分布网络中带有智能电表的差分隐私状态估计
Differentially Private State Estimation in Distribution Networks with Smart Meters
AI总结：
本文提出一种在分布网络中实现差分隐私的状态估计方法，通过优化不同性能指标，在估计性能与客户隐私之间取得平衡，适用于水和燃气网络等其他领域。
摘要：
状态估计通常用于高压输电电网以协助运行和检测故障设备。在低电压和中压配电电网中，传统上可用的实时测量较少，运行通常基于预测和历史数据。如今，许多地区已部署智能电表，其测量数据理论上可实时共享给运营商以改进状态估计。然而，客户可能因隐私问题而不愿共享。因此，本文提出了一种适用于分布电网模型的状态估计方案，确保客户隐私。具体而言，状态估计方案优化了不同的性能标准，并推导了估计性能下界与客户差分隐私之间的权衡。所提出的框架足够通用，也可应用于其他分布网络，如水和燃气网络。
英文摘要：
State estimation is routinely being performed in high-voltage power transmission grids in order to assist in operation and to detect faulty equipment. In low- and medium-voltage power distribution grids, on the other hand, few real-time measurements are traditionally available, and operation is often conducted based on predicted and historical data. Today, in many parts of the world, smart meters have been deployed at many customers, and their measurements could in principle be shared with the operators in real time to enable improved state estimation. However, customers may feel reluctance in doing so due to privacy concerns. We therefore propose state estimation schemes for a distribution grid model, which ensure differential privacy to the customers. In particular, the state estimation schemes optimize different performance criteria, and a trade-off between a lower bound on the estimation performance versus the customers' differential privacy is derived. The proposed framework is general enough to be applicable also to other distribution networks, such as water and gas networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
