Smart meter deployment optimization for efficient electrical appliance state monitoring

Abstract

Monitoring the energy consumptions of the massive electrical appliances in buildings has attracted great attentions for smart, green and sustainable living. Traditional approaches generally require large-scale smart sensor/meter networks, and thus suffer from the high deployment, maintenance and data collection costs. In this paper, we propose methodologies and algorithms to optimize the smart meter deployments to track the on/off states of the massive electrical appliances by using the minimal number of smart meters. Particularly, based on the tree structure of the power distribution networks we show the deployment of m meters will decompose the power distribution network into a forest of m mono-meter trees. Each mono-meter tree has depth 1, with one meter at the root and a set of appliances at the leaves. A mono-meter tree is clear if the meter at the root can decode the on/off states of its leaves without error. Based on it, the smart meter deployment optimization problem is to optimize the meter deployment locations, so as to minimize the number of required meters while keeping all the mono-meter trees being clear. We prove this problem is NP-hard, and propose a greedy algorithm to approach it by utilizing the bounds of degree and the maximum power of the load tree. We show the greedy algorithm has at most 2 approximation ratio. Finally,we assess our approach in different structure power networks by simulations. The simulation results suggest a reasonable good performance of our proposed smart meter deployment strategy.