A matrix-based VaR model for risk identification in power supply networks

This paper presents a value-at-risk (VaR) model based on the singular value decomposition (SVD) of a sparsity matrix for voltage risk identification in power supply networks. The matrix-based model provides a more computationally efficient risk assessment method than conventional models such as probability analysis and sensitivity analysis, for example, and provides decision makers in the power supply industry with sufficient information to minimize the risk of network collapse or blackouts. The VaR model is incorporated into a risk identification system (RIS) programmed in the MATLAB environment. The feasibility of the proposed approach is confirmed by performing a series of risk assessment simulations using the standard American Electric Power (AEP) test models (i.e. 14-, 30- and 57-node networks) and a real-world power network (Taiwan power network), respectively. In general, the simulated results confirm the ability of the matrix-based model VaR model to efficient identify risk of power supply networks.