Reactive Power Optimization of Partial Real-Time Visible Distribution Network Based on Data Driven

doi:10.12204/j.issn.1000-7229.2021.02.009

Abstract

Abstract:

At present, the coverage of measuring equipment in distribution network is low, so only part of the nodes’ load data can be collected in real time. This situation makes it impossible to use the optimization based on power flow calculation in the real-time reactive power optimization of distribution network. Considering the above situation, this paper proposes a data-driven reactive power optimization method based on partial real-time visible distribution network. According to the historical operation data, the optimal power flow is used to generate the reactive power optimization strategy offline, and the mapping between the real-time measured node load data and the reactive power optimization strategy is established by training the neural network to realize the real-time reactive power optimization of the partial real-time visible distribution network. Finally, in the modified IEEE 33-bus system, the proposed method is compared with the 9-zone diagram method to verify the effectiveness of the proposed method.

Key words: neural network, distribution network, reactive power optimization, data-driven

CLC Number:

TM712

WANG Jun, TIAN Endong, MA Jian, DOU Xiaobo, LIU Zhihan. Reactive Power Optimization of Partial Real-Time Visible Distribution Network Based on Data Driven[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(2): 68-76.

Figures/Tables 14

Fig.1 Framework of the reactive power optimization of partial real-time visible distribution network

Fig.2 Reactive power optimization module based on neural network

Fig.3 Flow chart of daily optimization and regular update

Fig.4 Modified IEEE 33-node case with DGs

Table 1 Node number and load type

Fig.5 Load curve and output curves of DGs

Fig.6 Training process of BP neural network

Table 2 Relative error of neural network output%

Table 3 Absolute error of capacitor input quantity%

Fig.7 Comparison of network loss before and after reactive power optimization

Fig.8 Node voltage before and after reactive power optimization

Fig.9 Node voltage profiles under different reactive power optimization methods

Table 4 Node properties under different real-time observation rates

Fig.10 Voltage control effect under different real-time observation rates

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