Sample-Based Conservative Bias Linear Power Flow Approximations
arxiv(2024)
摘要
The power flow equations are central to many problems in power system
planning, analysis, and control. However, their inherent non-linearity and
non-convexity present substantial challenges during problem-solving processes,
especially for optimization problems. Accordingly, linear approximations are
commonly employed to streamline computations, although this can often entail
compromises in accuracy and feasibility. This paper proposes an approach termed
Conservative Bias Linear Approximations (CBLA) for addressing these
limitations. By minimizing approximation errors across a specified operating
range while incorporating conservativeness (over- or under-estimating
quantities of interest), CBLA strikes a balance between accuracy and
tractability by maintaining linear constraints. By allowing users to design
loss functions tailored to the specific approximated function, the bias
approximation approach significantly enhances approximation accuracy. We
illustrate the effectiveness of our proposed approach through several test
cases.
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