Study of reactive power optimization strategy based on load characteristics

2012 ◽  
Author(s):  
Qiu Chen ◽  
Zhao Dongmei ◽  
Liu Chunlei ◽  
Qiu Yinghui
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Optimization Strategy ◽  
Reactive Power Optimization ◽  
Load Characteristics

scholarly journals Reactive power optimization strategy considering analytical impedance ratio

2017 ◽  
Author(s):  
Zhongchao Wu ◽  
Weibing Shen ◽  
Jinming Liu ◽  
Maoran Guo ◽  
Shoulin Zhang ◽  
...  
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Optimization Strategy ◽  
Reactive Power Optimization ◽  
Impedance Ratio

scholarly journals An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

2020 ◽  
Vol 10 (3) ◽  
pp. 1145
Author(s):  
Yaqiong Li ◽  
Tongxun Wang ◽  
Zhanfeng Deng
Keyword(s):  
Distribution Systems ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Compensation ◽  
Optimization Strategy ◽  
Reactive Power Optimization ◽  
Load Curve ◽  
Effective Strategies ◽  
Network Losses ◽  
Compensation Device

Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive power compensation devices cannot be adjusted too frequently due to their lifetime constraints. Thus, in this paper, an online reactive power optimization strategy based on the segmentation of multiple predicted load curves is proposed to address this issue, aiming to minimize network losses and at the same time to minimize reactive power-compensation device adjustment times. Based on forecasted time series of loads, the strategy first segments each load curve into several sections by means of thresholding a filtered signal, and then optimizes reactive power dispatch based on average load in each section. Through case studies using a modified IEEE 34-bus system and field measurement of loads, the merits of the proposed strategy is verified in terms of both optimization performance and computational efficiency compared with state-of-the-art methods.

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