Long-Time-Scale Photovoltaic Output Scenario Generation Method Based on Improved Transformer-CGAN
The inherent intermittency and volatility of photovoltaic (PV) power pose significant uncertainties for power system planning and operation. Existing generative adversarial network (GAN)-based scenario generation methods predominantly focus on short-term horizons and suffer from gradient vanishing, mode collapse, and boundary feature loss when extended to long time scales (e.g., annual 8760-hour sequences). This paper proposes a Transformer-CGAN (T-CGAN) model that integrates an improved Transformer architecture with a conditional GAN. Both generator and discriminator employ Transformer-based neural networks, with seasonal labels incorporated as conditional information. A multi-scale periodic attention mechanism is developed to capture diurnal and seasonal periodic features of PV output. The Wasserstein distance with gradient penalty is adopted as the loss function to enhance training convergence and stability. Validation using historical data from a PV station in northwestern China demonstrates that the proposed method effectively captures long- and short-term temporal dependencies, accurately generating annual PV output scenario sets. Comparative experiments against three typical models verify the method's superiority in statistical metrics, temporal correlation, and scenario validity.