Evaluating Connectable Capacity of Distributed Wind Generation in Distribution Networks Through a Bayesian Integrated Optimization Method

Abstract

The effective evaluation of the connectable capacity of renewable energy plays a vital role in the development of a sustainable distribution network. Quantifying the capacity while considering network security and the local renewable accommodation policy is a challenge. This article proposes a scenario-based bilevel mathematical model using a Bayesian integrated optimization method to evaluate and quantify the connectable capacity of distributed wind generation in distribution networks, which effectively integrates the characteristics of wind power and the local accommodation policy. The constraint on the generation curtailment ratio (CR) is innovatively designed to represent the renewable accommodation policy and integrated with network security constraints to coordinatively quantify the capacity. The model is solved by the Bayesian integrated optimization method. The regression-based algorithm greatly reduces the complexity of alternating iteration and improves the calculation efficiency. Practical cases are used to verify the effectiveness of the proposed method. Results indicate that the method is more efficient than traditional optimization algorithms, and CR integration ensures that the connectable capacity fits local renewable energy development policies well.