论文标题
具有不同隐私的分散和安全的生成维护
Decentralized and Secure Generation Maintenance with Differential Privacy
论文作者
论文摘要
分散的方法在电力系统中的数据驱动模型中获得了普及，因为它们提供了重要的计算可扩展性，同时保证了公用事业利益相关者的全部数据所有权。但是，分散的方法仍然需要共享有关公共沟通渠道网络流量估计的信息，这引起了隐私问题。在本文中，我们提出了一种差异隐私驱动的方法，该方法针对混合整数操作和维护优化问题，以保护网络流量估计。我们通过利用相角和流之间的线性关系来证明坚定的隐私。为了解决与问题的混合整数和动态性质相关的挑战，我们引入了一种基于指数的基于移动平均值的共识机制，以增强收敛性，并加上基于控制图表的收敛标准以提高稳定性。我们在IEEE 118公交案例上获得的实验结果表明，我们的隐私保留方法可在没有差异隐私的基准方法上获得解决方案质量。为了证明我们方法的计算鲁棒性，我们使用广泛的噪声水平和操作场景进行实验。
Decentralized methods are gaining popularity for data-driven models in power systems as they offer significant computational scalability while guaranteeing full data ownership by utility stakeholders. However, decentralized methods still require sharing information about network flow estimates over public facing communication channels, which raises privacy concerns. In this paper we propose a differential privacy driven approach geared towards decentralized formulations of mixed integer operations and maintenance optimization problems that protects network flow estimates. We prove strong privacy guarantees by leveraging the linear relationship between the phase angles and the flow. To address the challenges associated with the mixed integer and dynamic nature of the problem, we introduce an exponential moving average based consensus mechanism to enhance convergence, coupled with a control chart based convergence criteria to improve stability. Our experimental results obtained on the IEEE 118 bus case demonstrate that our privacy preserving approach yields solution qualities on par with benchmark methods without differential privacy. To demonstrate the computational robustness of our method, we conduct experiments using a wide range of noise levels and operational scenarios.
