annealing is used to realize the multi-objective optimization of dynamic load of energy network. Based on the co-optimization results of simulated annealing, the optimization application of the simulated annealing algorithm in the multi-objective co-scheduling of loads and energy storage in a new power system is realized. The experimental results show that after 400 iterations, the control convergence accuracy of the proposed method reaches 0.980, which is significantly better than that of the comparison method, and performs well in terms of scheduling efficiency improvement, load scheduling stability, scheduling time and energy waste ratio, proving that the method has good multi-objective integration and strong optimization ability in the scheduling process, and improves the load balanced scheduling and adaptive control ability of the power system.

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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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