Uncertainty Evaluation of Load Supply Capability for Distribution Network Based on Evidence Theory and Affine Analytic Recursion

In this paper, an evaluation method for load supply capacity based on evidence theory and affine analytic recursion is proposed to address the limitations of existing methods, which rely on initial values for the iteration convergence and struggle to comprehensively consider the objective uncertainty and cognitive uncertainty information. The method constructs distributed generation output model using evidence theory and employs the step-varied repeated power flow algorithm based on affine analytic recursion to calculate the focal elements of the maximum load growth percentage. The obtained results accurately represent the distribution characteristics of the maximum load growth percentage, thereby establishing confidence intervals for evaluating the supply risk across various operating conditions. Cases show that, the proposed method is effective and is capable of significantly reducing computation time.