Two-Stage Stochastic Unit Commitment Considering the Uncertainty of Wind Power and Electric Vehicle Travel Patterns

doi:10.12204/j.issn.1000-7229.2021.08.008

Abstract

Abstract:

The uncertainty of wind power output and charging demand of electric vehicles (EVs) brings great challenge for power system dispatching. Taking day-ahead dispatch as the target, this paper proposes a stochastic optimization scheduling model and corresponding reserve model on the basis of traditional unit commitment model. Firstly, for the uncertainty of wind power output, a two-stage stochastic unit commitment model is established in this paper according to scenario analysis based on generative adversarial network (GAN), while electric vehicles are divided into two categories: schedulable and non-schedulable EVs. Monte-Carlo method is adopted to simulate the behavior and the dynamic change of schedulable EVs on the basis of probability distribution of the travel patterns, and the model of EV aggregators is established in this paper. As for non-schedulable EVs, K-means cluster analysis is adopted to get a typical load curve, and then the charging demand is viewed as part of conventional load. Case study demonstrates the validity of the proposed model.

Key words: wind power generation, electric vehicle (EV), scenario analysis, generative adversarial network (GAN), EV aggregator, cluster analysis, unit commitment

CLC Number:

TM614

WANG Ruogu, CHEN Guo, WANG Xiuli, QIAN Tao, GAO Xin. Two-Stage Stochastic Unit Commitment Considering the Uncertainty of Wind Power and Electric Vehicle Travel Patterns[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(8): 63-70.

Figures/Tables 7

Fig.1 Scenario generation based on GAN

Fig.2 Flow chart of modeling and solving

Fig.3 Comparison of different reserve models

Fig.4 Comparison of result of 3 cases

Fig.5 Curve of conventional load and EV charging power

Fig.6 Influence of the quantity of EVs

Fig.7 Curve of conventional load and demand of non-schedulable EVs

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