We introduce a discrete-time model for electricity prices, which accounts for both spikes and temperature effects. The model allows for different mean-reversion rates, one around spikes, and another for the remainder of the process. We demonstrate how a Markov chain Monte Carlo approach can be used for parameter estimation, using data recorded in Allegheny County, Pennsylvania. For this data set, we also demonstrate that the model outperforms existing stochastic jump-diffusion models. Results also demonstrate the importance of model parameters corresponding to both the temperature effect and the two-level mean-reversion rate.