Regional nonintrusive load monitoring for low voltage substations and distributed energy resources
Regional nonintrusive load monitoring for low voltage substations and distributed energy resources
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DOI：
10.1016/j.apenergy.2019.114225
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发表时间：
2020-02
期刊：
影响因子：
11.2
通讯作者：
Shuangyuan Wang;Ran Li;A. Evans;Furong Li
中科院分区：
文献类型：
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作者：
Shuangyuan Wang;Ran Li;A. Evans;Furong Li
本文提出了一种新的扩展经典的非侵入式负载监测（NILM）问题，从家用电器级变电站级。提出了一种新的三阶段区域NILM方法，通过分解变电站需求来推断区域内不同类型负荷的状态。在这项研究中考虑了三种类型的负载：（i）传统负载;（ii）分布式发电，如光伏发电（PV）;和（iii）灵活的负载，如电动汽车（EV）。该方法首先利用长期历史数据对传统负荷进行预测，并采用谱分析方法提高信噪比。其次，通过负残差与当地太阳辐照度数据的峰值重合分析，推导出光伏容量。最后，提出了一种新的有限激活匹配追踪方法来估计电动汽车的状态，包括电动汽车的总负载和电动汽车的数量。该方法是评估真实的数据收集800变电站，10个PV和50电动汽车在英国。实验结果表明，该方法估计EV数的性能比基于稀疏编码、正交匹配追踪和非负匹配追踪的方法分别提高了16.5%、10.2%和10.0%。建议区域NILM解决方案提供了一个具有成本效益的方式，配电网络运营商了解网络的状态。因此，它可以显着提高网络可见性，而无需昂贵的监控和避免数据隐私问题。因此，它可以提高需求侧管理的效率，这是适应未来大量分布式能源连接所需要的。
This paper presents a novel extension of the classic nonintrusive load monitoring (NILM) problem from household-appliance level to substation level. A new three-stage regional-NILM method is proposed to deduce the states of different types of loads in a region by disaggregating its substation demand. Three types of loads are considered in this study: (i) traditional loads; (ii) distributed generation such as photovoltaics (PVs); and (iii) flexible loads like electric vehicles (EVs). The proposed method firstly forecasts the traditional load using the long-term historical data and employing spectral analysis to boost the signal-to-noise ratio. Secondly, the PV capacity is deduced by performing peak coincidence analysis between negative residuals and local solar irradiance data. Finally, a novel limited activation matching pursuit method is proposed to estimate the states of the EVs, including the total EV load and number of EVs. The method is assessed on real data collected from 800 substations, 10 PVs and 50 EVs in the UK. Results show the proposed method for estimating the number of EVs outperforms the approaches based on sparse coding, orthogonal matching pursuit and non-negative matching pursuit by 16.5%, 10.2% and 10.0%, respectively. The proposed Regional-NILM solution provides a cost-effective way for distribution network operators to understand the network’s state. It can therefore significantly increase the network visibility without requiring expensive monitoring and avoiding data privacy issues. As such, it can improve the efficiency of demand side management, which is required to accommodate the future large number of distributed energy resources connections.