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Application of the Nested Convex Programming to the Optimal Power Flow in MT-HVDC Grids.

机译:嵌套凸编程在MT-HVDC网格中的最优功率流中的应用。

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This paper deals with an application of the nested convex programming to the optimal power flow (OPF) in multi-terminal high-voltage direct-current grids (MT-HVDC). The real-world optimization problem under consideration is non-convex. This fact implies some possible inconsistencies of the conventional numerical minimization algorithms (such as interior point method). Moreover, the constructive numerical treatment of this problem is usually based on some approximative approaches, namely, on the suitable linearizations and problem relaxations. The resulting convex programming model constitutes an approximated model and can naturally involve the significant (approximation) errors. In difference to the strongly approximate computational approaches mentioned above, the numerical scheme we propose takes into account the specific bi-linear structure of the problem and operates with the originally given non-convex formulation of the problem. We implement the proposed nested optimization approach and study the numerical consistency of the resulting optimal design. The Python based numerical experiments demonstrate the imlementability of the proposed methodology. Optimization problem of the modified version of the CIGRE MT-HVDC is next used as a benchmark test for the approach we developed.
机译:本文涉及嵌套凸编程在多端子高压直流电网(MT-HVDC)中的最佳功率流量(OPF)的应用。正在考虑的真实优化问题是非凸。这一事实意味着传统数值最小化算法的一些可能不一致(例如内部点法)。此外,该问题的建设性数值处理通常基于一些近似方法,即,在合适的线性化和问题放松上。得到的凸编编型模型构成近似模型,并且可以自然地涉及显着的(近似)误差。与上述强烈的计算方法有所不同,我们提出的数值方案考虑了问题的特定双线性结构,并与原始给出的问题进行操作。我们实施了提议的嵌套优化方法,并研究了所得最佳设计的数值一致性。基于Python的数值实验证明了所提出的方法的可接触性。 CIGRE MT-HVDC的修改版本的优化问题将作为我们开发方法的基准测试。

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