Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning

TL;DR

This paper introduces a novel deep reinforcement learning method to estimate PQ flexibility at TSO-DSO interfaces, ensuring N-1 security and robustness, thereby enhancing grid congestion management in high voltage meshed grids.

Contribution

It is the first to apply DRL for PQ flexibility estimation considering N-1 security and robustness, offering a new approach beyond traditional optimal power flow methods.

Findings

Improved computational efficiency in PQ flexibility estimation.

Effective coordination of DERs for grid congestion management.

Enhanced robustness and security in grid operation planning.

Abstract

Nowadays, the PQ flexibility from the distributed energy resources (DERs) in the high voltage (HV) grids plays a more critical and significant role in grid congestion management in TSO grids. This work proposed a multi-stage deep reinforcement learning approach to estimate the PQ flexibility (PQ area) at the TSO-DSO interfaces and identifies the DER PQ setpoints for each operating point in a way, that DERs in the meshed HV grid can be coordinated to offer flexibility for the transmission grid. In the estimation process, we consider the steady-state grid limits and the robustness in the resulting voltage profile against uncertainties and the N-1 security criterion regarding thermal line loading, essential for real-life grid operational planning applications. Using deep reinforcement learning (DRL) for PQ flexibility estimation is the first of its kind. Furthermore, our approach of considering N-1 security criterion for meshed grids and robustness against uncertainty directly in the optimization tasks offers a new perspective besides the common relaxation schema in finding a solution with mathematical optimal power flow (OPF). Finally, significant improvements in the computational efficiency in estimation PQ area are the highlights of the proposed method.