
Aggregated Distribution Grid Flexibilities in the Overlaying Grid Operational Management
Author(s) -
Neelotpal Majumdar,
Lutz Hofmann
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3596884
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Distribution grid aggregated flexibilities or PQ-capabilities (active and reactive power capabilities) are termed in literature as Feasible Operating Regions (FORs). The FORs from underlying active distribution grids can effectively contribute to the operational management at the HV grid level. The HV buses are allocated aggregated FORs from the underlying MV grids, which are inherently nonlinear and non-convex. Therefore, two approaches are proposed in the paper to apply the FOR constraints in the HV grid operational management. First, a mixed integer linear programming (MILP) based optimization approach for alleviating the HV grid constraint violations is proposed, which addresses the non-convexity of the FOR using piecewise segmentation. Furthermore, the MILP method is enhanced to consider the influence of the HV bus voltage on the underlying MV grid flexibilities resulting in a 3D PQ(V)-FOR. Second, a convexification approach is proposed, which uses a convex approximation of the non-convex 3D PQ(V)-FOR for implementation in a linear optimization method. Results reveal a robust utilization of the distribution flexibilities to maintain grid security and reliability at the HV grid level. Comparisons present significantly reduced computation times for the convexification method.
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