Meteorological input parameters (monthly).
\r\n\t
",isbn:"978-1-83968-760-0",printIsbn:"978-1-83968-759-4",pdfIsbn:"978-1-83968-761-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"cc49d6034d85f8f2e2890c6acc3cc629",bookSignature:"Dr. Abhijit Biswas",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10285.jpg",keywords:"Mott Insulators, Semi Metals, Polycrystals, Single Crystals, Electronic Properties, Magnetic Properties, PLD, MBE, Topological Insulators, Topological Hall Effect, Devices Applications, Catalysis",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 9th 2020",dateEndSecondStepPublish:"October 7th 2020",dateEndThirdStepPublish:"December 6th 2020",dateEndFourthStepPublish:"February 24th 2021",dateEndFifthStepPublish:"April 25th 2021",remainingDaysToSecondStep:"5 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"A pioneering researcher in the field of tailoring metal oxide crystal surfaces and growth as well as engineering of thin films for various emergent phenomena and energy applications. Dr. Biswas received his Ph.D. from POSTECH, South Korea.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"194151",title:"Dr.",name:"Abhijit",middleName:null,surname:"Biswas",slug:"abhijit-biswas",fullName:"Abhijit Biswas",profilePictureURL:"https://mts.intechopen.com/storage/users/194151/images/system/194151.png",biography:"Dr. Abhijit Biswas is a research associate at the Indian Institute of Science Education and Research (IISER) Pune, in India. His research goal is to design and synthesize highest quality epitaxial heterostructures and superlattices, to play with their internal degrees of freedom to exploit the structure–property relationships, in order to find the next-generation multi-functional materials, in view of applications and of fundamental interest. His current research interest ranges from growth of novel perovskite oxides to non-oxides epitaxial films, down to its ultra-thin limit, to observe unforeseeable phenomena. He is also engaged in the growth of high quality epitaxial layered carbides and two-dimensional non-oxide thin films, to exploit the strain, dimension, and quantum confinement effect. His recent work also includes the metal-insulator transitions and magneto-transport phenomena in strong spin-orbit coupled epitaxial perovskite oxide thin films by reducing dimensionality as well as strain engineering. He is also extremely interested in the various energy related environment friendly future technological applications of thin films. In his early research career, he had also extensively worked on the tailoring of metal oxide crystal surfaces to obtain the atomic flatness with single terminating layer. Currently, he is also serving as a reviewer of several reputed peer-review journals.\nDr. Biswas received his B.Sc. in Physics from Kalyani University, followed by M.Sc in Physics (specialization in experimental condensed matter physics) from Indian Institute of Technology (IIT), Bombay. His Ph.D., also in experimental condensed matter physics, was awarded by POSTECH, South Korea for his work on the transport phenomena in perovskite oxide thin films. Before moving back to India as a national post-doctoral fellow, he was a post-doc at POSTECH working in the field of growth and characterizations of strong spin-orbit coupled metal oxide thin films.",institutionString:"Indian Institute of Science Education and Research Pune",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Indian Institute of Science Education and Research Pune",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"205697",firstName:"Kristina",lastName:"Kardum Cvitan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/205697/images/5186_n.jpg",email:"kristina.k@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"8356",title:"Metastable, Spintronics Materials and Mechanics of Deformable Bodies",subtitle:"Recent Progress",isOpenForSubmission:!1,hash:"1550f1986ce9bcc0db87d407a8b47078",slug:"solid-state-physics-metastable-spintronics-materials-and-mechanics-of-deformable-bodies-recent-progress",bookSignature:"Subbarayan Sivasankaran, Pramoda Kumar Nayak and Ezgi Günay",coverURL:"https://cdn.intechopen.com/books/images_new/8356.jpg",editedByType:"Edited by",editors:[{id:"190989",title:"Dr.",name:"Subbarayan",surname:"Sivasankaran",slug:"subbarayan-sivasankaran",fullName:"Subbarayan Sivasankaran"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"68958",title:"Methodology for Sizing Hybrid Battery-Backed Power Generation Systems in Off-Grid Areas",doi:"10.5772/intechopen.88830",slug:"methodology-for-sizing-hybrid-battery-backed-power-generation-systems-in-off-grid-areas",body:'\nDue to the technological and industrial worldwide progress and the growing industry and society need of power generation for the development and increment of life quality, it is of unquestionable importance to increase sustainable access to electrical energy. In developing countries, there are still many locations without power supply.
\nPower generation through fossil generators offers a continuous and reliable source of energy making it a very popular option for electrification in off-grid areas. This alternative presents an initial investment cost relatively low compared to other sources of power generation. However, fossil power generators are sized to meet peak demand and have a low performance when the load is quite below to its rated capacity. Additionally, operating and maintenance costs are high; the cost of energy (COE) is subject to changes according the national and international fuel markets. In addition, logistical challenges associated with fuel supply in remote areas can cause a significant increase in generation costs [1]. A solution for these disadvantages is the implementation of HRES which includes fossil and other energy sources. For warm and high-average daily radiation levels, photovoltaic solar energy with battery backup represents an attractive complementary source to diesel generation systems. This solution allows the reduction of generation costs and increased system reliability [2, 3].
\nHybrid systems have shown lower generation costs and greater reliability than dependent systems of a single source of energy [1, 2, 3, 4, 5, 6]. Each element of the system has to be properly sized to achieve a techno-economic profitability. Therefore, the penetration of renewable energy sources in the energy market depends mainly on the applied sizing methodology to optimize its design [7].
\nThe optimization of these systems could be complex, since many variables are naturally stochastic and linked to the selected location. Examples of these variables are temperature, solar resource, and load profile of the location [8]. Moreover, the optimization technique depends on the selected objective function, which can be oriented in seeking financial gain, increasing system reliability, and reducing the environmental impact [9].
\nThen, it is necessary to develop a methodology for optimizing the design of HRES that allows the integration of photovoltaic and diesel generation systems, with or without energy storage, allowing to reduce energy costs and maintaining a high reliability in energy supply in off-grid areas. The methodology requires a set of input information linked to the project site, as meteorological and load profile data, and also technical and economic information of the main equipment of the HRES. Then, an optimization process is necessary to determine the best combination of diesel power, PV power, and battery bank capacity. Economic and reliability parameters that support the solution obtained is expected to be presented with the solution.
\nIn the last decade, several optimization techniques have been used to obtain an optimal solution of the sizing of HRES [7, 10, 11, 12, 13]. The results among different approaches may vary depending on the characteristics of the model which permits to simulate the behavior of different elements of the system and also the economic and reliability model used as base on the optimization process.
\nThe main objective of this work is to develop an optimization methodology for sizing HRES in off-grid areas of developing countries. In contrast to other works, each step of the methodology is described in detail. Also, special condition will be considered on the development of the economic and reliable model to adjust it to the reality of Colombia, for example, the national and international physical distribution cost or the incentive proposed by the Act 1715 for electrification using non-conventional energy sources in Colombia.
\nIn this methodology, the grid can be formed either from the diesel unit or from a master inverter. The diesel generation is only required when the energy produced by the photovoltaic source and the energy backup in the battery bank is lower than the demanded load. The following items summarize the key characteristics of the dispatch strategy used in this work to model PV-diesel with battery storage systems: (1) the system is considered DC-coupled (Figure 1) and (2) the load following strategy is adopted [1]. The diesel generators are only used to supply the load when there is insufficient power from the PV source and the battery bank. Only the minimum DG unit required operates in every time step; (3) all DG units must operate over the minimum load ratio (\n
Schematic diagram of a hybrid solar/battery/diesel generation system.
The proposed methodology is composed of the following steps: (1) a dispatch strategy algorithm, (2) calculation of economic indicators, (3) calculation of reliability indicators, (4) calculation of fiscal incentives, and (5) a PSO optimization process given an objective function which optimizes the number of components of the installation and a calculation of economic and reliability indicators for the best solution. The following subsections detail the steps of the methodology. Figure 2 shows the schematic of the proposed methodology and the optimization process.
\nSchematic diagram of the proposed methodology.
Figure 3 shows the dispatch strategy flowchart used on the diesel-PV-battery model for a year which algorithm is described in detail below.
\nDispatch strategy flowchart.
1. Obtain or generate inputs of the system: load profile (\n
2. Introduce the following technical information of each element of the system and initialize variables.
\n2.1. According to the available location and its restrictions, introduce the following technical information: \n
2.2. Initialize the following variables: \n
3. Calculate the battery model which expresses the equations in the function of the energy each hour:
\n(1) The maximum amount of energy that the battery bank can be discharged in one time step (\n
(2) The maximum amount of energy that the battery can be charged in one time step (\n
4. Calculate the hourly generated energy of the PV system (\n
5. Calculate the difference between PV energy generated and the energy demanded by the load (\n
6. If \n
6.1. If \n
Go to step 10.
\n6.2. Else, the battery bank is fully charged; SOC is updated. There is excess of energy that cannot be used supplying the load or charging the battery, so energy wasted (\n
Go to step 10.
\n7. If \n
7.1. If \n
Go to step 10.
\n7.2. Otherwise, diesel generation is required. Go to step 8.
\n8. Diesel generation is necessary. Photovoltaic energy is used to charge the battery bank, and the diesel generation is used to supply the load. The energy stored in the battery bank and energy generated by the diesel unit is used to supply the load at night.
\n8.1. Case 1: \n
Go to step 10.
\n8.2. Case 2: \n
8.2.1. Case 2.1: \n
Go to step 9.
\n8.2.1.1. Case 2.1.1: \n
Go to step 9.
\n8.2.1.2. Case 2.1.2:\n
Go to step 9.
\n8.2.2. Case 2.2: \n
8.2.2.1. Case 2.2.1: \n
8.3. Case 3: (\n
8.3.1. Case 3.1: (\n
8.3.1.1. Case 3.1.1: (\n
8.3.2. Case 3.2: (\n
9. The fuel consumption \n
10. Increase the time step (\n
After run the previous algorithm; economic and reliability indicators should be calculated using the following procedure.
\nAn economic analysis is required to determine the optimum cost and benefit ratio of HRES. These systems generally require high capital investment, even though they have low operation and maintenance (O&M) costs and less fuel costs in comparison with systems relaying only on fossil fuels. In this study, the annualized cost of the system (ACS) and the cost of energy (COE) are considered as the economic criteria to evaluate the feasibility of this hybridized system configuration.
\nThe annualized cost of the system (ACS) is the sum of the annualized capital cost (\n
where \n
The real interest rate is used to convert between one-time costs and annualized costs. By defining the real discount rate, the inflation rate effect is factored out of the economic analysis. All costs, therefore, become real costs, which are in defined in terms of constant dollars. The real interest rate is calculated by
\nwhere \n
The capital cost for each component is described as follows:
\nwhere \n
The replacement cost is calculated for each element. The replacement cost of the photovoltaic system is assumed null, as the photovoltaic modules have a life cycle superior to the lifetime of the project and it is assumed in this model that the charge controllers and inverters do not need replacement during the lifetime of the project. The replacement cost of the battery system and the DG unit can be calculated as
\nwhere \n
where \n
The fixed mount PV systems do not have moving parts, so operating and maintenance costs consist of regular cleaning and monitoring of performance, the annual operation, and maintenance cost can be estimated as a percentage of the PV system total investment\n
In a similar way, the annual operation and maintenance cost for the battery system can be calculated as percentage of the total investment cost of the battery system. This cost can vary according to the technology of the battery bank. For example, the cost of operation and maintenance for vented lead-acid batteries is higher than maintenance-free sealed lead-acid batteries or Li-ion batteries. The percentage of the total investment cost, \n
The operation and maintenance cost for the diesel system components is divided in two values: a fixed cost, expressed as a percentage of the diesel initial investment, \n
The cost of energy (COE) can be defined as the average cost per kWh of useful electrical energy produced by the system [21]. It can be obtained as the ratio between the annualized cost of the system and the effective load served in 1 year. The economic model assumes that the yearly effective load served is constant over the lifetime of the project. COE can be calculated as follows:
\nThe dependency on nature and unpredictability of solar resources has a great impact on energy production which leads to unreliable power supply during cloudy days. A system is reliable if it can supply the required power to the electrical load within a specific time period.
\nThe loss of power supply probability (LPSP) is the most widely used method to evaluate the reliability in hybrid system, therefore is selected, in this work, as reliability criteria. The LPSP be calculated as the ratio of power supply deficit to the electric load demand during a certain period of time (normally a year). A ratio equal to zero means all load demand, during the period of time, is served by system (53). LPSP is given by
\nA method that takes into account the weight of reliability in the economic model includes a component of the cost of electricity interruptions or cost of load (\n
The cost of electricity lost for non-interconnected zone can vary with respect the reference cost and could be difficult to estimate, as depends on the willingness of users to pay for a more robust system. The cost of electricity not supply (\n
\n\n
Under the Colombian Renewable Energy Law, new clean energy projects will receive up to 50% tax credits, but they can only be applied during the first 5 years. In this work, when the fiscal incentives are considered, it is assumed that the company will receive the 50% of the tax credit equally distributed over the first 5 years of the project. In general, investment tax credits can be calculated as
\nIn a similar way, it is assumed that the effect of depreciation is equally distributed each year, and the useful life for accelerated depreciation purposes is 5 years; then
\nAssuming an effective corporate tax income rate of 33% and under the previous consideration, the tax reduction factor \n
where\n
Fiscal incentives granted by the Colombian Act 1715 only apply to not conventional energy source installation and its components. In this way, the incentive tax factor only applies to the capital cost of photovoltaic and battery components:
\nThe objective of this work is sizing hybrid power generation systems (solar-diesel) battery-backed, in non-interconnected zones, which minimizes the total cost of the solution and maximize the reliability of supply. To minimize the total cost of the system, the following objective function is used:
\nThis work aims to develop an optimization model for sizing an energy system to supply the energy demand on an off-grid location. The optimization of these systems could be complex, since many variables are naturally stochastic depending mostly on the characteristic of the solar resource and the load profile of the selected location. The objective is to minimize the total cost of the solution and maximize the reliability of the supply.
\nAs a result of the optimization problems, the following information are obtained: (1) amount of photovoltaic modules and therefore the total photovoltaic power in kWp, (2) amount of diesel generation units and the total diesel energy power in kWp, (3) amount of battery cell required and total capacity of the energy storage system in kWh, (4) energy flow in the system showing the different states of the system according to the dispatch strategy described in this work, (5) discriminated cost of each technology in terms of initial capital required and O&M cost, (6) annualized cost of energy of the best solution, and (7) amount and cost of energy not supplied and LPSP.
\n“Santa Cruz del Islote” in Bolivar, Colombia, was used as a location for the case study. This rural community is selected to evaluate the optimization model developed in this work.
\nThe monthly global irradiance over the horizontal and over the plane of the array was calculated using a MATLAB routine developed in this work and then compared with results obtained from Solargis. Table 1 shows the results obtained. The difference can be accounted to the simplicity of the transposition model used in our MATLAB routine; nevertheless the results are good enough for the purpose of this work.
\n\n | Global horizontal irradiation [kWh/m2] Solargis | \nGlobal horizontal irradiation [kWh/m2] calculated | \nDev [%] | \nGlobal tilted irradiation [kWh/m2] Solargis | \nGlobal tilted irradiation [kWh/m2] calculated | \nDev [%] | \n
---|---|---|---|---|---|---|
Jan | \n183.6 | \n182.0 | \n−0.88% | \n201.9 | \n198.6 | \n−1.65% | \n
Feb | \n175.6 | \n174.2 | \n−0.81% | \n186.9 | \n184.3 | \n−1.41% | \n
Mar | \n194.3 | \n193.0 | \n−0.68% | \n198.5 | \n196.2 | \n−1.14% | \n
Apr | \n177.2 | \n176.1 | \n−0.65% | \n175 | \n172.9 | \n−1.17% | \n
May | \n166.4 | \n165.2 | \n−0.70% | \n160.1 | \n158.8 | \n−0.83% | \n
Jun | \n161.9 | \n160.8 | \n−0.71% | \n153.6 | \n152.6 | \n−0.65% | \n
Jul | \n173.2 | \n172.0 | \n−0.69% | \n165.3 | \n163.9 | \n−0.84% | \n
Aug | \n171.7 | \n170.6 | \n−0.65% | \n167.8 | \n166.1 | \n−1.03% | \n
Sep | \n160.9 | \n159.8 | \n−0.70% | \n162 | \n160.1 | \n−1.17% | \n
Oct | \n155.8 | \n154.4 | \n−0.91% | \n162.4 | \n159.5 | \n−1.79% | \n
Nov | \n149.1 | \n147.7 | \n−0.96% | \n160.5 | \n157.1 | \n−2.13% | \n
Dec | \n161.2 | \n159.7 | \n−0.93% | \n177.8 | \n174.1 | \n−2.09% | \n
Year | \n2030.9 | \n2015.3 | \n−0.77% | \n2071.8 | \n2044.1 | \n−1.34% | \n
Meteorological input parameters (monthly).
The load profile data was obtained from the National Monitoring Center (CNM) of the IPSE [22]. Table 2 shows the input data used to generate the daily load profile curve. Figure 4 shows the daily load profile for a week generated by a MATLAB routine developed in this work.
\nHour | \nPower [%] | \nUncertainty factor [%] | \nHour | \nPower [%] | \nUncertainty factor [%] | \n
---|---|---|---|---|---|
\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
0 | \n7.78 | \n10 | \n12 | \n0.96 | \n10 | \n
1 | \n7.68 | \n10 | \n13 | \n2.88 | \n10 | \n
2 | \n7.40 | \n10 | \n14 | \n5.67 | \n10 | \n
3 | \n7.20 | \n10 | \n15 | \n5.86 | \n10 | \n
4 | \n6.34 | \n10 | \n16 | \n3.75 | \n10 | \n
5 | \n1.15 | \n10 | \n17 | \n1.54 | \n10 | \n
6 | \n0.00 | \n0 | \n18 | \n0.96 | \n10 | \n
7 | \n0.00 | \n0 | \n19 | \n6.24 | \n10 | \n
8 | \n0.00 | \n0 | \n20 | \n8.65 | \n10 | \n
9 | \n0.00 | \n0 | \n21 | \n8.65 | \n10 | \n
10 | \n0.00 | \n0 | \n22 | \n8.65 | \n10 | \n
11 | \n0.38 | \n10 | \n23 | \n8.26 | \n10 | \n
Yearly average daily energy demand [kWh], \n | \n520.5 | \n
Daily load profile for “Santa Cruz del Islote” July 2018.
Daily load profile for a week generated for “Santa Cruz del Islote.”
This subsection describes the technical inputs required by the photovoltaic, diesel, and battery model employed in the optimization model developed in this work.
\nA monocrystalline PV module of 300 Wp, reference JKM300M-60, from the company JINKO SOLAR, is used. Table 3 shows the technical characteristics of the PV module selected. The cost per Wp installed presented in Table 3 includes other costs not related to the price of the PV modules as the cost of charge controller, the PV inverters, and the mounting structure. Also this price includes indirect cost associated to the PV installation as engineering study costs, logistic costs, and certification costs. The cost per Wp presented is taken as reference and is provided by experts consulted in companies of energy sector.
\nSymbol | \nDescription | \nValue | \n
---|---|---|
\n\n | \nMaximum power [Wp] | \n300 | \n
\n\n | \nMaximum power voltage [V] | \n32.6 | \n
\n\n | \nMaximum power current [A] | \n9.21 | \n
\n\n | \nOpen-circuit voltage [V] | \n40.1 | \n
\n\n | \nShort-circuit current [A] | \n9.72 | \n
\n\n | \nModule efficiency (%) | \n18.33 | \n
\n\n | \nPower temperature coefficient [%/°C] | \n−0.39 | \n
\n\n | \nVoc temperature coefficient [%/°C] | \n−0.29 | \n
\n\n | \nIsc temperature coefficient [%/°C] | \n0.05 | \n
\n\n | \nNOCT [°C] | \n45 | \n
\n\n | \nCost per Wp installed [USD/Wp] | \n2 | \n
\n\n | \nFixed OM factor as ratio of the PV CC | \n0.01 | \n
\n\n | \nPhotovoltaic derating factor | \n0.85 | \n
\n\n | \nInverter efficiency | \n0.9 | \n
PV module technical inputs.
The input data required by the diesel generation model is presented in Table 4. This information is collected from expert opinions on companies in the energy sector. This information must be validated each time the optimization model is used since it can vary depending on the studied case.
\nDiesel input data | \n||
---|---|---|
Symbol | \nDescription | \nValue | \n
\n\n | \nMaximum number of DG unit | \n5 | \n
\n\n | \nMinimum load ratio allowed | \n0.3 | \n
\n\n | \nLifecycle [years] | \n10 | \n
\n\n | \nFixed OM value as percentage of the diesel initial investment [%] | \n0.1 | \n
\n\n | \nFuel cost [USD/l] | \n0.8 | \n
Diesel model technical inputs.
Table 5 shows a database of diesel generation units with the cost per kW and the fuel curve parameters. This table was built using information supplied by the Colombian Regulation Commission of Energy and Gas (CREG—Comisión de Regulación de Energía y Gas) in [23]. The cost per kW presented in Table 5 includes the direct and indirect costs related to the installation of a Diesel plant in non-interconnected zones.
\nDG power [kW] | \nCost per kW installed [USD/kW] | \nDerate factors of the initial capital cost invested [%] | \n1/2 load 1 hour in liters | \nFull-load 1 hour in liters | \nf0 [L/kWh] | \nf1 [L/kWh] | \n
---|---|---|---|---|---|---|
10 | \n2724.09 | \n31.83 | \n1.4 | \n2.6 | \n0.020 | \n0.240 | \n
20 | \n1697.26 | \n32.43 | \n3.4 | \n6.05 | \n0.037 | \n0.265 | \n
25 | \n1540.12 | \n31.63 | \n3.6 | \n6.4 | \n0.032 | \n0.224 | \n
30 | \n1934.44 | \n23.00 | \n6.8 | \n10.96 | \n0.088 | \n0.277 | \n
40 | \n1654.09 | \n23.71 | \n8.69 | \n15.12 | \n0.056 | \n0.321 | \n
50 | \n1434.92 | \n25.12 | \n9.825 | \n16.63 | \n0.060 | \n0.272 | \n
60 | \n1343.75 | \n25.26 | \n10.96 | \n18.14 | \n0.063 | \n0.239 | \n
70 | \n1788.83 | \n18.13 | \n11.43 | \n19.77 | \n0.044 | \n0.238 | \n
80 | \n1686.08 | \n18.56 | \n11.9 | \n21.4 | \n0.030 | \n0.237 | \n
100 | \n1723.40 | \n17.24 | \n12.85 | \n23.06 | \n0.026 | \n0.204 | \n
125 | \n1587.11 | \n17.92 | \n18.9 | \n34.4 | \n0.027 | \n0.248 | \n
150 | \n1572.63 | \n17.55 | \n22.3 | \n41.2 | \n0.022 | \n0.252 | \n
200 | \n1373.73 | \n19.32 | \n29.11 | \n54.43 | \n0.019 | \n0.253 | \n
Diesel genset unit database.
In this chapter book, vented lead-acid battery banks only are considered. This kind of battery cells are often selected for large energy storage banks due the low cost, low maintenance, and high cycle stability. Table 6 shows the input data required by the battery bank. The battery bank charge and discharge efficiency and the self-discharge ratio is taken from [24]. The maximum depth of discharge is set in 0.5 since the battery bank can accomplish 3000 cycles during its life service according the datasheet. Other values as maintenance cost,\n
Battery bank input data | \n||
---|---|---|
Symbol | \nDescription | \nValue | \n
\n\n | \nBattery voltage [V] | \n2 | \n
\n\n | \nDC system voltage [V] | \n48 | \n
\n\n | \nCapacity rate [h] | \n5 | \n
\n\n | \nCharge efficiency | \n0.9 | \n
\n\n | \nDischarge efficiency | \n1 | \n
\n\n | \nSelf-discharge rate | \n0.000083 | \n
\n\n | \nLifecycle [years] | \n10 | \n
\n\n | \nFactor of the initial capital cost invested for the battery bank | \n0.7 | \n
\n\n | \nFixed OM factor as ratio of the battery bank initial investment | \n0.02 | \n
\n\n | \nMaximum depth of discharge | \n0.5 | \n
Battery bank technical inputs.
The main characteristics and price of the battery cells of the reference used in this work are presented in Table 7. The information was obtained from inquiries to local companies.
\nBattery cell capacity [Ah] at C10 | \nBattery cell capacity [kWh] at C10 | \nBattery cell voltage [V] | \n# of cycles at 50% DOD | \nPrice per unit [USD] (€) | \nPrice per kWh [USD/kWh] (€) | \n
---|---|---|---|---|---|
280 | \n0.56 | \n2 | \n3000 | \n114.00 | \n203.57 | \n
350 | \n0.7 | \n2 | \n3000 | \n135.00 | \n192.86 | \n
420 | \n0.84 | \n2 | \n3000 | \n153.00 | \n182.14 | \n
520 | \n1.04 | \n2 | \n3000 | \n161.00 | \n154.81 | \n
620 | \n1.24 | \n2 | \n3000 | \n186.00 | \n150.00 | \n
730 | \n1.46 | \n2 | \n3000 | \n210.00 | \n143.84 | \n
910 | \n1.82 | \n2 | \n3000 | \n234.00 | \n128.57 | \n
1070 | \n2.14 | \n2 | \n3000 | \n303.00 | \n141.59 | \n
1220 | \n2.44 | \n2 | \n3000 | \n330.00 | \n135.25 | \n
1370 | \n2.74 | \n2 | \n3000 | \n361.00 | \n131.75 | \n
1520 | \n3.04 | \n2 | \n3000 | \n389.00 | \n127.96 | \n
1670 | \n3.34 | \n2 | \n3000 | \n426.00 | \n127.54 | \n
1820 | \n3.64 | \n2 | \n3000 | \n460.00 | \n126.37 | \n
2170 | \n4.34 | \n2 | \n3000 | \n538.00 | \n123.96 | \n
2540 | \n5.08 | \n2 | \n3000 | \n664.00 | \n130.71 | \n
2900 | \n5.8 | \n2 | \n3000 | \n744.00 | \n128.28 | \n
3250 | \n6.5 | \n2 | \n3000 | \n834.00 | \n128.31 | \n
3610 | \n7.22 | \n2 | \n3000 | \n906.00 | \n125.48 | \n
3980 | \n7.96 | \n2 | \n3000 | \n981.00 | \n123.24 | \n
4340 | \n8.68 | \n2 | \n3000 | \n1056.00 | \n121.66 | \n
4700 | \n9.4 | \n2 | \n3000 | \n1097.00 | \n116.70 | \n
Battery cell database.
The system input parameters are shown in Table 8. The cost of energy lost is assumed in 0.2 USD/kWh. This value depends on the necessities and characteristics of the users of the select location. The interest rate considered in this work is 8.08% taken in [25].
\nSystem inputs parameters | \n||
---|---|---|
Symbol | \nDescription | \nValue | \n
\n\n | \nTime of the project [years] | \n20 | \n
\n\n | \nReal interest rate [%] | \n8.08 | \n
\n\n | \nCost of energy loss [USD/kWh] | \n0.2 | \n
\n\n | \nFiscal incentive factor | \n0.9038 | \n
System input parameters.
Fiscal incentive factor is calculated applying an effective corporate tax income rate of 33%. The resulting incentive factor is 0.938.
\nThe parameters for the PSO algorithm and the boundaries for each decision variable are shown in Table 9.
\nPSO input parameters | \n||
---|---|---|
Symbol | \nDescription | \nValue | \n
\n\n | \nLower bound number of PV modules | \n0 | \n
\n\n | \nLower bound nominal power of diesel | \n0 | \n
\n\n | \nLower bound number of battery cell in parallel | \n0 | \n
\n\n | \nLower bound nominal capacity of battery cell [kWh] | \n0 | \n
\n\n | \nUpper bound number of PV modules | \n20,000 | \n
\n\n | \nUpper bound nominal power of diesel unit in [kW] | \n200 | \n
\n\n | \nUpper bound number of battery cell in parallel | \n10 | \n
\n\n | \nUpper bound nominal capacity of battery cell [kWh] | \n9.40 | \n
\n\n | \nMaximum number of iterations | \n50 | \n
\n\n | \nPopulation size | \n200 | \n
\n\n | \nInertia coefficient | \n1 | \n
\n\n | \nInertia coefficient max | \n0.9 | \n
\n\n | \nInertia coefficient min | \n0.5 | \n
\n\n | \nPersonal acceleration coefficient | \n2.5 | \n
\n\n | \nSocial acceleration coefficient | \n1.5 | \n
PSO input parameters.
Table 10 summarized the obtained results after applying the proposed sizing methodology. The best cost achieved was 0.2090 USD/kWh being the lowest obtained. The optimization results deliver no only the design (number of components) but also economic and reliability indicators.
\nComponent | \nDesign | \nUnit | \nIndicator | \nValue | \nUnit | \n
---|---|---|---|---|---|
\n\n | \n13 | \nUnits | \n\n\n | \n7800.00 | \nUSD | \n
\n\n | \n3.9 | \n[kWp] | \n\n\n | \n48257.99 | \nUSD | \n
\n\n | \n25 | \n[kW] | \n\n\n | \n3864.00 | \nUSD | \n
\n\n | \n2 | \nUnits | \n\n\n | \n78.00 | \nUSD/year | \n
\n\n | \n50 | \n[kW] | \n\n\n | \n4825.80 | \nUSD/year | \n
\n\n | \n1 | \nUnits | \n\n\n | \n26884.74 | \nUSD/year | \n
\n\n | \n24 | \nUnits | \n\n\n | \n31710.54 | \nUSD/year | \n
\n\n | \n24 | \nUnits | \n\n\n | \n77.28 | \nUSD/year | \n
\n\n | \n1.04 | \n[kWh] | \n\n\n | \n7019.48 | \nUSD | \n
\n\n | \n24.96 | \n[kWh] | \n\n\n | \n1243.60 | \nUSD | \n
\n | \n | \n | \n\n | \n38406.77 | \n[l] | \n
\n | \n | \n | \n\n | \n38737.05 | \nUSD/year | \n
\n | \n | \n | \n\n | \n1.25 | \n% | \n
\n | \n | \n | \n\n | \n0.26 | \nUSD/kWh | \n
\n | \n | \n | \n\n | \n475.03 | \nUSD/year | \n
\n | \n | \n | \n\n | \n0.00 | \n% | \n
\n | \n | \n | \n\n | \n0.21 | \nUSD/kWh | \n
Results of the case study.
In this work, an optimization methodology was developed and described in detail to help sizing HRSE integrated by photovoltaic and diesel generation with energy storage.
\nThe main features of the sizing methodology developed were as follows: (a) it allows the simulation of hybrid renewable systems and the evaluation of its economic and reliability integrated by diesel and photovoltaic generation with energy storage, (b) the dispatch strategy developed prioritize the use of renewable energy among other energy sources, and (c) fiscal incentives granted by the Act 1715 of 2014 in Colombia were considered on the calculation of the cost of energy using the fiscal incentive factor.
\nThe reliability of the system was included in the objective function of the PSO algorithm through the annual cost of the energy not supplied. Also a fiscal incentive factor was used to include the financial benefits granted by the Act 1715 of 2014 in Colombia to non-conventional renewable source of energy. The results were obtained after simulating the energy flow of the system for 1 year with 1-hour resolution.
\nDispatch strategy was described in detail, prioritizing the use of renewable resource over diesel generation to supply the load. Also diesel generation cannot be used to charge the battery bank. This condition was based on the fact that, in off-grid areas, the complications associated to supply the fuel and the maintenance of DG units are commonly underestimated.
\nIt is expected that this work will help the process of designing HRES in non-interconnected areas, thus contributing to the development of these locations and improving the life quality of the population living on these places.
\nThe authors gratefully acknowledge the financial support provided by the Colombia Scientific Program within the framework called “Ecosistema Científico” (Contract No. FP44842-218-2018).
\nTeeth are a topic of interest to paleontologists because they are very well preserved. As a matter of fact, the dental remains have made it possible to study the evolution of mammals by analyzing their morphology. In developmental biology, the mouse model is an interesting model for studying dental development.
Humans have two dentitions (temporary and permanent) and different types of teeth, incisor, canine, premolar, and molar with different morphologies, whereas mice only have two types (incisor and molar) separated by a diastema from which the incisors have unlimited growth. Despite these differences, the dental development process is similar in humans and mice, and regulatory phenomena have been maintained over the evolution.
Teeth, such as mammary glands, hair, and feathers, develop from two adjacent tissues: the epithelium and the mesenchyme, although they all have different morphologies. Indeed, during development, the specific shape of each organ is defined in relation to epithelial-mesenchymal proliferation and to all the changes that the epithelium undergoes [1].
The embryological aspect of the molars was addressed in order to clarify the etiopathogenic aspect and to adapt therapeutic attitudes according to the diagnosis.
The objective of this chapter is to address the embryology of human molars by focusing on its molecular and morphological characteristics.
Teeth represent a new morphological feature of mammals [2, 3]. Molars are complex teeth able to become occluded. Interlocking intercuspation between upper and lower molars allows food to be crushed [4]. Evolutionary dietary radiations are related to the great diversity of the current mammalian molars. They are clarified in the fossil record, where new molar organizations are often related to significant line diversifications. Several theories have been advanced to explain the evolution of molars. Like all primates, Man is a placental mammal, and the ancestor of contemporary humans is Homo sapiens. For 200 million years, in Therian mammals, the molars have trigonodontal morphology; in other words, the three tubercles are arranged in a triangle [5].
In 1965, the discovery of a fossil of a lower molar made it possible to show that on this Therian branch around 135 million years ago, these molars already existed. They were called tribosphenic by Simpson in 1936 [6]. These mandibular molars have six tubercles, three of which are pointed, high, sharp, and are arranged in a triangle and distal position. The three others tubercles are lower and are arranged in a central basin to receive the main palatal tubercle of the opposite teeth that have only three cusps. The fact of having six tubercles is of physiological interest when taking food.
Nearly 110 million years ago, the oldest placental mammals had a dental formula with 52 teeth, including 3 molars in a decreasing series, the first being the largest. This primitive disposition is found in modern man.
Around 75 million years ago, with the dinosaurs extinction, other species invaded space, and the dental formula was reduced to 44 teeth for all placental mammals including the man.
In the Catarrhini, the loss of one incisor and two premolars leads to a dental formula with 32 teeth found in monkeys of the ancient world (Afro-Eurasia), the Hominids, and the contemporary Men. It has been recognized for 45 million years [7].
In the genus Homo, the 32-teeth morphology does not differ much from the modern men, except for the great variability in size. Root morphology may vary from one group to another. The reduction in the number of cusps observed in humans can be considered as a specialization trait and not as a step backward. However, the reduction in the dental formula in the placentals and primates mainly affected the incisors, premolars, and even canines but not the molars.
Wisdom tooth agenesis, especially mandibular agenesis, is often considered as a sign of evolution. On the other hand, the presence of supernumerary teeth or hypergenesis is explained as a return to ancestral forms
The odontogenic epithelium is formed from the oral epithelium that lines the primary oral cavity called the “stomodeum.” It appears as a localized thickening of the oral epithelium, and it is formed by several cellular layers resulting from a series of localized mitoses affecting the oral epithelium. The mitotic spindle of dividing cells is oriented perpendicular to the basal membrane that separates the epithelium from the ectomesenchyma.
Epithelial thickening continues to proliferate and sinks into the underlying ectomesenchymal tissue forming a plunging wall (also called a primitive dental blade). This latter splits into two blades: vestibular and dental. The vestibular blade determines the formation of the buccal vestibule, which is the space between the cheek/lip and the dental arch.
In humans, as in rats and mice, the dental blade will give birth to the dental placodes that will be at the origin of the formation of future dental germs. Dental placodes are cellular clusters attached to the dental blade by a net of epithelial cells called the primary dental blade. Each dental arch initially contains 10 dental placodes. From the primary dental blade develops the secondary dental blade, which is at the origin of the 16 permanent teeth per arch.
Each placode will undergo morphological changes that are described as three successive stages: bud stage, cup stage, and bell stage [1].
Since the three molars are not preceded by temporary teeth, they evolve from the distal end of the initial dental blade, which proliferates in a posterior direction. The primary dental blade of the second temporary molar will cause the formation of four secondary dental blades. For each half of the arch, starting from the anterior area toward the posterior area, each of these four secondary dental blades will give the permanent germ of the following teeth: the first permanent molar, the second permanent molar, and the third permanent molar.
The secondary dental blades that are at the origin of the formation of the 1st and 2nd molar will orient themselves vertically as long as they have space that allows them to orient themselves in the mesenchyma. On the other hand, in most cases for the 3rd molar, orientation problems arise because there is not enough space for its secondary dental blade to be parallel to the other two blades [8].
All dental buds, with the exception of the second and third permanent molars, are present and begin to develop before birth [9]. The chronology of the appearance of molar germs remains variable according to the authors; however, it is often found that the germ of the first molar appears around the 4th or 5th month of intrauterine life. The one of the second molar appears around the 9th month or 1 year after birth.
The germ of the third molar does not appear until around 4 or 5 years of age. Mineralization begins between 7, 9, and 10 years, and the crown is completed between 12 and 16 years. The emergence in the oral cavity is between 17- and 21-year-olds; the tooth will then slide along the distal surface of the second molar to reach the occlusion level. Root building ends between the ages of 18 and 25 years. The place it has depends on the growth in the posterior region of the arch. The main activity of the dental blade is spread over a period of about 5 years. However, the dental blade near the third molar continues to be active until about 15 years of age [9].
A number of anomalies can occur during the development of the tooth. The development of excess dental blade can lead to an increase in the number of dental buds, resulting in too many teeth (supernumerary). A deficient dental blade can lead to a reduction in the number of teeth (hypodontia) [9].
Molars are multiradiculated teeth. Indeed, the vast majority of the first maxillary molars have three roots. The second maxillary molar has more frequent variations in the number of roots than the first maxillary molar, and the first mandibular molar and the second have two roots in the majority.
Root formation or radiculogenesis or rhizagenesis is the development of the root pulpo-dentinary organ in close relationship with cemenesis, the outline of the dentoalveolar ligament and the construction of the alveolar bone. It begins when the final dimensions are acquired. The Hertwig epithelial sheath is at the origin of root formation, depending on their number, shape, and size [10].
As for the crown, root development is governed by interactions involving the Hertwig epithelial sheath, basement membrane, mesenchymal papilla, and dental follicle.
The Hertwig epithelial sheath originates from the reflection zone or cervical loop which is the place where the external and internal adamantin epitheliums (EAE and EAI) meet to form a double epithelial layer. Hertwig epithelial sheath has an annular structure surrounded by a basal membrane that separates it from the pulpal and follicular mesenchyma. This basement membrane has anchoring fibrils on the pulp side. The internal epithelium faces the papilla and the external epithelium faces the dental follicle. The Hertwig epithelial sheath will emit tongues in the centripetal direction that will fuse in the central region of the papilla and form rings from which the roots can be identified. The number of strips emitted is proportional to the number of roots that each molar can have. For example, for the molar which will have two roots, two tongues are formed, and after fusion of two rings, each of the two will be at the origin of the formation of a root. These two leaves remain attached and progress in the underlying connective tissue in the apical direction defining the future shape of the dental root [11].
Root elongation and tissue formation are related to the coordinated proliferation of sheath epithelial cells and surrounding mesenchymal cells [12].
Root dentin forms in parallel with the proliferation in the apical direction of the Hertwig sheath. The latter gradually induces odontoblastic differentiation. The pulp parenchyma cells close to the anchor fibrils differentiate into odontoblasts. These odontoblasts produce preentine, which mineralizes to form dentin. The cells of the outer dental epithelium forming the outer layer of the sheath do not differentiate into ameloblasts as is the case for the crown. Then, the basement membrane degrades, and the epithelial blade involutes and gradually dissociates.
Developmental defects of the Hertwig sheath at the apical third of the root are at the origin of the formation of the lateral canals following a stop of dentinogenesis at this site due to the nondifferentiation of pulp fibroblasts into odontoblasts.
The cells of the sheath can undergo three spells: some can form the “Malassez epithelial debris,” others can die by apoptosis, while others can undergo epithelial-mesenchymal transformation.
As the sheath disintegrates, follicular cells near the surface of the root dentin differentiate into cementoblasts. These synthesize and deposit the cement matrix in contact with the dentin.
As the root development progresses, the epithelial ring forming the Hertwig epithelial sheath gradually shrinks as a result of a reduction in mitosis, thereby reducing the size of the root tube. This narrowing allows the development of one or more orifices (or foramina), which are the place where vascular and nervous elements intended for the pulp to pass through.
The development of the root ends with the construction of the apex, which is a slow process. In humans, for example, for the 1st permanent molar, this operation is performed until the age of 9–10 years. In the case of permanent teeth, this phenomenon lasts longer and requires more time than the development of the root itself.
In humans, dental development includes the morphogenesis of crowns and roots and results in the formation of the enamel organ, odontoblastic, ameloblastic, and cementoblastic differentiation. Huge advances in research have made it possible to understand the phenomena of molecular regulation of dental development.
Dental development follows a precisely controlled and regulated genetic program. The dental organ consists of an epithelial part that derives from the ectoderm and a mesenchymal part that derives from mesodermal cells on the one hand and cells from neural ridges on the other hand [13, 14, 15, 16].
The dental organ develops from a communication between the epithelium and the underlying mesenchyma. The communication language has been preserved throughout the evolution. This communication between the epithelium and the mesenchyma is done through signaling molecules and growth factors [17, 18, 19].
The studies carried out on the mouse molar have enabled us to gather a body of knowledge with many similarities to those of humans. However, the experimental data obtained in animals can be extrapolated relatively reliably to understand what is actually happening in humans.
Several families have been described, including:
TGF-beta (transforming growth factor beta) including BMP (bone morphogenetic proteins) activins and follistatin;
FGF (Fibroblast growth factors);
Hedgehog (only Sonic hedgehog (Shh) is known for its role in odontogenesis);
These molecules send their message to the nucleus through the signaling pathways and receptors on the cell membrane surface. Transcription factors will then modulate the expression of different target genes and induce changes in cell response and behavior (Figure 1) [25].
Signaling in tooth development [25].
Genes represented in “light blue” colored squares or rectangles are responsible, when inactivated, for stopping dental development.
It should be remembered that the odontogenic epithelium is formed at the first gill arch. The latter undergoes pharyngeal regionalization, resulting in the expression of Fgf8 and 9 (fibroblast growth factors 8 and 9) and Lhx-6 and -7 (LIM homeobox 6 and 7) in the oral part (rostral) and Gsc (goosecoid) in the aboral part (caudal). Indeed, the expression of Fgf8 in the odontogenic epithelium in the oral part of the first pharyngeal arch causes the expression of Lhx-7 in the underlying ectomesenchyma. In the aboral region, there is an important expression of Gsc in the ectomesenchyma. Gsc expression in the caudal region is not responsible for inhibiting Lhx-7 expression in this area; however, Lhx-7 expression in the rostral region will result in blocking Gsc gene expression in this.
In addition to Fgf8, a second BMP4 signaling molecule (bone morphogenetic protein 4) is expressed in the epithelium in the distal and therefore in the median region of the 1st arc.
The activation and inhibition of transcription factors allows the delimitation of the odontogenic territory by BMP4 and Fgf8a double signalling. (Figure 2) [19].
Pattern of gene expression in the developing tooth [19]. (a) Signaling within the epithelium and between the epithelium and the mesenchyme at embryonic day (E) 10.5. The diagram shows an isolated mandibular arch. Positive autoregulatory loops and mutual repression within the epithelium lead to the formation of strict boundaries of gene expression, which sets up the presumptive incisor and molar fields. Members of the bone morphogenetic protein (BMP) and fibroblast growth factor (FGF) families of protein in the epithelium induce and inhibit the expression of various homeobox genes. This results in a complex pattern of gene expression in the mesenchyme, across both the proximal–distal and oral–aboral/rostral–caudal axes. (b) The odontogenic homeobox code model of dental patterning. The nested expression pattern of homeobox genes in the mandible produces a homeobox code that defines tooth type. Bapx1 (bagpipe homeobox gene 1 homolog); Barx1 (BarH-like homeobox 1); Dlx (distalless homeobox); Gsc (goosecoid); Lhx (LIM homeodomain genes); Msx (homeobox, msh-like); Pitx (paired-related homeobox gene).
Mammalian teeth are meristic series. The determination of different morphology was explained by two theories:
The gradient theory proposed by Butler [26] which stipulates the presence of morphogenetic fields and that the determination of the shape of the tooth is a function of its position in the field independent of local factors.
The theory of clones proposed by Osborn [27] which stipulates that ectomesenchyma is already differentiated into three cellular clones, incisal, canine, and molar clones, before its migration. The proposal of this second concept suggested that the two theories are competing.
In 1995, the theory of odontogenic homeocode was developed by Sharpe [22], which represents a synthesis of the two theories: gradients and clones and shows that the latter two are complementary. These two concepts were explained in the light of the discovery of new genes and signaling molecules (Figure 3) [26, 27, 28].
(A) Regional field theory. (B) Clone theory. (C) Homeobox [26, 27, 28].
The identity of each tooth, including the molars, is characterized by its homeocode, which represents the combination of homeogens that defines the position and identity of the tooth. Indeed, different homeogens are expressed by the neural crest cells of the ectomesenchyma under the instructive induction of the oral epithelial cells. These homeogens are divergent and therefore of the nonhox type.
This odontogenic homeocode theory involves four homogenous genes: muscle segment homeodomain-homeobox 1 (Msx-1), muscle segment homeodomain-homeobox 2 (Msx-2), distal-less homeobox 1 (Dlx1), and goosecoide. In the molar sector, Msx-1 and Dlx-1 are expressed and Msx-2 and goosecoide are not expressed. In the canine sector, Msx-1, Msx-2, and goosecoide are expressed, and Dlx-1 is not expressed; in incisal sector, Msx-1 and goosecoide are expressed, Msx-2 and Dlx-1 are not expressed.
In the concept of morphogenetic fields, the consideration of various genetic factors and their epigenetic modulation influences dental development [29].
According to Mitsiadis’ work in 2006, the three models, gradients, clones, and homeocodes, could be grouped into a single model to explain dental identity. Indeed, dental identity, including molars, is given by the presence of morphogenetic fields defined by the diffusion of growth factors. The odontogenic epithelium expresses gradients of signaling molecules that are mainly Fgf, Bmp, Shh, and Wint that will diffuse to the underlying mesenchymal tissue containing neural peak cells. Depending on the location and instruction received by these cells, they will express a set of divergent genes in relation to concentrations of signaling molecules. The locally defined tooth type is related to the locally expressed divergent homeogen combinatorics of these ridge cells (Figure 4) [30].
Dental identity determination (adapted from Ref. [30]).
The Mitsiadis model combines the three concepts: morphogenetic fields, clone, and odontogenic homeocode.
These three models should be viewed as complementary rather than contradictory and propose that this unifying view can be extended into the clinical setting using findings on dental patterning in individuals with missing teeth. The proposals are compatible with the unifying etiological model developed by Brook in 1984 based on human epidemiological and clinical findings. Indeed, this new synthesis can provide a sound foundation for clinical diagnosis, counseling, and management of patients with various anomalies of dental development, as well as suggesting hypotheses for future studies.
The root development process involves a set of signaling cascades. Various growth factors, including BMPs (bone morphogenetic proteins), EGF (epidermal growth factor), IGF (insulin-like growth factor), FGF (fibroblast growth factor), transcription factors Msx1, Msx2, Runx-2, Sonic Hedgehog (Shh), enamel proteins (secreted by HGH cells), and other proteins such as follistatin and activin A, are involved in the root development process. Indeed, they are involved in the growth and/or differentiation of odontoblasts and cementoblasts and/or in the mineralization of dentin and/or cementum [21, 31, 32, 33, 34, 35, 36].
Dental morphology is controlled by an epithelial signaling center called the enamel node. The node of the enamel is a particular and transient histological structure formed by a cellular cluster that appears at the basal part of the internal dental epithelium. The node of the primary enamel is present in the dental germs of all types of teeth including incisors.
Because the enamel nodes link cell differentiation to morphogenesis, Thesleff suggests that the latter can be considered as central regulators of dental development [37].
During molar development, the node of the secondary enamel is formed during the bell stage at the location of future cusp areas. At this point, the expression of signaling molecules precedes the folding and growth of the dental epithelium [38, 39].
The Slit1 gene is expressed in the nodes of the primary and secondary enamel during the formation of molar cusps [40].
The approaches provided by Line and Mitsiadis have advanced the clinic’s understanding of dental identity establishment based on gradient, clone, and homeocode theories [29, 30].
The multifactorial model involving genetic, epigenetic, and environmental determinants has provided better explanations and helped to understand missing and supernumerary teeth in monozygotic twins [41].
In humans, dental problems are observed during pathologies of dental development or syndromes.
Mutations in genes known as divergent homeobox genes encoding transcription factors such as MSX1 and PAX9 (paired domain box gene 9) are at the origin of oligodontia. Indeed, a mutation in the homeobox of the MSX1 gene (substitution of an arginine by a proline in the homeodomain region) is associated with the agenesis of third molars, indicating the involvement of MSX1 in the dentition pattern [42, 43, 44].
Also, mutations in the PAX9 gene cause oligodontia characteristic of molars [45, 46, 47, 48]. The severity of dental agenesis appears to be correlated with the ability of the mutated PAX9 protein to bind to DNA [49].
A misdirection mutation during the sequencing of the PAX9 gene may explain a different phenotype of hereditary oligodontia observed in humans, which affects not only molars but also other tooth lines; and is characterized by tooth small size in both types of dentition. This mutation is characterized by a replacement of the amino acid arginine by tryptophan in a region entirely preserved in all genes of the matched sequenced box [50].
In humans, Pitx2 expression deficiency associated with Rieger syndrome is characterized by oligodontia [51].
The biological process is the same for all teeth, including molars, regardless of their identity, but epithelial signaling and homeogenic combination differ from one tooth type to another.
The study of first molar of the mouse has allowed us to better understand and follow the stages of dental development in humans. The general pattern remains the same, unlike the training time, the complexity of the dental system, the presence of two types of teeth in humans, and unlimited incisors growth in mice.
The multidisciplinary approach between fundamental and clinical research is essential to clarify the relationship between molecular involvement and clinical manifestations.
Understanding the molecular mechanisms of dental anomalies, including those affecting human molars, helps to propose diagnostic hypotheses and thus to improve patient management.
Future research should focus on synergizing molecular and genetic approaches to further analyze the action mechanisms of key genes involved in the development of human molars.
The authors declare that they have no conflicts of interest with the contents of this article.
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