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Privacy-Preserving Power Injection Over a Hybrid AMI/LTE Smart Grid Network

机译:混合AMI / LTE智能电网上的隐私保护功率注入

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摘要

The future smart grid will enable homes to have energy storage units that can store the excess power generated from renewable energy sources and sell it to the grid during the peak hours. Realization of this process, however, requires the utility company to be able to communicate with the storage units whenever needed. Nonetheless, the security and the privacy of this communication is essential to not only ensure a fair energy selling market but also eliminate any privacy concerns of the users due to potential exposure of their energy levels. In this paper, we propose a secure and privacy-preserving power injection querying scheme by exploiting the already available advanced metering infrastructure (AMI) and long-term evolution (LTE) cellular networks. The idea is based on collecting power injection bids from storage units and sending their aggregated value to the utility rather than the individual bids in order to preserve user privacy. We also develop a bilinear pairing-based technique to enable the utility company to ensure the integrity and authenticity of the aggregated bid without accessing the individual bids. In this way, no party will have access to the storage units’ individual bids and use them to achieve unfair financial gains. We implemented the proposed scheme in an integrated AMI/LTE network using the ns-3 network simulator. Our evaluations have demonstrated that the proposed scheme is secure and can protect user privacy with acceptable communication and computation overhead.
机译:未来的智能电网将使房屋拥有储能单元,该单元可以存储可再生能源产生的多余电力,并在高峰时段将其出售给电网。但是,该过程的实现要求公用事业公司能够在需要时与存储单元进行通信。尽管如此,这种通信的安全性和隐私性对于确保公平的能源销售市场以及消除由于用户潜在的能量水平所引起的用户隐私隐患至关重要。在本文中,我们通过利用已经可用的高级计量基础架构(AMI)和长期演进(LTE)蜂窝网络,提出了一种安全且保护隐私的功率注入查询方案。这个想法是基于从存储单元收集功率注入投标并将其总值发送给公用事业而不是单个投标,以保护用户隐私。我们还开发了一种基于双线性配对的技术,以使公用事业公司能够确保汇总投标的完整性和真实性,而无需访问单个投标。这样,任何一方都无法访问存储单元的单个出价并使用它们来获得不公平的财务收益。我们使用ns-3网络模拟器在集成的AMI / LTE网络中实现了该方案。我们的评估表明,提出的方案是安全的,并且可以通过可接受的通信和计算开销保护用户隐私。

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