Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
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Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
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"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
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Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"8678",leadTitle:null,fullTitle:"Mobile Computing",title:"Mobile Computing",subtitle:null,reviewType:"peer-reviewed",abstract:"Mobile computing is defined as the union between humans and mobile devices that allows people to be connected to the Internet through a network in order to transmit and receive information. This book presents a vision of the present and future of mobile computing. It identifies and examines the most pressing research issues in the field. Comprising chapters by leading researchers and academics, this volume includes recent publications in key areas of interest, including Flying Ad-Hoc Networks (FANETs), Vehicular Ad-Hoc Networks (VANETs), 5G, energy-efficient networks, localization in mobile networks, algorithms of mobile core networks, user interfaces, metabolic health analysis, and many others. This volume is suitable as a text for graduate students and professionals in the industrial sector and general engineering areas.",isbn:"978-1-78984-940-0",printIsbn:"978-1-78984-939-4",pdfIsbn:"978-1-83880-550-0",doi:"10.5772/intechopen.80095",price:119,priceEur:129,priceUsd:155,slug:"mobile-computing",numberOfPages:174,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"3c2cf4e62010e495199b294278d852c4",bookSignature:"Jesus Hamilton Ortiz",publishedDate:"May 20th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8678.jpg",numberOfDownloads:6822,numberOfWosCitations:0,numberOfCrossrefCitations:15,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:15,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:30,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"December 10th 2018",dateEndSecondStepPublish:"February 27th 2019",dateEndThirdStepPublish:"April 28th 2019",dateEndFourthStepPublish:"July 17th 2019",dateEndFifthStepPublish:"September 15th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"283288",title:"Dr.",name:"Jesus Hamilton",middleName:null,surname:"Ortiz",slug:"jesus-hamilton-ortiz",fullName:"Jesus Hamilton Ortiz",profilePictureURL:"https://mts.intechopen.com/storage/users/283288/images/system/283288.jpg",biography:"Jesus Hamilton Ortiz has a Ph.D. in Computer and Telecommunication Engineering. He is an international reviewer, editor, scientist, and entrepreneur in computer, telecommunication, and aerospace engineering. Dr. Ortiz has edited approximately nine books on ad hoc networks, telecommunication networks, wearables, Industry 4.0, mobile networks, mobile computing, and more. He is an associate editor of the journal IEEE Access. He is currently working on drone swarms, flying ad hoc networks (FANETs), bio-inspired algorithms, urban air mobility, air taxis, artificial intelligence, machine learning, the Volocopter, big data, blockchain, business intelligence, vertical–horizontal integration, and 5.0 society, and different disruptive technologies including cobots and humanoid robots.",institutionString:"CloseMobile R&D",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"536",title:"Mobile Computing",slug:"communications-and-security-mobile-computing"}],chapters:[{id:"67574",title:"Wireless Communications Challenges to Flying Ad Hoc Networks (FANET)",doi:"10.5772/intechopen.86544",slug:"wireless-communications-challenges-to-flying-ad-hoc-networks-fanet-",totalDownloads:1328,totalCrossrefCites:6,totalDimensionsCites:6,hasAltmetrics:0,abstract:"The increasing demand for Internet access from more and more different devices in recent years has provided a challenge for companies and the academic community to research and develop new solutions that support the increasing flow in the network, applications that require very low latencies and more dynamic and scalable infrastructures, in this context the mobile ad hoc networks (MANETs) emerged as a possible solution and applying this technology in unmanned aerial vehicles (UAVs) was developed the flying ad hoc networks (FANETs) which are wireless networks independent, its main characteristics are to have high mobility, scalability for different applications and scenarios and robustness to deal with possible communication failures. However, they still have several constraints such as limited flight time of UAVs and routing protocols that are capable of supporting network dynamics. To analyze this scenario, two simulations were developed where it was possible to observe the behavior of FANET with different routing protocols both during data transmission and video transmission. The results show that the choice of the best routing protocol must take into account the mobility of the UAVs and the necessary communication priority in the network.",signatures:"Miguel Itallo B. Azevedo, Carlos Coutinho, Eylon Martins Toda, Tassio Costa Carvalho and José Jailton",downloadPdfUrl:"/chapter/pdf-download/67574",previewPdfUrl:"/chapter/pdf-preview/67574",authors:[{id:"290776",title:"Dr.",name:"José",surname:"Jailton",slug:"jose-jailton",fullName:"José Jailton"},{id:"290799",title:"Dr.",name:"Tassio",surname:"Costa Carvalho",slug:"tassio-costa-carvalho",fullName:"Tassio Costa Carvalho"},{id:"298486",title:"BSc.",name:"Miguel Itallo B.",surname:"Azevedo",slug:"miguel-itallo-b.-azevedo",fullName:"Miguel Itallo B. Azevedo"},{id:"298488",title:"BSc.",name:"Carlos",surname:"Coutinho",slug:"carlos-coutinho",fullName:"Carlos Coutinho"},{id:"298489",title:"BSc.",name:"Eylon Martins",surname:"Toda",slug:"eylon-martins-toda",fullName:"Eylon Martins Toda"}],corrections:null},{id:"69849",title:"An Overview of Query-Broadcasting Techniques in Ad Hoc Networks",doi:"10.5772/intechopen.89609",slug:"an-overview-of-query-broadcasting-techniques-in-ad-hoc-networks",totalDownloads:709,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter presents query-broadcasting techniques used to minimize expenses of the route discovery in ad hoc networks. A broad variety of such techniques have been proposed that improved the effectiveness and efficiency in various aspects of route discovery considering time and energy. Time-to-live based broadcast is the most common controlled flooding scheme widely used in routing protocols. One category of such techniques leveraged the routing history, while other category used broadcast repealing strategy to cancel the query-broadcast after successful route discovery.",signatures:"Naeem Ahmad and Shuchi Sethi",downloadPdfUrl:"/chapter/pdf-download/69849",previewPdfUrl:"/chapter/pdf-preview/69849",authors:[{id:"296384",title:"Dr.",name:"Naeem",surname:"Ahmad",slug:"naeem-ahmad",fullName:"Naeem Ahmad"},{id:"308250",title:"Dr.",name:"Shuchi",surname:"Sethi",slug:"shuchi-sethi",fullName:"Shuchi Sethi"}],corrections:null},{id:"69128",title:"Importance of Fifth Generation Wireless Systems",doi:"10.5772/intechopen.89345",slug:"importance-of-fifth-generation-wireless-systems",totalDownloads:813,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Fifth generation wireless communications are denoted by 5G technology. 5G schemes are coming from first generation analog communication, 2G of Global System for Mobile communication (GSM), then 3G of Code Division Multiple Access (CDMA), after that fourth generation of long-term evaluation (LTE), and now fifth generation World Wide Wireless Web (WWWW). This research investigation presents issues, challenges, and the importance of 5G Wifi communication. In the 5G digital cellular network, the coverage area of the service providers is separated into small area called cells. All the audio, video, and image files are digitized and converted by an ADC (Analog to Digital Converter) and transmitted through stream of bits. 5G wireless devices are communicated using radio waves in a geographically reusable common pool of frequency band. Using wireless backhaul connection, the local antennas are connected with the internet/telephone network. Spectrum speed is substantially higher in millimeter wave. Hence, this was considered in this work.",signatures:"K. Sakthidasan Sankaran, G. Ramprabu and V.R. Prakash",downloadPdfUrl:"/chapter/pdf-download/69128",previewPdfUrl:"/chapter/pdf-preview/69128",authors:[{id:"302194",title:"Dr.",name:"K. Sakthidasan",surname:"Sankaran",slug:"k.-sakthidasan-sankaran",fullName:"K. Sakthidasan Sankaran"},{id:"302195",title:"Dr.",name:"V.R.",surname:"Prakash",slug:"v.r.-prakash",fullName:"V.R. Prakash"},{id:"302202",title:"Prof.",name:"G.",surname:"Ramprabu",slug:"g.-ramprabu",fullName:"G. Ramprabu"}],corrections:null},{id:"69979",title:"Softwarization in Future Mobile Networks and Energy Efficient Networks",doi:"10.5772/intechopen.89607",slug:"softwarization-in-future-mobile-networks-and-energy-efficient-networks",totalDownloads:573,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The data growth generated by pervasive mobile devices and the Internet of Things at the network edge (i.e., closer to mobile users), couple with the demand for ultra-low latency, requires high computation resources which are not available at the end-user device. This demands a new network design paradigm in order to handle user demands. As a remedy, a new MN network design paradigm has emerged, called Mobile Edge Computing (MEC), to enable low-latency and location-aware data processing at the network edge. MEC is based on network function virtualization (NFV) technology, where mobile network functions (NFs) that formerly existed in the evolved packet core (EPC) are moved to the access network [i.e., they are deployed on local cloud platforms in proximity to the base stations (BSs)]. In order to reap the full benefits of the virtualized infrastructure, the NFV technology shall be combined with intelligent mechanisms for handling network resources. Despite the potential benefits presented by MEC, energy consumption is a challenge due to the foreseen dense deployment of BSs empowered with computation capabilities. In the effort to build greener 5G mobile network (MN), we advocate the integration of energy harvesting (EH) into future edge systems.",signatures:"Thembelihle Dlamini",downloadPdfUrl:"/chapter/pdf-download/69979",previewPdfUrl:"/chapter/pdf-preview/69979",authors:[{id:"291620",title:"Dr.",name:"Thembelihle",surname:"Dlamini",slug:"thembelihle-dlamini",fullName:"Thembelihle Dlamini"}],corrections:null},{id:"67993",title:"Localization Enhanced Mobile Networks",doi:"10.5772/intechopen.88110",slug:"localization-enhanced-mobile-networks",totalDownloads:722,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The interest in mobile ad-hoc networks (MANETs) and often more precisely vehicular ad-hoc networks (VANETs) is steadily growing with many new applications, and even anticipated support in the emerging 5G networks. Particularly in outdoor scenarios, there are different mechanisms to make the mobile nodes aware of their geographical location at all times. The location information can be utilized at different layers of the protocol stack to enhance communication services in the network. Specifically, geographical routing can facilitate route management with smaller overhead than the traditional proactive and reactive routing protocols. In order to achieve similar advantages for radio resource management (RRM) and multiple access protocols, the concept of virtual cells is devised to exploit fully distributed knowledge of node locations. The virtual cells define clusters of MANET nodes assuming a predefined set of geographically distributed anchor points. It enables fast response of the network to changes in the nodes spatial configuration. More importantly, the notion of geographical location can be generalized to other shared contexts which can be learned or otherwise acquired by the network nodes. The strategy of enhancing communication services by shared contexts is likely to be one of the key features in the beyond-5G networks.",signatures:"Salman Al-Shehri, Pavel Loskot and Michael J. Hirsch",downloadPdfUrl:"/chapter/pdf-download/67993",previewPdfUrl:"/chapter/pdf-preview/67993",authors:[{id:"17608",title:"Dr.",name:"Pavel",surname:"Loskot",slug:"pavel-loskot",fullName:"Pavel Loskot"},{id:"212143",title:"Mr.",name:"Salman",surname:"Al-Shehri",slug:"salman-al-shehri",fullName:"Salman Al-Shehri"},{id:"297181",title:"Dr.",name:"Michael J.",surname:"Hirsch",slug:"michael-j.-hirsch",fullName:"Michael J. Hirsch"}],corrections:null},{id:"69472",title:"Architecture and Operation Algorithms of Mobile Core Network with Virtualization",doi:"10.5772/intechopen.89608",slug:"architecture-and-operation-algorithms-of-mobile-core-network-with-virtualization",totalDownloads:830,totalCrossrefCites:4,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The analysis of the current situation in the wireless communication market shows an increase in the workload, which leads to an increase in the need in additional resources. However, the uneven loading of the infrastructure nodes leads to their loss of use; so, there is a need in introducing technologies that both do not lead to downtime of equipment and ensure the quality of load service during the day. An overview of the NFV virtualization technology has shown that it is appropriate to build wireless networks, since it provides the necessary flexibility and scalability. The method for determining the location and capacity of reserved computer resources of virtual network functions in the data centers of the mobile communication operator, method for determining the size of computing resources constant configuration time interval, and distributed method of local reconfiguration of the virtual network computing resources in the case of a failure or overload are proposed. Thus, configuration, operation, and reconfiguration processes in mobile core network with virtualized functions are described.",signatures:"Larysa Globa, Svitlana Sulima, Mariia Skulysh, Stanislav Dovgyi and Oleksandr Stryzhak",downloadPdfUrl:"/chapter/pdf-download/69472",previewPdfUrl:"/chapter/pdf-preview/69472",authors:[{id:"105085",title:"Prof.",name:"Larysa",surname:"Globa",slug:"larysa-globa",fullName:"Larysa Globa"},{id:"281033",title:"Dr.",name:"Mariia",surname:"Skulysh",slug:"mariia-skulysh",fullName:"Mariia Skulysh"},{id:"295095",title:"Ph.D. Student",name:"Svitlana",surname:"Sulima",slug:"svitlana-sulima",fullName:"Svitlana Sulima"},{id:"295097",title:"Dr.",name:"Oleksandr",surname:"Stryzhak",slug:"oleksandr-stryzhak",fullName:"Oleksandr Stryzhak"},{id:"312644",title:"Prof.",name:"Stanislav",surname:"Dovgyi",slug:"stanislav-dovgyi",fullName:"Stanislav Dovgyi"}],corrections:null},{id:"67391",title:"Mobile Distributed User Interfaces",doi:"10.5772/intechopen.86563",slug:"mobile-distributed-user-interfaces",totalDownloads:662,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The success of a mobile application is due to the usability that the graphical user interface provides. A feature of mobile devices is the limited space for the interaction and the deployment of the graphical user interface. For this reason, user interfaces can have different interaction modalities. However, to work with information that can be complex to display, the use of modalities may not solve this problem. A possible alternative to provide more workspace to the users is through a distributed user interface (DUI). A mobile DUI allows the mobile applications to use two or more devices to execute the user interface. These devices can be Smart TVs or wearable such as smart watches. In this work the concepts of mobile DUI design are discussed, some use cases are presented and it is shown that its development in mobile devices is feasible.",signatures:"Erika Hernández-Rubio, Amilcar Meneses-Viveros and Sonia G. Mendoza-Chapa",downloadPdfUrl:"/chapter/pdf-download/67391",previewPdfUrl:"/chapter/pdf-preview/67391",authors:[{id:"289740",title:"M.Sc.",name:"Erika",surname:"Hernández-Rubio",slug:"erika-hernandez-rubio",fullName:"Erika Hernández-Rubio"},{id:"289742",title:"Dr.",name:"Amilcar",surname:"Meneses-Viveros",slug:"amilcar-meneses-viveros",fullName:"Amilcar Meneses-Viveros"},{id:"301053",title:"Dr.",name:"Sonia G.",surname:"Mendoza-Chapa",slug:"sonia-g.-mendoza-chapa",fullName:"Sonia G. Mendoza-Chapa"}],corrections:null},{id:"69042",title:"Metabolic Health Analysis and Forecasting with Mobile Computing",doi:"10.5772/intechopen.88872",slug:"metabolic-health-analysis-and-forecasting-with-mobile-computing",totalDownloads:540,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The goal of this paper is to demonstrate feasibility of a concept of mobile computing to help users to reach and maintain metabolic health. For this purpose, we analyze data from 12 clinical studies with a total of 39 study arms from the international literature to show that insulin resistance measured by HOMA-IR could be followed and its changes could be predicted using our weight-fat mass-energy balance calculations taking advantage of the significant and strong correlation between changes of HOMA-IR and state variables of the energy metabolism like changes of weight, fat mass, R-ratio, Rw-ratio, and fat burning fraction of the energy production. We introduce here our extended weight-fat mass-energy balance calculation to assess de novo lipogenesis, adaptive thermogenesis, and the 24-hour nonprotein respiratory quotient. We show how we can analyze and predict individualized state variables of the metabolism, which serve as metrics for the quantification of the interrelationship between energy metabolism and insulin resistance facilitating management and self-management of insulin-resistance related conditions including obesity, fatty liver, prediabetes, metabolic syndrome, and type 2 diabetes. The feedback of individualized metrics using tools of the digital health era may amount to channeling focus also to patient-centered individualized care and to accelerating nutrition research.",signatures:"Zsolt P. Ori",downloadPdfUrl:"/chapter/pdf-download/69042",previewPdfUrl:"/chapter/pdf-preview/69042",authors:[{id:"273754",title:"M.D.",name:"Zsolt P.",surname:"Ori",slug:"zsolt-p.-ori",fullName:"Zsolt P. Ori"}],corrections:null},{id:"71868",title:"Energy Consumption Model for Green Computing",doi:"10.5772/intechopen.92129",slug:"energy-consumption-model-for-green-computing",totalDownloads:646,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"This chapter shows the environmental measurement factors that define the indicators within an architecture composed of Goods, Networks and Services (GNS). These references are obtained from the energy consumptions in the measurement processes. This lets obtaining several analyses important from the behavior in the energy consumption schemes. The application of these factors was determined from the energy generation processes, whose units are in metrics adequate for electricity and heat according to each system observed, and the same from the energetic consumption of the same data centers that helped to determine these characteristics. These indicators applied in an environment of Information and Communications Technology (ICT) define comparisons and be specified as the basis characterizing the analysis of energetic performance. The tests show the energy consumption and carbon footprint. This experiment seeks to increase the quality of services and decrease the energy consumption. This let us use efficiently the computational resources minimizing the environment impact. To achieve this target, it was used to apply indicators in green computing environment, like it can be mentioned: The Power Usage Effectiveness (PUE) and Data Center Effectiveness (DCE). With the use of these indicators can derive a generic model of energy consumption for a GNS system.",signatures:"Jesus Hamilton Ortiz, Fernando Velez Varela and Bazil Taha Ahmed",downloadPdfUrl:"/chapter/pdf-download/71868",previewPdfUrl:"/chapter/pdf-preview/71868",authors:[{id:"283288",title:"Dr.",name:"Jesus Hamilton",surname:"Ortiz",slug:"jesus-hamilton-ortiz",fullName:"Jesus Hamilton Ortiz"},{id:"26364",title:"Dr.",name:"Bazil Taha",surname:"Ahmed",slug:"bazil-taha-ahmed",fullName:"Bazil Taha Ahmed"},{id:"319427",title:"Dr.",name:"Fernando",surname:"Velez Varela",slug:"fernando-velez-varela",fullName:"Fernando Velez Varela"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"10417",title:"Collaborative and Humanoid Robots",subtitle:null,isOpenForSubmission:!1,hash:"dd42dd44dc386e591e8ff04956762023",slug:"collaborative-and-humanoid-robots",bookSignature:"Jesús Hamilton Ortiz and Ramana Kumar Vinjamuri",coverURL:"https://cdn.intechopen.com/books/images_new/10417.jpg",editedByType:"Edited by",editors:[{id:"283288",title:"Dr.",name:"Jesus Hamilton",surname:"Ortiz",slug:"jesus-hamilton-ortiz",fullName:"Jesus Hamilton Ortiz"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1636",title:"Telecommunications Networks",subtitle:"Current Status and Future Trends",isOpenForSubmission:!1,hash:"3cd52027cd1f450d5770cede2b712b46",slug:"telecommunications-networks-current-status-and-future-trends",bookSignature:"Jesus Hamilton Ortiz",coverURL:"https://cdn.intechopen.com/books/images_new/1636.jpg",editedByType:"Edited by",editors:[{id:"97704",title:"Dr.",name:"Jesús Hamilton",surname:"Ortiz",slug:"jesus-hamilton-ortiz",fullName:"Jesús Hamilton Ortiz"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"858",title:"Recent Developments in Mobile Communications",subtitle:"A Multidisciplinary Approach",isOpenForSubmission:!1,hash:"d9051720bd0c3f7ff7f171bcbbb599f0",slug:"recent-developments-in-mobile-communications-a-multidisciplinary-approach",bookSignature:"Juan P. 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1. Introduction
Ongoing rapid advances in power electronics and communication technologies are facilitating the development of smart grids that are characterized by hosting distributed generation units (DGs). There are many benefits of smart grids such as enhancing power reliability and power quality, improving safety and cyber security, maximizing energy utilization and efficiency, environment protection and conservation, and increasing financial revenues [1]. For instance, as reported in [2], smart grids could reduce greenhouse gas emissions by up to 18%. The integration of renewable-based DGs alters distribution systems so that rather than having passive structures, with unidirectional power flow, they become active distribution networks (ADNs), with multidirectional power flow. Voltage regulation is considered one of the main operational challenges that accompany high penetration levels of renewable-based DGs. RES, such as wind and solar energy, can significantly change the voltage profile of smart grids and interact negatively with conventional schemes of controlling onload tap changers (OLTCs). Another factor is the growing penetration of PEVs, which creates additional stress on voltage control devices due to their stochastic and concentrated power profiles. These combined generation and load power profiles can lead to overvoltages, undervoltages, high system losses, excessive tap operation, infeasible solutions (hunting) with respect to OLTCs, and/or limits on the integration of either PEVs or RES.
2. Background
One of the main objectives of electric utilities is to maintain the grid voltage within standard levels to guarantee customers’ satisfaction. Many equipment are deployed in the grid, such as OLTCs and capacitor banks, to properly fulfill this objective. In general, voltage regulation in a smart grid can be classified as local or communication-assisted [3]. The local voltage regulation is referred to as the conventional control, where the reference values and measurements for voltage control are locally determined. In this section, the fundamentals of the conventional control of OLTC and inverter-interfaced DGs are explained.
2.1 OLTC conventional controller
OLTCs are one of the main voltage regulators in distribution systems. A tap changer equipped with an automatic control system usually regulates the transformer’s secondary voltage to maintain an acceptable voltage near the load center. The tap changer mechanically varies the tap position from zero (no voltage compensation) to Nmax (maximum voltage compensation). This allows for changing the transformer’s turn ratio in discrete steps; each step adjustment may take 3–20 seconds. Figure 1 displays the schematic diagram of an OLTC, where the tap changer is placed on the primary side. The tap changer is usually installed on the high voltage side of the transformer (i.e., the primary side in Figure 1) because of the low current of that side. A reversing switch is employed to change the polarity of the tap winding for positive and negative voltage compensation. In steady state, the transformer model can be given by
Figure 1.
Transformer with tap changer.
V2=V1a−I2ZTaE1
where V1 and V2 are primary and secondary voltages, respectively, I2 is secondary current, a denotes the transformer’s tap ratio, and ZT (a) is the transformer series impedance (referred to the secondary side). The tap changer allows a to vary linearly; thus it can be expressed in terms of the nominal turn ratio (i.e., a0=1.0):
a=a0+niΔaE2
where Δa is the change in a as a result of a step change in the tap position ni, which is given by
ni=ni−1+ΔnE3
where ni is the present tap position and ni−1 is the previous tap position.Δn is the change in the tap position which is defined by
where Tm denotes mechanical time delay which is required by the motor driver unit to change the tap position by only one step and b is the control signal that is applied to the tap-changing mechanism (see Figure 2) and is given by
Figure 2.
The schematic diagram of the conventional discrete control of the OLTC.
where Td is the time delay introduced by the OLTC controller and e is the output of the hysteresis controller, that is,
e=0forΔV≤DB1forΔV>DB−1forΔV<−DBE6
The mechanical time delay (Tm) has a constant value; usually, it varies from 3 to 10 seconds, and ΔV is the voltage error. In some models of the OLTC, the controller time delay (Td) is considered constant:
Td=τ0E7
However, Td is usually variable and depends on the voltage error and the controller’s dead band DB [4]:
Td=τ0DBΔVE8
Td is inversely proportional to the voltage error ΔV to avoid unnecessary operation during transient voltages and temporary load disturbances. DB must be greater than ∆a to ensure stability when the regulated voltage approaches the reference value Vref; otherwise, the OLTC will suffer from hunting. Vref is typically between 0.95 p.u. and 1.0 p.u. since it determines the steady-state voltage near the load center (Vk).
2.2 Conventional voltage control of inverter-based DGs
Energy processing strategies for inverter-based DGs typically involve two cascaded loops: inner and outer. The inner loop is a current control loop, which regulates the DG inverter current in the d−q reference frame. The outer control loop, on the other hand, can fulfill different control objectives depending on the hosing grid, such as voltage regulation and power management control.
Figure 3 shows an inverter-based DG that is controlled in the current injection mode, which is the typical strategy adopted with RES. A DG inverter model in the d−q synchronous frame represents the dynamics of the interfacing LC filter [5], that is,
Figure 3.
Power circuit and current control diagram for inverter-based DG.
LfdIddt=−RfId+Vd−Vod+ωLfIqE9
LfdIqdt=−RfIq+Vq−Voq−ωLfIdE10
CfdVoddt=Id−Iod+ωCfVoqE11
CfdVoqdt=Iq−Ioq−ωCfVodE12
ω=dθdtE13
where Idq and Iodq represent the d−q components of the inverter output current and DG current at the PCC, respectively; Vdq and Vodq are the components of the inverter terminal voltage and DG voltage at the PCC, respectively; Rf, Lf, and Cf are the resistance, inductance, and capacitance of the DG interfacing filter, respectively; and ω is the grid frequency.
The current equations, that is, (9) and (10), are coupled through the ωLfIq and −ωLfId terms. For independent control of both the Id and Iq currents, the decoupled terms must be eliminated, which can be accomplished if new variables Vd′ and Vq′ are defined by
Equations (16) and (17) represent decoupled first-order differential equations for Id and Iq, respectively. Therefore, PI current controllers can be designed based on the transfer functions derived from (16) and (17). If the gains of the PI current controllers are selected as
Kip=LfτiKii=Rfτi,E18
then the equivalent closed-loop transfer functions for the current loops can be given by
IdsIdrefs=IqsIqrefs=1τis+1E19
where τi is the time constant of the closed-loop system. The vector magnitude of the reference current idref2+iqref2 should be limited according to the maximum allowable current, typically 20% greater than the rated current of the inverter [5], in order to provide overcurrent protection. The vector magnitude of the modulation index M=Md2+Mq2 should also be limited to Mmax=1.0 p.u. so that DG operates in a linear modulation region. Figure 3 shows the vector magnitude limiter, which implies a limit on the magnitude of M without a change in the phase angle between Md and Mq.
Determination of the reference currents, Idref and Iqref, is usually affiliated to the DG output power. In the synchronous d−q frame, the output active PG and reactive QG powers are given by
PG=1.5VodId+VoqIqE20
QG=1.5VoqId−VodIqE21
For decoupled control of the active and reactive powers, a phase-locked loop (PLL) should be used for aligning Vod with the output voltage of phase a so that Voq=0 [6]. To determine Idref, a maximum power point tracking (MPPT) algorithm along with a DC link voltage controller is employed [7]. On the other hand, Idref can be calculated, as in (22), to regulate the output reactive power, thus, providing voltage support.
Idref=QGref1.5VodE22
where QGref is the reference value of the DG reactive power which can be determined using an outer voltage control loop or received from a communication-assisted voltage regulation scheme [8].
2.3 DG contribution to voltage violation
Usually, distribution networks have unidirectional power flow from the substation to customers. This leads to a descending voltage profile which may only suffer from an undervoltage near the load center, a problem typically tackled using OLTC and capacitor banks. On the other hand, DG integration into distribution networks makes the power flow bi-directional; thus an overvoltage problem may also occur. To understand the impact of DGs on the system voltage, the simplified distribution network in Figure 4(a) is used, where a DG is connected at a load bus. In this figure, R and X are the feeder resistance and reactance, respectively; IR is the feeder current; V1, V2 are the primary and secondary voltage of the distribution transformer, respectively; and VG is the DG output voltage. The phasor diagram of the simplified distribution network is shown in Figure 4(b), in which δ is the power angle and φ is the phase shift between VG and IR.
Figure 4.
Simplified distribution network with DG. (a) Single-line diagram and (b) phasor diagram.
Using the phasor diagram, the relation between the DG voltage and V2 can be formulated:
VG′=V2+IRRcosφ+IRXsinφ=VGcosδE23
The power angle (δ) is very small; hence (23) can be approximated by
VG≈V2+IRRcosφ+IRXsinφE24
Therefore, the voltage rise caused by the DG, that is, ΔVG=VG−V2, is given by.
ΔVG≈IRRcosφ+IRXsinφ=PRR+QRXVG=PG−PLR+QG−QLXVGE25
where PG and QG are the DG output active and reactive powers, respectively, while PL and QL are the load active and reactive powers, respectively. It can be seen from (25) that the highest overvoltage happens when the DG generates its maximum power during a light load condition. This problem is mainly associated with the excessive reverse power flow caused by the DG.
3. Communication-assisted voltage regulation
Due to the intermittency of RES and PEVs, the conventional control schemes for OLTC and DGs fail to provide proper voltage regulation. This shortcoming can be compensated using communication-assisted voltage regulation schemes. In the literature, the communication-assisted schemes fall under two approaches: distributed and centralized [3]. Both approaches involve investment in communication links and remote terminal units. The distributed (intelligent) approach is considered to be an expert-based control or model-free approach, which coordinates a variety of voltage control devices with the goal of providing effective and nonoptimal voltage regulation with fewer communication requirements [9]. On the other hand, the centralized approach relies on a central point that monitors the system status and optimizes the operation of voltage control equipment. Typically, a centralized optimization problem is solved to dispatch the reactive power of different voltage control equipment based on (i) load forecasting and (ii) generation monitoring. Several solutions have been proposed in the literature to provide optimal reactive power dispatch for DGs [10, 11, 12]. In this section, the role of PEVs in optimal voltage regulation is explained as in [13].
3.1 PEV impact on voltage regulation
Figure 5 represents a simplified multi-feeder distribution network connected to a substation through an OLTC. The test network has a photovoltaic (PV)-based DG and a PEV parking lot, which are connected at different feeder terminals. Following the derivation of (25), the per-unit voltage deviation for both DG and PEV busses can be approximated by
where PPV, PEV, and PL are DG, PEV, and load active powers, respectively, and QPV, QEV, and QL are DG, PEV, and load reactive powers, respectively.
Equation (26) shows that two worst-case scenarios may occur: (i) overvoltage, when the DG generates its maximum power during light loads and (ii) undervoltage, during a peak load demand and low DG output. The integration of DGs changes the voltage profile significantly and complicates the voltage regulation. This is due to two reasons: (i) the voltage trend not descending from the substation to the feeder terminal, thereby invalidating the target point (reference) and (ii) the voltage estimation, based on local measurements, becoming inaccurate because of the stochastic power natures of RES and EVs [13]. Moreover, the stochastic power nature of EVs makes the voltage estimation inferior and aggravates the undervoltage problem. Therefore, OLTCs may suffer from wear and tear due to excessive operations. This problem worsens when feeders suffer from overvoltage due to high DG penetration, while others suffer from undervoltage during high demand, such as PEV charging. In this instance, the OLTC will have two contradicting solutions. Increasing the transformer’s secondary voltage mitigates the undervoltage problem at the expense of the system’s overvoltage and vice versa. Figure 6 shows two daily power profiles for uncontrolled1 PEV charging demand and a PV-based DG. The PEV demand is generated based on practical arrival/departure times from the Toronto Parking Authority (TPA), Toronto, Canada. Since the power profiles of commercial parking lots and PV-based DGs naturally coincide, there is a high chance that the system simultaneously suffers from overvoltage and undervoltage. A partial solution for this problem can be realized if a centralized-based controller for the OLTC exploits the system’s maximum and minimum voltages. However, this controller may not prevent the OLTC hunting problem during high PV power generation and peak EV demand, resulting in excessive tap operation.
Figure 6.
DG and PEV power profiles.
For that reason, the power electronic converters that interface DGs and PEVs should be utilized in voltage regulation. The DG can support the voltage regulation through two options: (i) absorbing reactive power and/or (ii) curtailment of active power. The first option is preferred since active power curtailment represents an energy waste. However, the capacity of the DG converter may limit the reactive power support and force the second option. To increase the reactive power support, the interfacing converter of the PEV can be employed to inject its surplus reactive power, thus reducing the DG active power curtailment [13]. A novel optimal coordinated voltage regulation scheme is presented to coordinate PEV, DG, and OLTC to achieve optimal voltage regulation and satisfy the self-objectives of each voltage control device.
3.2 OLTC centralized control
As shown in Figure 7, the OLTC is represented by a π-circuit model [14]. The taps are assumed to be at the primary side (high voltage). Subsequently, the OLTC secondary voltage and current can be calculated by
Figure 7.
Equivalent π-circuit model of OLTC.
V1tI1t=1a−aYT0−aV0tI0t′E27
where YT is the transformer series admittance, a is the turns ratio given in (2), and t denotes the time instant. To take the physical busses into account, (27) can be rewritten as
I0tI1t=gF+jbμ+YTa2−YTa−YTaYT︸YOLTCV0tV1tE28
where YOLTC is the OLTC Y-bus admittance matrix, which represents the OLTC admittance in the power flow equations.
The conventional OLTC controller, shown in Figure 8(a), is modified to emulate an adaptive reference by considering the system’s minimum and maximum voltages, that is, Vminsys and Vmaxsys, respectively. This modification forms the centralized OLTC controllers (COC) proposed in [13] and shown in Figure 8(b), which allows the OLTC to deal with multiple feeders with voltage problems resulted from DGs and PEVs. The COC emulates an adaptive reference because the error ΔV changes such that Vminsys and Vmaxsys are within the standard limits VUpper and VLower (i.e., 1.05 and 0.95 p.u.), respectively. Vminsys and Vmaxsys can be estimated using the state estimation algorithm in [15] or attained through the central voltage control unit explained in the next subsection.
Figure 8.
The OLTC control: (a) conventional local controller and (b) centralized OLTC controller (COC).
In the case of an overvoltage, ΔV is negative, and the primary controller decreases the transformer secondary voltage (by increasing the tap position) and vice versa. During normal conditions, ΔV is zero because both Vminsys and Vmaxsys are within the standard limits; thus, the tap position remains unchanged. When both overvoltage and undervoltage occur simultaneously, the COC should be disabled to avoid hunting [16]. In the next subsection, the roles of DGs and PEVs in voltage regulation are explained to mitigate the shortcoming of the COC.
3.3 Optimal coordinated voltage regulation
Both power electronic converters of PEVs and DGs can support the grid with reactive power to relax the OLTC. A vehicle-to-grid reactive power support (V2GQ) strategy is proposed in [13] to incorporate PEVs and DGs in voltage regulation, as shown in Figure 9. The main difference between vehicle-to-grid (V2G) strategies and the V2GQ is that the latter injects only reactive power to the grid. Thus, it preserves the battery life of PEV, that is, the highest priority of the vehicles’ owners. Nevertheless, the V2GQ cannot be flexibly employed in power management because PEVs do not export active power to the grid. V2G strategies are avoided in voltage regulation to elongate the battery life, which is considered a priority in this study. The V2GQ comprises a three-stage nonlinear programming, in which Stage (I) aims at maximizing the energy delivered to PEVs, Stage (II) minimizes the DG active power curtailment, and Stage (III) minimizes the voltage deviations. The COC is coordinated such that it acts after Stage (III) to ensure that all bus voltages are within the standard limits.
Figure 9.
PEV/DG voltage support scheme.
3.3.1 Problem formulation of stage (I)
The main objective of Stage (I) is maximizing the energy delivered to PEV owners, that is,
maxγ∑i∈IPEV∑chi∈CHiEDchit∀tE29
where EDchit is the energy delivered to the PEV connected to charger chi∈CHi at PEV bus i∈IPEV, IPEV is the set of busses with PEV charger connections, CHi is the set of chargers connected to bus i, and γ is the decision variable vector. The PEV and DG voltage support depends mainly on γ, which can generally take the following form:
γ=XchitQoitPEVPoitQoitE30
where Xchit and QoitPEV are the vector of the charger decisions and PEV reactive power at bus i∈IPEV, respectively; Poit and Qoit are the DG active and reactive powers at bus i∈IDG, respectively; and IDG is the set of busses with DG connections. The charging decisions are continuous, that is, X∈01 where “0” stands for no charging and “1” stands for full charging. According to the grid operator, γ can be partially constrained. For instance, the PEV reactive powers can be set to zero, that is, QoitPEV=0,∀i∈IPEV,t, when the PEV voltage support is disregarded. Stage (I) should satisfy the power flow constraints, as given by
PGit−PLit=∑j∈IbVitVjtYijcosθij+δjt−δit∀i∈Ib,tE31
QGit−QLit=∑j∈IbVitVjtYijsinθij+δjt−δit∀i∈Ib,tE32
where PGit and QGit denote the generated active and reactive powers, respectively; PLit and QLit are the active and reactive power demands, respectively; Vit and δit denote the magnitude and angle of the voltage, respectively; Ib is the set of system busses; and Yij and θij are the magnitude and angle of the Y-bus admittance matrix, respectively.
The voltage and feeder thermal limits should also hold, and thus,
Vmin≤Vit≤Vmax,∀i∈Ib,tE33
Ilt≤IlCAP,∀l∈ℒ,tE34
where Vmin and Vmax are the maximum and minimum voltage limits, that is, 0.9 and 1.1 p.u., respectively; Ilt denotes the per-unit current through line l∈ℒ; ℒ is the set of system lines; and IlCAP is the current carrying capacity.
Typically, two back-to-back power electronic converters are used to interface PEVs and PVs, that is, DC/DC and DC/AC converters. The DC/DC converter performs MPPT with PV-based DGs or controls the PEV charging. The DC/AC converter regulates the DC link voltage and is responsible for the reactive power support [7]. The power injected to a bus should be equal to the output power of the DG installed at that bus:
PGit=PoitQGit=Qoit,∀i∈IDG,tE35
Poit≤PoitMPPT,∀i∈IDG,tE36
where PoitMPPT denotes the DG maximum power available. In both PEVs and PVs, the DC/AC converter is similar to that used with Typ. 4 wind farms. Therefore, the reactive power capability limits, defined in [17], should be used as constraints. These limits depend on the converter’s power rating and DC link voltage, as follows:
Qoit2≤Soit2−Poit2,∀i∈IDG,tE37
Qoit+Vit2Xi2≤VcimaxVitXi2−Poit2,∀i∈IDG,tE38
where Vcimax represents the maximum converter voltage which depends on the converter DC link voltage [17, 18], Soit denotes the DG rated power, and Xi is the total reactance of the DG filter and interfacing transformer at bus i . If the DC/AC converter increases the set point for the DC link voltage to relax Constraint (38), the DC/DC converter will operate at a high duty cycle, which decreases its efficiency [19]. Hence, the reactive power support from the DC/AC converter is limited by the DC link voltage.
The load power at a bus should be equal to the total power consumed by regular loads and PEV:
PLit=PNLit+PoitPEV,∀i∈Ib,tE39
QLit=QNLit+QoitPEV,∀i∈Ib,tE40
where PoitPEV is the PEV active power and PNLit and QNLit are the active and reactive powers of normal loads, respectively. The PV power profile relies mainly on solar insolation, whereas PoitPEV depends on charging decisions Xchit, the charging power limit in kW PCHchit, and the charging efficiency ηCHchi, as given by.
where Sbase is the base power for the per-unit system in kW. The charging power limit PCH is a function of the PEV battery state of charge (SOC) and is limited by the capacity of the charger, that is, PCH≤PratedCharger. This function is dependent on the characteristics of the battery, which can be expressed as
PCHchit=fchitSOCchitF,∀i∈IPEV,chi,tE42
where fchit is the function that represents the characteristics of the PEV battery and SOCchitF is the reached SOC. The relationship between the energy delivered to a PEV battery and its SOC can be given by
where ΔT is the time step to collect the system data, run the program, and implement the decisions. Similar to DGs, the injected reactive powers from the PEVs should be limited by their converter ratings and DC link voltages, as given by
where SoitPEV is the rated power of the PEV converter. In addition, the final achieved SOC, that is, SOCchitF, should be limited by the SOC desired by the PEV owners SOCchitD:
SOCchitF≤SOCchitD,∀i∈IPEV,chi,tE47
3.3.2 Problem formulation of stage (II)
In Stage (II), the objective is to minimize the DG active power curtailment, where the final SOC reached in Stage (I), that is, SOCchitR, must be attained to ensure maximum customer satisfaction, which is the highest priority of the V2GQ technology. Therefore, this stage is subject to all of the constraints in Stage (I) except for (47), which is replaced by
SOCchitF=SOCchitR,∀i∈IPEV,chi,tE48
Thus, the objective function of Stage (II) is
maxγ∑i∈IDGPoit,∀tE49
3.3.3 Problem formulation of stage (III)
Stage (III) aims at minimizing the voltage deviation using the DGs and PEVs to restore a feasible solution for the COC and relax the tap operation. Thus,
minγ∑i∈Ib1−Vit2,∀i,tE50
Besides all the constraints in Stage (II), this problem is subject to the constraint defined in (50), in which the maximum injected powers from the DGs reached in Stage (II), that is, PoitR, should be maintained.
Poit=PoitR,∀i∈IDG,tE51
3.4 Coordination between V2GQ and COC
The charging decisions and active/reactive dispatch signals produced in Stage (III) are sent to all PEV parking lots and DGs, as shown in Figure 9. To ensure that the PEV and DG converters settle at the desired active and reactive power references, a time delay Δtconv is introduced. The converter settling time may vary from 50 to 100 ms, depending on the primary controllers of the DC/AC converter [7]. For slow automatic interactions, such as voltage regulation, the maximum communication time delay is 100 ms as per the IEC 61850 [20]. Thus, Δtconv is assumed to be 200 ms (100 ms for the converter settling time + 100 ms for the communication latency). Lastly, the COC refines the output solution from Stage (III) to ensure that both Vminsys and Vmaxsys are within the standard voltage limits. The total execution time of the coordination algorithm ΔT is 5 minutes.
3.5 Real-time results
In this section, various case studies are presented to validate the robustness and effectiveness of the optimal coordinated voltage regulation algorithm. The 38-bus 12.66-kV distribution system is used as a test system, as shown in Figure 10. The system data can be found in [21]. The system is modified to accommodate two PEV parking lots and four PV-based DGs, with power ratings as given in Figure 10. The power demands of the two parking lots are extracted from data provided by the TPA for a weekday in 2013. Both parking lots are commercial, where P1 represents a lot in the vicinity of a train station and P2 is a lot located near downtown Toronto. The total number of PEVs during a day is displayed in Figure 11. Due to confidentiality, the addresses of the real parking lots are not mentioned. The central control unit receives the desired SOCs and sends the charging decisions to all vehicles in the parking lots. An OPAL real-time simulator (RTS) is used to model the visual test network using the SimPowerSystems blockset, which is available in Simulink/Matlab, and an ARTEMiS plug-in [22]. The network, PEV, and DG models are distributed between the RTS cores for performing parallel computations. The RTS is used to perform a hardware-in-the-loop (HiL) realization, where a central control unit, emulated by a host computer running GAMs, exchanges real-time data with the test network modeled in the RTS. The sampling time used to realize the HiL application is 100 μs.
Figure 10.
Test network with an HiL realization.
Figure 11.
Number of vehicles in the parking lots.
3.5.1 OLTC control without PEV/DG voltage support
This section compares the responses of the conventional and COC controllers for the OLTC. The voltage support from PEVs and DGs is disabled to study their impacts of the OLTC. Figure 12(a) and (b) illustrate the response of the conventional controller for the OLTC over 24 hours. Although the OLTC does not suffer from an excessive tap operation (13 taps/day), undervoltage and overvoltage problems occur.
Figure 12.
OLTC response: (a & b) conventional control and (c & d) COC.
As expected, the overvoltage happens during the peak power generation from DGs, while the undervoltage coincides with the peak demand of PEV. The COC is enabled to mitigate these voltage violations. Figure 12(c) and (d) demonstrate that the COC can limit the voltage violations without the PEV/DG voltage support, but it suffers from a hunting problem. This problem happens when the overvoltage and undervoltage occur concurrently. In this situation, the COC should be deactivated.
3.5.2 OLTC control with PEV/DG voltage support
To address the hunting problem, presented in the previous case, the V2GQ is coordinated with the COC. Two case studies dealing with PEV/DG voltage support are carried out, as follows:
DG active power curtailment, without PEV and DG reactive power supports, that is, γ=XchitPoit, where QoitPEV=0,∀i∈IPEV and Qoit=0,∀i∈IDG
PEV and DG reactive power dispatch, that is, γ=XchitQoitPEVPoitQoit
Figure 13 demonstrates the performance of the coordination algorithm when the voltage support is merely achieved via the DG active power curtailment (i.e., the first case study). The coordination algorithm keeps the bus voltages within the standard limits, as shown in Figure 13(a). The hunting problem is avoided with a reasonable daily tap operation, that is, 16 taps/day; see Figure 13(b). Further, the required PEV charging demand is satisfied, as illustrated in Figure 13(c). However, 6.14% of the DG available energy is curtailed because the priority is given to supplying the PEV demand, as shown in Figure 13(d). This privilege is considered to comply with the distribution system code developed by the Ontario Energy Board [23]. It states that electric utilities should deliver the required energy to supply their loads (such as PEVs) unless there is a technical limit violation. The only solution to maximize the energy extraction is therefore to incorporate both the PEVs and DGs in the voltage support. Figure 14 illustrates the response of the coordination algorithm for the second case when PEV and DG reactive powers are employed for voltage regulation. Utilizing the full features of the V2GQ results in a proper voltage regulation using only 4 taps/day, extracting all DG power and charging all PEVs.
Figure 13.
Response of coordination algorithm, assuming DG active power curtailment.
Figure 14.
Response of coordination algorithm, activating both PEV and DG reactive power dispatch.
4. Conclusion
This chapter explained the conventional voltage regulation schemes used in smart grids with inverter-based DGs and PEVs. High penetration levels of PEVs and DGs may lead to negative impacts on the conventional voltage control devices such as tap-changing transformers and capacitor banks. The main reason behind these negative impacts is the stochastic power profiles of PEVs and renewable-based DGs that stimulate chronological overvoltage and undervoltage and make load forecasting erroneous. In the literature, there are two approaches to mitigating the voltage violations associated with DGs and PEVs: (i) centralized and (ii) distributed voltage regulation schemes. These schemes necessitate communication and, thus, may benefit from the communication infrastructure embedded within smart grids. The centralized approach employs state estimation and solves an optimization problem to dispatch DG reactive power for optimal voltage regulation. On the other hand, the distributed approach is an expert-based control or model-free approach, which coordinates a variety of voltage control devices with the goal of providing effective and nonoptimal voltage regulation with fewer communication requirements. Case studies for a centralized voltage control scheme illuminated the role of PEV and DG reactive powers in providing optimal voltage regulation with relaxed tap operation.
\n',keywords:"distributed generation, active distribution networks, voltage regulation, electric vehicles, onload tap changers",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66100.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66100.xml",downloadPdfUrl:"/chapter/pdf-download/66100",previewPdfUrl:"/chapter/pdf-preview/66100",totalDownloads:977,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:1,impactScore:0,impactScorePercentile:37,impactScoreQuartile:2,hasAltmetrics:1,dateSubmitted:"September 17th 2018",dateReviewed:"February 11th 2019",datePrePublished:"May 22nd 2019",datePublished:"January 15th 2020",dateFinished:"March 12th 2019",readingETA:"0",abstract:"The intermittent nature of renewable power sources (RES) can significantly change the voltage profile of smart grids and adversely impact the conventional voltage control devices such as tap-changing transformers and capacitor banks. Furthermore, the growing penetration of plug-in electric vehicles (PEVs) can add high stress on voltage control devices due to the PEV stochastic and concentrated power profiles. Such power profiles may lead to high maintenance costs and reduced lifetimes for voltage control devices and limit actions on accommodation of high penetration levels of RES and PEVs. This chapter explains the basic background of voltage regulation in smart grids. The typical approaches, which are employed by utilities for voltage regulation, are reviewed. Then, the impact of RES and PEVs on voltage regulation is analyzed. Lastly, remedies for voltage violations in smart grids, such as optimal reactive power control and coordination between voltage control devices, are discussed.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66100",risUrl:"/chapter/ris/66100",book:{id:"7613",slug:"research-trends-and-challenges-in-smart-grids"},signatures:"Maher Azzouz",authors:[{id:"275562",title:"Dr.",name:"Maher",middleName:null,surname:"Azzouz",fullName:"Maher Azzouz",slug:"maher-azzouz",email:"mazzouz@uwindsor.ca",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Background",level:"1"},{id:"sec_2_2",title:"2.1 OLTC conventional controller",level:"2"},{id:"sec_3_2",title:"2.2 Conventional voltage control of inverter-based DGs",level:"2"},{id:"sec_4_2",title:"2.3 DG contribution to voltage violation",level:"2"},{id:"sec_6",title:"3. Communication-assisted voltage regulation",level:"1"},{id:"sec_6_2",title:"3.1 PEV impact on voltage regulation",level:"2"},{id:"sec_7_2",title:"3.2 OLTC centralized control",level:"2"},{id:"sec_8_2",title:"3.3 Optimal coordinated voltage regulation",level:"2"},{id:"sec_8_3",title:"3.3.1 Problem formulation of stage (I)",level:"3"},{id:"sec_9_3",title:"3.3.2 Problem formulation of stage (II)",level:"3"},{id:"sec_10_3",title:"3.3.3 Problem formulation of stage (III)",level:"3"},{id:"sec_12_2",title:"3.4 Coordination between V2GQ and COC",level:"2"},{id:"sec_13_2",title:"3.5 Real-time results",level:"2"},{id:"sec_13_3",title:"3.5.1 OLTC control without PEV/DG voltage support",level:"3"},{id:"sec_14_3",title:"3.5.2 OLTC control with PEV/DG voltage support",level:"3"},{id:"sec_17",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Estimating the Costs and Benefits of the Smart Grid: A Preliminary Estimate of the Investment Requirements and the Resultant Benefits of a Fully Functioning Smart Grid. Palo Alto, CA: EPRI; 2011'},{id:"B2",body:'The Smart Grid: An Estimation of the Energy and CO2 Benefits. Pacific Northwest National Lab; 2010'},{id:"B3",body:'Vovos PN, Kiprakis AE, Wallace AR, Harrison GP. Centralized and distributed voltage control: Impact on distributed generation penetration. IEEE Transactions on Power Apparatus and Systems. 2007;22(1):476-483'},{id:"B4",body:'Faiz J, Siahkolah B. Differences between conventional and electronic tap-changers and modifications of controller. IEEE Transactions on Power Delivery. 2006;21(3):1342-1349'},{id:"B5",body:'Zamani MA, Yazdani A, Sidhu TS. A control strategy for enhanced operation of inverter-based microgrids under transient disturbances and network faults. IEEE Transactions on Power Delivery. 2012;27(4):1737-1747'},{id:"B6",body:'Karimi-Ghartemani M, Iravani MR. A method for synchronization of power electronic converters in polluted and variable-frequency environments. IEEE Transactions on Power Apparatus and Systems. 2004;19(3):1263-1270'},{id:"B7",body:'Yazdani A, Iravani R. Voltage-Sourced Converters in Power Systems. Wiley-IEEE Press; Feb 2010. ISBN-10: 0470521562'},{id:"B8",body:'Katiraei F, Iravani R, Hatziargyriou N, Dimeas A. Microgrids management. The IEEE Power & Energy Magazine. 2008;6(3):54-65'},{id:"B9",body:'Azzouz MA, Farag HE, El-Saadany EF. Real-time fuzzy voltage regulation for distribution networks incorporating high penetration of renewable sources. IEEE Systems Journal. Sep 2017;1(3):1702-1711'},{id:"B10",body:'Daratha N, Das B, Sharma J. Coordination between OLTC and SVC for voltage regulation in unbalanced distribution system distributed generation. IEEE Transactions on Power Apparatus and Systems. 2013;29(1):1-11'},{id:"B11",body:'Agalgaonkar YP, Pal BC, Jabr RA. Distribution voltage control considering the impact of PV generation on tap changers and autonomous regulators. IEEE Transactions on Power Apparatus and Systems. 2014;29(1):182-192'},{id:"B12",body:'Bonfiglio A, Brignone M, Delfino F, Procopio R. Optimal control and operation of grid-connected photovoltaic production units for voltage support in medium-voltage networks. The IEEE Transactions on Sustainable Energy. 2014;5(1):254-263'},{id:"B13",body:'Azzouz MA, Shaaban MF, El-Saadany EF. Real-time optimal voltage regulation for distribution networks incorporating high penetration of PEVs. IEEE Transactions on Power Apparatus and Systems. 2015;30(6)'},{id:"B14",body:'Milano F. Hybrid control model of under load tap changers. IEEE Transactions on Power Delivery. 2011;26(4):2837-2844'},{id:"B15",body:'Elkhatib ME, El-Shatshat R, Salama MMA. Novel coordinated voltage control for smart distribution networks with DG. IEEE Transactions on Smart Grid. 2011;2(4):598-605'},{id:"B16",body:'Farag HE, El-Saadany EF, Seethapathy R. A two ways communication-based distributed control for voltage regulation in smart distribution feeders. IEEE Transactions on Smart Grid. 2012;3(1):271-281'},{id:"B17",body:'Ullah NR, Bhattacharya K, Thiringer T. Wind farms as reactive power ancillary service providers—Technical and economic issues. IEEE Transactions on Energy Conversion. 2009;24(3):661-672'},{id:"B18",body:'Zhou K, Wang D. Relationship between space-vector modulation and three-phase carrier-based PWM: A comprehensive analysis [three-phase inverters]. IEEE Transactions on Industrial Electronics. 2002;49(1):186-196'},{id:"B19",body:'Kazimierczuk MK. Pulse-Width Modulated DC-DC Power Converters. John Wiley & Sons; 2008'},{id:"B20",body:'Communication Networks and Systems in Substations—Part 5: Communication Requirements for Functions and Device Models. IEC 61850-5 ed2; 2003'},{id:"B21",body:'Singh D, Misra RK, Singh D. Effect of load models in distributed generation planning. IEEE Transactions on Power Apparatus and Systems. 2007;22(4):2204-2212'},{id:"B22",body:'Azzouz MA, El-Saadany EF. Multivariable grid admittance identification for impedance stabilization of active distribution networks. IEEE Transactions on Smart Grid. 2016:1-13. in press'},{id:"B23",body:'Ontario Energy Board. Distribution system code. 2013. Available from: http://www.ontarioenergyboard.ca/OEB/Documents/Regulatory/DistributionSystemCode.pdf'}],footnotes:[{id:"fn1",explanation:"In uncontrolled charging schemes, PEVs start charging as soon as they are plugged in."}],contributors:[{corresp:"yes",contributorFullName:"Maher Azzouz",address:"mazzouz@uwindsor.ca",affiliation:'
University of Windsor, Windsor, Ontario, Canada
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1. Introduction: what every clinician needs to know
The intrinsic circadian system synchronizes basic physiologic functions such as temperature regulation, appetite, and hormonal homeostasis and is responsible for the stable sleep and wake states that occur at regular times with respect to day and night. The term circadian derives from the Latin words “circa,” meaning approximately, and “diem,” meaning day, which emphasize that the intrinsic cycle is usually not exactly 24 hours in length [1]. The average circadian cycle length is generally 24.2 hours, which means that the cycle always requires entrainment to the environment because the day is exactly 24 hours long. Light is the most potent mechanism of entrainment, but meals and exercise also have an impact on entrainment [1, 2, 3]. Dysynchrony between a person’s internal circadian system and their desired wake and sleep periods can lead to one of 6 different types of circadian rhythm sleep–wake disorders (CRSWDs). These disorders can present clinically with symptoms of insomnia and/or excessive daytime sleepiness, along with impairments in cognitive, emotional, and social functioning. A key feature of these conditions is that re-alignment of the intrinsic circadian period to the desired circadian period leads to resolution of symptoms.
CRSWDs may be due to a primary problem with the circadian system, such as altered sensitivity of the circadian system to light, and genetic and/or age-related factors that disrupt the intrinsic period of the system.
The diagnosis of CRSWDs can be difficult, due to the overlapping symptoms with other sleep disorders and medical conditions. Recognizing consistent patterns in abnormal sleep schedules is key to helping differentiate CRSWDs from other disorders. CRSWDs are primarily clinical diagnoses and use of a detailed sleep diary is an important part of the evaluation. Other objective measures such as actigraphy and melatonin measurements can supplement information obtained from the patient’s history [4]. Polysomnography is usually not indicated unless there is a suspicion for a comorbid sleep disorder, such as a sleep-related breathing disorder.
Management of circadian rhythm sleep–wake disorders involves a combination of behavioral interventions, light therapy, and timed melatonin therapy. Treatment is individualized to the specific circadian rhythm sleep–wake disorder [5]. The goal of therapy is to gradually realign the patient’s sleep and wake times with the desired schedule. The timing of light and melatonin therapies is critical to determining their biologic effects. The key biologic markers are the dim light melatonin onset (DLMO), which typically occurs approximately two hours prior to habitual sleeptime, and the core body temperature minimum (CBT-min), which typically occurs 2-3 hours prior to habitual wake up time. Exposure to light prior to the body temperature minimum will cause the circadian rhythm to delay (i.e. the next night, there will be a tendency to go to bed and wake up later). Light exposure after the core body temperature minimum will cause the circadian system to advance (i.e. the next night, there will be a tendency to go to bed and to wake up earlier). The effect of light is most potent when it is in the blue spectrum and administered close to the CBT-min. Melatonin has the opposite phase response relationship that light has; melatonin given prior to the CBT-min will cause the circadian rhythm to advance (i.e., the next night, there will be a tendency to go to bed and wake up earlier) whereas melatonin administration after the CBT-min will cause the circadian system to delay (i.e., the next night, there will be a tendency to go to bed and wake up later).
The International Classification of Sleep Disorders, third edition (ICSD-3), has the following three diagnostic criteria for all circadian rhythm sleep–wake disorders [6]:
A disrupted sleep–wake pattern, thought to be due to misalignment or malfunction of the circadian system;
A complaint of insomnia, excessive sleepiness, or both;
Suboptimal performance in an important area of functioning (e.g. occupation, education, social life, mental or physical life).
2. Classification of the circadian rhythm sleep-wake disorders
Intrinsic CRSWDs include the following: 1) advanced sleep–wake phase disorder (extreme early bird), 2) delayed sleep–wake phase disorder (extreme night owl), 3) non-24-hour sleep–wake rhythm disorder (drifting circadian rhythm), and 4) irregular sleep–wake rhythm disorder (no rhythm). The two circadian disorders caused by extrinsic factors are 1) shift work disorder and 2) jet lag disorder, both of which are due to behaviorally mediated misalignments of circadian system.
3. Are you sure your patient has a circadian rhythm sleep-wake disorder? What should you expect to find?
Circadian rhythm disorders can result in clinically significant symptoms of insomnia, excessive daytime sleepiness, and cognitive impairment. In addition, a drastic misalignment of one’s circadian clock with societal norms can often have implications for one’s emotional well-being and social functioning. There are 6 major categories of circadian rhythm sleep–wake disorders which are grouped based on patterns of sleep time:
An advance (early timing) in the phase of the major sleep episode in relation to the desired or required sleep time and wake-up time, (i.e. the patient feels sleepy too early and wakes up too early).
Symptoms are present for at least three months.
When the patient sleeps of his/her own accord, sleep quality and duration are improved with a consistent but advanced timing of the major sleep episode.
Sleep log and, whenever possible, actigraphy monitoring for at least seven days show a stable advance in sleep period. Both work/school days and free days must be included during the actigraphy monitoring period.
The patient’s symptoms are not better explained by another current sleep, medical, neurological disorder or mental disorder.
Clinical Characteristics: ASWPD is an intrinsic defect of the circadian system in which sleep duration and quality are normal, but where sleep and wake up times are earlier than desired or socially acceptable. Generally, the intrinsic circadian rhythm period is actually shorter than normal.
Patients with ASWPD often report that they are unable to stay awake past 7 PM and tend to wake up around 2-5 AM. If a patient is able to set their own schedule they will obtain adequate sleep and feel refreshed in the morning. However, since due to social obligations they often stay up later than naturally desired while still waking early. This can lead to a sleep deficit over time that leads to daytime sleepiness.
3.2 Delayed sleep-wake phase disorder (DSWPD)
ICSD-3 Diagnostic Criteria [6]: A delay (late timing) in the phase of the major sleep episode in relation to the desired or required sleep time and wake-up time (i.e. the patient does not feel sleepy at bed time and wakes up too late).
The symptoms are present for at least three months.
When patients sleep of their own accord, sleep quality and duration are improved with a consistent but delayed timing of the major sleep episode.
Sleep log and, whenever possible, actigraphy monitoring for at least seven days show a stable delay in sleep period. Both work/school days and free days must be included during the actigraphy monitoring period.
The patient’s symptoms are not better explained by another current sleep, medical, neurological disorder or mental disorder
Clinical Characteristics: DSWPD is one of the most common circadian rhythm sleep–wake disorders, often affecting adolescents and young adults when eveningness tendencies are typically the strongest. This is a defect of the circadian system whereby the sleep/wake cycle is misaligned with the patient’s desired schedule by more than 2 hours. Individuals go to sleep and wake up at substantially later times than desired, which can lead to social consequences such as chronic tardiness at work or school. Delayed bedtimes (usually between 1 and 6 AM), coupled with early awakenings to meet social/occupational/academic obligations, result in a sleep debt that accumulates over time. There is often a high prevalence of comorbid depression, and higher degrees of circadian misalignment correlate with greater severity of depression.
The intrinsic circadian period is generally longer than normal. DSWPD typically emerges during adolescence and can continue into adulthood.
Chronic or recurrent pattern of irregular sleep and wake episodes throughout the 24-hour period.
Symptoms are present for at least three months.
Sleep log and, whenever possible, actigraphy monitoring for at least seven days, show no major sleep period and multiple irregular sleep bouts (at least three) during a 24-hour period.
The patient’s symptoms are not better explained by another current sleep, medical, neurological disorder or mental disorder.
Clinical Characteristics: ISWRD is characterized by a temporally disorganized sleep and wake pattern such that there are no clearly defined periods of wake and/or sleep. Multiple sleep and wake periods can occur throughout the day, and usually consist of 3 or more short intervals, approximately 1-4 hours each. The longest period generally occurs in the morning between 2 and 6 AM; however, the distribution of sleep and wake periods can vary per individual. The total sleep duration throughout a 24-hour period is generally normal for the individual’s age. Because of the fragmented nature of sleep, individuals can experience frequent napping, excessive daytime sleepiness, and difficulty staying asleep at night.
History of insomnia, excessive daytime sleepiness, or both, which alternate with asymptomatic episodes, due to misalignment between the 24-hour light–dark cycle and the non-entrained endogenous circadian rhythm of sleep–wake propensity.
Symptoms persist over the course of at least three months.
Daily sleep logs and actigraphy for at least 14 days, preferably longer for blind persons, show a pattern of sleep and wake times that typically delay each day.
The patient’s symptoms are not better explained by another current sleep, medical, neurological disorder or mental disorder.
Clinical Characteristics: N24SWD, also known as non-trained rhythm disorder, is characterized by chronic cycles of sleep and wake that are not always synchronized with the 24-hour environment. There is a gradual, but consistent drift of sleep and wake times later into the day. Attempting to maintain a regular sleep–wake schedule can lead to symptoms of excessive daytime sleepiness, chronic fatigue, early morning awakenings and insomnia. These symptoms alternate with days to weeks in which the patient is asymptomatic, owing to the patient’s endogenous circadian system coinciding with the external 24-hour cycle. Napping is common, and patients often report impairment of social and occupational functioning due to non-entrained sleep–wake schedule.
N24SWD occurs most often in blind individuals. Onset of symptoms usually occurs in 2nd or 3rd decade of life, and men are disproportionally affected compared to women at a ratio of >2: 1 [7].
Insomnia and/or excessive sleepiness, accompanied by a reduction of total sleep time, which is associated with a recurring work schedule that overlaps with the usual time for sleep.
Symptoms are present and associated with the shift work schedule for at least three months.
Symptoms cause clinically significant impairment in mental, physical, social, occupational, education, or other important areas of functioning.
Sleep log and, whenever possible, actigraphy monitoring for at least 14 days (work and free days) demonstrate a disturbed sleep and wake pattern.
The patient’s symptoms are not better explained by another current sleep, medical, neurological disorder or mental disorder.
Clinical Characteristics: Individuals who work night shift experience difficulty with sleep and alertness at desired times, and are at greater risk for the variety of adverse health outcomes associated with poor sleep. Shift workers generally report 30-90 minutes less sleep compared to those not working shifts, and their sleep quality tends to be more fragmented and of poorer quality. Shift workers also experience difficulty falling and staying asleep, with as many as 20% of shift workers having clinically significant insomnia [8]. During waking hours, night shift workers are more prone to increased sleepiness, decreased neurocognitive function and more significant changes to mood than their non-night shift counterparts.
Insomnia or excessive daytime sleepiness associated with a reduction of total sleep time coinciding with jet travel across at least two time zones
Impaired daytime function, general fatigue, or somatic symptoms that begin within two days of travel
The sleep disturbance cannot be explained by another disorder.
Clinical Characteristics: JLD occurs when an individual travels through time zones faster that the endogenous circadian rhythm can adjust, resulting in desynchrony between the external light–dark cycle and one’s internal clock. Symptoms of JLD include difficulty falling and staying asleep, excessive daytime sleepiness, generalized fatigue, impaired daytime performance, and various somatic symptoms (most commonly gastrointestinal) that begin 1 to 2 days post-travel [8].
In addition to number of time zones traveled, the severity of jet lag is affected by the direction of travel. Eastward travel leads to more difficulty with falling asleep and is more difficult to adjust to, while westward travel is more disruptive to sleep maintenance.
4. How and/or why did the patient develop a circadian rhythm sleep-wake disorder? Which individuals are of greatest risk of developing a circadian rhythm sleep-wake disorder?
4.1 Advanced sleep-wake phase disorder (ASWPD)
Genetic studies have shown possible links to mutations in the PER2 and CSNK1D clock genes, both of which are inherited in autosomal dominant pattern [2, 3]. There is an estimated 1% prevalence, with males and females being equally affected. Older adults are more likely to have advanced tendencies. Other contributing factors include decreased responsiveness to evening light, and increased responsiveness to morning light, both of which can shift the sleep–wake cycle earlier than desired.
4.2 Delayed sleep–wake phase disorder (DSWPD)
The pathophysiology of DSWPD is multifactorial [2, 3] l. It is hypothesized that certain exogenous factors, such as increased exposure to evening light and greater sensitivity to evening light may play a role in development of DSWPD. Genetic factors may play a role, but their exact contribution is less understood than is the case for ASWPD.
DSWPD typically emerges during adolescence and can continue into adulthood. Males and females seem to be affected equally. Peak age appears to be 21 years old in males and 17 years old in females. There is a large variability in its reported prevalence, with population study estimates ranging from <1% up to 10% [1, 2, 3]. Patients with hepatic cirrhosis are also affected at much higher rates (33%).
4.3 Irregular sleep-wake rhythm disorder (ISWRD)
ISWRD is most commonly seen in individuals with neurodegenerative conditions, particularly those with Alzheimer’s disease and late-afternoon sundowning [1, 2, 3]. It is also more common in those with traumatic brain injury, in children with developmental delay, in patients with schizophrenia, and particularly in patients who are institutionalized. It is hypothesized that intrinsic circadian dysfunction, coupled with decreased exposure to external synchronizing agents such as light and social activity, are factors in ISWRD.
4.4 Non-24 sleep-wake rhythm disorder (N24SWRD)
The etiology of N24SWD is related to disruption of the portion of the circadian system responsible for capturing photic stimuli in the retina. Pathologies that lead to vision loss often but not necessarily impact this system. Thus, N24SWD occurs most often in blind individuals, with reports of 50% of blind patients diagnosed with N24SWD and up to 70% of blind individuals having symptoms of chronic sleep disturbance [2, 3, 7]. Men are disproportionally affected compared to women at a ratio of >2:1. N24SWD is rare, but has been reported, in sighted individuals. In sighted individuals, N24SWD is thought to be a severe form of delayed sleep phase disorder where entrainment to light can no longer be effective [7].
4.5 Shift work sleep-wake disorder (SWD)
The mechanism behind SWD is thought to be governed by disruptions of 2 physiologic processes [1, 2, 3]. The first is related to one’s homeostatic drive for sleep, which increases throughout wakefulness. The second is one’s intrinsic rhythmic oscillations for sleep and wake periods, which is governed by the circadian pacemaker. This latter process is calibrated by environmental clues such as light. Both the homeostatic process and the circadian process are disrupted in SWD.
Individuals at greatest risk for SWD are those that work rotating night shifts, rather than permanent night shifts, because they are never able to adapt to a stable sleep–wake pattern [8]. In addition, those with other sleep comorbidities can have synergistic effects of sleep disturbance, which lead to symptomatic worsening.
4.6 Jet lag disorder (JLD)
JLD results from desynchrony between the external light–dark cycle and an individual’s internal clock when travel across time zones occurs more quickly than the endogenous circadian rhythm can adjust. The prevalence of this condition is poorly defined [8]. Factors that affect jet lag severity include the direction of travel and number of time zones traveled, the person’s ability to sleep during travel, individual variations in circadian timing, presence of light cues at destination, and intake of alcohol and caffeine.
5. What diagnostic studies will be helpful in making or excluding the diagnosis of a circadian rhythm sleep-wake disorder? What other diseases can mimic circadian rhythm sleep-wake disorders?
5.1 Advanced sleep-wake phase disorder (ASWPD)
ASWPD is a clinical diagnosis and should be suspected in individuals who have a history of early sleep onset and wake times. It is important to obtain a detailed sleep history that addresses sleep patterns, napping habits, and daytime symptoms of sleepiness, cognitive changes, or mood changes. Targeted questions should be asked about difficulty falling or staying asleep and sleep quality both during the patient’s current schedule and during times when he/she has followed the preferred schedule. Obtaining collateral history from a bed partner is often useful.
Patients with suspected ASWPD should keep a sleep log for at least 7 days, preferably 14 days, including both weekdays and weekends (Figure 1). Actigraphy is helpful to supplement the sleep diary, especially if the history is unreliable. Melatonin levels, through salivary or plasma sampling, may show early melatonin onset or earlier phase of melatonin metabolite excretion via urinary 6-sulfatoxymelatonin, although these tests are not widely available for clinical purposes [2, 3, 9].
Figure 1.
This 14-day sleep diary of a patient with advanced sleep–wake phase disorder (ASWPD) depicts an early sleep onset (7-8 pm) and early wake time (3-4 am). The total duration of sleep time (shaded box) remains normal at 7-8 hours. Individuals with ASWPD usually do not have symptoms if they are allowed to sleep per their preferred schedule; however, when tasked with staying up later than their usual bedtime, they can have significant difficulty. This circadian rhythm is more prevalent in older adults who may not have the same work or school obligations (“ret” represents “retired”) that can contribute to other circadian rhythm disorders, such as delayed sleep–wake phase disorder.
5.2 Delayed sleep-wake phase disorder (DSWPD)
DSWPD is a clinical diagnosis and should be suspected when individuals report consistent bedtime and wake times that are significantly later than social norms. Bedtimes are often more informative than wake times, which are usually dictated by social or work/school obligations. It is also helpful to ask about sleep patterns during weekends, and during unrestricted periods such as vacations, when patients are able to sleep based on their own circadian preference (Figure 2). Sleep logs of at least 7 days, including both school/workdays and weekends, are needed to identify specific patterns [9]. It is important to inquire about other social factors, such as caffeine use later in the day, or excessive use of light-emitting devices before bedtime, which can also delay sleep onset.
Figure 2.
This 14-day sleep diary of a patient with delayed sleep–wake phase disorder (DSWPD) depicts variable sleep depending on the day of the week. The upwards arrow indicates “time in bed” while the downwards arrow indicates “time out of bed”. The shaded area represents sleep time of the patient. For example, in this diagram, the individual goes to bed between 9 and 10 pm on school days but is not able to sleep until 2-3 am. Due to his fixed school start time, he has to get out of bed between 6 and 7 am, leaving only 4-5 hours of sleep time (shaded box). However, on weekends (Friday and Saturday nights), he goes to sleep at his desired time of 2-3 am and wakes up at his desired time between 1 and 2 pm, accounting for a total sleep time of 10-11 hours. This variation in sleep pattern, based on day of the week, is classic for DSWPD, and results in the symptoms described by those with this condition.
Wrist actigraphy is another means of obtaining more quantitative data [9]. If the actigraph has a photo sensor it can provide information about the correlation between an individual’s light exposure and sleep time. Polysomnography is not typically indicated, unless there is clinical suspicion for another comorbid sleep disorder, such as sleep-disordered breathing. Salivary melatonin assays are available; however, these assays are used primarily as research tools and not for clinical diagnosis.
5.3 Irregular sleep-wake rhythm disorder (ISWRD)
The diagnosis of ISWRD is made by clinical history, with supplemental information from wrist actigraphy. There must be a reported chronic or recurrent pattern of irregular sleep and wake episodes throughout a 24-hour period, with a minimum of 3 cycles occurring during that time (Figure 3). A sleep log, and/or actigraphy must document these cycles for at least 7 days (preferably 14 days), and symptoms must be present for at least 3 months [9].
Figure 3.
This 14-day sleep diary of a patient with irregular sleep-wake rhythm disorder (ISWRD) depicts an irregular pattern of sleep throughout each 24-hour period. During each day, there are at least 3 sleep cycles occurring in a recurrent, but irregular fashion. The total sleep duration (shaded box) is usually normal for an individual’s age, however there is no clearly defined pattern. This disorder is more prevalent in older individuals and in those with dementia.
Polysomnography is not usually indicated, unless there is concern that the sleep disturbance is better explained by another disorder (e.g., a sleep-related breathing disorder).
5.4 Non-24 sleep-wake rhythm disorder (N24SWRD)
Sleep diary and actigraphy are important in confirming a non-entrained sleep pattern and will also show a gradual drift of onset and offset of the sleep–wake rhythm (Figure 4). In order to appreciate the drift, this data should be obtained for at least 2 weeks, and symptoms should be present for at least 3 months [9].
Figure 4.
This 14-day sleep diary of a patient with non-24 sleep–wake phase disorder (N24SWPD) depicts a gradual drift in onset and offset of the sleep duration (shaded box), usually by 30 minutes each day. In order to best appreciate this drift, a sleep diary or actigraphy should be obtained for at least 2 weeks, and ideally more if possible. This circadian rhythm disorder is most prevalent in individuals who are blind.
Other measurements such as continuous core body temperature or serial measurements of melatonin can be confirmatory as they also exhibit a similarly non-24-hour drifting rhythm. However, these procedures are not required to make the diagnosis of N24SWD.
Attention should be paid to distinguish N24SWD from DSWPD, as these patients can display a similar evening phenotype and up to 25% of N24SWD are often initially misdiagnosed as DSWPD [2, 3, 9].
5.5 Shift work sleep-wake disorder (SWD)
SWD is best assessed through careful sleep history and sleep diary. Particular attention should be paid to a patient’s occupation, history with shift work disorder with prior jobs, and impaired task performance at work. Factors specific to the patient’s home environment (i.e. lack of dedicated dark space for sleeping, noise levels during the day, etc.) can further reduce the likelihood that the patient can obtain restorative sleep. The clinical history should also inquire about features of other comorbid sleep, medical, and mental disorders. A sleep diary should be obtained for at least 2 weeks and should capture both work and non-work days [9]. Validated questionnaires, such as the Insomnia Severity Index and Epworth Sleepiness Scale, can be used but are not required to diagnose SWD.
Wrist actigraphy, especially when performed with an actigraphy that includes a photosensor, can better quantify sleep duration. There is no need for polysomnography unless there is a clinical suspicion for a comorbid sleep disorder, such as a sleep-related breathing disorder. Melatonin sampling is done in research settings, but is not routinely used in the clinical setting.
5.6 Jet lag disorder (JLD)
Clinical history is the most important tool. The clinician should specifically obtain information about number of time zones crossed and the timeline of symptom occurrence. JLD is often confused with travel fatigue since there are many overlapping symptoms; however, the distinction is that travel fatigue is not dependent on the number of time zones traveled and tends to resolve quicker.
Since JLD is transient, and clinical history is usually clear, there is typically no role for formal diagnostic testing.
6. If you decide the patient has a circadian rhythm sleep-wake disorder, how should the patient be managed?
6.1 Advanced sleep-wake phase disorder (ASWPD)
Bright light therapy in the early evening is the primary treatment for ASWPD, with the goal to delay the circadian phase so it is better aligned to desired sleep and wake times [5, 9]. Patients should use a bright light that filters out ultraviolet rays (2,500 to 10,000 lux) for 1-3 hours per day, starting at the time when they first experience sleepiness in the evening (usually around 7-9 pm). This should be done to gradually delay bedtime by 1-2 hours each day until desired times are met (Figure 5).
Figure 5.
For individuals with advanced sleep–wake phase disorder (ASWPD), bright light therapy should be given for 1-3 hours per day in the evening starting at the time of sleepiness. This will gradually shift the sleep time until the desired schedule is met.
There is no strong evidence to support pharmacologic therapy in ASWPD. Melatonin in the morning can theoretically shift the clock to a later phase, however the sedating effect of melatonin often limits morning use. Early morning hypnotics to resume sleep can lead to daytime grogginess and hangover effect and thus are generally discouraged.
6.2 Delayed sleep-wake phase disorder (DSWPD)
Management of DSWPD consists of behavioral modifications to realign one’s circadian system with desired sleep–wake times, and concurrent melatonin use and light therapy. Easily modifiable behaviors should be targeted first. These behaviors include avoiding daytime naps, eliminating excessive use of caffeine and/or alcohol, reducing stimulating activity, and minimizing light (particularly blue light) exposure 2 hours prior to bedtime.
For those who fail to respond to behavioral therapies alone, timed melatonin can supplement these behavioral modifications. One reasonable approach would be to take melatonin daily, approximately 3-5 hours prior to desired sleep time [5]. Doses may vary from 0.5-5 mg, though it is best to use the lowest effective dose. Melatonin dose and timing can be strategically adjusted based on clinical response. Relapse tends to occur in high rates (80-90%) once melatonin is discontinued [3, 5].
Morning light therapy can be coupled with the above interventions. Commercial light boxes that either contain a broad spectrum of white light or narrow spectrum of blue light (2,000-10,000 lux) should be used every morning between just after typical wake-up time, with gradual advancing of the sleep–wake time until the desired time is reached [5, 9]. Care should be taken to avoid light exposure earlier than 2-3 hours before the habitual wake up time because light prior to the core body temperature minimum may cause further delays in circadian phase (Figure 6).
Figure 6.
For individuals with delayed sleep–wake phase disorder (DSWPD), treatment can consist of low-dose melatonin (to be taken ~3-5 hours prior to desired sleep time), along with phototherapy in the morning upon awakening. This combination can cause gradual advancement in sleep phase.
6.3 Irregular sleep–wake rhythm disorder (ISWRD)
Treatment of ISWRD involves using behavioral strategies to restructure an individual’s daily routine, and to optimize sleep hygiene to better consolidate sleep times and improve daytime alertness. It is important to create a cognitively enriched and socially interactive environment during the day to maintain alertness and prevent excessive napping. At night, measures should be taken to reduce noise and light, and to prevent sleep disturbances caused by other factors (e.g., nocturia).
Light therapy remains the most effective intervention during the day. Exposure to 3,000-5,000 lux of light for at least 2 hours in the morning has been shown to improve daytime alertness, reduce napping, and consolidate nightly sleep [5].
Melatonin can also be used for management of ISWRD, though positive results with melatonin use are less consistent than with treatment of other CRSWDs. Melatonin doses of 1-5 mg can be used 30 min prior to bedtime to facilitate sleep, and melatonin is more effective if used in conjunction with light therapy [3, 5]. Controlled-release formulation can be more effective than immediate release in this specific patient population with ISWRD.
6.4 Non-24 sleep-wake rhythm disorder (N24SWRD)
Treatment for N24SWD includes attempting to re-synchronize the circadian pacemaker using behavioral approaches and pharmacologic therapy. Education regarding proper sleep hygiene and maintenance of regularly scheduled timing of meals, social activities and physical exercise is important.
Tasimelteon, a melatonin agonist with affinity for the MT1 and MT2 receptors, is Food and Drug Administration (FDA) approved for use in N24SWD [5]. Melatonin can also be used to achieve gradual re-alignment. Higher doses (3-10 mg) may be given 1-2 hours prior to the desired bedtime for the first month. Entrainment usually occurs within 5-10 weeks, after which low-dose melatonin (0.5-1 mg) should be maintained to prevent relapse [5, 7].
Less robust evidence exists for bright light therapy, though it has been shown to be effective in sighted individuals with N24SWD and can be used in the early morning after awakenings.
6.5 Shift work sleep-wake disorder (SWD)
Management of SWD should first start with changes to work schedule, if possible, with adjustments to sleep hygiene. A regular daytime sleep schedule that can be followed, even during non-working days, is recommended to promote stability to the circadian system. This can be organized around an individual’s personal schedule, but should ideally incorporate at least a 5 to 6-hour block of uninterrupted sleep. The sleep environment should be optimized to reduce sound, light, unfavorable temperatures and other factors that can interfere with sleep. Light blocking window shades can be used, and a temperature setting between 65 and 70 degrees Fahrenheit is optimal.
While at work, individuals can use continuous exposure to high-intensity light (2,000-10,000 lux) for as little as four 20-minute periods during the first part of the nigh [8] t. Use of blue light-blocking goggles in the morning and general avoidance of morning light help the circadian rhythm remain adapted to the shift work schedule. Additionally, short naps (<45 minutes) prior to the start of the work shift are a low-risk intervention that can be used to promote wakefulness during the shift.
If these behavioral measures fail, medication therapy is the next step. Modafinil (200 mg) and armodafinil (150 mg) are FDA approved for shift work disorder and can be used during the first part of the night [5, 8]. Small doses of caffeine (100-250 mg, equivalent to a small cup of coffee) can also be used during the first part of the night. Short-acting hypnotics such as zolpidem and zaleplon can be used to promote sleep during the daytime; use of hypnotics must be introduced cautiously to prevent nocturnal grogginess (during work hours). Exogenous melatonin 30 min prior to desired bedtime can be used, although the evidence for this is poor [5].
6.6 Jet lag disorder (JLD)
The treatment strategy depends on the length of trip, number of time zones traveled, and direction of travel [9]. Trips less than 3 days are usually too short to create problematic jet lag symptoms. Treatment planning is best done in anticipation of travel.
For eastward travel on trips longer than 3 days and up to 7 time zones, a strategy using timed light exposure and melatonin can help advance the circadian rhythm (making natural bedtime and wake time earlier) to the new time zone. Bright light therapy can be started up to 3 days prior to departure to start the advancing process. Individuals should wake up about 1 hour prior to usual wake-up time and expose themselves to bright light for at least an hour. Upon arrival at destination, strategic light exposure throughout the afternoon and light avoidance during the early morning can help with this circadian realignment. The specific timing of light exposure is based on when the patient’s habitual core body temperature minimum (CBT-min) occurs, which is usually 3 hours before habitual wake-up time. Light exposure after the CBT-min causes circadian phase advancement, whereas light exposure before the CBT-min causes a circadian phase delay and is thus counterproductive for eastward travel [8].
Timed melatonin (3-5 mg) taken at the desired destination bedtime can be used concurrently with prescribed light exposure. Melatonin should first be taken on the evening of arrival and continued for up to 5 days. Hypnotics have been used by patients during travel, but these medications can lead to impaired daytime performance, and thus are generally not recommended for management of JLD. Caffeine can help mitigate daytime sleepiness. Other stimulants (e.g. modafinil, armodafinil) can offset daytime sleepiness though these medications are not FDA approved for JLD. For eastward travel that crosses more than 8 time zones, it is often easier to pursue a circadian phase delay, rather than a circadian phase advance, to mitigate symptoms of JLD. This approach generally involves seeking out early morning light at the destination and avoiding light exposure in the afternoon.
Westward travel requires a delay in the circadian rhythm (later bed times and wake times) to adjust to the destination time zone. It is advised to seek bright light before the calculated CBT-min at the destination (based on the home time zone). For example, if one’s habitual wake up time in Boston is 8 am Eastern Standard Time (EST), the habitual CBT-min will be at 5 am EST. If that individual travels to Westward to Hawaii (5-hour time zone delay), the CBT-min will occur at midnight EST, thus bright light exposure should be given prior to that time. Melatonin or hypnotics are generally not required in JLD associated with westward travel.
Resources such as www.jetlagrooster.com and the British Airways jet lag advisor [10] can help individuals plan light and melatonin exposure including in preparation for travel.
7. How do patients generally respond to treatment? What other considerations exist for patients with circadian rhythm disorders?
Treatment of circadian rhythm sleep wake disorders can often be challenging, and requires an individualized and multimodal approach, incorporating behavioral strategies such as directed light exposure, and appropriately timed melatonin. Overall effectiveness can be improved by combining these measures with chronotherapy, which gradually and progressively shifts the circadian clock.
Patients tend to respond well initially, however often require significant personal investment to maintain their newly desired schedule, and relapse to prior sleep schedule is not uncommon. It is essential to have the support of family, friends, teachers and coworkers to establish and maintain a sustainable new sleeping schedule.
When treating circadian rhythm sleep wake disorders, it is important to optimize the treatment of other comorbid medical conditions, especially psychiatric and mood disorders. This is especially true in adolescents and older patients. The importance of addressing sleep hygiene and one’s sleep environment cannot be overstated as a critical component of treatment.
The treatment of CRSWDs remain a challenge, in part because of the scarcity of large, multicenter placebo-controlled trials using phototherapy and pharmacotherapy. However there have been recent rapid advances in our understanding of the genetics of circadian rhythm regulation, which may lead to improved diagnostic tools and treatments. For example, technology involving DNA manipulation has been used to generate Cry1/Cry2 knockout animals to further study the expression of specific genes on circadian function [11]. This opens up avenue for new therapeutic approaches in many disorders, specifically neuropsychiatric conditions, associated with circadian rhythm disturbance.
8. Conclusions
Circadian rhythm disorders are common conditions that occur as a result of misalignment between an individual’s intrinsic time-keeping system, and extrinsic cues. There are usually multifactorial contributions, including genetic influences, and behaviorally induced elements. Effective treatment approaches largely revolve around strategically timed melatonin and phototherapy to shift one’s sleep phase to a more desired time. Consultation with a sleep medicine clinician may be helpful if symptoms persist, or to clarify a suspected circadian rhythm disorder.
Acknowledgments
The authors wish to acknowledge faculty at the Northwestern University Center for Circadian & Sleep Medicine, and the UCLA Sleep Disorders Center for their significant contributions towards our training and sleep medicine education.
\n',keywords:"circadian rhythm disorder, advanced sleep–wake phase disorder, delayed sleep–wake phase disorder, irregular sleep–wake rhythm disorder, non-24-hour sleep–wake rhythm disorder, shift work disorder, jet lag disorder, melatonin, phototherapy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/78358.pdf",chapterXML:"https://mts.intechopen.com/source/xml/78358.xml",downloadPdfUrl:"/chapter/pdf-download/78358",previewPdfUrl:"/chapter/pdf-preview/78358",totalDownloads:121,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 13th 2021",dateReviewed:"August 5th 2021",datePrePublished:"September 1st 2021",datePublished:"January 7th 2022",dateFinished:"September 1st 2021",readingETA:"0",abstract:"Circadian rhythm disorders are a group of sleep conditions that involve a misalignment of an individual’s internal timekeeping system with that of one’s desired sleep-wake time. This desynchrony can compromise sleep health as well as the functioning of other organ system, and significantly diminish one’s quality of life. There are six well-defined circadian rhythm disorders that can be classified as either intrinsic or extrinsic, based on the underlying factors that contribute to the condition. Intrinsic circadian disorders include the following: 1) advanced sleep-wake phase disorder, 2) delayed sleep-wake phase disorder, 3) irregular sleep-wake rhythm disorder, and 4) non-24-hour sleep-wake rhythm disorder. The two circadian disorders caused by external factors include 1) shift work disorder, and 2) jet lag disorder, both of which are due to behaviorally mediated misalignments of circadian system. This chapter serves to summarize these disorders, guide clinicians towards screening and evaluation of these conditions, and introduce basic treatment strategies that can be applied by non-sleep medicine clinicians.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/78358",risUrl:"/chapter/ris/78358",signatures:"Ajay Sampat and Armand Ryden",book:{id:"10350",type:"book",title:"Sleep Medicine and the Evolution of Contemporary Sleep Pharmacotherapy",subtitle:null,fullTitle:"Sleep Medicine and the Evolution of Contemporary Sleep Pharmacotherapy",slug:"sleep-medicine-and-the-evolution-of-contemporary-sleep-pharmacotherapy",publishedDate:"January 7th 2022",bookSignature:"Denis Larrivee",coverURL:"https://cdn.intechopen.com/books/images_new/10350.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-822-4",printIsbn:"978-1-83969-821-7",pdfIsbn:"978-1-83969-823-1",isAvailableForWebshopOrdering:!0,editors:[{id:"206412",title:"Prof.",name:"Denis",middleName:null,surname:"Larrivee",slug:"denis-larrivee",fullName:"Denis Larrivee"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"416407",title:"Assistant Prof.",name:"Ajay",middleName:null,surname:"Sampat",fullName:"Ajay Sampat",slug:"ajay-sampat",email:"acsampat@ucdavis.edu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"426799",title:"Dr.",name:"Armand",middleName:null,surname:"Ryden",fullName:"Armand Ryden",slug:"armand-ryden",email:"armand.ryden@va.gov",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"VA Greater Los Angeles Healthcare System",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction: what every clinician needs to know",level:"1"},{id:"sec_2",title:"2. Classification of the circadian rhythm sleep-wake disorders",level:"1"},{id:"sec_3",title:"3. Are you sure your patient has a circadian rhythm sleep-wake disorder? What should you expect to find?",level:"1"},{id:"sec_3_2",title:"3.1 Advanced sleep–wake phase disorder (ASWPD)",level:"2"},{id:"sec_4_2",title:"3.2 Delayed sleep-wake phase disorder (DSWPD)",level:"2"},{id:"sec_5_2",title:"3.3 Irregular sleep-wake rhythm disorder (ISWRD)",level:"2"},{id:"sec_6_2",title:"3.4 Non-24 sleep-wake rhythm disorder (N24SWRD)",level:"2"},{id:"sec_7_2",title:"3.5 Shift work sleep-wake disorder (SWD)",level:"2"},{id:"sec_8_2",title:"3.6 Jet lag disorder (JLD)",level:"2"},{id:"sec_10",title:"4. How and/or why did the patient develop a circadian rhythm sleep-wake disorder? Which individuals are of greatest risk of developing a circadian rhythm sleep-wake disorder?",level:"1"},{id:"sec_10_2",title:"4.1 Advanced sleep-wake phase disorder (ASWPD)",level:"2"},{id:"sec_11_2",title:"4.2 Delayed sleep–wake phase disorder (DSWPD)",level:"2"},{id:"sec_12_2",title:"4.3 Irregular sleep-wake rhythm disorder (ISWRD)",level:"2"},{id:"sec_13_2",title:"4.4 Non-24 sleep-wake rhythm disorder (N24SWRD)",level:"2"},{id:"sec_14_2",title:"4.5 Shift work sleep-wake disorder (SWD)",level:"2"},{id:"sec_15_2",title:"4.6 Jet lag disorder (JLD)",level:"2"},{id:"sec_17",title:"5. What diagnostic studies will be helpful in making or excluding the diagnosis of a circadian rhythm sleep-wake disorder? What other diseases can mimic circadian rhythm sleep-wake disorders?",level:"1"},{id:"sec_17_2",title:"5.1 Advanced sleep-wake phase disorder (ASWPD)",level:"2"},{id:"sec_18_2",title:"5.2 Delayed sleep-wake phase disorder (DSWPD)",level:"2"},{id:"sec_19_2",title:"5.3 Irregular sleep-wake rhythm disorder (ISWRD)",level:"2"},{id:"sec_20_2",title:"5.4 Non-24 sleep-wake rhythm disorder (N24SWRD)",level:"2"},{id:"sec_21_2",title:"5.5 Shift work sleep-wake disorder (SWD)",level:"2"},{id:"sec_22_2",title:"5.6 Jet lag disorder (JLD)",level:"2"},{id:"sec_24",title:"6. If you decide the patient has a circadian rhythm sleep-wake disorder, how should the patient be managed?",level:"1"},{id:"sec_24_2",title:"6.1 Advanced sleep-wake phase disorder (ASWPD)",level:"2"},{id:"sec_25_2",title:"6.2 Delayed sleep-wake phase disorder (DSWPD)",level:"2"},{id:"sec_26_2",title:"6.3 Irregular sleep–wake rhythm disorder (ISWRD)",level:"2"},{id:"sec_27_2",title:"6.4 Non-24 sleep-wake rhythm disorder (N24SWRD)",level:"2"},{id:"sec_28_2",title:"6.5 Shift work sleep-wake disorder (SWD)",level:"2"},{id:"sec_29_2",title:"6.6 Jet lag disorder (JLD)",level:"2"},{id:"sec_31",title:"7. How do patients generally respond to treatment? What other considerations exist for patients with circadian rhythm disorders?",level:"1"},{id:"sec_32",title:"8. Conclusions",level:"1"},{id:"sec_33",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Zee PC, Attarian H, Videnovic A. Circadian Rhythm Abnormalities. Continuum (Minneap Minn). 2013 Feb;19(1 Sleep Disorders): 132-47'},{id:"B2",body:'Reid KJ, Zee PC. Circadian rhythm sleep disorders. Handb Clin Neurol 2011;99: 963-977'},{id:"B3",body:'Barion A, Zee PC. A clinical approach to circadian rhythm sleep disorders. Sleep Med. 2007 Sep;8(6):566-77'},{id:"B4",body:'Bjorvatn B, Pallesen S. A practical approach to circadian rhythm sleep disorders. Sleep Med Rev 2009;13(1):47-60'},{id:"B5",body:'Dodson ER, Zee PC. Therapeutics for circadian rhythm sleep disorders. Sleep Med Clin 2010;5(4):701-715'},{id:"B6",body:'American Academy of Sleep Medicine. International Classification of Sleep Disorders. 3rd ed. Darien, IL: American Academy of Sleepy Medicine; 2014'},{id:"B7",body:'Uchiyama M, Lockley SW. Non–24-Hour Sleep–Wake Syndrome in Sighted and Blind Patients. Sleep Medicine Clinics 2009; 4(2): 195 – 211'},{id:"B8",body:'Kolla BP, Auger RR. Jet lag and shift work sleep disorders: how to help reset the internal clock. Cleve Clin J Med 2011;78(10): 675-684'},{id:"B9",body:'Morgenthaler TI, Lee-Chiong T, Alessi C, et al. Practice parameters for the clinical evaluation and treatment of circadian rhythm sleep disorders. An American Academy of Sleep Medicine report. Sleep 2007;30(11):1445-1459'},{id:"B10",body:'British Airways Jet Lag Advisor. (2021, June 15). Retrieved from https://www.britishairways.com/travel/drsleep/public/en_gb'},{id:"B11",body:'Palm D, Uzoni A, Simon F, et al. Evolutionary conservations, changes of circadian rhythms and their effect on circadian disturbances and therapeutic approaches. Neuroscience & Biobehavioral Reviews 2021; Sep 2021 128(21-34)'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ajay Sampat",address:"acsampat@ucdavis.edu",affiliation:'
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The properties of the proposed estimator have been analysed. To judge the performance of the proposed model, I have considered a real data set and it is to be pointed out that the proposed model is more efficient in terms of relative efficiencies and privacy protection of respondents as well. 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IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
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Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
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If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. 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Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science and Technology from the Department of Chemistry, National University of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013. She relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the National Institute of Fundamental Studies from April 2013 to October 2016. She was a senior lecturer on a temporary basis at the Department of Food Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is currently Deputy Principal of the Australian College of Business and Technology – Kandy Campus, Sri Lanka. 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Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. He is especially interested in the genetic differentiation pattern and speciation process that correlate to the flashing pattern and mating behavior of some fireflies in Japan. He then worked for Olympus Corporation, a Japanese manufacturer of optics and imaging products, where he was involved in the development of luminescence technology and produced a bioluminescence microscope that is currently being used for gene expression analysis in chronobiology, neurobiology, and developmental biology. Dr. Suzuki currently serves as a visiting researcher at Kogakuin University, Japan, and also a vice president of the Japan Firefly Society.",institutionString:"Kogakuin University",institution:null}]}]},openForSubmissionBooks:{},onlineFirstChapters:{},subseriesFiltersForOFChapters:[],publishedBooks:{},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{}},subseries:{item:{id:"40",type:"subseries",title:"Ecosystems and Biodiversity",keywords:"Ecosystems, Biodiversity, Fauna, Taxonomy, Invasive species, Destruction of habitats, Overexploitation of natural resources, Pollution, Global warming, Conservation of natural spaces, Bioremediation",scope:"
\r\n\tIn general, the harsher the environmental conditions in an ecosystem, the lower the biodiversity. Changes in the environment caused by human activity accelerate the impoverishment of biodiversity.
\r\n
\r\n\tBiodiversity refers to “the variability of living organisms from any source, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; it includes diversity within each species, between species, and that of ecosystems”.
\r\n
\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
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