DERs participation in TSO-DSO coordinated operation considering dynamic reconfiguration of distribution networks
Year: 2025
Type: article
Abstract: • The proposed model enables DERs to support TN flexibility by dynamically adjusting DN topology. • TSDRL method optimizes large-scale discrete DN topology with MST cut-set and local search. • Interactive training captures DER response potential with topology decisions. With the continuous improvement of distributed resource configuration capabilities and the continuous increase in the flexibility requirements of power systems, the integration of flexible resources on the distribution network (DN) side for coordinated dispatching is of great significance to improving the local consumption of distributed power sources and improving the real-time supply and demand balance of power systems. However, existing research rarely addresses distributed energy resources (DERs) in different distribution networks (DNs), which can participate in transmission network (TN) and DN coordination by considering DN topology to provide flexibility for various transmission-distribution interfaces (TDIs). To address this problem, this paper proposes a model for DER participation in transmission network operator (TSO) and distribution network operator (DSO) coordinated operation based on dynamic topology adjustment of DN, enabling DSOs to dynamically optimize DN topology and DER output based on TSO dispatch instructions. A two-stage deep reinforcement learning algorithm (TSDRL) is developed to optimize network topology and DER output, overcoming challenges in large-scale topology optimization and non-convex problem handling. The simulation results on the modified IEEE 14-bus and IEEE 118-bus systems demonstrate that the proposed method significantly improves the DN ability to respond to the flexibility requirements of the TN, enabling real-time decision-making.
Authors: Haojing Tian, Shuya Lei, Xiao Liang, Beibei Wang, Tiewei Song
Institutions: Southeast University, China Electric Power Research Institute
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
OpenAlex ID: W4416449442