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arXiv 1802.04456eess.SYcs.SY
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大规模电力传输网络上的全局最优功率流

Global Optimal Power Flow over Large-Scale Power Transmission Network

University of Technology, Sydney(悉尼科技大学);CERT-ONERA, Control System Department(法国国家航空航天研究中心控制系);the University of New South Wales(新南威尔士大学)

Y. Shi, H. D. Tuan, P. Apkarian, A. V. Savkin

AI总结:

本文提出一种迭代方法解决大规模非线性优化问题,通过半定规划生成收敛于秩一解的改进点序列,验证了该方法在数千节点网络上的高效性。

摘要:

最优功率流(OPF)在电力传输网络上提出具有挑战性的大规模非线性优化问题,涉及大量二次等式和不定二次不等式约束。这些计算上不可行的约束通常通过线性约束加上电压向量外积的矩阵秩一约束来表达。现有的凸松弛技术因忽略难处理的秩一约束而无法得到可行点。我们通过迭代过程解决这些计算困难的问题,生成一系列改进点,收敛于秩一解。每次迭代调用半定规划。对数千节点网络上的OPF问题进行大量模拟以证明该方法的有效性。通过我们的计算过程找到的OPF问题次优值被发现为全局最优值,计算误差小于0.01%。

英文摘要:

Optimal power flow (OPF) over power transmission networks poses challenging large-scale nonlinear optimization problems, which involve a large number of quadratic equality and indefinite quadratic inequality constraints. These computationally intractable constraints are often expressed by linear constraints plus matrix additional rank-one constraints on the outer products of the voltage vectors. The existing convex relaxation technique, which drops the difficult rank-one constraints for tractable computation, cannot yield even a feasible point. We address these computationally difficult problems by an iterative procedure, which generates a sequence of improved points that converge to a rank-one solution. Each iteration calls a semi-definite program. Intensive simulations for the OPF problems over networks with a few thousands of buses are provided to demonstrate the efficiency of our approach. The suboptimal values of the OPF problems found by our computational procedure turn out to be the global optimal value with computational tolerance less than 0.01%.

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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