Model Predictive Control-Based Active/Reactive Power Regulation of Inverter Air Conditioners for Improving Voltage Quality of Distribution Systems

The distribution system's voltage has more fluctuations due to the increasing intermittent and uncertain generation of renewable energy sources. To deal with massive and abrupt voltage issues, more operating reserves should be established. Compared with traditional regulation resources from generators, demand response by regulating the power consumption of demand-side resources is getting greater attention. On the demand-side, inverter air conditioners (IACs) account for a high power consumption percentage and have huge regulation potential. However, it remains a significant challenge to control large-scale IACs. Traditional control methods only consider active power and do not consider the compressor's complex characteristics combining active and reactive power. To address this issue, this article proposes a two-stage method considering system voltage quality. The first stage is using the photovoltaics' operating reserve for maintaining the system voltage in the safe range. The second stage is under more serious voltage deviations to regulate IACs' active power and reactive power based on model predictive control. Finally, hardware-in-the-loop experiments with realistic IAC are conducted to verify the proposed method. The proposed method improves the voltage fluctuation by 23.45% compared to the traditional method. The experimental results demonstrate that the proposed method can effectively maintain the distribution system's voltage within the allowable range.

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Publication Details

Journal
IEEE Transactions on Industrial Informatics
Published
2024-10-16
DOI
https://doi.org/10.1109/tii.2024.3468475
Citations
8
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
2.63

Funders

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article

Model Predictive Control-Based Active/Reactive Power Regulation of Inverter Air Conditioners for Improving Voltage Quality of Distribution Systems

Lunshu Chen, Hongxun Hui
8 citations
IEEE Transactions on Industrial Informatics
Microgrid Control and Optimization
2.63
article

Model Predictive Control-Based Active/Reactive Power Regulation of Inverter Air Conditioners for Improving Voltage Quality of Distribution Systems

Lunshu Chen, Hongxun Hui
article en
8 citations

Abstract

The distribution system's voltage has more fluctuations due to the increasing intermittent and uncertain generation of renewable energy sources. To deal with massive and abrupt voltage issues, more operating reserves should be established. Compared with traditional regulation resources from generators, demand response by regulating the power consumption of demand-side resources is getting greater attention. On the demand-side, inverter air conditioners (IACs) account for a high power consumption percentage and have huge regulation potential. However, it remains a significant challenge to control large-scale IACs. Traditional control methods only consider active power and do not consider the compressor's complex characteristics combining active and reactive power. To address this issue, this article proposes a two-stage method considering system voltage quality. The first stage is using the photovoltaics' operating reserve for maintaining the system voltage in the safe range. The second stage is under more serious voltage deviations to regulate IACs' active power and reactive power based on model predictive control. Finally, hardware-in-the-loop experiments with realistic IAC are conducted to verify the proposed method. The proposed method improves the voltage fluctuation by 23.45% compared to the traditional method. The experimental results demonstrate that the proposed method can effectively maintain the distribution system's voltage within the allowable range.

IEEE Transactions on Industrial InformaticsVol. 21(1)
University of Macau (MO), City University of Macau (MO)
National Natural Science Foundation of China, Universidade de Macau
Affordable and clean energy
Openalex Percentile: Top 10%
Microgrid Control and Optimization
2.63
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