Abstract
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.
Highlights
With the introduction of more and more uncertainties, such as load fluctuations, variations of distributed generation (DG) into power systems, there is an increasing need to analyze the power system using probabilistic load flow (PLF) methods
Simulation-based methods require a huge amount of steady-state load flow calculations to determine the statistical characteristics of those random variables, while analytical methods can work out the probabilistic features of the random variables through one-time calculation
A novel parallel cumulants-based PLF method considering nodal correlations is proposed in this paper, and it is able to deal with the correlations among all the system nodes, and avoid the Jacobian matrix inversion and full matrix computation in the traditional cumulants-based PLF methods
Summary
With the introduction of more and more uncertainties, such as load fluctuations, variations of distributed generation (DG) into power systems, there is an increasing need to analyze the power system using probabilistic load flow (PLF) methods. Fan et al [13] proposed joint cumulants to deal with the photovoltaic generation, and other approaches to deal with correlation such as extended point estimate method [14], Hybrid Latin Hypercube Sampling with Cholesky decomposition [15] were proposed These schemes are proposed to deal with specific nodal injection correlations [11,12,13,14,15], such as the wind speed correlations between neighboring wind power plants, seldom have any studies taken into account the correlations among all the nodes at a power system level [16]. This paper focuses on the improvement of the analytical PLF method, and a novel algorithm for cumulants-based PLF is proposed, which can deal with the correlations among all system nodes, and avoid the the Jacobian matrix inversion and full matrix manipulation as well.
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