Operation risk assessment of distribution network considering time dependence correlation coefficient. Issue 13 (18th December 2017)

• Record Type: Journal Article
• Authors: Xiao, Tianying; Pei, Wei; Ye, Hua; Niu, Geng; Xiao, Hao; Qi, Zhiping
• Is Part Of: Journal of engineering. Volume 2017:Issue 13(2017)
• Page Start: 2489
• Page End: 2495
• Publication Date: 2017-12-18
• Subjects: Monte Carlo methods -- risk analysis -- distributed power generation -- wind turbines -- distribution networks -- risk management -- load flow -- autoregressive processes; optimal scheduling -- risk assessment method -- time‐dependent correlation -- input random variables -- distribution network -- distributed generation power fluctuations -- multiple‐variable generalised autoregressive conditional heteroscedasticity model -- DCC MVGARCH -- risk evaluation -- time dependence correlation coefficient -- operation risk assessment -- photovoltaic power -- modified IEEE 123‐bus power system -- power limit -- operational risk level -- Cholesky method -- Latin hypercube sampling -- Monte Carlo simulation method -- conditional power flow calculation -- time‐varying correlation coefficient -- dynamic correlation coefficient -- time 5.0 min -- time 15.0 min; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2017.0776
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : The accuracy and reliability of the risk assessment results plays an important role in accurately determining the operational status of the system and risk‐based optimal scheduling. To improve the accuracy of the risk evaluation results, a risk assessment method considering time‐dependent correlation between input random variables for distribution network is presented, validated, and applied. Firstly, a direct analysis on the correlation between random variables, such as distributed generation power fluctuations and the forecast error, is carried out, then a multiple‐variable generalised autoregressive conditional heteroscedasticity model based on dynamic correlation coefficient (DCC_MVGARCH) is proposed to calculate the time‐varying correlation coefficient. Secondly, the conditional power flow calculation is then implemented by using the Monte Carlo simulation method based on Latin hypercube sampling and Cholesky method. In addition, according to the results of the power flow calculation, the operational risk level of the system is evaluated by the voltage limit and the power limit. Finally, a modified IEEE 123‐bus power system with a certain penetration of wind turbine and photovoltaic power is employed to validate the proposed method. The effectiveness of the method is verified through the simulation results performed during a horizon of 5 and 15 min, respectively.