AI 中文总结
针对分布式能源资源集成带来的大规模输配电交流最优潮流问题，提出平滑两阶段分解优化器，通过分解问题结构并行求解，不依赖近似或松弛，经实验验证求解时间减少、可扩展性增加，有效助力DER集成。
AI 中文摘要
分布式能源资源（DER）集成到电网给交流最优潮流（AC-OPF）问题带来新挑战。传统OPF优化仅考虑输电系统，将配电网络视为静态负载。但DER的增加使精确配电系统建模对电网运行至关重要。本文提出平滑两阶段分解优化器（StsDOpt），通过将输配电AC-OPF问题分解为主子问题结构来解决这些复杂性，实现并行求解。它不依赖近似或松弛，利用平滑技术使子问题响应相对于主问题可微，借助原始对偶内点法的障碍问题属性。该方法对准确建模和求解多相、不平衡且非线性的配电系统至关重要。集成到PowerModelsITD框架中，通过数值实验验证，结果表明其求解时间减少、可扩展性增加，是大规模输配电AC-OPF问题的有效解决方案，有助于DER可靠集成到复杂输配电系统。
英文摘要
The integration of distributed energy resources (DERs) into the power grid has introduced new challenges to AC optimal power flow (AC-OPF) problems. Traditional OPF optimize consider transmission systems, treating distribution networks as static loads. However, the growing presence of DERs makes accurate distribution system modeling crucial for grid operations. Consequently, efficiently solving the resultant large-scale, nonconvex transmission and distribution (T&D) AC-OPF problem remains a significant challenge. This paper proposes a Smoothed Two-Stage Decomposition Optimizer (StsDOpt) to address these complexities by decomposing the T&D AC-OPF problem into a master-subproblem(s) structure, enabling parallel solving. Unlike traditional methods, StsDOpt does not rely on approximations or relaxations. It uses a smoothing technique to render the subproblems responses differentiable with respect to the master problem, leveraging the barrier problem properties inherent in primal-dual interior point methods. This approach is crucial for accurately modeling and solving distribution systems, which are multiphase, unbalanced, and nonlinear, distinguishing StsDOpt apart from other methods. Integrated into the PowerModelsITD framework, StsDOpt has been validated through numerical experiments, demonstrating reduced wall-clock solve time and increased scalability. Results highlight its efficacy as a robust, scalable solution for large-scale T&D AC-OPF problems, facilitating the reliable integration of DERs into complex T&D systems.
