Performance evaluation of photovoltaic scenario generation

Photovoltaic scenario generation plays a critical role in power systems characterized by high diversity and fluctuation. Despite recent theoretical advancements, effectively evaluating the performance of photovoltaic scenario generation remains a significant challenge. Existing studies predominantly rely on metrics such as mean, variance, and probability density functions for assessment. However, these approaches struggle to disentangle the underlying mechanisms of morphological features and environmental stochastic factors (e.g., cloud cover, seasonal variations) from individual or batch-generated samples. To address these limitations, this paper proposes an evaluation framework based on the wide-sense stationary process. By analyzing historical photovoltaic scenario data, a solar irradiance distribution model is first constructed to characterize its dynamic behavior. Subsequently, an autoregressive model is employed to quantify the influence of environmental randomness on photovoltaic scenarios. The proposed evaluation model not only comprehensively validates the reliability of various photovoltaic scenario generation techniques but also identifies the corresponding month or season of generated samples through scenario feature analysis. Experimental results demonstrate that, compared to conventional probability-based metrics, the proposed model more effectively reveals the performance characteristics of photovoltaic scenario generation technologies. This advancement provides a novel technical foundation for optimizing photovoltaic scenario generation in practical power systems.