Measurements-Feedback Linear Power Flow Model Considering Distribution System Parameter Errors

Accurate parameters of the distribution system are difficult to obtain, yielding significant challenges for existing model-based linear power flow (LPF) models. To cope with this problem, this paper proposes a measurements-feedback LPF model with model correction for distribution systems. Specifically, the inaccurate distribution system branch parameters are considered in the system admittance matrix, which also affects the coefficient matrix of the LPF model. Then, measurements - feedback formulation optimization is proposed to correct the coefficient matrix, yielding the improved LPF model. The integration of both physical system information and measurements allows us to deal with inaccurate system model parameters and reduces the requirement for the number of measurement samples. Numerical results carried out on the IEEE 13-node and 123-node systems show that the proposed LPF model achieves significantly reduced linearization error under different degrees of parameter uncertainties as compared to existing model-based and hybrid LPF models. Moreover, the sensitivity of the linearization error to measurement sample size is discussed and it is found that only a small size of measurement samples is needed to maintain high LPF accuracy.