发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于Mosaik框架的智能电网协同仿真平台，对比同步策略并优化NS-3事件处理，在IEEE基准系统上验证优化后方法可降执行时间最多50%，还总结了标准化多仿真器配置的领域本体。
AI 中文摘要
电网向“智能电网”的演进是新兴大规模网络物理系统的典型案例。为研究网络攻击或其他极端事件的潜在影响，高保真协同仿真是唯一可行策略。本研究展示了一种适用于智能电网的实用高性能协同仿真策略，其提供的协同仿真集成经验可推广至其他网络物理系统。具体而言，我们提出了一种基于Mosaik框架的协同仿真平台，该平台集成了多个联邦程序，包括用于潮流计算的OpenDSS、优化后的NS-3通信网络仿真器，以及基于Python的自定义仿真器，用于分接开关控制、分布式状态估计和数据采集。我们对比了同步策略：穷尽式锁步时间推进与利用下界时间戳（LBTS）和下一个事件预测的优化事件驱动方法，并对NS-3的事件处理和内部事件的相关性过滤引入了针对性改进。在两个标准IEEE基准系统上评估性能：带分接开关电压调节的13节点测试馈线，以及增强了32个欧洲低压馈线（共1793个节点）的大规模33节点系统，该系统执行分布式状态估计，涉及数千个相量和智能电表。实验结果表明，与朴素锁步方法相比，采用基于LBTS的NS-3事件过滤的优化事件驱动同步可将执行时间最多降低50%（在较小场景中超过4倍），同时严格保持时间正确性和仿真精度。我们还总结了一种领域本体，用于标准化电力、通信和控制领域的多仿真器配置和实体映射。
英文摘要
The evolution of the electric power grid towards a "smart grid" is an example of an emerging large-scale cyber-physical system. To study the potential of cyberattacks or other sources of extreme events, high-fidelity co-simulation is the only realistic strategy. In this work, we demonstrate a practical and high-performance co-simulation strategy for smart grids, which provides lessons about co-simulation integration that can be carried over to other cyber-physical systems. Specifically, we present a co-simulation platform based on the Mosaik framework that integrates several federates including OpenDSS for power flow, a refined NS-3 for communication networks, and custom Python-based simulators for on-load tap changer control, distributed state estimation, and data collection. We compare synchronization strategies -- exhaustive lock-step time advancement versus an optimized event-driven approach that exploits lower-bound time stamps (LBTS) and next-event prediction -- and introduce targeted refinements to NS-3 event handling and relevance filtering of internal events. Performance is evaluated on two standard IEEE benchmark systems: the 13-node test feeder with tap-changer voltage regulation and a large-scale 33-bus system augmented with 32 European low-voltage feeders (1,793 nodes total) performing distributed state estimation with thousands of phasors and smart meters. Experimental results demonstrate that the refined event-based synchronization that uses LBTS-based NS-3 event filtering reduces execution time by up to 50% (and more than 4$\times$ in smaller scenarios) compared to naïve lock-step methods, while strictly preserving temporal correctness and simulation accuracy. We additionally summarize a domain ontology that standardizes multi-simulator configuration and entity mapping across power, communication, and control domains.
Comments15 pages
