Data-driven unit commitment model incorporating the uncertainty of wind-PV-load

doi:10.3969/j.issn.2097-0706.2022.01.003

Abstract

Abstract:

The traditional probability distribution model mainly relies on the choice of model in uncertainty modelling, but the assumed single model often cannot accurately describe the complex variations of random quantities. In addition, the parametric probability model does not adequately describe the temporal and spatial correlations between random variables, while the correlation modelling methods like Copula functions are too complicated in describing the correlations of multiple random variables, which adds difficulty to the practical applications of the model. Taking a data-driven uncertainty modelling method, a data-driven two-stage unit commitment model is proposed. The non-parametric Dirichlet process Gaussian mixture model （DPGMM） and the variational Bayesian inference （VBI） method are used to describe the uncertainty of wind power, photovoltaics and load. Taking the correlations between multiple wind farms and the load of each node into consideration, the traditional mathematical optimization method is applied to solve the established unit commitment model. Finally, the simulation verification is carried out on the IEEE-30 node test system. The results show that the DPGMM model can fit the probability distribution of random quantities and describe the correlation between them.

Key words: wind power, PV, load, uncertainty, random variable, data-driven, unit commitment, DP, GMM

CLC Number:

TK01⁺9:TM732

SHI Libao, ZHAI Fang. Data-driven unit commitment model incorporating the uncertainty of wind-PV-load[J]. Integrated Intelligent Energy, 2022, 44(1): 18-25.

Figures/Tables 12

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概率模型	2006-06-10	2006-06-11
Gamma	58.06	214.01
Weibull	61.48	215.12
Lognorm	58.29	198.14
GMM-1	58.44	198.14
GMM-2	72.75	144.04
GMM-3	108.20	247.10
GMM-4	107.48	231.95
DPGMM	115.53	251.63

概率模型	2006-06-10	2006-06-11
Gamma	134.78	81.80
Weibull	129.60	81.08
Lognorm	135.00	98.45
GMM-1	134.81	234.95
GMM-2	111.03	122.70
GMM-3	30.13	35.44
GMM-4	20.80	27.91
DPGMM	18.57	20.09

机组编号	启停计划(01:00—24:00)
G1	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
G2	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
G3	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
G4	0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
G5	0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0
G6	0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

穿透率/%	负荷波动率/%
穿透率/%	10	15	20
10	1.198 7×10⁴	1.283 9×10⁴	1.356 4×10⁴
20	1.029 3×10⁴	1.112 0×10⁴	1.193 7×10⁴
30	9.042 0×10³	9.850 1×10³	1.056 0×10⁴
40	8.348 0×10³	9.117 2×10³	9.743 0×10³