Day-Ahead Temporal Scenario Generation and Quality Evaluation of Photovoltaic and Loads Based on Volatility Interval Correction

To further enhance the quality of temporally generated scenarios, this paper proposes a method for generating typical PV-load temporal scenarios by modifying the sampling probability interval. First, based on the analysis of PV-load influencing factors from the previous day, a comprehensive correlation index is introduced to identify a set of similar days from historical samples and model the fluctuation probability function. Second, the temporal sampling probability interval of PV-load power is adjusted by considering historical fluctuation characteristics, and typical temporal scenarios are generated using clustering reduction methods. Finally, different scenario quality indicators are proposed, and based on actual microgrid examples, the results demonstrate that the proposed method comprehensively improves scenario quality as well as operational economics compared to the traditional sampling-reduction method.