Numerical and Computational Aspects of Cosmo-Based Activity Coefficient Models

Possani, Luiz Felipe Kusler; Soares, Rafael de Pelegrini

doi:10.1590/0104-6632.20190361s20170574

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…In general, as shown in Fig ( 5-c), the PM method is faster than NR method for large grids. And, it is worth noting that, the fact that the system structure of the proposed method is faster than Newton-Raphson technique is also observed in a similar evaluation has been done in [24] when the number of elements in the Jacobian matrix is large.…”

Section: B Solution Convergencesupporting

confidence: 54%

Linear Iterative Power Flow Approach Based on the Current Injection Model of Load and Generator

et al. 2021

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Power flow (PF) is a fundamental tool for operation, automation and optimization of the power systems. Due to the nonlinearity of the PF system equations, the classical PF solutions are computationally very demanding. As a common approach in solving the nonlinear equations, linearization is a potential technique which can simplify and accelerate the PF calculations. In this context, this paper proposes a linear fast iterative method based on the fixed-point iteration technique in which a linearized model of generator along with a ZI load model are integrated in a simplified system of linear equations (SLE) of Yv = i. The relaxation method is used during the deriving process of generator equivalent current in this approach. However, the already developed ZI load model based on the curve-fitting technique has been exploited in this work. The accuracy of the proposed PF method has been compared with calculated results from DIgSILENT PowerFactory on the benchmark IEEE 33-bus test system and on a large medium voltage network in Germany.INDEX TERMS Linearization, fixed-point iteration technique, current injection model, relaxation method. NOMENCLATUREThe main notation used in this paper is provided below; other symbols are defined as required.

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Section: B Solution Convergencesupporting

confidence: 54%

Linear Iterative Power Flow Approach Based on the Current Injection Model of Load and Generator

et al. 2021

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“…Analysis of the successive substitution method and a comparison with the Newton-Raphson method is provided in Possani and de P. Soares. 46 To solve the segment activity coefficient of Eqs. (28) and (35), initial values have to be specified for Γ S (σ n ) and Γ s S (σ s n ), respectively.…”

Section: Model Of Hsieh At El -Cosmo-sac-dspmentioning

confidence: 99%

“…Successive substitution is characterized as being both reliable and slowly convergent. Analysis of the successive substitution method and a comparison with the Newton–Raphson method is provided in the work of Possani and de P. Soares …”

Section: Implementation Details For the Cosmo-sac Modelsmentioning

confidence: 99%

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A Benchmark Open-Source Implementation of COSMO-SAC

Bell

Mickoleit

Hsieh

et al. 2020

J. Chem. Theory Comput.

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The COSMO-SAC modeling approach has found wide application in science as well as in a range of industries due to its good predictive capabilities. While other models for liquid phases, as for example UNIFAC, are in general more accurate than COSMO-SAC, these models typically contain many adjustable parameters and can be limited in their applicability. In contrast, the COSMO-SAC model only contains a few universal parameters and subdivides the molecular surface area into charged segments that interact with each other. In recent years, additional improvements to the construction of the sigma profiles and evaluation of activity coefficients have been made. In this work, we present a comprehensive description how to postprocess the results of a COSMO calculation through to the evaluation of thermodynamic properties. We also assembled a large database of COSMO files, consisting of 2261 compounds, freely available to academic and noncommercial users. We especially focus on the documentation of the implementation and provide the optimized source code in C++, wrappers in Python, sample sigma profiles calculated from each approach, as well as tests and validation results. The misunderstandings in the literature relating to COSMO-SAC are described and corrected. The computational efficiency of the implementation is demonstrated.

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