Probabilistic power flow analysis of microgrid with renewable energy

With the development of renewable-based distributed generation (RDG), there are increasing uncertainties in the operation of microgrids (MGs), and stochastic evaluation methods are attracting more attention nowadays. In this paper, a probabilistic power flow (PPF) analysis method is proposed to evaluate the influence of uncertainties on the power flow of MGs. First, the MG PPF model is established considering different operation modes of MGs and uncertainties of RDG and load demands. Then, the Borgonovo method, which is a density-based global sensitivity analysis (GSA) method, is used to evaluate the importance of input variables in PPF calculation. To improve the computational efficiency of GSA, the sparse polynomial chaos expansion (SPCE) is used to establish the surrogate model of MG PPF, and the Borgonovo index is calculated based on the surrogate model. Finally, the procedure of applying GSA to MG power flow is established. The proposed method is tested using 33-node and 123-node MGs, and is compared with other methods to validate its effectiveness. Simulation results indicate that the proposed method identifies critical uncertainties that affect MG power flow. Based on the rankings of input variables, the influences of critical uncertainties are diminished with energy storage systems.