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arXiv 2103.00692math.OC
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不平衡配电系统网络级Optimization:近似与松弛

Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

Rahul Ranjan Jha, Anamika Dubey

摘要:

配电层最优潮流(D-OPF)的非线性规划(NLP)问题存在收敛性问题,且不适用于不平衡配电系统。现有的可扩展的D-OPF算法要么使用近似,是无效的不平衡的配电系统或应用松弛技术的非线性潮流方程,不保证一个可行的潮流解决方案。在本文中,我们提出了可扩展的D-OPF算法,同时实现最佳和可行解,通过解决多次迭代的近似,或放松,D-OPF子问题的低复杂性。第一种算法是基于一个连续的线性逼近的非线性潮流方程的当前操作点,其中的D-OPF的解决方案是通过求解多次迭代的线性规划(LP)问题。第二种算法是基于松弛的非线性潮流方程的圆锥约束和方向约束,实现了最优和可行解的二阶锥规划(SOCP)问题的多次迭代。结果表明,所提出的算法是能够达到一个最佳的和可行解,同时显着减少计算时间相比,一个等效的NLP D-OPF模型为同一配电系统。

英文摘要:

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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