How to effectively evaluate price of volatility risk is the basis of risk management in electricity market. Electricity price connotes a grey system, due to uncertainty and incomplete information for partial external or inner parameters. A two-stage model for estimating value-at-risk based on grey system and extreme value theory is proposed. Firstly, in order to capture the dependencies, seasonalities and volatility-clustering, an GM(1,1) model is used to filter electricity price series. In this way, an approximately independently and identically distributed residual series with better statistical properties is acquired. Then extreme value theory is adopted to explicitly model the tails of the residuals of GM(1,1) model, and accurate estimates of electricity market value-at-risk can be produced. The empirical analysis shows that the proposed model can be rapidly reflect the most recent and relevant changes of electricity prices and produce accurate forecasts of value-at-risk at all confidence levels, and the computational cost is far less than the existing two-stage value-at-risk estimating models, further improving the ability of risk management for electricity market participants.