On-Going Project by Jie Wu
Smart grid, managed by intelligent devices, are unarguably the backbone of national infrastructure to provide a vital support for residential customers to optimally schedule and manage the appliances' energy consumption. Due to the fine-grained power consumption information collected by smart meter, the customers' privacy becomes a major security concern. With the highly accurate power consumption information, an attacker could identify customers' personal behavior patterns, such as residential occupancy and social activities. Moreover, household appliances are not homogeneous in terms of schedulability and can be classified into schedulable appliances and non-schedulable appliances. The operations of schedulable appliances can be controlled by a scheduler. On the other hand, the non-schedulable appliances must be turned on immediately upon the customers' request, the timing of which cannot be known by the scheduler ahead of time. The non-schedulable appliances, such as TV and computers, introduce operation uncertainties. Therefore, we focus an on-line appliance scheduling design to protect customers' privacy in a cost-effective way, while taking into account the influences of non-schedulable appliances' operation uncertainties. We formulate the problem by minimizing the expected sum of electricity cost and achieving acceptable privacy protection. Without knowledge of future electricity consumptions, an on-line scheduling framework (PACES) is proposed based on the only current observations by using a stochastic dynamic programming technique. The PACES is illustrated in Fig.1.
Fig.1: PACES for on-line privacy-aware cost-effective scheduling