Online Assessment of Conservation Voltage Reduction Effects With Micro-perturbation
Jian Xu, Boyu Xie, Siyang Liao, Yuanzhang Sun, Deping Ke, Jun Yang, Peng Li, Li Yu, Quan Xu, Xiyuan Ma
Abstract
Online assessment of conservation voltage reduction (CVR) has great effects on the performance of CVR-based applications, especially for real-time CVR-based controls. The challenges of online CVR assessment mainly come from time-varying load composition and inevitable noise. Besides, the dynamic process resulting from motors further complicates this problem. This article proposes a micro-perturbation based load-to-voltage (LTV) process identification method to online assess the CVR effects. In particular, we construct a well-designed pseudo-random-binary-sequence voltage perturbation to excite the CVR process continually and fully. With the experimental data, the CVR factor and transfer function of LTV are estimated by the cross-correlation method, which can reflect both steady-state and dynamic CVR effects without being affected by noise. The proposed method is verified on a specific hardware-in-the-loop testbed under various cases. In addition, through the energy-saving and power smoothing applications, the impact of CVR assessment on performance of CVR-based applications is also analyzed, which further verify the validity and economic benefits of the proposed assessment method.