发表机构
Karlstad University; Tarbiat Modares University; Deggendorf Institute of Technology（卡尔斯特德大学; 塔比尔·莫达雷斯大学; 德根多夫应用技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出双层优化框架，识别配电网络中多社区本地能源市场对虚假数据注入的最坏情况攻击，并通过IEEE 33节点案例证明攻击影响取决于数据的时空放置而非均匀分布。
AI 中文摘要
社区型本地能源市场的出清依赖于电力需求和光伏预测，这使得其容易受到协调性虚假数据注入（FDI）攻击。本文提出了一种双层优化框架，用于识别配电网络中互联多社区电力市场中最坏情况下的节点级虚假数据注入。上层攻击者以最大化物理影响为目标，该影响通过累积电压偏差来衡量，同时考虑可检测性。下层则在运行和网络约束下重新出清互联的多社区市场。该双层模型通过Karush-Kuhn-Tucker条件被重构为单层混合整数规划，并使用ε约束方法来刻画物理影响与可检测性之间的权衡。在包含三个社区的IEEE 33节点系统上的案例研究表明，即使是有界的、系统级的零和攻击也能重塑本地交易、降低电压安全裕度，并产生不对称的社区级市场结果。结果进一步表明，脆弱性在很大程度上取决于虚假数据在时空上的放置位置，而非其在各节点和时间段上的均匀分布。
英文摘要
Market clearing in community-based local energy markets relies on power demand and PV forecasts, making it vulnerable to coordinated false data injection (FDI) attacks. This paper proposes a bilevel optimization framework to identify worst-case bus-level FDI against interconnected multi-community electricity markets within a distribution network. The upper level attacker maximizes physical impact, measured by cumulative voltage deviation, while accounting for detectability. The lower level re-clears the interconnected multi-community markets subject to operational and network constraints. The bilevel model is reformulated as a single-level mixed-integer program through Karush-Kuhn-Tucker conditions and solved using the epsilon constraint method to characterize the trade-off between physical impact and detectability. Case studies on the IEEE 33-bus system with three communities show that even bounded, system-level zero-sum attacks can reshape local trading, reduce voltage security margins, and produce asymmetric community-level market outcomes. The results further show that vulnerability depends strongly on the spatiotemporal placement of falsified data rather than on uniform spreading across buses and time periods.
