Regional grid voltage reactive power optimization strategy based on voltage qualification rate evaluation function

Liu, Qianjin; Yu, Liangliang; Li, Zhuohuan; Zeng, Jian Min; Chen, Siyuan

doi:10.1109/powercon.2018.8601955

Cited by 1 publication

(1 citation statement)

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“…Referring to the evaluation system of voltage and reactive power operation, the regional power grid under automatic voltage control (AVC) is evaluated successively in references [6][7][8][9], from the aspects of control effect, loss reduction benefits, equipment configuration and strategy evaluation, etc. Based on the evaluation model and evaluation indexes containing reactive power equipment configuration, the reactive power operation of the regional power grid is quantitatively evaluated in reference [9]. The above studies mostly focused on the optimization analysis and evaluation of the reactive power operation of the system, but the multi-scenario operation of renewable energy is not considered.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Ji,

Chen,

Xiao

et al. 2021

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The output of renewable energy is strongly uncertain and random, and the distribution of voltage and reactive power in regional power grids is changed with the access to large-scale renewable energy. In order to quantitatively evaluate the influence of renewable energy access on voltage and reactive power operation, a novel combinational evaluation method of voltage and reactive power in regional power grids containing renewable energy is proposed. Firstly, the actual operation data of renewable energy and load demand are clustered based on the K-means algorithm, and several typical scenarios are divided. Then, the entropy weight method (EWM) and the analytic hierarchy process (AHP) are combined to evaluate the voltage qualified rate, voltage fluctuation, power factor qualified rate and reactive power reserve in typical scenarios. Besides, the evaluation results are used as the training samples for back-propagation (BP) neural networks. The proposed combinational evaluation method can calculate the weight coefficient of the indexes adaptively with the change of samples, which simplifies the calculation process of the indexes' weight. At last, the case simulation of an actual regional power grid is provided, and the historical data of one year is taken as the sample for training, evaluating and analyzing. And finally, the effectiveness of the proposed method is verified based on the comparison with the existing method. The evaluated results could provide reference and guidance to the operation analysis and planning of renewable energy.

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