Combing data-driven and model-driven methods for high proportion renewable energy distribution network reliability evaluation

Abstract

To accurately evaluate the reliability of the renewable energy distribution network, a combined data-driven and model-driven method of distribution network reliability evaluation are proposed. The approach improves the traditional reliability evaluation method in the uncertainty scenario generation and fault consequence analysis stages. In the scenario generation stage, an interpretable network architecture capable of adaptively learning the inherent randomness, volatility, and time variability properties of renewable energy is designed. The proposed DG output uncertainty generation method integrates the Conditional Wasserstein Generative Adversarial Network (CWGAN) based scenario generation and clustering-based scenario reduction. It accurately portrays uncertainty in distributed generation (DG) output power. In the fault consequence analysis stage, an optimization model for the reliability evaluation of distribution networks based on mixed integer linear programming is proposed. The model fully accounts for substation protection, branch line protection, network reconfiguration, DG, and energy storage system (ESS) components. Time-series analytical expressions for the corresponding reliability indices are established. Finally, with the combination of data-driven and model-driven methods, a distribution network reliability evaluation framework considering the uncertainty of renewable energy is proposed. Simulation tests are performed on a modified IEEE 123 bus system. The results verify the effectiveness and feasibility of the method. The proposed simulation analysis method can effectively quantify the distribution networks’ reliability compared to the traditional method. It has higher calculation accuracy.