A novel linear correlation clustering method for stochastic power flow studies

Nowadays as quantities of renewable energy resources are integrated to the power grid, their fluctuation and uncertainties influence violently to power system operation. Stochastic power flow is utilized to handle this issue. However, conventional stochastic power flow did not consider correlation in renewable energies, especially wind farms, leading to errors in power flow analysis. To address this problem, this paper proposes a linear correlation clustering method, which decomposes the non-linear correlated wind power data to several linear models to solve. The data generated from the nonlinear probabilistic distribution is clustered according to similar linear property, still keeping their major features and characteristics. Through analysis in a case study, it is proved that the proposed linear correlation clustering method performs better than existing Gaussian mixture model in both accuracy and computational efficiency. It is insightful to be utilized for stochastic power flow calculation in power system planning and operation.