Research on Reconfiguration Strategy of Distributed Distribution Network With Self-Healing Performance Under High-Proportion Renewable Energy Access

doi:10.12096/j.2096-4528.pgt.23112

Abstract

Abstract:

Objectives As the proportion of renewable energy in power grids increases year by year, the volatility and uncertainty of the grid are significantly heightened, posing challenges to the safe operation of distribution networks. To address the issue of distributed network reconfiguration in high-proportion renewable energy grids, this paper proposed an online rolling optimization framework. Methods The framework utilized a distributed consensus protocol to obtain network topology and node operation information. It can enable automatic reconfiguration in the event of N-1 and N-2 line failures, allowing the distribution network to automatically restore normal operation without the need for additional external triggering signals, thus ensuring economic operation of the grid. Additionally, a rolling optimization method was employed to handle grid fluctuations caused by the high proportion of renewable energy, and generative adversarial network (GAN) technology was used to generate new data, which combined with historical data. It can help to achieve high-precision forecasting of grid operation data. Results The proposed method can achieve automatic economic optimization and self-healing in normal, single-point failure, and two-point failure scenarios. Conclusions This method provides an effective solution for ensuring the safe operation of distributed networks in high-proportion renewable energy grids.

Key words: renewable energy, distributed generator, distribution network reconstruction, distribution network self-healing, high proportion of new energy, distributed optimization, look-ahead optimization

CLC Number:

TK 01

Renbo WU, Yijun HUANG. Research on Reconfiguration Strategy of Distributed Distribution Network With Self-Healing Performance Under High-Proportion Renewable Energy Access[J]. Power Generation Technology, 2024, 45(5): 975-982.

Figures/Tables 6

Fig. 1 IEEE-33 system with photovoltaic and wind turbine

Tab. 1 Parameters related to the testing system

参数	数值	参数	数值
C^G/(美元/MW)	100	$η ̲ i, η ¯ i$	0.95, 1.05
C^M/(美元/MW)	25	$P ̲ i M, P ¯ i M$	0, 2
C^Loss/(美元/MW)	130	$Q ̲ i M, Q ¯ i M$	0, 1
C^λ/(美元/MW)	2 000	$n q i$	2×10^-5

Tab. 1 Parameters related to the testing system

参数	数值	参数	数值
C^G/(美元/MW)	100	$η ̲ i, η ¯ i$	0.95, 1.05
C^M/(美元/MW)	25	$P ̲ i M, P ¯ i M$	0, 2
C^Loss/(美元/MW)	130	$Q ̲ i M, Q ¯ i M$	0, 1
C^λ/(美元/MW)	2 000	$n q i$	2×10^-5

Fig. 2 Normal operation status

Fig. 3 Comparison of network loss indicators between conventional one-step method and advanced 4-step prediction method

Fig. 4 Distributed network state under a line fault

Fig. 5 Distributed network status under two line faults

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