\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"2024",leadTitle:null,fullTitle:"Heat Transfer - Engineering Applications",title:"Heat Transfer",subtitle:"Engineering Applications",reviewType:"peer-reviewed",abstract:"Heat transfer is involved in numerous industrial technologies. 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\r\n\r\n\tThis book will be a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, PhD students and other scientists. The contents of the book will be written by multiple authors and edited by experts in the field. Authors are not limited in terms of topic, but encouraged to present a chapter proposal that best suits their current research efforts. Later, when all chapter proposals are collected, the editor will provide a more specific direction of the book.
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He works at the General Hospital of Northern Theater Command (formerly General Hospital of Shenyang Military Area) now. His major research interests are the clinical researches in the etiology, diagnosis and management of liver cirrhosis, portal hypertension (especially portal vein thrombosis and Budd-Chiari syndrome), and hepatocellular carcinoma. His major work was published in several international journals. He served as an associate editor of Annals of Hepatology, a lead guest editor in Gastroenterology Research and Practice and BioMed Research International, and a guest editor in Canadian Journal of Gastroenterology and Hepatology. He also served as a peer-reviewer in more than 100 journals. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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This new concept of smarter grids, known as smart grids, is incorporating automation technologies and communication in real time to the grid, with more intelligent devices, distributed generators’ connection and instant forward actions to electrical problems. These changes in the distribution system enable greater diversity in services related to energy, such as demand management, the use of distributed generation from renewable sources, connection of electric vehicles, and voltage maintenance after self‐healing, among others.
\nIn this sense, this chapter presents a new methodology and a software tool for real‐time reconfiguration of distribution network considering distributed generation units the renewable sources. The work of [1, 2] indicates that this system has a good performance, applying smart grids concepts, from the information and functionality of remote‐controlled equipment installed in the distribution test systems.
\nThis chapter is divided into five sections. The second section presents the main concepts that guide the smart grids. Following is shown the distributed generation technologies to the application exposed in this work. The fourth section presents the reconfiguration methodology developed in real time. Then, the results and discussions are exposed considering a large real distribution system. Finally, the main conclusions are presented in the last section.
\nThe smart grids represent a new paradigm in the way power systems are designed and operated. They are characterized by the integration of well‐established technologies and concepts that together seek to respond to the increased demand and requirements regarding quality and sustainability in the energy sector [3]. The resources available from technological advances in telecommunication and automation have brought benefits to the grid, especially: fast processing and information exchange between network devices, allowing real‐time monitoring and control; cost and time reduction with maintenance teams; the possibility of creating more robust and reliable systems and less susceptible to human limitations; and the integration of network structures, such as distributed generation, stability control, and demand control.
\nHowever, the benefits of the technology are often economically viable only if the automated system is part of a larger whole, provided with a degree of intelligence. This applies, for example, for the automatic network restoration, in which the automatic operation of remotely controlled switches must be integrated with the detection and isolation of the fault systems, ensuring the restoration of the largest possible number of consumers. In this case, the system\'s intelligence can be represented by the response of computer programs that employ optimization techniques and support decision‐making.
\nA general illustration of some features and elements of the smart grids is highlighted in Figure 1. From the bottom to the top, the figure shows the main interfaces between the power utility services and equipment, and the consumer and/or micro generator. The middle layer of the figure gives some examples of structures that can be related either to power utilities or to consumers. A brief discussion on the main concepts shown in the Figure 1 is presented in this section.
\nA general view of a smart grid.
The demand response refers to the ability of management and control of power system loads. The primary goal of DR was to reduce peak consumption by turning off loads in higher power consumption schedules. Alternatively, consumption can be shifted to off‐peak periods or even can be increased at certain periods, to maintain the stability of generation or to make the most of the available resources, such as energy from distributed generation [4].
\nThe implementation of DR is done through electronic devices that communicate with the power utility and receive commands to turn off the loads connected to them, according to the request of the utility or to the energy rate variation. Previously, a priority study of the loads must be done to avoid interruptions that may harm important services to the consumer.
\nThe demand response is one of the challenges of the energy market facing the smart grids. It can promote greater pressure to reduce prices and increase concern about the use of energy by consumers. From the utility\'s viewpoint, it may result in a change in load curves and reduction of the peak consumption, which implies lower need for investment in energy reserves. On the other hand, the biggest challenges are the consumers’ behavior change, the need for compatible technology—such as advanced metering structures—and the efficiency of government agencies in regulating and supervising the process.
\nAdvanced metering infrastructure comprises all the elements needed for the measurement and communication between consumers and suppliers. The communication in this case is bidirectional and allows, for example, that a power utility sends to the consumer the real‐time energy pricing. Integration with demand response devices allows load management according to the change in the price or according to the need of the power utility.
\nThe advanced metering infrastructure consists of electronic meters, communication networks, and a data management system (MDMS, meter data management system), which is responsible for storing and managing the large amount of data from the power meters and establish interface between the data collected and other features of the network, such as the billing system and maintenance teams, for example.
\nThe advantages of AMIs include the possibility of monitoring the energy billing in real time by the consumer, fast detection of measurement failures and non‐technical losses, and the creation of consumer profiles for demand response programs and fast response to energy restoration systems. AMIs also allow the creation of more accurate consumer databases for profiles and demand estimation studies and provide real‐time measurements that help in decision‐making regarding the system operation. The technological challenges of AMIs include the need for standardization of communication and interfaces between devices, and security issues to ensure that only authorized devices could access the network information [5].
\nThe smart meter may be considered as an evolution of the automatic electronic power meter. The main feature of smart meters is the two‐way communication, which makes it possible to receive real‐time power utility commands.
\nThe standard of communication varies depending on the smart meter application project. In some countries, for example, power meters communicate through wire with a data concentrator and the data concentrator communicates with a central by a wireless network. Many smart meters, however, have wireless communication capability directly to the operation center.
\nCommunication can be considered the biggest challenge of deploying smart meters. A wide variety of protocols and possible ways of communication is used, and there is no universal standardization of power meters. Possible arrangements of networks include the use of cellular networks, satellite communications, radio frequency, Wi‐Fi, power line communication, directly to a central or a data concentrator, or in cascade mode communicating through a mesh network. The main protocols used are defined by ANSI C12.18 standard in the United States, and IEC 61107/62056 in Europe. Regardless of the type of communication, the discussion about the project to be adopted involves cost, safety, and health.
\nSome common features in smart meters include possibility of remote connection and shutdown of the energy point, grid failure warning, fraud warning, real‐time monitoring of energy bill, and demand control. In some projects, smart meters also have the ability to communicate with user internal devices. For example, in a residence, the smart meter can receive or send information to and from appliances, air‐conditioning units. This concept is based on the home area network and enables the timely management of user loads.
\nThe smart grids led to concerns about changes in consumer load profiles, and the prospect of increased plug‐in hybrid electric vehicles (PHEV) connected to the power system is one of the most discussed points [6]. Electric vehicles are characterized by substituting fully or partially (in case of hybrid vehicles) the traditional internal combustion motor by electric motors for vehicle propulsion. Electric motors are typically powered by batteries; and if the batteries are recharged through the electric distribution network, the vehicles are called plug‐in.
\nThe impact of PHEVs in power systems is significant, and several studies highlight the need for planning of battery recharges, to avoid concentration of PHEVs overloading the system, and solutions based on the use of the vehicle to inject energy into the network during peak periods (vehicle‐to‐grid).
\nThe smart grids shall be prepared to absorb this new type of demand. Some aggravating quality factors in power systems can be cited: power imbalances (in case of single‐phase chargers), harmonics, and increased voltage drops and losses, particularly in feeders with large extensions. Furthermore, the capacity of distributions transformers may be easily extrapolated if a large amount of PHEVs is loaded simultaneously.
\nDistributed generation (DG) normally employs renewable energy sources, mainly wind and photovoltaic, due to difficulties of building small conventional power plants, such as hydro and thermal coal, close to consumption centers.
\nIn residences, photovoltaic energy has the advantages of cost and modularity to install in roofs—in the power range up to 10 kW. Figure 2 shows a typical arrangement of a photovoltaic microgeneration connected to the grid, which includes a set of batteries for energy storage, power electronics circuits (charge control and inverters), and a bidirectional power meter.
\nSmall‐scale generation connected to the grid.
To take advantage of DG, smart grids have to include advanced mechanisms of generation estimation, short‐ and long‐term ability to quickly regulate power and energy storage, and management of distributed resources in conjunction with the demand response [7]. The main technical issues involved are related to power quality, stability, and protection, due to the intermittent characteristic of the primary sources.
\nA high level of automation and information technology (IT) is expected in a smart grid. The network infrastructure must support data management of electronic meters (MDMS), monitoring and control of network status (overload, reactive power control, etc.), load and distributed generation management, charging of PHEVs, among other features. Information systems should communicate with each other at different levels of implementation, such as power regulation strategies, billing, maintenance management, consumer and network databases, geographic information systems (GIS), among others. Interoperability between standards and communication protocols plays a critical role in the advancement of smart grids. An important reference guide is published by the National Institute of Standards and Technology (NIST) [8].
\nAt the operation center, the supervisory systems (SCADA) perform the interface between the technical team (operator, planner, supervisor, etc.) and the network devices. At the distribution network, some automation features may include automatic adjustment of protection devices, automatic regulation of voltage levels, control of capacitor banks and transformers’ taps, control of distributed generation, self‐reconfiguration, in addition to the automatic management of loads and consumption measurements. Furthermore, the automation requires the employment of remote‐controlled equipment (switches, reclosers, circuit breakers, etc.), and digital controllers and intelligent electronic devices (IEDs).
\nWith the advancement of the smart grids, the profile of the load curves of distribution feeders will be subject to a different dynamic behavior. Some features, such as increased use of distributed generation, demand response, and charging of electric vehicles, will require a fast network response for new generation and load scenarios. The automatic reconfiguration in real time will help to improve the network performance and to promote more efficient use of the smart grids resources.
\nThe concept of distributed generation refers to the use of small generators directly connected to the distribution network or the local network of consumers. Among the current generation sources stand out wind power, photovoltaic, hydroelectric, and diesel. Thermal power plants and biomass have also been employed in DG, but on a smaller scale.
\nThe wind power harnessing is obtained from the kinetic energy conversion of the air masses through translational movement in rotational kinetic energy, using a wind turbine or wind generator for electricity generation. Figure 3 illustrates the typical components of a wind turbine: rotor, nacelle, generator, sensors, among others.
\nThe rotor is responsible for transforming the kinetic energy of wind in mechanical energy of rotation. The turbine blades are fixed in the rotor, which in turn is connected to a hub interconnected to the generator through a gearbox. The mechanisms to allow operation of the generator are located in the nacelle, for example, the turning control, the brake system, the wind sensors, among others.
\nOnly part of the extracted wind power can be used to generate electricity. The amount of energy that can be converted into electricity is obtained by the power coefficient of the wind turbine Cp, which is the ratio between the possible power to be extracted from the wind and the total amount of power contained therein [9, 10]. Equation (1) defines the effective output power of a wind turbine (kg m/s) (where 1 kg m/s is 9.81 W):\n
where ρ is the specific air density (kg/m3), A is the cross‐sectional area (m2) swept by the propeller blades or the turbine blades, e v is the wind speed (m/s).
\nSchematic of a wind turbine generating power.
Photovoltaic generation uses semiconductor elements capable to generate electricity from the direct conversion of solar energy in electrical energy through solar cells (photovoltaic). Although it can be straightforward, this conversion process depends on the characteristics of each semiconductor and quality of the materials employed in manufacturing technology.
\nOne of the key aspects for the photovoltaic systems implementation is the knowledge of local solar radiation characteristics. These data may be obtained through the meteorological data base information. Solar radiation and temperature are the major variables that affect the generated power of the photovoltaic cells. To illustrate these effects, characteristic curves were obtained with the PV module parameters (245 W KD245GH) of the Kyocera manufacturer [11]. Figure 4a shows the I–V curves as a function of solar radiation. As can be seen, the solar radiation modifies the available power by changing the output current of a photovoltaic module. Besides to the solar radiation, the operating temperature of the cells also influences the amount of generated power, since the output voltage of the photovoltaic cell is changed depending on the ambient temperature. Figure 4b shows the I–V characteristic curve considering the temperature variation.
\n(a) Current and (b) voltage characteristics for the KD245GH module.
The photovoltaic systems’ behavior is usually characterized by measuring the voltage and current curves (I–V curves) of the PV modules from standard test conditions (STC). These conditions establish the reference values for the radiation (L) 1000 W/m2, temperature (T) 25°C, and air mass (AM) 1.5.
\nHowever, STC conditions rarely occur in real operating conditions. Consequently, the estimation of the PV modules behavior requires extrapolation from the standpoint of real operating conditions.
\nCurrently, there is no standard methodology for assessing the electricity production of PV modules. There are typically two methods of assessing the amount of maximum power that modules produce numerical methods and algebraic methods [12, 13]. The numerical procedures are used to calculate the instantaneous power peak of the curve I–V in specific conditions, such as STC. Since the algebraic procedures use data regression analysis using historical data and can be used for any operating condition, they are more suitable for real data applications.
\nThe method of Osterwald is one of the algebraic methods most commonly used because of the simplicity. It allows calculating the output power of a photovoltaic system for any amount of irradiation and cell temperature. Equation (2) shows the method of Osterwald [14]:\n
where PSTC is the maximum power generated by the module (Watts), usually being the rated power of the manufacturer datasheet. GSTC is the overall radiation to the condition of the STC; Gi and Ti is the overall radiation; and air temperature condition TSTC is measured and the temperature for STC condition. Knowing that the STC conditions are given in restricted conditions, it is necessary to apply a power factor correction for temperature, which is represented by γ and corresponds to the value of the interval -0.005 to -0.003°C-1.
\nThe energy generation through the hydraulic potential for exploitation in a river can be characterized in different ways: When there is a concentrated unevenness in a waterfall, featuring a natural advantage, through a barrage with small unevenness, or through diversion of river from its natural course. The water is conducted through canals, tunnels, or penstocks and transformed into kinetic energy by spinning of turbine blades; this motion produces electrical power from the drive shaft of a generator.
\nSmall hydro power (SHP) currently accounts for a fast and efficient way to promote the expansion of supply of electricity. This type of project enables better compliance with the consumers’ requirement of small urban centers and rural areas, complementing the power supply performed by the conventional system.
\nRegarding operating philosophies, SHPs have great flexibility, having two main forms of reservoirs regularization: the river or storage, with daily regulation reservoir, and the dispatched power depends on the physical characteristics and techniques and also the central philosophy of the undertaking that holds.
\nThe estimated output power for a hydraulic turbine can be obtained in relation to the height of the available downfall and of the flow of the hydraulic turbine, considered constant. Generally, the turbines models of Pelton, Francis, Kaplan, and Bulb are used for small hydro projects. The choice considering one or other model is defined according to the height of fall characteristics, water flow, and rotation of the turbine generator set.
\nIn general terms, the power of a turbine can be expressed as the sum of the three forms of energy of Bernoulli\'s theorem [9], as represented in Eq. (3).\n
where v is the flow velocity (m/s), g represents the gravitational constant (m/s), p is the water pressure (N/m2) at a height h of water (m) with ρ density (kg/m3) and a water flow Q (m3/s).
\nThe output power of the turbine shaft in a hydroelectric system can be obtained through Eq. (4) [15].\n
where ρ represents the specific mass of water (1000 kg/m3), g is the gravity acceleration (9.81 m/s2), Q is the flow rate (m3/s) of the turbine at a drop height Hliq (m) with an efficiency of ηT, which depend on the chosen hydraulic turbine model.
\nThe reconfiguration of the distribution network is considered an optimization problem in that search, among the various solutions (topologies) possible, the solution that leads to better performance, considering the ultimate goal of reconfiguration and observing the network constraints. One factor that increases the complexity of the problem is the high number of switching devices in a real network, which results in a lot of different possible configurations to be analyzed.
\nIn general, it may be impractical to test all possible combinations and perform, for each of the calculations needed—such as power flow and reliability indicators—in order to identify the setting that results in the best performance. To solve the problem, optimization methods that reduce the search space of the optimal solution are used.
\nAnother problem is that the optimal solution found meets a given situation of power generation and consumption, which typically varies over a period. The load variation during the day, for example, can modify the parameters for which the topology is optimized, resulting in a new optimum configuration. At this point, the solution for the reconfiguration of the network must come from at least two premises:
The network must be flexible to allow the reconfiguration, whenever observed the need; and
It is necessary to establish a cost–benefit relationship to determine the necessity and the effectiveness of the reconfiguration;
The diagram in Figure 5 shows the architecture employed in this work to meet these premises. The SCADA program is the main interface between the real‐time reconfiguration program developed and the equipment in distribution network.
\nThe first premise is to facilitate the implementation of the reconfiguration, so there is an effective gain with the network topology change. The use of remote‐controlled equipment such as switches and reclosers is a solution that meets this premise on two aspects: The reconfiguration can be automated without the need to displacement teams to operate the equipment, and immediate, or can be performed at the time wherein determining its need.
\nArchitecture of the developed system.
The second premise aims to limit the wear of the switching equipment. One solution is to establish relevant levels change in the network (e.g., demand) and parameters utilized as the aim of reconfiguration (e.g., energy losses), and conditioning reset only to cases in which the alteration levels exceed the reference values.
\nIn order to obtain a good discretization of the demand curve and generation curve to avoid frequent reconfigurations in the network, it is employed a set of six demand rates. The rates are constructed from the average of historical data (typical daily demand curve). The representative demand for one entire period is the maximum value observed in it and the case of generation curve it is used the average output power.
\nFigure 6 exemplifies the discretization of typical curves; Figure 6a shows the demand curves of an industrial feeder; and Figure 6b shows a wind turbine generation curve.
\nDiscretization of typical curves of (a) an industrial feeder, (b) a wind turbine.
The first stage of the optimization process is to define the objective function (FO) and constraints of the problem. The OF includes the minimization of network indicators:
Expected loss of energy in the primary network (ELosses);
Expected index of interruption frequency in the system (ESAIFI); and
Expected value of energy not supplied (EENS).
These three indicators can identify each of the alternatives, and reconfiguration is shown in Eq. (5).\n
Subject to:\n
where i corresponds to the period of analysis: 1...6; w1...w3 are weights of the criteria in multicriteria method; Ii is current equipment or driver; Vj is operating voltage in permanent state; PDGn is active and reactive power limits provided by the distributed generator, the minimum power equations Pmin, and maximum power Pmax presented in Section 3, depend on the DG technology considered.
\nSeveral optimization methods are proposed to solve the reconfiguration problem of distribution networks. The search techniques can be classified into different categories: heuristics, meta‐heuristics, expert systems, and mathematical programming [16]. Following is detailed one of heuristic search techniques, and considerations are then presented to illustrate a reconfiguration of application example using this technique in a real network model. The heuristic search technique, also known as branch exchange, is based on a local search, where the algorithm looks for a new solution from neighboring configurations in each iteration.
\nIn power systems, the method is premised on the radial configuration of the network. This method consists in carrying out successive changes in network configuration (e.g., opening a switch and closing another), so that each search tree node represents a possible solution of the problem. If the objective function indicators decrease, there is a new solution, and the algorithm continues the search process until no further improvement occurs. The technique can best be understood in two stages:
Step A: Analysis of interconnections between feeders in situations where there are not distributed generators connected to the distribution network [17] and
Step B: Analysis of interconnections in situations where there are distributed generators connected to the distribution network [1, 2].
Figure 7 illustrates the flow chart includes the Stage A and Stage B of heuristic search technique for reconfiguration of distribution networks.
\nFlowchart from step A and step B of heuristic search technique for reconfiguration of distribution networks.
The reconfiguration of the distribution network can involve optimization criteria that result in conflicting solutions. For example, considering the minimization of losses and increased reliability, a network topology can represent the optimal solution that meets the first criterion is, however, not represent the optimum solution to the second criterion.
\nA usual method of multicriteria analysis‐based decision‐making is the analytic hierarchy process (AHP) proposed by Saaty [18]. In AHP, the degree of preference of one over another objective is quantified through a table of the method suggested by the author, and the relationship between alternatives is represented by a matrix. A detailed description of AHP calculation methodology used in this work is presented in [17], which obtained as results for the indicators of the OF: w1 (ELosses) = 0.64; w2 (ESAIFI) = 0.26 and w3 (EENS) = 0.10.
\nThe proposed methodology was verified through several tests on the concession area of a power utility of Brazil. The real distribution network model presented in Figure 8 is used as a case study, and this network has the following:\n
Two substations with voltage 69/13.8 kV;
Five feeders: FD‐101; FD‐102; and FD 103 connected from the Substation A; and FD‐104 and FD‐105 connected from the Substation B;
15 tie‐switches remote‐controlled (TS) in normally open state, named and numbered as TS‐1 to TS‐15;
143 remote‐controlled switches (S) in normally closed state[1] -;
Solar photovoltaic plant of 500 kW, located between the S11 and S12 switches;
Wind power plant of 1600 kW, located between the TS‐10 and S39 switches;
Hydroelectric power plant (SHP) of 1000 kW, located between the TS‐15 and S61 switches.
The original configuration of this system is shown in Figure 8, where it is important to highlight the interconnection switches and the distributed generation plants.
\nThe analysis is done considering the expected maximum values of demand feeders and the average availability of active power generation by source of DG during the period corresponding to each of the six levels, as shown in Figure 9. For the developed methodology exemplification, only the results of level 5 are detailed (18h00–20h59).
\nNetwork distribution with DG.
Demand evaluation and generation.
The reconfiguration algorithm is applied considering the individual analysis of each switch interconnection shown in Figure 8. The individual analysis of the tie‐switches leads to the results shown in Figure 10. Only the cases where the objective functions analyzed presented positive evolution are shown.
\nThe results of objective function in Eq. (5) are sorted from lowest to highest value to give the best switching sequence. These results of the objective function represent the individual changes made in the network and its effects, as can be seen in Figure 10a for energy losses, Figure 10b for ESAIFI and Figure 10c for EENS.
\nThese sorted results represent one switching sequence that should be performed by reapplying the branch exchange according to the defined order. The best configuration achieved with one tie‐switch is preserved as the initial configuration to the following tie‐switch to be tested. The final result of the network optimization to the analyzed rate demand is shown in Figure 11.
\nResults for the individual analysis of tie-switches. (a) Energy losses. (b) ESAIFI. (c) EENS.
Final results of reconfiguration analysis.
The final result of the optimization of the network to the rate demand analyzed is shown in Figure 12 which shows the loads distribution between the analyzed feeders; Figure 12a shows the original configuration; and Figure 12b shows the network configuration after changes. All procedures of the main software were successfully performed to give the best network topology that improves the FO indicators.
\nLoad distribution between the distribution network feeders: (a) original configuration and (b) after changes reconfiguration.
In this chapter, a novel methodology for real‐time reconfiguration of power distribution networks considering distributed generation units was presented. The main advantages of the proposed system are automatic change of the network topology based on load rate analysis, modelling, and DG profile from distinct generation sources; multicriteria decision‐making is given by the AHP method to choose the switching sequence and, finally, the computational analysis, the supervisory control, and the data acquisition of remote‐controlled switches are integrated to perform the real‐time reconfiguration. The switching is performed automatically, in the sequence determined by the software. Additionally, case studies are performed with real data from a power utility in Brazil, with the use of different operational scenarios that guarantee a real evaluation of the developed software performance. The results included in this chapter shown the feasibility of the proposed methodology, which assure the use of this system to other real networks with DG.
\nThe authors would like to thank the technical and financial support of AES Sul Distribuidora Gaúcha de Energia SA by project “Solução Inovadora para Gerenciamento Ativo de Sistemas de Distribuição” (P&D/ANEEL), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
\nThe mankind has relied on different sources of energy during its economic development throughout the centuries. Whereas coal has been the main energy source in the nineteenth century, oil was in twentieth one. The possible scenarios for remediation of greenhouse effect due to carbon dioxide released by energy production and industry are rendered to minimization of emissions and its recycling. The latter is accomplished by the production of energy sources and chemicals of practical importance from carbon dioxide.
The emission minimization consists in two approaches: replacement of the fossil fuels by renewable ones (solar, wind energies, biomass, etc.) or improvement of energy efficiency in all human activities in different ways. The distribution of energy sources for the European Union for the year 2016 is shown in Figure 1. One can see that the share of renewables is bigger than the powerful nuclear energy with a leading role in energy production. The biggest part (more than 60%) of the renewable energy sources is assigned to the biomass and waste utilization.
Production of primary energy, EU-28, 2016 (% of total, based on tons of oil equivalent). Source: Eurostat (nrg_100a) and (nrg_107a) [1].
One of the ways to cope with the problem of carbon dioxide emissions is to close the carbon cycle using renewable fuels from presently grown biomass, by recycling the released carbon dioxide by the present vegetation by photosynthesis. This is the philosophy of biomass utilization as energy source. The most spread biofuels in the present period are biogas, produced by anaerobic digestion of organic waste, bioethanol, produced from cereals and/or lignocellulosic residues and biodiesel, produced by trans-esterification of lipids with methanol or ethanol.
In this review, we shall concentrate ourselves to the application of biogas as renewable energy source and also as a feedstock for the production of chemicals and other fuels.
Biogas is produced by anaerobic digestion of organic matter of natural origin [2, 3, 4]. The main advantage of this process consists in the combined environmental and energy effect.
Biogas consists mainly of methane, carbon dioxide, and traces of hydrogen sulfide and mercaptanes, as well as residual amounts of oxygen and nitrogen. Small amounts of ethane and hydrogen are possible too. Biogas is obtained by anaerobic digestion of organic waste of biologic origin. The most exploited ones are of agricultural origin (manure, poultry litter, hay, and straw) [5], from food industry, stillage from ethanol production [6], landfill gas, activated sludge from wastewater treatment plants, etc. One of the simplest and the mostly spread flow sheets for biogas production and utilization is shown in Figure 2 [7].
Illustration of biogas cycle, formation, and applications. Scheme taken from [7].
The main fuel in the scheme, shown in Figure 2, is biogas, utilized for energy (thermal one and electricity) or fuel for transport. The carbon dioxide released after combustion is absorbed by the vegetation by photosynthesis, thus closing the carbon cycle. The residual sludge from the digester is rich of organic nitrogen, and therefore, it is suitable for fertilizing the soil.
In the past, biogas has been widely spread as an energy source in the households in the countries of Africa and Asia. Although quite primitive as design, the anaerobic digesters have solved the problems with autonomous energy supply for many households in India, Pakistan, Indo-China, etc.
Later, biogas became very important and essential share as energy source for the countries in Western Europe and Northern America. Besides heating, biogas is now more frequently used for the production of electricity and transport fuel in many municipalities. It is already added to the pipelines for natural gas distribution of household purposes.
A new trend in biogas production and utilization is the so-called biorefinery concept. This concept not only presumes the use of renewable biomass as energy source but also combines it with the production of chemicals, such as plastics, solvents, and synthetic fuels [8]. An example for this is the Danish Bioethanol Concept presented by Zafar [9]. It comprises the ethanol production from lignocellulosic biomass with biogas production of the stillage and cellulose waste. The residual cellulose waste is additionally recycled after wet-oxidation for additional conversion into biogas. A detailed review on biogas applications is published recently by Sawyerr et al. [10].
The variety of anaerobic digesters for biogas production is very broad: from the very primitive pits to most sophisticated bioreactors, such as the floating drum reactor, the upflow anaerobic sludge blanket (UASB) reactor [11, 12, 13], and multistage bioreactor with separated compartments [14, 15]. The choice for anaerobic digester depends on the origin of substrate, and the intermediates are converted during the consecutive steps of hydrolysis, acidification, acetogenesis, and final methanation. In case an accumulation of fatty acids takes place, the reactor with separated compartments is preferable. The most exploited digester for biogas production from domestic waste, activated sludge, and manure is the UASB reactor.
The mostly used substrates for biogas production are the manure from cattle, pigs, and poultry litter. This application competes with the traditional use of manure for soil fertilization. When the amounts of manure prevail the demand for fertilization, biogas production is welcome because double problem is solved: on the one hand, the waste is destroyed and removed, and on the other hand, renewable energy is produced saving money and contributing for carbon cycle closing. That is why attention is paid to the utilization of cattle dung, lignocellulose waste, waste from food and beverage processing, activated sludge from wastewater treatment plants, and household solid waste with landfill gas use. The waste treatment is associated with energy production and reduction of the energy demand of the main enterprise.
Crude glycerol is the main residue from biodiesel production. The amount of this waste product is about 10% from the produced fuel. The poor quality of this glycerol, containing water, potassium hydroxide, and some methanol makes it non-suitable for market purposes even after purification. One alternative utilization of this residual glycerol is in its direct conversion into biogas, thus supplying the biodiesel plant with energy simultaneously. However, as a very simple and digestible substrate, glycerol yields large amounts of organic acids as intermediates, leading to strong inhibition of methanogenic bacteria [16, 17, 18]. That is why glycerol must be used as substrate for biogas production very cautiously with the addition of small amounts, thus making this process with little practical use. It is reported, however, that small additions of glycerol to other basic substrates, i.e. manure, can boost biogas production, as reported by Robra et al. [19] and Astals et al. [20].
Food industry is also a good source for biogas production.
Traditional biogas contains approximately 60% (vol.) methane, almost 40% carbon dioxide, small amounts of ethane and hydrogen (less than 0.5% together), hydrogen sulfide and mercaptanes (some ppm), humidity, and traces of oxygen. Its net energy capacity is ca. 24 MJ/nm3 at methane content of 60% (vol.). The first and most direct use of biogas is for heating purposes for maintenance of the equipment and the farm, where the animal dung is treated. The same applies for its use for domestic purposes, besides heating, e.g., cooking and lighting, as firstly used in Asian and African countries.
Another more sophisticated use of the biogas heating capacity is its utilization as heat energy in beverage and ethanol production. There the stillage remaining after distillation is recycled for biogas production. The resulting biogas is combusted for boiler heating and for energy for operation of distillation columns. Thus, the problems with the treatment of the residual stillage are solved by conversion into biogas, thus mitigating the problems with energy supply and spending. Calculations show that in some cases, stillage utilization as biogas can cover almost the whole energy demand for heating the distillation process. Besides these straightforward applications, biogas is also injected into the grid for natural gas supply for domestic use [21, 22]. For this purpose, a preliminary scrubbing of the carbon dioxide and sulfur compounds is necessary.
Biogas is suitable for generation of electric power in combination with heat recovery. Usually the gas is combusted in engines with internal combustion coupled to turbine. The released heat (being around 60% of the utilized energy) is used for heating purposes for maintenance of the anaerobic digester or for household needs. This method is widely applied for the treatment of activated sludge, a residue from municipal wastewater treatment plants [23, 24].
Electricity production by gas turbines can be applied by biogas as a fuel, thus replacing the natural gas for small-scale applications (or power within 25–100 kW).
The use of biogas as a fuel for civil transport and road vehicles instead of natural gas is already spread in Western Europe and the United States [25]. There are many vehicles in Sweden operating on biogas in the urban public transport [26].
Another very attractive application of biogas for electricity production is its use in fuel cells. The specialized cells for these purposes are described briefly by O’Hayre et al. [27]. Prior to biogas feed, carbon dioxide and sulfur compounds must be removed by scrubbing to avoid corrosion and catalyst poisoning and to rise the gas energy capacity. A sketch of such a fuel cell is shown in Figure 3, cf. [28].
Principal sketch of methane-driven fuel cell, from [28].
The classic process for methane-driven fuel cells is to convert catalytically by steam reforming methane into a mixture of carbon monoxide and hydrogen and to use the latter in a traditional hydrogen/oxygen fuel cell to generate electricity. The advantages of fuel cell applications with methane as a fuel compared to the traditional heat power stations consist in their higher efficiency, clean waste gases (containing almost only carbon dioxide), and higher efficiency at low loads than the gas turbine equipment [29]. Moreover, the released heat can be utilized for different purposes; the main one is to maintain the temperature regime in the fuel cell. There are many practical applications of these methods. It is already widely commercialized. A disadvantage of this method is the necessity of consequent reactions of steam reforming and carbon monoxide removal as well as the operation at high temperatures (about 750°C), being harmful for the metal parts of the equipment [30, 31]. Higher temperatures are preferred to avoid coke deposition on the catalyst [31].
There are new efforts to lower the operation temperature to 500°C in order to keep the equipment durability [32, 33]. Another improvement of the technology is to use the mixture of carbon monoxide and hydrogen as a fuel simultaneously, thus simplifying the whole process, but applying new catalytic process.
The most attractive option is to convert methane (biogas, respectively) into electricity in one step, thus avoiding the steam reforming and carbon dioxide removal. There are some new studies showing direct catalytic oxidation of methane in the anodic space of solid oxide fuel cells (SOFCs), with direct activation of the C-H bonds in the methane molecule [28, 34, 35, 36]. A platinum catalyst was used for this purpose at low temperatures, e.g., 80°C. However, the catalyst deactivates, and the process is limited by methane diffusion in the anodic space. As a result, the power density is still low for practical use.
Besides as a fuel, biogas could be used as a feedstock for synthetic organic fuel production. There are studies claiming for biogas recovery as fuels applying catalytic auto-reforming. Another approach is the dry reforming consisting in converting the equimolar mixture of methane and carbon dioxide into synthesis gas (an equimolar mixture of carbon monoxide and hydrogen).
Afterward, this synthesis gas is converted into a mixture of light hydrocarbons by the catalytic Fischer-Tropsch process. The resulting Fischer-Tropsch process yields liquid hydrocarbon fuels (methanol and dimethyl ether). The intrinsically high-energy density of these fuels and their transportability make them highly desirable. Such synthetic fuels do not contain any sulfur. In addition, methanol (arguably the “simplest” synthetic carbonaceous fuel) is a candidate both as a hydrogen source for a fuel cell vehicle and indeed as a transport fuel, and dimethyl ether is viewed as a “superclean” diesel fuel [36]. It is well known that methanol is a starting material in chemical industry. It is a liquid at room temperature and has much easier storage and transport capabilities than alternatives such as methane and hydrogen. Methanol is used as solvent, gasoline additive, and a chemical feedstock for production of biodiesel and other chemicals of high value. Therefore, the wide application of methanol motivates its large-scale production, which is ever increasing.
However, presently, the dominant technology of methanol is a two-step catalytic process, which is too expensive. A large number of industrial-scale chemical manufacturing processes are currently operated worldwide on the basis of strongly endothermic chemical reactions. The steam reforming of hydrocarbons to yield syngas and hydrogen is a classic example:
The above, highly endothermic reaction is used worldwide for the high-volume production of “merchant hydrogen” in the gas, food, and fertilizer industries, i.e., other portions of energy have to be spent with the consequent air pollution by carbon dioxide.
At present, a relevant technology for methanol production resides in the transformation of CO2 and CH4 to molecules having industrial added values. Among such technologies, a great attention is focused on the production of synthesis gas (gaseous mixture of CO and H2) that constitutes a versatile building block for subsequent production of methanol or chemical intermediates in petrochemical industries. Methanol is still produced on a world scale from synthesis gas, which is combination of varying amounts of H2, CO, and CO2 (at 200–300°C, 50–100 bar), which is itself product of steam reforming of methane (SRM; at ca. 800°C over Ni-based catalyst), followed by further conversion processes such as Fischer-Tropsch (FT) synthesis. This two-step process incurs high energy and capital demands. Additionally, this process gives many other light and heavy weight co-products along with the methanol product. Therefore, additional energy and cost in the conventional methanol plants are directed to the separation of these coproducts from methanol prior to the final deposition of product.
The direct synthesis of methanol from syngas requires a H2/CO ratio of about 2 [37, 38]. Since the syngas produced by dry reforming of methane (DRM) is too poor of H2 (H2/CO ≤ 1) to be fed to a FT synthesis unit, the bi-reforming of methane (BRM), combining DRM with steam reforming of methane (SRM) (H2/CO = 3) and the utilization of the most important two greenhouse gases CH4 and CO2 with water, may yield a syngas with ratio close to 2, the so-called “metgas”:
To date, only one plant with the combination of steam and dry reforming has been recently demonstrated by the Japan Oil, Gas, and Metals National Cooperation. No other industrial technology for DRM has been developed because the selection and design of suitable reforming catalyst remain an important challenge. Ni-based catalysts are the most attractive candidates for large-scale industrial applications due to their high activity in DRM and SRM [39, 40, 41, 42, 43], low cost, and wide availability compared to noble metals. However, they are sensitive to deactivation caused by the metal particles sintering and carbon formation at high reaction temperature of reforming processes. Development of selective and coke-resistance modified Ni-based reforming catalysts is a key challenge for successful application of bi-reforming for methanol production. Modifying Ni catalysts with suitable promoters and supported on reducible metal oxide carriers will give the opportunity to develop active and stable catalysts for bi-reforming of methane.
A “super-dry” CH4 reforming reaction for enhanced CO production from CH4 and CO2 was developed [44]. Ni/MgAl2O4 was used as a CH4 reforming catalyst, Fe2O3/MgAl2O4 was used as a solid oxygen carrier, and CaO/Al2O3 was used as a CO2 sorbent. The isothermal coupling of these three different processes resulted in a higher CO production than conventional dry reforming by avoiding back reactions with water. Equation (3) shows the global reaction of this two-step process, in which CO and H2O are inherently separated because of the two-step process configuration:
It is important to note that despite the apparently higher endothermic effect of the super-dry reforming process than conventional DRM (Eq. 1), the required heat input per mole CO2 converted is much lower (110 kJ/mol CO2 compared to 247 kJ/mol CO2). Finally, given the availability of a renewable source of H2, applications are possible where CO and H2 can be combined in different ratios for the formation of chemicals or fuels [45, 46]. Indeed, an efficient and separate production of high purity CO and H2 would further establish the role of syngas as a versatile and flexible platform mixture.
All these methods and techniques are applicable when biogas is available. Some other applications are described briefly below.
First of all, biogas must be purified for sulfur compounds prior to its use [47]. Afterward, methane and carbon dioxide have to be separated by membrane processes using gas-liquid systems [48] or swing pressure adsorption [49]. Once methane and carbon dioxide are separated, each of them has its own route for further application. Besides the already mentioned applications as a fuel for transport and energy purposes, dry reforming and steam reforming to obtain synthesis gas, the purified methane can be converted into light hydrocarbons, e.g., ethane and ethylene by advanced methods, like the so-called VYJ process [50, 51, 52, 53]. By this method, methane is converted in one step into ethylene by catalytic or electrocatalytic reaction [54, 55, 56].
High yields up to 88% in total are attained [50]. The rest of nonreacted methane is trapped in molecular sieves and recycled to the reactor [50, 53, 54]. In this way, the use of methane reaches 97% with an ethylene yield of 85% [50].
As ethylene is a basic feedstock for the mostly spread polymerizations and many value-added chemicals, it is clear that this way of biogas utilization is quite promising one.
The usual criteria for the feasibility of an anaerobic digestion technology are the type of digester, the operation temperature, the necessary retention time of the substrate in the reactor, the substrate acidity (the initial pH value), and the presence of certain chemicals in the inlet slurry.
However, the most important one is energy demand for the biogas formation and the energy potential of the produced biogas.
There are two typical temperature ranges for biogas production: mesophilic one (at 30–35°C) and thermophilic one (at 55–60°C). Different genera of methanogenic microorganisms are capable to accomplish the processes in those two cases. The advantages of the thermophilic regime are in the higher production rate and the lack of pathogens in the outlet slurry. However, the energy input for maintenance of this regime is higher than for the mesophilic one.
The question of the energy demand for any industrial process is of crucial importance for its economic reliability. The same applies to biogas production.
There are some methodologies for the estimation of the feasibility of biogas production [57, 58]. They all involve the demand of heat for temperature maintenance and electricity for mechanical operations (stirring, pumping, and transport) and comparison to the energy yield after anaerobic digestion.
Generally, the operations for a certain flowsheet are separated into production processes and support ones. The production processes in the considered case are the reception of the substrate and its storage, pre-treatment of feed (dilution, pH adjustment, acid hydrolysis, etc.), and anaerobic digestion with biogas production. The removal of the digestate and its storage and processing are also included. This set of processes is called as Level 1 [57].
Once biogas is produced, it could be used for direct heat and/or electricity production and supplied to customers or for own use (Level 2). More sophisticated operations, such as gas cleaning, upgrading (i.e., removal of carbon dioxide), and compressing the upgraded gas, are required if the gas will be distributed by the gas distribution grid or for some chemical applications.
The methodologies for energy demand evaluation consist in the inventory of all such processes and auxiliary ones with their energy demand per unit production (i.e., amount of produced biogas with certain energy potential). Then, the overall energy demand is compared to the biogas yield with its energy potential, and the percentage of the energy input to the overall yield is a measure for feasibility of the studied technology.
The structures of the energy demand for different flow sheets and the weight of different subprocesses depend on the substrate properties (particles size, chemical structure and content, moisture, and total solid content) and the amount to be treated, the digester construction and design.
Berglund and Borjesson [58] proposed a methodology based on the life-cycle perspective including the energy required for the production of the substrates (including crop growth, harvesting, transport, etc.). The energy efficiency is defined by the ratio of the energy input to the energy yield of the produced biogas. It was found that the energy input corresponds mainly to 15–40% of the energy content of the produced biogas. The subprocesses of extensive handling of raw materials may lead to considerably increase the energy input and thus to undermine the feasibility of the entire technology.
In case the gas will be used as a feedstock for other chemical applications (e.g., dry reforming and steam reforming), the operational costs of the processes at Levels 1 and 2 have to be compared to the operational costs for the chemical processes and the prices of the produced chemicals or other final products.
The main disadvantage of biomass produced fuels is the inevitable release of CO2 in the atmosphere after combustion. Therefore, big efforts are made in the recent years for remediation of this adverse effect of greenhouse gas. The best way to cope with this problem is the natural assimilation by the vegetation by photosynthesis, but it is not sufficient due to the very large emissions from industrial sources, energy production, transport, and household. That is why many other methods are proposed and studied in the recent years.
One of them is the direct use of pure carbon dioxide as a solvent in supercritical extraction in the pharmaceutical industry. However, this application is limited and cannot be a substantial solution of the problem. There are many efforts to recycle carbon dioxide to produce different organic chemicals: formic acid, methanol, dimethyl-ether, poly-carbonates, acrylic acid, etc. [59, 60]. All of these methods are applicable for the residual carbon dioxide after separation from biogas. Therefore, not only methane but also carbon dioxide in biogas is valuable source of energy and value-added product.
The data presented here illustrate one of the very important biorefinery approaches to produce simultaneous energy and value-added chemicals from biomass, thus reducing the demand of fossil fuels and resulting in overloading of atmosphere by greenhouse gases. The same applies to the water and soil pollution, since those resulting from biomass processing are nature compatible and facilitate the formation of close energy and material cycle. One of the ways to do it is biogas production from such waste.
At the end, we can say that biogas extends its area of application leading simultaneously to protect the environment by waste treatment, natural gas, and fossil fuel saving, as well as to replace, at least partially, the oil as a feedstock for organic value-added products.
This work was supported by the Bulgarian Ministry of Education and Science under the National Research Program Eplus: Low Carbon Energy for the Transport and Households, grant agreement D01-214/2018.
The authors declare no conflict of interest.
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
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\\n\\n3.1. ERRATUM
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\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
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\\n\\nPolicy last updated: 2017-09-11
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\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
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\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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