Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System

doi:10.12096/j.2096-4528.pgt.20105

Abstract

Abstract:

Renewable energy is gradually replacing traditional power plants to provide electricity for users, but it also brings potential risks to the safe operation of the power grid. Thus, in the planning stage, it is necessary to comprehensively evaluate the probability of violation risk of maximum frequency deviation. Planning method based on Monte Carlo simulation (MCS) is inefficient, while artificial neural network (ANN) can make fast and effective prediction by learning data. Therefore, this paper proposed an MCS-ANN algorithm to realize the rapid assessment of violation risk of regional maximum frequency deviation. Firstly, a large number of stochastic disturbances were generated, and only a small part of disturbances were used for MCS. Then, these data were sent to the neural network for training, and most of the remaining disturbances were sent to the trained neural network for output prediction. The above training and prediction processes were repeated. The average of multiple prediction results was used as the final prediction output, and the probability distribution of each risk interval was obtained. Finally, the effectiveness of the proposed MCS-ANN algorithm was verified on IEEE 10-machine 39-node system.

Key words: renewable energy, power grid security, frequency risk assessment, Monte Carlo simulation (MCS), artificial neural network (ANN)

CLC Number:

TK81

Jiaxin WEN, Siqi BU, Qiyu CHEN, Bowen ZHOU. Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System[J]. Power Generation Technology, 2021, 42(1): 40-47.

Figures/Tables 16

Fig.1 Stochastic models of wind energy and solar energy for different planning objectives

Fig.2 The relationship between wind speed and wind power

Fig.3 Frequency responses of multi-machines after disturbances

Fig.4 Structure of a neural network

Fig.5 Structure of ANN

Fig.6 Regional frequency probability assessment process based on MCS-ANN

Fig.7 IEEE 10-machine 39-node test system with 3 wind farms

Fig.8 Histogram of system maximum frequency deviation error distribution

Fig.9 Histogram of Area 1 maximum frequency deviation error distribution

Fig.10 Histogram of Area 2 maximum frequency deviation error distribution

Fig.11 Histogram of Area 3 maximum frequency deviation error distribution

Tab. 1 Probabilistic distribution of system maximum frequency deviation %

最大频率偏差/Hz	MCS-ANN	MCS	绝对误差
< 49.5	1.74	1.80	0.06
[49.5, 49.8)	21.74	21.04	0.70
[49.8, 50.2]	63.64	64.18	0.54
(50.2, 50.5]	12.88	12.98	0.10
> 50.5	0.00	0.00	0.00

Tab. 2 Probabilistic distribution of Area 1 maximum frequency deviation %

最大频率偏差/Hz	MCS-ANN	MCS	绝对误差
< 49.5	1.84	1.88	0.04
[49.5, 49.8)	22.37	22.04	0.33
[49.8, 50.2]	62.57	62.78	0.21
(50.2, 50.5]	13.22	13.30	0.08
> 50.5	0.00	0.00	0.00

Tab. 3 Probabilistic distribution of Area 2 maximum frequency deviation %

最大频率偏差/Hz	MCS-ANN	MCS	绝对误差
< 49.5	1.75	1.80	0.05
[49.5, 49.8)	21.85	21.22	0.63
[49.8, 50.2]	63.57	63.96	0.39
(50.2, 50.5]	12.83	13.02	0.19
> 50.5	0.00	0.00	0.00

Tab. 4 Probabilistic distribution of Area 3 maximum frequency deviation %

最大频率偏差/Hz	MCS-ANN	MCS	绝对误差
< 49.5	1.78	1.84	0.06
[49.5, 49.8)	21.87	21.36	0.51
[49.8, 50.2]	63.27	63.64	0.37
(50.2, 50.5]	13.07	13.16	0.09
> 50.5	0.00	0.00	0.00

Tab. 5 Comparison of computational time between MCS-ANN and MCS

方法	计算时间/s
MCS-ANN	714.4=700(100次MCS)+14.4(10次ANN训练与预测)
MCS	34 998.7(5 000次MCS)

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