首页|含高比例分布式资源的乡村配电系统有功-无功协同优化调控方法

含高比例分布式资源的乡村配电系统有功-无功协同优化调控方法

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随着光伏、电动汽车、储能等分布式资源规模化接入乡村配电系统,给系统的运行和调控带来巨大挑战,为保障电网安全稳定运行,有必要预测和评估含高比例分布式资源的乡村配电系统其有功和无功支撑能力.因此,本文提出了一种基于Transformer的Informer预测模型,利用最大互信息和历史功率数据对未来点的功率数据进行准确预测;利用节点电压的灵敏度和可调节的无功功率容量,评估节点的电压支撑程度和无功功率的调节能力;同时引入了新的指标来量化和评估配电系统的有功和无功支持能力.最后,在贵州某含高比例分布式资源的乡村配电系统进行仿真试验,结果表明该优化控制方法具有更好的安全性和经济性,有利于促进新农村配电系统的建设和运行.
Active-reactive Power Coordinated Optimization Control Method of Rural Power Distribution System with High Proportion of Distributed Resources
With the large-scale access of distributed resources such as photovoltaics,electric vehicles,and energy storage to rural power distribution systems,it brings great challenges to the operation and regulation of system.Predicting and evaluating the active and reactive power support capacity of rural distribution systems with high proportion of distributed resources has become an important issue to ensure the safe and stable operation of the power grid.Therefore,this paper proposes an informer prediction model based on transformer,which uses the maximum mutual information and historical power data to accurately predict the power data of future points.The voltage support degree and reactive power regulation ability of the node are evaluated by using the sensitivity of the node voltage and the adjustable reactive power capacity.At the same time,new indicators and dynamic indicators are introduced to quantify and evaluate the active and reactive power support capabilities of the distribution system.Finally,the simulation experiment is carried out in a rural power distribution system with high proportion of distributed resources in Guizhou.The results show that the optimal control method proposed in this paper has better safety and economy,which is conducive to promoting the construction and operation of the new rural power distribution system.

informer modelpower forecastingactive support capabilitydistributed resourcesoptimal power flow

姚倩倩、李新皓

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华北电力大学电气与电子工程学院,北京 102206

贵州电网有限责任公司电力科学研究院,贵州 贵阳 550002

Informer模型 功率预测 主动支撑能力 分布式资源 最优潮流

2024

电力大数据
贵州电力试验研究院 贵州省电机工程学会

电力大数据

影响因子:0.047
ISSN:2096-4633
年,卷(期):2024.27(9)