<p>This paper develops an advanced stochastic-fuzzy framework for modelling electricity prices in the Palo Verde day-ahead peak market. The proposed model comprehensively captures key market characteristics – seasonality, continuous fluctuations, and discontinuous jumps – by integrating multiple risk sources into a unified jump-diffusion process. A significant methodological contribution is the analytical derivation of the probability that prices hit a predetermined threshold. The interpretation of this result provides a crucial risk assessment tool, enabling stakeholders to quantitatively gauge exposure to extreme price events and refine their hedging strategies accordingly. Furthermore, we derive closed-form solutions for pricing electricity forward contracts under both stochastic and fuzzy environments. By representing key parameters as fuzzy numbers, the model incorporates inherent market uncertainties, yielding price intervals paired with belief degrees. This approach offers a more nuanced interpretation of forward prices, moving beyond single-point estimates to reflect the spectrum of plausible market outcomes, which is vital for robust financial decision-making. Empirical analysis demonstrates the model’s superior performance over traditional benchmarks. The results obtained are a significant step towards more resilient and interpretable energy price modelling, directly aiding market participants in risk management and strategic planning.</p>