期刊
IEEE INTERNET OF THINGS JOURNAL
卷 5, 期 1, 页码 69-78出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2017.2771370
关键词
Electric vehicle (EV); Internet of Things (IoT); Markov decision process (MDP); privacy; Q-learning; smart metering
资金
- Natural Sciences and Engineering Research Council of Canada
The Internet of Things (IoT) extends network connectivity and computing capability to physical devices. However, data from IoT devices may increase the risk of privacy violations. In this paper, we consider smart meters as a prominent early instance of the IoT, and we investigate their privacy protection solutions at customer premises. In particular, we design a load hiding approach that obscures household consumption with the help of energy storage units. For this purpose, we leverage the opportunistic use of existing household energy storage units to render load hiding less costly. We propose combining the use of electric vehicles (EVs) and heating, ventilating, and air conditioning (HVAC) systems to reduce or eliminate the reliance on local rechargeable batteries for load hiding. To this end, we formulate a Markov decision process to account for the stochastic nature of customer demand and use a Q-learning algorithm to adapt the control policies for the energy storage units. We also provide an idealized benchmark system by formulating a deterministic optimization problem and deriving its equivalent convex form. We evaluate the performance of our approach for different combinations of storage units and with different benchmark methods. Our results show that the opportunistic joint use of EV and HVAC units can reduce the need of dedicated large-capacity or fast-charging-cycle batteries for load hiding.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据