To address the impact of photovoltaic (PV) output randomness on the economic performance of active distribution networks

an adaptive robust optimization dispatch strategy based on Gaussian mixture model (GMM) is proposed to reduce system operating costs. First

PV output is categorized into two scenarios: sufficient illumination and insufficient illumination. GMM is used to cluster historical PV output data

generating the mean and standard deviation of PV output uncertainty sets for different time periods under varying illumination conditions. Based on the PauTa criterion

accurate uncertainty sets are constructed for each lighting condition. Next

an adaptive robust optimization dispatch model is established with the objective of minimizing the total scheduling cost of the distribution network. The model fully considers the uncertainty of PV output and uses an affine decision rule for solving

enhancing its adaptability to PV fluctuations. Finally

simulations are conducted on an improved IEEE 33-node distribution network system. The results show that the proposed model ensures system security while effectively reducing operating costs compared to traditional interval and polyhedral sets

with lower conservatism in the optimization results.