Probabilistic Modeling for Prediction Errors to Enhance Balancing Market Participation of Photovoltaic Systems: Error Threshold Estimation, Multisite Aggregation, and Overloading Effects

The variation in solar output due to weather hinders both reliability and profitability, particularly because an increase in photovoltaic (PV) penetration reduces daytime electricity prices. In this study, a method to generate a reserve value by managing prediction errors is developed. A model to estimate the error threshold corresponding to a specific cumulative probability is constructed using a polynomial approximation based on the historical distribution of prediction errors at a single site. This threshold is considered as the headroom required to absorb the prediction errors. The model is then applied to multiple sites to evaluate its overall applicability and the smoothing effect of site aggregation. The output is divided into clipped and nonclipped portions under overloading conditions; the former is modeled with a gamma distribution and the latter with a polynomial surface. Two models are tested: one using overall errors and another incorporating conditional probability with the clearness index. The results indicate that site aggregation reduces imbalance events and increases bid amounts. Under overloading, the optimal DC/AC ratio depends on the specified cumulative probability. Future works must focus on improving characterizing prediction errors under overloading and developing methods for predicting clipping to enhance balancing market participation of PV plants.