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Hybrid computation of corrective security-constrained optimal power flow problems

机译:校正安全性约束的最优潮流问题的混合计算

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摘要

Corrective security-constrained optimal power flow (CSCOPF) considers the use of corrective control to remove system security violations in the post-contingency state. Its optimality not only depends on the pre-contingency state, but also the post-contingency state as well as the involved corrective control actions. This study first gives a comprehensive review on the relevant OPF models and then proposes a hybrid method to solve the CSCOPF problem. It makes use of the evolutionary algorithms to randomly search the maximum feasible region and state-of-the-art OPF solution technique (interior-point method) to provide deterministic solutions in the found region. The two interact iteratively to progressively approach the final solution. The proposed method is verified on the IEEE 14-bus and 118-bus systems. Comparison studies show that (i) CSCOPF can better balance the security and economy and (ii) the hybrid method is overall superior (in solution quality, robustness and convergence characteristic) over the single evolutionary algorithm. Parallel processing is applied to speed-up the computations.
机译:纠正性安全受限的最佳潮流(CSCOPF)考虑使用纠正性控制来消除应急后状态下的系统安全违规情况。它的最优性不仅取决于应变前状态,还取决于应变后状态以及所涉及的纠正控制措施。这项研究首先对相关的OPF模型进行了全面的综述,然后提出了一种解决CSCOPF问题的混合方法。它利用进化算法随机搜索最大可行区域,并采用最新的OPF解法技术(内点法)在找到的区域中提供确定性解。两者进行迭代交互以逐步接近最终解决方案。该方法在IEEE 14总线和118总线系统上得到了验证。比较研究表明,(i)CSCOPF可以更好地平衡安全性和经济性;(ii)混合方法在整体上(在解决方案质量,鲁棒性和收敛性方面)优于单一进化算法。并行处理可加快计算速度。

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