AC Power Flow Informed Parameter Learning for DC Power Flow Network Equivalents
2024 IEEE Texas Power and Energy Conference (TPEC)(2023)
摘要
This paper presents an algorithm to optimize the parameters of power systems
equivalents to enhance the accuracy of the DC power flow approximation in
reduced networks. Based on a zonal division of the network, the algorithm
produces a reduced power system equivalent that captures inter-zonal flows with
aggregated buses and equivalent transmission lines. The algorithm refines
coefficient and bias parameters for the DC power flow model of the reduced
network, aiming to minimize discrepancies between inter-zonal flows in DC and
AC power flow results. Using optimization methods like BFGS, L-BFGS, and TNC in
an offline training phase, these parameters boost the accuracy of online DC
power flow computations. In contrast to existing network equivalencing methods,
the proposed algorithm optimizes accuracy over a specified range of operation
as opposed to only considering a single nominal point. Numerical tests
demonstrate substantial accuracy improvements over traditional equivalencing
and approximation methods.
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关键词
Network reduction,DC power flow,machine learning,parameter optimization,power system equivalents
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