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

arXiv: https://arxiv.org/abs/2211.05855 · HTML: https://mlsanity.com/paper/2211.05855/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow](https://mlsanity.com/paper/1609.02194/) (2016)
- [Evaluating Machine Learning Models for the Fast Identification of Contingency Cases](https://mlsanity.com/paper/2008.09384/) (2020)
- [QuickFlex: a Fast Algorithm for Flexible Region Construction for the TSO-DSO Coordination](https://mlsanity.com/paper/2107.00114/) (2021)
- [Fast Parallel Newton-Raphson Power Flow Solver for Large Number of System Calculations with CPU and GPU](https://mlsanity.com/paper/2101.02270/) (2021)

## Built on by (most cited first, 1 of 1)

- [Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid](https://mlsanity.com/paper/2310.01181/) (2023)

## Abstract (arXiv)

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.