Modern power grids incorporate renewable energy at an increased pace, placing greater stress on the power grid equipment and shifting their operational conditions towards their limits. As a result, failures of any network component, such as a transmission line or power generator, can be critical to the overall grid operation. The security constrained optimal power flow (SCOPF) aims for the long term precontingency operating state, such that in the event of any contingency, the power grid will remain secure. For a realistic power network, however, with numerous contingencies considered, the overall problem size becomes intractable for single-core optimization tools in short time frames established by real-time industrial operations. We propose a parallel distributed memory structure exploiting framework, BELTISTOS-SC, which accelerates the solution of SCOPF problems over state of the art techniques. The acceleration on single-core execution is achieved by a structure-exploiting interior point method, employing successive Schur complement evaluations to further reduce the size of the systems solved at each iteration while maintaining sparsity, resulting in lower computational resources for the linear system solution. Additionally the parallel, distributed memory implementation of the proposed framework is also presented in detail and validated through several large-scale examples, demonstrating its efficiency for large-scale SCOPF problems.

中文翻译：

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求解安全约束最优潮流问题的两级并行增强Schur补内点算法

现代电网以更快的速度整合可再生能源，给电网设备带来了更大的压力，并将其运行条件转变为极限。因此，任何网络组件（例如传输线或发电机）的故障对整个电网运行都至关重要。安全约束最优潮流（SCOPF）旨在长期预应急运行状态，以便在发生任何突发事件时，电网将保持安全。然而，对于现实的电力网络，考虑到许多突发事件，在实时工业运营建立的短时间内，单核优化工具的整体问题规模变得难以处理。我们提出了一种并行分布式内存结构开发框架 BELTISTOS-SC，这加速了 SCOPF 问题的解决方案，而不是最先进的技术。单核执行的加速是通过结构开发内点方法实现的，采用连续的 Schur 补充评估来进一步减小每次迭代求解的系统的大小，同时保持稀疏性，从而减少线性系统解决方案的计算资源。此外，还详细介绍了所提出框架的并行分布式内存实现，并通过几个大规模示例进行了验证，证明了其对大规模 SCOPF 问题的效率。使用连续的 Schur 补充评估来进一步减小每次迭代求解的系统的大小，同时保持稀疏性，从而减少线性系统解决方案的计算资源。此外，还详细介绍了所提出框架的并行分布式内存实现，并通过几个大规模示例进行了验证，证明了其对大规模 SCOPF 问题的效率。使用连续的 Schur 补充评估来进一步减小每次迭代求解的系统的大小，同时保持稀疏性，从而减少线性系统解决方案的计算资源。此外，还详细介绍了所提出框架的并行分布式内存实现，并通过几个大规模示例进行了验证，证明了其对大规模 SCOPF 问题的效率。