During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, often lacks numerical convergence guarantees or is limited to single-machine numerical simulation. To address this, we present a distributed approach for solving AC Optimal Power Flow (OPF) problems within a geographically distributed environment. This involves integrating the energy system Co-Simulation (eCoSim) module in the eASiMOV framework with the convergence-guaranteed distributed optimization algorithm, i.e., the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). Comprehensive evaluations across multiple system scenarios reveal a marginal performance slowdown compared to the centralized approach and the distributed approach executed on single machines -- a justified trade-off for enhanced data privacy. This investigation serves as empirical validation of the successful execution of distributed AC OPF within a geographically distributed environment, highlighting potential directions for future research.

翻译：在能源转型过程中，机构间协同管理的重要性日益凸显，但面临数据隐私问题带来的挑战。当前针对分布式方法（作为集中式管理的替代方案）的主流研究，往往缺乏数值收敛性保证，或局限于单机数值仿真。为解决这一问题，我们提出了一种在地理分布式环境中求解交流最优潮流（OPF）问题的分布式方法。该方法将eASiMOV框架中的能源系统协同仿真（eCoSim）模块与具有收敛保证的分布式优化算法——即基于增广拉格朗日的交替方向非精确牛顿法（ALADIN）相结合。在多种系统场景下的综合评估表明，与集中式方法及单机执行的分布式方法相比，该方法的性能略有下降——这是为提升数据隐私性而付出的合理权衡。本研究为在地理分布式环境中成功执行分布式交流最优潮流提供了实证验证，并指明了未来研究的潜在方向。