Optimizing DER Aggregate Flexibility via Network Reconfiguration
Feixiang Zhang, Hongyi Li, Bai Cui, Zhaoyu Wang
TL;DR
The paper tackles unlocking large-scale DER flexibility by optimally reconfiguring distribution networks under load uncertainty. It develops a unified adaptive robust optimization framework that yields an exact convex reformulation into a max-volume ellipsoid, solvable via a tailored Benders decomposition that handles topology and binary switching. The authors demonstrate substantial improvements in the aggregate flexibility region on the IEEE 123-bus feeder (over 90% gain with reconfiguration) and validate robustness through disaggregation and uncertainty analyses, while achieving notable computational efficiency gains via problem reformulations. The work highlights network reconfiguration as a powerful, scalable lever to enhance grid services and reliability in DER-rich systems.
Abstract
The aggregate flexibility region of distributed energy resources (DERs) quantifies the aggregate power shaping capabilities of DERs. It characterizes the distribution network's potential for wholesale market participation and grid service provision at the transmission level. To enhance flexibility and fully exploit the potential of DERs, this paper proposes a method to optimize the aggregate flexibility region through distribution network reconfiguration. First, we formulate the ellipsoidal aggregate flexibility region characterization problem as a two-stage adaptive robust optimization problem and derive an exact convex reformulation with a large number of second-order cone constraints. By exploiting the problem structure, we propose a scalable Benders decomposition algorithm with provable finite convergence to the optimal solution. Finally, we propose an optimal reconfiguration problem for aggregate flexibility region optimization and solve it using the custom Benders decomposition. Numerical simulations on the IEEE 123-bus test feeder demonstrate that, compared to existing approaches, substantial improvements in the aggregate flexibility region can be achieved over multiple scenarios with the optimized topology.