Enhanced Probabilistic Power Flow Method Considering Multiple Stochastic Factors and Their Correlations

As increasing integration of stochastic factors such as renewable energy, storage system and controllable load, the consequent randomness and variability bring great challenges to power system planning and operation. Probabilistic power flow (PPF) is proposed to deal with those uncertainties by means of probability distributions and probabilistic methods. However, some stochastic factors like energy storage and controllable load are subject to the control strategies, which makes their distributions and correlations vary a lot and adds difficulties to PPF analysis. To address the problem, this paper combines alternative control strategies, a novel polynomial distribution fitting method and the Copula function theory to establish a general probability model. Based on the model, an enhanced probabilistic power flow procedure with all of these stochastic factors and correlations is performed, and a subsequent evaluation scheme for transmission expansion plans are developed. Case study shows that the proposed PPF method can help make more reasonable and reliable decisions to transmission expansion plans under multiple stochastic factors.