Abstract
The implementation of innovative technologies provides more secure, efficient,safe and sustainable activities and services to improve human life and productivity towards a "smart city". More and more attention is being paid on developing a "smart network", which will improve the building automation, life safety, wireless connectivity and communications. Reduction in energy consumption and minimization of environmental impact can also be achieved by smart networks. The intelligence of smart networks combined with new advances in low carbon fuel sources, electric vehicles, energy storage, and the proliferation of smart appliances or facilities has the potential to transform the way we use natural energy. The research results of the designs and the applications of smart networks in this thesis presents concerns, solutions and improvement measures in the fields of wireless connectivity in smart-grid design, ZigBee network design, CO2 emission reduction, plug-in hybrid electric vehicle and building energy efficiency.A WiFi ZigBee hybrid Building Area Network solution, namely WiZBAN, is proposed and implemented to cater to the development of high traffic Advance Metering Infrastructure (AMI) for smart grid application. It is important to highlight that the major challenge of WiZBAN is to handle the high density environment which results in heavy traffic loading and weak signal propagation. To overcome the captioned problem, Vertical Backbone Communication (VBC) and Horizontal Floor Communication (HFC) are defined for WiZBAN. The WiZBAN consists of WiZBAN Gateway (WiZGW), WiZBAN Meter Hub (WiZMH) and WiZBAN In Home Display (WiZIHD) which caters to the smart grids services including smart metering and demand response. The WiZGW is the entry way of WiZBAN and connects WiZBAN to utilities. The WiZGW also teams up with WiZMH to enable VBC. On the other hand, WiZMH serves as the interception point of VBC and HFC. It interacts with smart meters and sets up the HFC together with WiZIHD to provide user interface for end users. To shorten the transmission time, WiFi is adopted for VBC while ZigBee is applied to HCF to overcome the weak signal propagation. To investigate the performance of WiZBAN, a case study has been conducted based on an existing 23 floor residential building. From the measured and simulated results, the average round trip delay of demand response and smart metering are found to be 0.6 s and 9 s respectively.
There are increasing demands for mobile health applications. The mobile health profile is developed in association with the ZigBee Health Care profile and the ISO/IEEE 11073 standard which is normally applied to non-mobile applications. Since mobile sensors have to be carried by patients, the mobile health profile must facilitate mobility. In this investigation, a ZigBee fixed-mobile network (ZFMN) is defined and developed to supplement the ZigBee Health Care Profile for patient monitoring. The mobility study of ZigBee is performed using a random waypoint OPNET simulation model. In a ZFMN, the critical issue of address shortage is identified and discussed. It is estimated that the problematic address shortage in a ZFMN may generate a huge amount of orphaned end devices and thus the packet drop percentage may potentially rise to 70%, rendering the network unable to function properly. Without introducing additional governing schemes, it is evaluated that the communication of the entire ZigBee network may paralyze. Further, vigorous test are performed (by OPNET) on the communication capability of ZFMN when devices are randomly moving and sending data in 1 s. It is vital to point out that under the adverse condition of address shortage that the performance of a ZFMN is still encouraging as long as the packet drop percentage has been kept below 3% before running out of address. The conclusion drawn in this analysis is that the packet drop percentage should be kept below 3% to provide a satisfactory Quality of Service (QoS) for an effective mobile health application using ZFMN such as patient monitoring. Such finding is also important for other future mobile application designs of ZigBee.
To be more cost effective and environmental friendly, electric cooking is a new trend in both commercial and residential properties. Utility states that electric cooking can reduce 20% to 50% energy cost in all kinds of restaurants. However, a wide adoption of electric cooking would exact a heavy load from electrical energy supply. Also, some buildings with geriatric electrical system designs may not be able to support a large electrical energy demand imposed by electric cooking. The understanding of the energy usage pattern of electric cooking is important to tackle with this potential problem. An energy profile of electric kitchen utensils is essential to minimize the energy demand. The research result gives the overview of the recent market growth in electric cooking and also introduces the potential toward wireless sensor network technology for energy auditing in smart kitchen. Even more important, this study analyses the energy usage of electric kitchen utensils such as stove, microwave oven, refrigerator and freezer. An experiment would be set up by using the existing energy auditing system. The energy consumption pattern of each kitchen utensil is collected with a current transformer which will be displayed as instant consumption reading. By using the collected consumption data, the energy profile of each electric kitchen utensil can be generated. Having the energy profile of each electric kitchen utensil is critical to develop a real time energy demand forecast and which helps the utilities conduct the energy demand response effectively.
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEV s) was developed in this research. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme was designed to manage the energy resource usage. The objective function of the genetic algorithm was implemented by designing a fuzzy logic controller which closely monitored and resembled the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. The comparison between calculated results and publicized data showed that the achieved efficiency of the fuzzified genetic algorithm improved 10% over existing schemes. The developed scheme, if fully adopted, helps to reduce over 600 tons of CO2 emission worldwide every day.
In the U.S., the Office of Energy Efficiency and Renewable Energy of Department of Energy are promoting a "Green your school" program. America's schools spend more than US$7.5 billion annually on energy - more than they spend on textbooks and computers combined. Energy costs are the largest operating expense for school buildings after the salaries and benefits, but energy is one of the few expenses that can be decreased without negatively affecting classroom teaching. A tertiary level education building in Hong Kong was selected to evaluate for a case study in this thesis, after the walk-through energy audit on the educational building, 11 energy management opportunities (EMOs) on their facilities were identified. Several inefficient facilities would be replaced, by higher efficient facilities or by adding new facilities to existing facilities, to improve the energy efficiency by investment. The investment have different budgets and decisions must be made by considering the payback back period and how much energy could be saved. A multi-objective optimization model was applied to find the solutions with different budgets. Under different budget investment cases, the solution of what facilities and the quantity that should be retrofitted can be found from the simulation results. By applying a multi-objective optimization model, software SolveXL was used in this study to find the solutions for the optimization problems. In order to determine what inefficient facilities should be retrofitted under different budget cases the following objectives were examined: (i) maximising annual energy saving; and (ii) minimising the payback period of the investment. The optimization model is subjected to the constraints of a payback period, a budget and an energy saving target. The cases solutions for different budget were found in the research results. The sensitivity analysis was performed by analyzing the influences of wrongly specified energy savings, the changes in interest rate and the changes of electricity price, on the requirements of maximizing energy savings and minimizing the payback period of the investment.
| Date of Award | 31 Aug 2015 |
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| Original language | English |
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| Supervisor | Kim Fung TSANG (Supervisor) |