Probability Evaluation of Available Transfer Capability Considering Wind Power Correlation

doi:10.12204/j.issn.1000-7229.2021.07.014

Abstract

Abstract:

Large scale wind power integration brings new challenges to the assessment of available transfer capability (ATC) of power system. In order to evaluate the ATC under the uncertain conditions of high wind power permeability more accurately, a method is proposed to evaluate the probability of ATC that takes the correlation of wind power into consideration. Firstly, the historical data are processed using the conditional probability principle and the wind correlation is modeled by the Copula function. Secondly, the evaluation process of ATC involves the calculation of the total transfer capability (TTC) and the existing transfer commitments (ETC), so this paper proposes a bi-level optimization model, on the basis of which, using the Karush-Kuhn-Tucker (KKT) optimal conditions, the bi-level model is transformed into mathematical program with equilibrium constraint (MPEC) model. Then, the MPEC model is transformed into a mixed integer second-order cone planning problem. Finally, the Monte-carlo simulation is used to evaluate the probability of inter-regional ATC, and the IEEE 30-bus system is used as an example to analyze the influence of wind power correlation on the assessment of inter-regional ATC and verify the correctness and validity of the proposed model.

Key words: power system, available transfer capacity(ATC), correlated wind power, bi-level model, Monte-Carlo simulation

CLC Number:

TM72

SONG Xiaozhe, LI Jiaqi, SUN Fushou, FU Jiyue, XU Duo. Probability Evaluation of Available Transfer Capability Considering Wind Power Correlation[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(7): 118-126.

Figures/Tables 12

Fig.1 Flowchart of ATC calculation

Fig.2 Wind power scenarios of sample

Fig.3 Probability density curve of wind power scenarios

Fig.4 Probability density of wind power output of wind farm 2 considering the correlation between wind farms

Fig.5 Samples out of wind power output with correlation

Fig.6 Structure of IEEE 30-bus system

Table 1 Result comparison applying different ATC indices

Fig.7 Probability density curve of ATC under different scenarios

Fig.8 Probability density curves of ATC under different correlation coefficients when wind farms are integrated in power supply area

Fig.9 Probability density curves of ATC under different correlation coefficients when wind farms are integrated in the load area

Table 2 Result comparison applying different ATC indices (Case 1)

Table 3 Result comparison applying different ATC indices (Case 2)

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