Reactive Power Optimization of Urban Distribution Network Considering Multiple Reactive Power Scenarios of Loads

doi:10.12204/j.issn.1000-7229.2022.08.005

Abstract

Abstract:

The wide access of the high proportion of power electronic equipment and the high proportion of distributed photovoltaic power, and the improvement of the urban cabling rate make the reactive power characteristics on the user side of the distribution network complicate. The increased uncertainty of the load reactive power consumption is not conducive to the safe operation of distribution network. Therefore, to better optimize reactive power, the combined scenarios and their probabilities of daily power-factor variation curves of different loads are used to reflect the uncertainty of reactive power. Taking the minimum expected value of operation cost as the objective function, an optimal configuration model for the expected value of multiple reactive power scenarios is established. Firstly, multiple one-dimensional convolutional autoencoders (1D-CAEs) is used to extract the low-dimensional representation of the daily power factor data of different users. Then, the k-means method is used for scene reduction to obtain typical daily power-factor variation scenes, and multi-user scenario set is combined. Finally, the expected value reactive power optimization model is established, and the particle swarm algorithm is used to solve it to determine the optimal configuration scheme. According to the reactive power consumption scenarios of users in a distribution network in Shanghai, the modified IEEE 33-node system is taken as an example to verify the effectiveness of the proposed method.

Key words: power electronization, distributed photovoltaic power, high cable ratio, urban distribution network, load reactive power, power factor, reactive power optimization, data driven

CLC Number:

TM732

YANG Xiu, JIAO Kaidan, SUN Gaiping, CHEN Xiaoyi, DU Jiawei, QIU Zhixin. Reactive Power Optimization of Urban Distribution Network Considering Multiple Reactive Power Scenarios of Loads[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 42-52.

Figures/Tables 18

Fig.1 General optimization framework

Fig.2 Data dimensionality reduction principle of 1D-CAEs

Fig.3 Flowchart of the PSO to solve the reactive power optimization model

Table 1 Parameters of distribution PV and load access

Table 2 Dimensionality reduction process of load’s power factor sequence based on 1D-CAEs

Fig.4 Clustering results of load power-factor curve

Fig.5 Typical reactive power scenarios of loads

Fig.6 Combination scenario sets

Fig.7 The line charging power and the system average reactive power

Table 3 Capacitor capacity of each model

Table 4 Reactor capacity of each model

Table 5 Average network loss of different models

Table 6 Cost of different models

Table 7 The average voltage fluctuation range of different models

Fig.8 The average voltage of nodes in 9 scenarios of different models

Fig.9 Probability distribution of voltage amplitudes at node 16

Fig.10 Probability distribution of voltage amplitudes at node 31

Table 8 Average reactive power reverse of different models

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