Smart grid is recently proposed as an enhancement for the next generation power grid. To achieve efficient status monitoring, control, and billing, a large number of smart meters are deployed and they would produce a huge amount of data. To efficiently collect them imposes a great challenge on the communication networks. In this paper, we study the efficient meter data collection problem by exploring the secondary spectrum market in cellular networks. The electricity power reserved by sending meter data via leased secondary channels would be charged at a lower price. With the objective of reducing the overall cost of both power and communication, we formulate a problem called cost minimization for meter data collection (CMM) that is to find optimal solution of channel selection and transmission scheduling. The CMM problem under a linear power pricing model is formulated as a mixed integer linear programming problem and is then solved by a branch-and-bound algorithm. Under a nonlinear power pricing model, we formulate it as a nonconvex mixed integer nonlinear programming problem and propose an optimal algorithm by integrating the sequential parametric convex approximation method into the branch-and-bound framework. Extensive simulation results show that our proposal can significantly reduce the overall cost.
- Meter data collection
- Smart grid
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Information Systems
- Human-Computer Interaction
- Computer Science Applications