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Regional nonintrusive load monitoring for low voltage substations and distributed energy resources

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  • Wang, Shuangyuan
  • Li, Ran
  • Evans, Adrian
  • Li, Furong

Abstract

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.

Suggested Citation

  • Wang, Shuangyuan & Li, Ran & Evans, Adrian & Li, Furong, 2020. "Regional nonintrusive load monitoring for low voltage substations and distributed energy resources," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919319129
    DOI: 10.1016/j.apenergy.2019.114225
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    1. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

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