Robust N-1 secure HV grid flexibility estimation for TSO-DSO coordinated congestion management with deep reinforcement learning

Konferenz: NEIS 2022 - Conference on Sustainable Energy Supply and Energy Storage Systems
26.09.2022 - 27.09.2022 in Hamburg, Germany

Tagungsband: NEIS 2022

Seiten: 7Sprache: EnglischTyp: PDF

Wang, Zhenqi (University of Kassel, Germany)
Wende-von Berg, Sebastian; Braun, Martin (University of Kassel, Germany & Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Kassel, Germany)

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 PQflexibility (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 PQarea are the highlights of the proposed method.