Operation Risk Evaluation on Distributed Wind Power Connected to Distribution Network Considering Temporal-Spatial Correlation

doi:10.12204/j.issn.1000-7229.2021.07.015

Abstract

Abstract:

With the massive access of distributed power generation, in order to study the impact of the correlation of distributed wind power on the risk assessment of the distribution network, a risk assessment method for distributed power generations connected to distribution network considering temporal-spatial correlation is proposed. Firstly, considering the temporal-spatial correlation of wind power generators in different seasons, a two-stage wind power scenario generation model considering temporal-spatial correlation in different seasons is established on the basis of Copula function and Markov chain model. Secondly, in order to improve the computational efficiency of risk assessment, the dynamic fault set is established on the basis of the red-black tree structure. By searching and storing the sampled state sequence and its evaluation results, it avoids repeatedly calling the optimal power flow in the same state, and improves the speed of state evaluation. Finally, the operational risk indices such as over-voltage, low-voltage, line overload and loss of load are calculated in IEEE 33-bus distribution network system, and the comprehensive operational risk indices are established on the basis of analytic hierarchy process. The risk caused by strongly correlated wind power connected to distribution network is reasonably and comprehensively evaluated, and the effectiveness and accuracy of the proposed model and method are verified.

Key words: distributed wind power generation, temporal-spatial correlation, Copula function, Markov chain, red-black tree

CLC Number:

TM744

HE Zhaohui, CAO Rui, ZHOU Cheng, LIU Haipeng. Operation Risk Evaluation on Distributed Wind Power Connected to Distribution Network Considering Temporal-Spatial Correlation[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(7): 127-136.

Figures/Tables 17

Fig.1 Simplified structure of red-black tree

Fig.A1 Flow chart of operation risk assessment

Fig.2 Structure of IEEE 33-node distribution network

Fig.3 Monthly average output of wind power

Table 1 Correlations of wind power outputs in different seasons

Fig.4 Joint probability density function of two wind farms

Table 2 Evaluation indices of different Copula functions

Fig.A2 Different copula probability density functions oftwo wind farms in summer

Fig.A3 Joint probability density function of two wind farms in spring, fall and winter

Fig.5 Autocorrelation of simulated output of two wind farms

Fig.A4 Typical wind power scenarios in spring, summer, fall and winter

Fig.6 Convergence process of risk assessment indices

Table 3 Risk assessment results of distribution network

Table 4 Computing time of system

Fig.7 Probability distribution functions of operational risk indices of distribution network

Fig.8 Comparison of risk indices with different wind power capacity

Fig.9 Comparison of risk assessment results after the refinement of wind power capacity range

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