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Energy Technology and Management ENERGY TECHNOLOGY AND MANAGEMENT Edited by Tauseef Aized Energy Technology and Management Edited by Tauseef Aized Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Iva Simcic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright Sideways Design, 2011. Used under license from Shutterstock.com First published September, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from [email protected] Energy Technology and Management, Edited by Tauseef Aized p. cm. ISBN 978-953-307-742-0 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Part 1 Energy Technology 1 Chapter 1 Centralizing the Power Saving Mode for 802.11 Infrastructure Networks 3 Yi Xie, Xiapu Luo and Rocky K. C. Chang Chapter 2 A Study on Design of Fiber-Reinforced Plastic (FRP) Tubes as Energy Absorption Element in Vehicles 25 Yuqiu Yang and Hiroyuki Hamada Chapter 3 Optimal Feeder Reconfiguration with Distributed Generation in Three-Phase Distribution System by Fuzzy Multiobjective and Tabu Search 59 Nattachote Rugthaicharoencheep and Somporn Sirisumranukul Chapter 4 Energy Managements in the Chemical and Biochemical World, as It may be Understood from the Systems Chemistry Point of View 79 Zoltán Mucsi, Péter Ábrányi Balogh, Béla Viskolcz and Imre G. Csizmadia Chapter 5 Energy Planning for Distributed Generation Energy System: The Optimization Work 111 Behdad Kiani Chapter 6 Network Reconfiguration for Distribution System with Micro-Grid 125 Yu Xiaodan, Chen Huanfei, Liu Zhao and Jia Hongjie Chapter 7 A Camera-Based Energy Management of Computer Displays and TV Sets 137 Vasily G. Moshnyaga VI Contents Chapter 8 Enhancement of Power System State Estimation 157 Bei Gou and Weibiao Wu Chapter 9 Smart Home Services for a Smart Grid 171 Young-Myoung Kim and Young-Woo Lee Part 2 Energy Management 185 Chapter 10 Management Crisis in Partial Deregulation of Energy Sector and Modeling the Technical and Economic Results of Organizational Management Structure 187 Joseph Yakubu Oricha Chapter 11 Methodology Development for a Comprehensive and Cost-Effective Energy Management in Public Administrations 201 Capobianchi Simona, Andreassi Luca, Introna Vito, Martini Fabrizio and Ubertini Stefano Preface Energy is one of the most important issue of modern civilization. All material developments are strongly linked with energy availability and efficient utilization. Unfortunately, energy resources are not unlimited, especially conventional energy resources are depleting at an enormous pace. Hence, efficient utilization of available resources and development of new energy resources are extremely important in order to maintain material development of human civilization. Energy management, saving and efficient utilization are important in the backdrop of current energy shortfalls. Additionally, energy studies have a wider scope than merely concentrating on technological issues of energy resource development and also include energy policy and planning issues. This book is compiled to address both technology and policy issues and presents a collection of articles from experts belonging to different parts of the world. The articles range from policy to technological issues of energy development and efficient utilization. In order to comprehend this book, some background of energy related issues is required. Students, researchers, academics, policy makers and practitioners may get benefit from this book. Prof. Tauseef Aized University of Engineering and Technology (UET) Lahore Pakistan Part 1 Energy Technology 10 Centralizing the Power Saving Mode for 802.11 Infrastructure Networks Yi Xie1, Xiapu Luo2 and Rocky K. C. Chang2 1Department of Computer Science, Xiamen University 2Department of Computing, The Hong Kong Polytechnic University 1China 2Hong Kong, SAR China 1. Introduction With the rapid development of wireless networks, efficient energy management for wireless LAN (WLAN) has become an important problem, because mobile devices’ availability is determined by their stringent batteries power. Quite a few sources of energy consumption have been identified (Narseo et al., 2010), among which the wireless communication component uses up a significant amount of energy. For instance, the Motorola Droid phone consumes around 200mW with the backlight off, close to 400mW with the backlight on, and over 800mW when the Wi-Fi radio is active (Zeng et al., 2011). This chapter focuses on improving the energy efficiency of wireless communication component, because they may consume up to 50% of the total energy. Various mechanisms have been proposed to balance between communication quality and energy consumption for wireless devices, for example, power saving mode (PSM) that puts an idle client into a low-power mode (Gast, 2005), transmission power control (Nuggehalli et al., 2002), packet transmission scheduling (Qiao et al., 2003; Tarello et al., 2005), and some cross-layer methods (Anastasi et al., 2007). They investigate the trade-off between energy consumption and throughput (Gao et al., 2010; Zhang & Chanson, 2003), delay (Guha et al., 2010; Nuggehalli et al., 2002; 2006), or network utility (Chiang & Bell, 2004). In this chapter, we propose a centralized PSM (C-PSM), an AP-centric deployment of the IEEE 802.11 PSM, to optimize power saving and multiple performance metrics for infrastructure networks which are widely deployed in enterprise, campus, and metropolitan networks. In these networks, wireless clients (e.g., laptops, PDAs and mobile phones) using the IEEE 802.11 infrastructure mode connect to the Internet through an access point (AP). The experiment results show that significant improvements can be obtained from the new deployment of C-PSM. The IEEE 802.11 PSM, widely used in WLAN, allows an idle client to go into a sleep mode. Hereafter, we use PSM to refer to the IEEE 802.11 PSM. The clients save energy by sleeping while wakes up periodically to receive beacon frames from AP. The beacon frame, sent by an access point (AP) every beacon interval (BI), indicates whether clients have frames buffered at the AP. Each client’s wake-up frequency is determined by a PSM parameter listen interval (LI). Both BI and LI are configurable, and their settings directly influence the PSM’s performance shown by the analysis of section 4. Unfortunately, the protocol does not prescribe how the BI 42 Energy Technology andWill-be-set-by-IN-TECH Management and LI should be configured in PSM; therefore, default values are often used. Obviously, the PSM using default settings cannot adapt to the traffic and configuration dynamics inherent in typical wireless networks. Worse yet, the PSM was reported to have adverse impact on application performance, such as short TCP connections (Krashinsky & Balakrishnan, 2005). To address these shortcomings, a number of new power-saving schemes that put idle clients into sleep have been proposed. A class of them (e.g., (Nath et al., 2004); (Qiao & Shin, 2005); (Krashinsky & Balakrishnan, 2005)) enables each client to save energy by reducing the number of unnecessary wake-ups ( i.e., design an optimal wake-up schedule). These user-centric schemes, however, do not address energy consumption due to channel contention which, as we will show in section 3, is another major source of energy wastage. Another class adopts an AP-centric approach which exploits AP to improve the energy efficiency of all clients in the network. Within this class, some schemes design a packet transmission schedule to minimize channel contention (e.g., (Lin et al., 2006); (He et al., 2007); (Zeng et al., 2011)). Others redesign beacon frame and poll clients one by one, which totally avoids channel contention (Lee et al., 2006). However, most AP-centric schemes are not compatible with the standard PSM scheme or difficult to implement, because they employ precise transmission schedule. Unlike the previous works on the power saving mode (PSM), our C-PSM optimizes the beacon interval, listen interval, minimal congestion window, and sequence of first wake-up time for each device according to the traffic characteristics. Firstly, the AP chooses the optimal BI and LIs for clients based on the pattern of arriving packets to reduce energy consumption due to both unnecessary wake-ups and channel contentions. Especially, the energy wasted in channel contentions could be very significant, because all clients involved cannot go to sleep throughout the contention period which could be very long. Secondly, the AP assigns congestion windows to the clients which are involved in collisions, such that a client that wakes up less frequently will be able to retransmit earlier. Finally, C-PSM provides an additional wakeup schedule to further reduce simultaneous wakeups of clients. Having the AP control the PSM parameters, C-PSM is therefore able to maximize the total energy efficiency for all clients and facilitates various aspects of network management and operations. Our extensive simulation results show that the C-PSM is very promising under four traditional distributions of inter-frame arrival times: deterministic, uniform, exponential, and Pareto.
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