In the competitive electricity market

the stochastic electricity price model originated form financial engineering theory is crucial to the pricing of the electricity derivatives

the electricity market risk management

the appraisal of asset and the investment decision under uncertainty. This paper proposes the stochastic electricity price models based on the affine jump-diffusion process with two or three jump components and a novel approximate parameter calibration method

with which the model parameters can be solved quickly using the historical electricity price data and the calculation needed will not increase with the number of the historical electricity prices. This calibration method will not cause the accumulation of computational error because it solves the model parameters directly from some numerical characteristic values of the historical electricity price. The calibration results for the Powernext

EEX and APX market using hourly spot prices are presented and Monte Carlo simulations are performed to validate the effectiveness of the model. The results illustrate that the calibrated models are capable of depicting the probability distribution of the hourly electricity price accurately and provide a useful tool for further research.