Optimized LinDistFlow for High-Fidelity Power Flow Modeling of Distribution Networks
arxiv(2024)
摘要
The DistFlow model accurately represents power flows in distribution systems,
but the model's nonlinearities result in computational challenges for many
optimization applications. Accordingly, a linear approximation known as
LinDistFlow is commonly employed. This paper introduces an algorithm for
enhancing the accuracy of the LinDistFlow approximation, with the goal of
aligning the outputs more closely with those from the nonlinear DistFlow model.
Using sensitivity information, our algorithm optimizes the LinDistFlow
approximation's coefficient and bias parameters to minimize discrepancies in
predictions of voltage magnitudes relative to the nonlinear DistFlow model. The
algorithm employs the Truncated Newton Conjugate-Gradient (TNC) optimization
method to fine-tune coefficients and bias parameters during an offline training
phase in order to improve the LinDistFlow approximation's accuracy in
optimization applications. This improves the model's effectiveness across
various system scenarios, leading to a marked improvement in predictive
accuracy. Numerical results underscore the algorithm's efficacy, showcasing
accuracy improvements in L_1-norm and L_∞-norm losses of up to
92% and 88%, respectively, relative to the traditional LinDistFlow model.
We assess how the optimized parameters perform under changes in the network
topology and also validate the optimized LinDistFlow approximation's efficacy
in a hosting capacity optimization problem.
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