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Global optimal power flow over large-scale power transmission networks

机译:全局最优功率流过大型电力传输网络

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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 converges 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%. (C) 2018 Elsevier B.V. All rights reserved.
机译:通过电力传输网络的最佳功率流量(OPF)构成了挑战大规模非线性优化问题,这涉及大量二次平等和无限的二次不等式约束。这些计算上的难以应变约束通常由线性约束加矩阵附加秩一对电压矢量的附加约束表示。现有的凸弛豫技术,下降难度排列的难度计算,甚至不能产生可行点。我们通过迭代过程解决了这些计算困难问题,它产生了一系列改进的点,该节点会聚到秩一解决方案。每次迭代都调用半定程序。提供了在具有几个公共汽车的网络上的OPF问题的密集模拟,以展示我们的方法的效率。我们的计算过程发现的OPF问题的次优值将成为全局最优值,计算公差小于0.01%。 (c)2018 Elsevier B.v.保留所有权利。

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