Optimized Dispatch of a Photovoltaic-Inclusive Virtual Power Plant Based on a Weighted Solar Irradiance Probability Model

Under China’s dual-carbon strategic objectives, virtual power plants (VPPs) actively participate in the coupled electricity–carbon market through the optimized scheduling of distributed energy resources, simultaneously stabilizing grid operations and reducing carbon emissions. Photovoltaic (PV) generation, a cornerstone resource within VPP systems, introduces significant challenges in scheduling due to its inherent output variability. To increase the accuracy in the characterization of the PV output uncertainty, a weighted probability distribution of solar irradiance, based on historical irradiance data, is newly proposed. The leveraging rejection sampling technique is applied to generate solar irradiance scenarios that are consistent with the proposed weighted solar irradiance probability model. Further, a confidence interval-based filtering mechanism is applied to eliminate extreme scenarios, ensuring statistical credibility and enhancing practicability in actual dispatch scenarios. Based on the filtered scenarios, a novel dispatch strategy for the VPP operation in the electricity–carbon market is proposed. Numerical case studies verify that scenarios generated by the weighted solar irradiance probability model are capable of closely replicating historical PV characteristics, and the confidence interval filter effectively excludes improbable extreme scenarios. Compared to conventional normal distribution-based methods, the proposed approach yields dispatch solutions that are more closely aligned with the optimal dispatch of the historical irradiance data, demonstrating the improved accuracy in the probabilistic modelling of the PV output uncertainty. Consequently, the obtained dispatch strategy shows the improved capability to ensure the market revenue of the VPP considering the fluctuations of the PV output.