A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources

TL;DR

This paper introduces a data-driven, distributionally robust optimization method to estimate and visualize the flexibility region of aggregated distributed energy resources, accounting for forecast uncertainty and operational limits.

Contribution

It proposes a novel multi-directional search algorithm for real-time estimation of DER flexibility regions considering operational and network constraints.

Findings

Algorithm effectively estimates flexibility regions under uncertainty.

Simulation shows robustness and computational efficiency.

Method enables controllability assessment and risk quantification.

Abstract

This paper presents a data-driven, distributionally robust chance-constrained optimization method for estimating the real and reactive power controllability of aggregated distributed energy resources (DER). At the DER-level, a two-dimensional flexibility region can be formed based on the real and reactive power regulating limits of each DER considering forecast uncertainty. At the feeder-level, an aggregated flexibility region is computed via a multi-directional search method. In each search direction, extend the real and reactive power of each controllable DER towards its operational limits until: i) all DERs' maximum operational limits are reached or, ii) one or more of the distribution network operational limits are violated. The method enables three key features for operating aggregated DER resources: controllability estimation, visualization, and risk quantification. Simulation results demonstrated the effectiveness of the algorithm and quantified the impact of different parameter settings on the flexibility region boundary changes. The proposed algorithm is robust and computationally efficient and can meet real-time computing needs.