• Energy Research

There are significant challenges as well as great opportunities for research at both policy and technology levels on the efficient use of energy. Most existing power generation and distribution systems are based on a century old mechanism where power grids are managed by vertically integrated utilities. Intelligent power grids known as smart grids are required as the demand for energy continues to grow and more and more emphasis is being placed on the supply of renewable energy. The main ingredient of smart grids is the integration of information and communication technology (ICT) into the grids to monitor and regulate power generation and demand. This article provides an overview of demand management with a particular focus on the necessary enabling wireless technologies. Various mechanisms and algorithms for the optimal demand management in smart grids using these wireless technologies are also reviewed.

There are significant challenges as well as great opportunities for research at both policy and technology levels on the efficient use of energy. Most existing power generation and distribution systems are based on a century old mechanism where power grids are managed by vertically integrated utilities. Intelligent power grids known as smart grids are required as the demand for energy continues to grow and more and more emphasis is being placed on the supply of renewable energy. The main ingredient of smart grids is the integration of information and communication technology (ICT) into the grids to monitor and regulate power generation and demand. This article provides an overview of demand management with a particular focus on the necessary enabling wireless technologies. Various mechanisms and algorithms for the optimal demand management in smart grids using these wireless technologies are also reviewed.

This work proposes a novel method based on compressive sensing homotopy to dynamically track power line parameters with voltage phasor angle data from phasor measurement units (PMUs). In the literature, compressive sensing has already been successfully applied to locate multiple fault lines in power systems. Our solution substantially extends this work to the case where power line parameters are to be tracked in real time using phasor angle data from a subset of all buses. The new method offers a better performance both in locating the error lines and tracking the changes of line impedances.

This work proposes a novel method based on compressive sensing homotopy to dynamically track power line parameters with voltage phasor angle data from phasor measurement units (PMUs). In the literature, compressive sensing has already been successfully applied to locate multiple fault lines in power systems. Our solution substantially extends this work to the case where power line parameters are to be tracked in real time using phasor angle data from a subset of all buses. The new method offers a better performance both in locating the error lines and tracking the changes of line impedances.

We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.

We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.