Error Bound Restriction of Linear Power Flow Model

Linear power flow models are widely used in power system analysis. To control the maximum influence of linearization error on decision making, it is important to evaluate the worst-case linearization error (i.e., the error bound). This letter theoretically derives the error bound of linear power flow models based on the truncation error analysis of Taylor series expansion. A cold-start approach is proposed to restrain the error bound. Case studies show that 1) the proposed error bound is tight and effective and 2) the maximum linearization error can be successfully reduced using the proposed method.