Risk Assessment of Voltage Limit Violation Based on Probabilistic Load Flow in Active Distribution Network
This paper mainly investigates risk assessment of voltage limit violation in active distribution network with integration of wind generation (WG), photovoltaic generation (PVG) and electric vehicles (EVs). Firstly, to avoid additional peak load caused by random EV charging, a controlled EV charging and discharging strategy is designed. Then, for the correlations of spatially near WGs and PVGs, Nataf transformation and orthogonal transformation (OT) are integrated to solve the problem, and this provides a path for point estimate method (PEM) based probabilistic load flow (PLF) to obtain steady-state voltage of active distribution network. Furthermore, based on the voltage results, a model for quantifying the risk of voltage limit violation is developed by considering loss of load caused by voltage limit violation, which is different from the previous risk indices calculated by possibility and severity of voltage limit violation. Finally, the proposed model is tested on the modified IEEE 33-bus system. Simulation results confirm that the effective EV charging/discharging strategies and penetration increment of WG and PVG help to decrease operation risk of active distribution network.
KeywordsRisk assessment Voltage limit violation Active distribution network EV charging/discharging strategy Correlation Loss of load
This work was supported in part by the national Science Foundation of China under Grant No. 61773253, and project of Science and technology Commission of Shanghai Municipality under Grants No. 15JC1401900, 14JC1402200, and 17511107002.
- 2.Liu, X., Shahidehpour, M., Li, Z., Liu, X., Cao, Y., Li, Z.: Power system risk assessment in cyber attacks considering the role of protection systems. IEEE Trans. Smart Grid 8(2), 572–580 (2017)Google Scholar
- 14.Li, X., Chen, H., Lu, P., Du, D.: Space transformation-based interdependency modelling for probabilistic load flow analysis of power systems. J. Donghua Univ. 33(5), 734–739 (2016)Google Scholar