Accurate PV power forecasting is essential for modern power system operations. While Transformers have shown strong potential for capturing temporal patterns, many existing models remain deterministic and overlook predictive uncertainty. This work proposes a unified Transformer-based framework for day-ahead PV forecasting. We first enhance deterministic performance through an encoder-only architecture, temporal embeddings, and nighttime zero-padding, yielding substantial accuracy gains across 11 PV sites. We then extend the model using quantile regression to produce multiple conditional quantiles, enabling empirical 90% and 95% prediction intervals without distributional assumptions. Compared to a traditional SARIMAX baseline, our Transformer-based models consistently achieve higher point accuracy and better-calibrated prediction intervals, offering a practical solution for uncertainty-aware solar forecasting.