Integration flexibility of renewable distributed energy resources in active distribution networks: A two-module data-driven characterization method.

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the ADN operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems. • A novel characterization framework for the IFR with learning and updating modules. • A new loss function in the learning module from the physical information of the IFR. • A strategy in the updating module for the improved characterization of the IFR. • A better tradeoff between security and conservatism of the IFR characterization. • Significant potential in violation reduction under incorrect justifications. [ABSTRACT FROM AUTHOR]