A Parallel Probabilistic Load Flow Method Considering Nodal Correlations

• Main Authors: Jun Liu, Xudong Hao, Peifen Cheng, Wanliang Fang, Shuanbao Niu
• Format: Article
• Language: English
• Published: MDPI AG 2016-12-01
• Series: Energies
• Subjects: correlation matrix; Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS); cumulants; distributed generation (DG); parallel computing; probabilistic load flow (PLF)
• Online Access: http://www.mdpi.com/1996-1073/9/12/1041

With the introduction of more and more random factors in power systems, probabilistic load flow (PLF) has become one of the most important tasks for power system planning and operation. Cumulants-based PLF is an effective algorithm to calculate PLF in an analytical way, however, the correlations among the nodal injections to the system level have rarely been studied. A novel parallel cumulants-based PLF method considering nodal correlations is proposed in this paper, which is able to deal with the correlations among all system nodes, and avoid the Jacobian matrix inversion in the traditional cumulants-based PLF as well. In addition, parallel computing is introduced to improve the efficiency of the numerical calculations. The accuracy of the proposed method is validated by numerical tests on the standard IEEE-14 system, comparing with the results from Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS) method. And the efficiency and parallel performance is proven by the tests on the modified IEEE-300, C703, N1047 systems with distributed generation (DG). Numerical simulations show that the proposed parallel cumulants-based PLF method considering nodal correlations is able to get more accurate results using less computational time and physical memory, and have higher efficiency and better parallel performance than the traditional one.