\r\n\tComputational fluid dynamics is composed of turbulence and modeling, turbulent heat transfer, fluid-solid interaction, chemical reactions and combustion, the finite volume method for unsteady flows, sports engineering problem and simulations - Aerodynamics, fluid dynamics, biomechanics, blood flow.
",isbn:"978-1-83968-248-3",printIsbn:"978-1-83968-247-6",pdfIsbn:"978-1-83968-321-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"1f8fd29e4b72dbfe632f47840b369b11",bookSignature:"Dr. Suvanjan Bhattacharyya",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10695.jpg",keywords:"Free Turbulent Flow, Discretisation Methods, Aerodynamics, Phase Flow, Bluff-Body, Complex Geometries, Drag Force, Flow Separation, Laminar Diffusion Flame, Non-Premixed Combustion, Fluid Dynamics, Biomechanics",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 28th 2021",dateEndSecondStepPublish:"February 25th 2021",dateEndThirdStepPublish:"April 26th 2021",dateEndFourthStepPublish:"July 15th 2021",dateEndFifthStepPublish:"September 13th 2021",remainingDaysToSecondStep:"3 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Suvanjan Bhattacharyya is currently working as an Assistant Professor in the Department of Mechanical Engineering of BITS Pilani, Pilani Campus. His research interest lies in computational fluid dynamics, experimental heat transfer enhancement, solar energy, renewable energy, etc.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"233630",title:"Dr.",name:"Suvanjan",middleName:null,surname:"Bhattacharyya",slug:"suvanjan-bhattacharyya",fullName:"Suvanjan Bhattacharyya",profilePictureURL:"https://mts.intechopen.com/storage/users/233630/images/system/233630.png",biography:"Dr. Suvanjan Bhattacharyya is currently working as an Assistant Professor in the Department of Mechanical Engineering of BITS Pilani, Pilani Campus, India. Dr. Bhattacharyya completed his post-doctoral research at the Department of Mechanical and Aeronautical Engineering, University of Pretoria, South Africa. Dr. Bhattacharyya completed his Ph.D. in Mechanical Engineering from Jadavpur University, Kolkata, India and with the collaboration of Duesseldorf University of Applied Sciences, Germany. He received his Master’s degree from the Indian Institute of Engineering, Science and Technology, India (Formerly known as Bengal Engineering and Science University), on Heat-Power Engineering.\nHis research interest lies in computational fluid dynamics in fluid flow and heat transfer, specializing on laminar, turbulent, transition, steady, unsteady separated flows and convective heat transfer, experimental heat transfer enhancement, solar energy and renewable energy. He is the author and co-author of 107 papers in high ranked journals and prestigious conference proceedings. He has bagged the best paper award in a number of international conferences as well. 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1. Introduction
The Rho family of small GTP-binding proteins is comprised of 22 members, including the most well characterized members RhoA, Rac1 and Cdc42 (Jaffe and Hall 2005). The Rho family proteins share a high degree of homology with the Ras proto-oncogene, and indeed were first identified as a result of this similarity (Ras homologue). Activity of these proteins is dependent upon their nucleotide binding state; inactive when associated with GDP but active following exchange of GDP for GTP, which induces conformational changes that promote association/activation of downstream effector proteins. The GDP/GTP cycle is regulated by GAPs that accelerate GTP hydrolysis by providing a critical catalytic amino acid leading to a return to the inactive state (Bernards and Settleman 2005), and GEFs that promote guanine nucleotide exchange and consequent Rho activation (Rossman et al. 2005). The number of GAPs and GEFs far exceeds the number of Rho proteins, and the roles of individual GAPs and GEFs in specific cell types and biological processes is currently an intensively studied field.
Although united by homology and function as regulators of the actin cytoskeleton, each of RhoA, Rac1 and Cdc42 has a distinct role in the organization of actin structures (Figure 1). RhoA is principally involved with the production of actin-myosin bundles and the generation of actomyosin contractile force. Rac1 contributes to the formation of actin meshworks that result in the emergence of large protrusive structures that lead to spreading or, if occurring in a polarized manner, will contribute to motility. Cdc42 promotes the formation of actin-rich filopodia. Together, coordinated programs of RhoA, Rac1 and Cdc42 activation/inactivation play prominent roles in processes such as endocytosis/exocytosis, adhesion and motility, which may subsequently impact upon proliferation and death/survival. Recent advances in the development of activation-state sensitive fluorescent probes have allowed temporal and spatial analysis of Rho protein activation, which has added significantly to our appreciation of Rho regulation and function (Hodgson et al. 2010). Much of the early research on Rho protein function relied upon over-expression of dominant-negative mutants that reduced affinity for GTP and constitutively-active mutants that reduced GTP hydrolysis; however, more refined analysis has become possible with the rise of RNAi and knockout methodologies (Heasman and Ridley 2008).
The study of Rho family proteins has historically focused on their roles as molecular switches acting downstream of cell surface receptors to regulate the actin cytoskeleton (Jaffe and Hall 2005). Significant effort has gone into classifying signaling from Rho proteins into
Figure 1.
Diagram of actin structures regulated by RhoA, Rac1 and Cdc42.
linear cascades, similarly to the classical Ras/Raf/MEK/ERK kinase cascade. However, recently a greater appreciation of the role of mechanical forces as fundamental influences in biology has emerged (Puceat et al. 2003). As central regulators of the actin-myosin cytoskeleton, an emerging concept is that many of the activities of Rho proteins may not be attributable to simple linear pathways, but instead are the product of modulating contraction and relaxation at the cellular and subcellular levels, with consequent effects on development and function at the tissue and organismal levels.
2. Embryonic Stem Cells
Pluripotent stem cells were first isolated from testicular teratocarcinoma (Pierce and Dixon 1959), a germ cell tumor type containing a population of pluripotent stem cells together with embryonic and extra-embryonic tissues that arise from these stem cells. Pluripotent stem cells of testicular teratocarcinomas are termed Embryonal Carcinoma (EC) cells and can give rise to collections of tumor cells having morphological characteristics of each of the three embryonic germ layers. In mice, EC cells have been demonstrated to be capable of contributing to every germ layer including the germ-line when injected into host blastocysts (Brinster 1974; Mintz and Illmensee 1975; Illmensee and Mintz 1976). Interestingly, under these conditions, EC cells are non-malignant, and chimeric mice containing tissues differentiated from EC cells are generally healthy. These observations formed the basis for the isolation of Embryonic Stem (ES) cells, which were derived from the pre-implantation embryo, arising when cells constituting the inner cell mass (ICM) of the pre-implantation blastocyst or the epiblast of the post-implantation blastocyst were placed in 2D-culture (Evans and Kaufman 1981; Martin 1981). Like EC cells, ES cells are pluripotent, being capable of giving rise to all tissues of the adult organism originating from the three germ layers, upon injection into a host blastocyst (Bradley et al. 1984). The great similarities observed between EC cells and ES cells led to an appreciation of the importance of the tissue microenvironment in informing cell behavior and fate.
A major attraction of murine ESC (mESC) research stemmed from the realization that mutations introduced into the mESC genome would be readily transmitted through the germ-line, enabling the establishment of strains of mice harboring specific genetic mutations (Capecchi 1989), thereby facilitating the elegant functional characterization of virtually any gene of interest. The first gene to be targeted and inactivated in mES cells was the X-linked gene Hprt, which encodes hypoxanthine guanine phosphoribosyltransferase, an enzyme involved in purine metabolism (Thomas and Capecchi 1987). In turn, an Hprt-deficient ES cell line was engineered to re-introduce the Hprt coding sequence and used to produce knock-in gene-targeted mice for the first time, which faithfully recapitulated the wild-type Hprt expression pattern (Thompson et al. 1989). Following on from these pioneering studies, techniques for establishing gene-targeted mice have been considerably improved and refined. Gene targeting in mES cells to generate loss of function or gain of function mutations with an exquisite degree of subtlety and control is now an established tool in biological research.
2.1. Maintenance of pluripotency
ES cells express markers of their undifferentiated state such as the octamer binding protein 4 (Oct4) (Rosner et al. 1990; Scholer et al. 1990), the SRY-related HMG-box gene 2 (Sox2) (Yuan et al. 1995), signal transducer and activator of transcription 3 (Stat3) (Niwa et al. 1998), the homeobox protein Nanog (Chambers et al. 2003; Mitsui et al. 2003) and alkaline phosphatase (AP) (Hahnel et al. 1990) that denote their capacity for both self-renewal and pluripotency. Of these, Oct4 and Sox2 have key roles in the maintenance of ES cell self-renewing capacity such that their expression is essential for the maintenance of pluripotency and their ectopic expression in somatic cells contributes to the generation of induced pluripotent (iPS) cells (Takahashi and Yamanaka 2006; Yu et al. 2007; Nakagawa et al. 2008).
Oct4 is a POU-domain transcription factor also termed POU5F1 and is indispensable for plutipotency. Oct4 deficient embryos develop to the morula stage, but are unable to form an ICM (Nichols et al. 1998) and in vitro culture of Oct4 deficient embryos failed to yield ES cells (Nichols et al. 1998). These observations are further elaborated by more recent work showing that selective deletion of the Oct4 gene in primordial germ cells (PGC) results in their death by apoptosis (Kehler et al. 2004). Oct4 expression is very tightly regulated and its transient increase and decrease during early stages of embryonic development have been termed the totipotent cycle (Yeom et al. 1996). While evidence for the absolute requirement for Oct4 in the maintenance of ES cells is very strong, there is controversy on whether it is required for the maintenance of adult stem cells. Although there are numerous reports of Oct4 expression in adult stem cells including in hematopoietic and mesenchymal stem cells and stem cells of epithelial tissues such as the pancreas, kidney, breast, uterus, lung and skin, a recent study in which its expression was systematically abrogated in several of these tissues has revealed that Oct4 is required for neither the maintenance of adult stem cells nor for wound healing (Lengner et al. 2007).
Sox2 is a HMG-box containing transcription factor closely related to the Y-chromosome located sex determining gene SRY. Its main role in the maintenance of pluripotency is thought to be closely related to the regulation of Oct4 transcription. Indeed Sox2 and Oct4 can jointly bind regulatory chromosomal regions associated with both the Oct4 and Sox2 genes (Chew et al. 2005; Masui et al. 2007) as well as regulating Nanog expression (Kuroda et al. 2005; Rodda et al. 2005).
2.2. Culturing ES cells
Since the initial isolation of ICM-derived mESCs in the early 1980s (Evans and Kaufman 1981; Martin 1981), conditions for the culture of ESCs have been developed and progressively refined. mESCs are propagated on a feeder layer of murine embryonic fibroblasts (MEFs) or in media containing leukemia inhibitory factor (LIF), under which conditions they maintain a pluripotent state (Williams et al. 1988). Withdrawal of LIF or culture in the absence of fibroblasts results in spontaneous differentiation of mESCs into a variety of lineages (Evans and Kaufman 1981; Martin 1981; Williams et al. 1988). The dependence of mES cells on LIF is thought to be related to LIF mediated activation of STAT3 signaling (Smith et al. 1988) which together with Oct4/Sox2, has a possible role in the regulation of Nanog expression.
Human ESCs (hESCs), which have been isolated from the epiblasts of human blastocysts (Thomson et al. 1998; Reubinoff et al. 2000) are also propagated on a feeder layer of MEFs, but LIF has no role in maintaining their pluripotency (Thomson et al. 1998; Reubinoff et al. 2000). Instead, a balance between Tgfβ/activin/nodal signaling and suppression of BMP signaling together with the FGF signaling pathway are important for self-renewal and the maintenance of pluripotency in this system (James et al. 2005; Vallier et al. 2005; Xu et al. 2005). However, as yet no reliable defined medium has been developed to enable the culture of hES cells in the absence of feeder cells. Like mESCs, hESCs spontaneously differentiate if cultured in the absence of a feeder layer, but unlike mESCs they undergo blebbing and apoptosis when maintained in a dissociated state (Watanabe et al. 2007).
hESCs are not only a valuable tool for the study of human development, but also have applications in regenerative medicine, toxicology and the development of new drugs to target human disease (Murry and Keller 2008). mESCs and hESCs are thus examples of the two major types of pluripotent stem cells, derived as they are from the ICM and the epiblast respectively.
3. Rho family GTPases in embryonic stem cells
One of the most interesting recent developments in ES research is the revelation that signaling through RhoA plays a key role in the survival of human embryonic stem cells. This was first appreciated in 2007, following a cell-based screen of biologically active compounds that promoted survival and proliferation of dissociated hESCs that identified Y27632, a selective inhibitor of the Rho-effector protein ROCK (Watanabe et al. 2007). The ROCK1 and ROCK2 serine/threonine kinases are central and critical regulators of actomyosin contractility (Coleman et al. 2001). Typically, these kinases are activated by association with active GTP-bound Rho proteins. Active ROCK promotes actomyosin contractility through a dual mechanism of simultaneously phosphorylating and activating the contractile force-generating regulatory myosin light chain (MLC) and the LIM kinases (Sugihara et al. 1998), which modulate filamentous actin stability. In contrast to hESC, mES cells do not require ROCK inhibition for survival even when disaggregated to a single cell suspension. Since that initial study, subsequent screens have identified additional ROCK selective inhibitors that promote the survival of hESC (Andrews et al. 2010; Pakzad et al. 2010) and neural stem cells (Xu et al. 2010), thereby independently validating the role of ROCK as a key regulator of ESC survival. The addition of Y27632 to the culture media is now standard practice and has greatly improved the reliability of hES cell survival (Olson 2008; Krawetz et al. 2009). The addition of Y-27632 can be directly to the cell culture medium or into the extracellular matrix upon which the hESCs are plated (Danovi et al. 2010). ROCK inhibitors have also been shown to improve recovery of cryopreserved ESC (Scott and Olson 2007; Wickman et al. 2010) and increase the efficiency of adenovirus-mediated gene transfer (Patwari and Lee 2008).
3.1. Rho signaling in ES cells
Recently, it has become clear that the actomyosin machinery downstream of Rho activation is essential for the blebbing and apoptosis that follow dissociation of hESCs (Martin 1981; Chen et al. 2010; Ohgushi et al. 2010), as inhibition of the myosin heavy chain ATPase with Blebbistatin, the use of actin disruption drugs or selective knock-down of ROCK1, ROCK2 or the myosin heavy and light chains all prolong survival of dissociated hESCs. Rho activation, coupled with Rac inhibition, was determined to be the driver of dissociation-induced hESC apoptosis via ROCK-mediated myosin light chain phosphorylation (Ohgushi et al. 2010). Activation of ROCK1 by caspase-mediated cleavage (Buecker et al. 2010) does not appear to contribute to apoptosis induced in this manner (Ohgushi et al. 2010). Overexpression of an active form of Ezrin, which strengthens the physical coupling between the plasma membrane and cortical actin cytoskeleton, was sufficient to block blebbing but not the dissociation-induced cell death, indicating that apoptosis was not caused by blebbing itself but the result of actomyosin contraction (Ohgushi et al. 2010). Although the dissociation-induced cell death was linked back to mitochondrial depolarization and cytochrome c release, further study will be required to determine how actomyosin contractility is coupled to the mitochondrial pathway of apoptosis (Ohgushi et al. 2010). It is also becoming clear that the particular sub-embryonic origin of the embryonic stem cell line determines whether Rho signaling is detrimental to survival on dissociation. While epiblast-derived hESCs are acutely sensitive to Rho signaling following dissociation, ICM-derived mESC have the capacity to survive dissociation without the need for inhibition of the actomyosin machinery (Ohgushi et al. 2010), a characteristic they share with human induced pluripotent stem cells (hiPSC), which display mESC-like morphological features (Evans and Kaufman 1981). On the other hand, epiblast-derived murine epiblast stem cells (mEpiSC) or mESCs differentiated into epiblast-like cells acquire a dependence on ROCK-inhibition in order to survive dissociation (Ohgushi et al. 2010). One theoretical possibility to account for these observations is that external pulling forces from adjacent cells in an epithelial sheet counteract the internal actomyosin contractile forces within individual cells such that the internal and external mechanical forces become balanced in all directions along the epithelial plane, thereby limiting their pro-apoptotic effects. Since mESCs are derived from the ICM prior to differentiation into epithelial-type cells and grow in disorganized three-dimensional cell collectives similar to the bona fide inner cell mass, they may not be dependent on external tension derived from cell-cell adhesions, such as those that occur in an epithelial sheet, for survival. In contrast, hESCs grow as tightly adherent two-dimensional sheets similar to the epiblast where pulling forces from adjacent cells would be sensed. In agreement with this model, when human induced pluriopotent stem cells (hiPSCs) were reprogrammed from fibroblasts through the expression of five reprogramming factors plus LIF, they acquired the ability to grow at low density or in suspension in parallel with changed in vitro growth characteristics to mESC-like disorganized three-dimensional structures (Tashiro et al. 2010). This exquisite sensitivity of epiblast and epiblast-like stem cells may reflect the critical importance of proper differentiation and spatial organization of the epiblast stage during embryonic development. If any individual cell in the epiblast layer were improperly positioned in the epithelial sheet, the potential consequences to the subsequent developmental stages and ultimately to the organism as a whole could be catastrophic.
3.2. Rac signaling in ES cells
The pro-apoptotic effect of Rho signaling in dissociated hESC is strongly counteracted by signaling through Rac. Indeed it has been shown that Rac1 is required for the survival of epiblast cells within the blastocyst during morphogenesis of the murine peri-implantation egg cylinder (He et al. 2010). During this process, the apoptosis mediated clearance of cells that are not in contact with the basement membrane (known as cavitation) is counteracted by signaling through Rac in those cells that remain apposed to the basement membrane (BM). In the absence of Rac1, cells in contact with the BM undergo apoptosis despite the survival signals that it normally provides (Kim et al. 2011). It is these BM-associated cells that give rise to the epiblast (He et al. 2010). Activation of Rac in the epiblast is mediated by the recruitment of the Crk adaptor protein and DOCK180 GEF (He et al. 2010). In turn, active Rac signals via PI3K and Akt to promote survival (He et al. 2010). Interestingly, a single dual-function protein, Abr, acts as Rho-GEF and Rac-GAP within dissociated hES cells in culture, simultaneously activating Rho and inactivating Rac upon cell dissociation, in a manner dependent on cell-cell interactions involving E-cadherin (Martin 1981; Ohgushi et al. 2010). The role of E-cadherin in hESC survival was also revealed in a chemical biology screen for small molecules that affected survival (Pakzad et al. 2010). One compound increased the survival of dissociated cells by reducing E-cadherin endocytosis, thus increasing the levels of cell-surface E-cadherin and consequently promoting cell-cell adhesions. In agreement with these observations, ectopic over-expression of E-cadherin was also sufficient to increase survival of dissociated hESCs (Rizzino 2010). However, when dissociated hESCs were grown on E-cadherin coated plates, they still underwent membrane blebbing and had significantly lower survival, indicating that homotypic E-cadherin interactions alone were not sufficient to promote survival (Ohgushi et al. 2010). These observations suggest the existence of a yet uncharacterized sensor that transmits a complementary signal derived from cell-cell adhesion that acts in concert with, or in parallel to, E-cadherin activation to repress actomyosin contractility and consequent cell death. Although mESCs are not sensitive to the same sort of dissociation-induced cell death, constitutive Rac1 deletion was found to induce membrane blebbing and eventual apoptosis of epiblast derived stem cells, possibly due to the lack of Rac1 activity to counter-balance the effect of RhoA activation (Kim et al. 2011). These Rac1 deleted cells also were defective in the formation of actin cytoskeleton structures such as lamellipodia and were significantly slower in migrating on collagen I coated dishes, revealing the critical role played by Rac1 in these biological activities. Similarly, Rac1 was found to be an important contributor to mESC migration on laminin (Li et al. 2010).
3.3. Cdc42 signaling in ES cells
Also implicated in murine peri-implantation development is the Cdc42 GTP-binding protein. Mouse embryoid bodies deficient for Cdc42 exhibited polarization defects characterized by aberrant adherens and tight cell-cell junction formation and failure of cavitation (Wu et al. 2007), in a process mediated by the atypical protein kinase C (aPKC) family of kinases. Despite the polarization defects, basement membrane formation, which requires polarized deposition and assembly of basement membrane components at the basal side of a cell layer, was unaffected by deletion of Cdc42 (Wu et al. 2007). Interestingly mES cells lacking Cdc42 had lower levels of active Rac1 although total Rac1 protein levels were unaffected (Wu et al. 2007), suggesting that some of the observed defects could be the result of reduced Rac1 activity. However, unlike Rac1 deficient mES cells that would undergo apoptosis while in contact with the basement membrane (Kim et al. 2011), deletion of Cdc42 still allowed survival of cells in contact with the BM (Wu et al. 2007). Additional defects in PIP2-induced actin polymerization and cytoskeletal organization were likely to also contribute to defective adhesion and migration of mESC deleted of Cdc42 (Chambers et al. 2003; Wu et al. 2007). The motility of mESC plated on plated on laminin also were dependent on Cdc42 as revealed by siRNA-mediated knockdown (Li et al. 2010). These morphological, polarization and motility defects almost certainly contributed to early embryonic lethality in Cdc42 deficient mice (Chambers et al. 2003). These tantalizing observations point to complementary functions for Rho, Rac and Cdc42 during the processes of cavitation and the appearance of the epiblast, and underscore the importance of these proteins in appropriately mediating the survival or apoptotic clearance of cells during early morphogenesis. It therefore appears that the activity of the Rho family GTPases crucially determines the fate of pluripotent stem cells within the early developing embryo.
4. Additional functions of Rho proteins in ES cells
An interesting aspect of ESC is that under the right conditions, such as hanging drop suspension leading to the formation of embryoid bodies (Kurosawa 2007), differentiation results in the production of cardiomyocytes that spontaneously contact and relax (beating) as they would in an intact heart (Wobus et al. 1991). Human ESC can also be differentiated into cardiomyocytes, which has generated considerable excitement in the field because of their value in examining the role of specific proteins in cardiac disease phenotypes, and also due to the eventual possibility that they might have therapeutic utility (Brinster 1974). To examine the role of Rac1 in the differentiation of mESC into cardiomyocytes, ectopic expression of constitutively-active Rac 1 deficient in GTPase activity (Rac1V12) or dominant-negative Rac1 with reduced affinity for GTP (Rac1N17 was used to elucidate the consequences of Rac1 gain-of-function and loss-of-function, respectively (Puceat et al. 2003). Expression of active Rac1V12 blocked the characteristic beating of embryoid bodies, due to a differentiation defect as indicated by reduced expression of cardiomyocyte differentiation markers such as MEF2C and ventricular myosin light chain 2 (MLCv2). In contrast, expression of a constitutively active form of RhoA did not block cardiomyocyte differentiation. Previous research had revealed that Rac1 regulates the activity of the NADPH oxidase that generates reactive oxygen species (ROS) (Di-Poi et al. 2001), and when H2O2 was added to embryoid bodies for up to 7 days the effect on blocking cardiomyocyte differentiation by active Rac1V12 was mimicked, while the ROS scavenger catalase reduced the differentiation block induced by active Rac1V12 (Puceat et al. 2003). Consistent with this conclusion, expression of a point-mutant form of Rac1 that does not activate the NADPH oxidase (Rac1V12D38) did not block cardiomyocyte differentiation. Expression of the dominant-negative Rac1N17 to examine loss-of-function did not affect differentiation but did impair beating by interfering with the organization of sarcomeric units required for contraction (Puceat et al. 2003). In contrast to what occurred when Rac1 was expressed early, when the MLCv2 promoter was used to express active Rac1 in differentiated cardiomyocytes, increased beating was observed due to a facilitation of differentiation and prolonged proliferation (Puceat et al. 2003). Expression of dominant-negative Rac1N17 from the MLCv2 promoter had a similar effect as early expression on the organization of sarcomeric units. These results revealed that the role of Rac1 in cardiac differentiation is likely dependent on the developmental stage. Given the availability of mESC in which Rac1 can be conditionally deleted (Yuan et al. 1995), more refined analysis of the role of Rac1 in cardiac differentiation and disease should be possible.
5. Activating ROCK in mouse ICM-derived ES cells
Mechanical forces are increasingly appreciated as major influences in embryonic development. External mechanical forces can be produced by physical alterations to the microenvironment. These external forces are sensed by cells, leading to responses that allow the cell to adapt to the changed environmental circumstances. One way that cells respond to mechanical force is via integrin-mediated activation of Rho and ROCK resulting in increased cellular stiffness via increased actomyosin contracility, which is also known as reinforcement (Guilluy et al. 2011). There is considerable evidence that suppression of
Figure 2.
Mechanism of conditional activation of ROCK. 1: Diagram of ROCK domains, RBD = Rho Binding Domain, PH = Pleckstrin Homology domain, CRD = Cysteine-Rich Domain. 2: Kinase domain of ROCK2 was fused to Enhanced Green Fluorescent Protein (EGFP) and the hormone-binding domain of Estrogen Receptor (ER) to create conditionally regulated ROCK:ER. 3: In the absence of ligand, Heat Shock Protein 90 (Hsp90) binds to the ER domain and represses catalytic activity. 4: Upon binding of estrogen analogues such as 4-hydroxytamoxifen (4HT), 5: Hsp90 is displaced thereby allowing for ROCK catalytic activity.
actomyosin contractility by inhibition of ROCK promotes the survival and continued proliferation of epiblast-derived hES cells. It is suggested, however, that this signaling axis is less important in ICM-derived mES cells. We therefore decided to take advantage of a system to conditionally activate ROCK within mES cells to determine whether ROCK activation and consequent actomyosin contractility had a role in their proliferation, survival and/or maintenance of pluripotency. Accordingly, we transduced G4 mES cells (George et al. 2007) with a pBabe-Puro retroviral vector (Morgenstern and Land 1990) encoding a conditionally-active version of ROCK fused to the hormone-binding domain of the estrogen receptor (Figure 2) (Croft and Olson 2006) to establish the pBabe-Puro-ROCK:ER mES cell line in which ROCK activity could be elicited by treatment with the estrogen analog 4-hydroxytamoxifen (4HT). As a negative control, cells were transduced with pBabe-Puro encoding a kinase-dead counterpart (KD:ER) to produce control pBabe-Puro-KD:ER mES cells that express of catalytically inactive control ROCK protein.
When maintained in 4HT, pBabe-Puro-ROCK:ER mES cells exhibited robust growth and a large number of colonies exhibiting a refractive colony morphology under transmitted light and fewer colonies exhibiting a differentiated morphology, consistent with a high degree of pluripotency (Figure 3). Consistent with this observation, 4HT treated pBabe-Puro-ROCK:ER mES cells express significantly higher levels of the pluripotency marker alkaline phosphatase (ALP) than 4HT treated pBabe-Puro-KD:ER mES cells or vehicle treated pBabe-Puro-ROCK:ER
Figure 3.
Conditional ROCK activation in mES cells elicits a highly refractive colony morphology. Panels show brightfield images of pBabe-Puro-ROCK:ER and pBabe-Puro-KD:ER mES cells treated with Vehicle or 4HT. Flat colonies containing mainly differentiated cells (purple arrows) and raised colonies containing mainly undifferentiated cells (white arrows) are indicated. Scale bar denotes 500µm.
and pBabe-Puro-KD:ER mES cells (Figure 4A). To determine whether the increased ALP activity observed upon ROCK activation correlated with an increase in stemness, we then assessed the expression of two classical markers of pluripotency, Oct4 and Nanog. 4HT treated pBabe-Puro-ROCK:ER mES cells express significantly higher levels of Oct4 and Nanog than 4HT treated pBabe-Puro-KD:ER mES cells or vehicle treated pBabe-Puro-ROCK:ER and pBabe-Puro-KD:ER mES cells (Figure 4B). Consistent with this effect being mediated by the activity of ROCK, co-treatment of pBabe-Puro-ROCK:ER mES cells with 4HT and the selective ROCK inhibitor Y-27632 failed to induce Oct4 or Nanog expression (Figure 4B).
Taken together, these results strongly suggest that ROCK activation in mES cells promotes stemness and facilitates proliferation and survival. These observations are consistent with a previous report that inhibition of ROCK activity or silencing of ROCK expression in mESC causes a reduction in stem like properties including alkaline phosphatase activity and Oct3/4 expression, and increased expression of differentiation markers SOX-1, nestin and MAP2c when grown at high seeding densities (Chang et al. 2010). Interestingly, the effects of ROCK inhibition on morphology and colony formation were reversible if cells had been
Figure 4.
Conditional ROCK activation in mES cells increases stemness. (A) Histogram shows alkaline phosphatase activity in pBabe-Puro-ROCK:ER and pBabe-Puro-KD:ER mES cells treated with Vehicle or 4HT. (B) Histograms show expression at the mRNA level of the stem cell markers Oct4 and Nanog in pBabe-Puro-ROCK:ER and pBabe-Puro-KD:ER mES cells treated with Vehicle or 4HT. All values are expressed as mean ± SD. P values were calculated using the Student’s t-test.
grown at low initial densities where the reduction in stem like properties were not observed. However, at high cell densities where ROCK inhibition had repressed stem cell properties the effects were not reversible, suggesting that epigenetic reprogramming had occurred. It would be very interesting to determine whether the effects of ROCK activation on the maintenance of stemness would persist upon removal of tamoxifen and return of actomyosin contractility to basal levels.
6. Rho signalling in ES cell maintenance, proliferation, survival
There have been significant recent advances in our understanding of the requirement for specific Rho GTPases and downstream signaling pathways in ES cells from gene knockouts, RNAi and small molecule inhibitors. However, what has been missing is an understanding of where and when Rho proteins are activated and inactivated, for example during adhesion or differentiation. Activation-state sensitive fluorescent probes have been developed and used to characterize the temporal and spatial patterns of Rho activation during tumor cell migration and invasion (Vega et al. 2011). One exciting complementary area of research will be the determination of Rho protein activation with spatial and temporal resolution during ES cell growth and differentiation, ultimately through progressive developmental stages
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Samuel",slug:"michael-s.-samuel",email:"Michael.Samuel@health.sa.gov.au",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Embryonic Stem Cells",level:"1"},{id:"sec_2_2",title:"2.1. Maintenance of pluripotency",level:"2"},{id:"sec_3_2",title:"2.2. Culturing ES cells",level:"2"},{id:"sec_5",title:"3. Rho family GTPases in embryonic stem cells",level:"1"},{id:"sec_5_2",title:"3.1. Rho signaling in ES cells",level:"2"},{id:"sec_6_2",title:"3.2. Rac signaling in ES cells",level:"2"},{id:"sec_7_2",title:"3.3. Cdc42 signaling in ES cells",level:"2"},{id:"sec_9",title:"4. Additional functions of Rho proteins in ES cells",level:"1"},{id:"sec_10",title:"5. Activating ROCK in mouse ICM-derived ES cells",level:"1"},{id:"sec_11",title:"6. Rho signalling in ES cell maintenance, proliferation, survival",level:"1"}],chapterReferences:[{id:"B1",body:'AndrewsP. 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Genes Dev 92126352645'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Michael S. Samuel",address:"",affiliation:'
Centre for Cancer Biology, SA Pathology, Adelaide, Australia
'},{corresp:"yes",contributorFullName:"Michael F. Olson",address:"",affiliation:'
The Beatson Institute for Cancer Research, Glasgow, Uk
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Cheung",authors:[{id:"60384",title:"Dr.",name:"Tammy",middleName:null,surname:"Laberge",fullName:"Tammy Laberge",slug:"tammy-laberge"},{id:"60386",title:"Dr.",name:"Herman S.",middleName:null,surname:"Cheung",fullName:"Herman S. Cheung",slug:"herman-s.-cheung"}]},{id:"21372",title:"Pluripotent Stem Cells from Testis",slug:"pluripotent-stem-cells-from-testis",signatures:"Sandeep Goel and Hiroshi Imai",authors:[{id:"61246",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Imai",fullName:"Hiroshi Imai",slug:"hiroshi-imai"},{id:"62272",title:"Dr.",name:"Sandeep",middleName:null,surname:"Goel",fullName:"Sandeep Goel",slug:"sandeep-goel"}]},{id:"21373",title:"Amniotic Fluid Stem Cells",slug:"amniotic-fluid-stem-cells",signatures:"Gianni Carraro, Orquidea H. Garcia, Laura Perin, Roger De Filippo and David Warburton",authors:[{id:"55435",title:"Prof.",name:"David",middleName:null,surname:"Warburton",fullName:"David Warburton",slug:"david-warburton"},{id:"55934",title:"Dr.",name:"Gianni",middleName:null,surname:"Carraro",fullName:"Gianni Carraro",slug:"gianni-carraro"},{id:"59776",title:"Dr.",name:"Laura",middleName:null,surname:"Perin",fullName:"Laura Perin",slug:"laura-perin"},{id:"109076",title:"MSc.",name:"Orquidea H.",middleName:null,surname:"Garcia",fullName:"Orquidea H. Garcia",slug:"orquidea-h.-garcia"},{id:"118878",title:"Dr.",name:"Roger",middleName:null,surname:"De Filippo",fullName:"Roger De Filippo",slug:"roger-de-filippo"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"60785",title:"Renewable Energy Microgrid Design for Shared Loads",doi:"10.5772/intechopen.75980",slug:"renewable-energy-microgrid-design-for-shared-loads",body:'
1. Introduction
1.1. Distributed electricity generation
Electrical power is historically generated at a few large power stations and transmitted over long distances to end users. However, in recent years, there is an increase in decentralized or distributed electric power generation, where the power is produced and used at the same location [1]. Often, this decentralized power is produced with renewable energy technologies, such as wind and solar, due to the decreasing costs of these technologies [1].
There are many advantages to a power distribution system that relies on many small generation facilities rather than a few large power plants. Transmitting power over long distances is inefficient and requires expensive infrastructure. Smaller facilities that are close to where the power is used can provide higher quality power, with fewer blackouts and a more steady voltage. Since many of the small generating stations are natural gas powered or powered with renewable sources, there is less pollution than large plants that often run on coal. Finally, a distributed energy generation model is more secure than a centralized model.
The challenge to expanding distributed electricity generation is partly economic and partly technical. The capital costs for building a small-scale facility is large relative to the power it can produce. Many renewable technologies produce irregular power that varies with the weather, and this power is difficult to incorporate into the grid [2].
1.2. Motivation for expanding distributed generation
Increasing attention is given to designs for integrating renewable energy with traditional power to satisfy electrical loads from individual or multiple buildings. Research in this area is driven by several factors. First, costs for photovoltaic (PV) panels and wind micro-turbines (MT) are steadily dropping, along with battery energy storage systems (BESS). For example, the reported cost of installed solar PV systems fell by an average of 6–12% per year from 1998 to 2014, depending on the scale of the system [3]. Similarly, the price of wind power is dropping significantly, as more turbines are brought online, and currently about 5% of the energy requirements for the United States is supplied through wind power. In the previous decade, more than two-thirds of all wind installations in the United States have been small- or mid-sized wind turbines [4]. Battery storage has also dropped to a level of about $100 per kWh of capacity [5]. Another motivating factor is the various grid-pricing structures available, which often create an incentive for using less power at certain times of the day or making the overall electrical demand more uniform. Microgrids can achieve this, reducing the cost of grid electrical power.
In addition to economic motivation, there is increased recognition of the damage to the environment due to CO2 emission from traditional power generation. The benefit of using RER microgrids for buildings is the potential reduction in carbon and other pollutants because buildings consume over 40% of end-use energy worldwide [6]. In order to address the problem of efficient building energy use and to reduce pollution in buildings, the United States has set a zero net energy target on 50% of commercial buildings by 2040 and on all commercial buildings by 2050 [6]. A zero net energy building is one that produces some renewable energy on-site, such that the building sometimes uses grid power and at other times produces extra renewable power. The average amount of renewable power produced annually is the same as the building’s average annual consumption.
Finally, another advantage for microgrids is the improved reliability that they offer. There are multiple sources of power in a microgrid, such that there is less chance of a complete power outage.
1.3. Problem statement
Several studies consider the optimal design and sizing of the RER system for residential or commercial building individually [7, 8]. The RER microgrid design in this work is applied to a mixed commercial and residential building. Residential loads peak in the evening and early morning times, whereas commercial loads peak in the daytime. A shared residential and commercial load therefore has the potential to be more uniform than one or the other alone. A uniform load is easier to efficiently match to the RER supply, and it may also help lower the grid power costs. This chapter studies the effect of combining loads for an RER microgrid on the full cost of the microgrid. The size of the RER system partly depends on the shape of the load profile, such that irregular profiles require larger RER systems than smooth profiles. For example, an irregular load profile requires more energy storage to satisfy peak loads. The concept explored in this work is the benefit of combining different realistic load profiles in order to develop a total profile that is smoother and less costly to satisfy with an RER microgrid.
2. Building energy demand model
The residential load profile used for this work is generated from measured aggregate hourly consumption data for 12 apartments in a residential building in Columbus, Ohio [8]. The apartments are on the third floor of a three-story building, which means that they will have higher heating loads in the winter and cooling loads in the summer. This choice represents a worst-case scenario in terms of the peaks in the residential load. One year of hourly metered power use for these apartments is available, starting at 12 am on Sunday, June 9, 2013. These apartments use electricity for hot water, heating, and cooling. The hourly commercial load profile is synthesized from typical load profiles for commercial kitchens [9]. The average demand from the commercial load is selected to be nearly the same as the average demand from the 12 residential apartments.
2.1. Historical residential demand
Figure 1 illustrates the weekly average for the aggregate residential load data. Each day of the week, there is a peak in the morning at about 8 am, representing the electricity consumption as residents prepare for the workday. At the end of the day, at about 8 pm, there is a larger peak as residents return home for dinner and other electricity-consuming activities.
Figure 1.
Weekly average aggregate residential load.
To determine the temperature-dependent component of the residential data, a piecewise linear regression is used. Each week, aggregate residential consumption data are averaged to create 52 single values. The same is done for the temperature. Figure 2 shows a plot of the average weekly power versus average weekly outdoor temperature. The fit shown in Figure 2 has five parameters as follows: a heating slope (HS), cooling slope (CS), heating temperature (HT), cooling temperature (CT), and baseline (B) [8]. The baseline component defines the hourly expected weather independent demand. The heating and cooling slopes KW per Fahrenheit degree (kW/°F) enable respective prediction of the hourly heating and cooling demand for a typical weather year.
Figure 2.
Temperature dependence for residential data, along with a five-parameter fit.
The threshold temperatures and heating/cooling slopes depend on many factors, such as building construction and size. Values for the temperature-dependent five-parameter model were calculated to be: HS=0.16kW/°F.,CS=0.12kW/F∘, HT=57F∘, and CT=48F∘.
2.2. Commercial kitchen demand model
Figure 3 illustrates the weekly average baseline consumption for the commercial kitchen. The same basic profile shape is used for each day, scaled to represent the different amounts of customer traffic for each day of the week. The peak consumption occurs at 8 pm as the kitchen serves dinner, and there is also a peak at 1 pm for lunch. The kitchen uses power more consistently than the residential load through the afternoon hours.
Figure 3.
Weekly average commercial load (baseline).
2.3. Controlling load profile characteristics
In order to compare the three individual loads, the load factor (LF) is used. LF is defined as the ratio of the average per-month consumption to the peak hourly consumption for that month. For the data used in this study, an average yearly LF is found by averaging the 12 monthly LF values. Figures 4 and 5 show the comparison of the monthly LF values for the three types of loads considered: residential, commercial, and combined. Figure 4 shows the effect of combining the residential load with only the baseline commercial load. In this case, the load factor of the combined is between the residential and commercial load factors each month. Figure 5 shows the effect of combining the residential load with the commercial load, where the weather-dependent load is included with the commercial load. The load factor behavior varies more in this case. The LF for the combined load is still between the LF for the residential and commercial loads, except for January, March, and April.
Figure 4.
Comparison of monthly load factors for the three loads with no weather-dependent component.
Figure 5.
Comparison of monthly load factors for the three loads with weather-dependent component.
3. Proposed microgrid structure
The main components of the renewable energy resource (RER) microgrid examined here are solar photovoltaics (PV), micro-wind turbines (MT), and a battery energy storage system (BESS). These are utilized for electricity generation and energy storage, and they supply energy to a load that is normally satisfied with grid power alone. The microgrid is studied for two types of operating conditions. First is an isolated RER which provides all of the load power. The second system is an RER with a diesel generator backup in which the generator is activated if the RER cannot supply the full load.
For each system, the hourly power flow to or from each element of the microgrid is simulated. The capacity of each RER component is chosen to minimize the overall cost.
3.1. Isolated RER
The first type of microgrid is the isolated RER, which is shown in Figure 6. The RER operates independently from the local utility grid to provide all of the electricity to residential and commercial building. This system requires large battery storage to use during low wind and solar output times. The RER for the isolated mode must be large enough to produce energy to cover 100% of the building’s energy needs.
Figure 6.
Isolated grid model.
3.2. RER and diesel generator
Figure 7 illustrates the second configuration and RER with a diesel generator backup. The diesel generator has the option of charging the battery, which is significantly different from the other scenarios.
Figure 7.
RER and diesel generator model.
3.3. RER and battery modeling
3.3.1. Photovoltaic model
The power generated by a photovoltaic panel depends on two fundamental parameters: the solar irradiation and the ambient temperature. In order to simplify the model, the power produced by a PV panel, the following equation is used [8].
Pst=ηsAsGtE1
whereηs: Energy conversion efficiency (%), As: PV panel area (m2), Gt: Solar irradiation (W/m2), and Pst: Power generated by solar PV (W).
The solar irradiation, sampled hourly, is found from typical meteorological year (TMY) data, for Columbus, Ohio [10]. The number of solar PV panels is Ns.
3.3.2. Micro-wind turbine model
The electrical power generated by a micro-wind turbine depends on the wind speed, air density, area swept by the turbine blades, and an efficiency factor called the Betz coefficient [9]. A constant air density of 1.225 kg/m3 is used, and the Betz coefficient is taken to be 59%.
Pwt=0.5ρAwCpVw3tE2
where ρ: Air density (kg/m3), Aw: Micro-turbine swept area (m2), Cp: Betz coefficient, Vwt: Wind speed (m/s), and Pwt: Power generated by micro-turbine (W)
The wind speed, sampled hourly, is found from typical meteorological year (TMY) data, for Columbus, Ohio [10]. The number of micro-turbines is Nw.
3.3.3. Battery energy storage model
Whenever the RER output exceeds the load demand, the extra power is stored in a battery. This power is then used whenever the RER is unable to supply the load demand. The charge level of the battery is PBt, which is restricted to be in the range of 20–80% of the battery capacity, Bcap(kWh).
0.2Bcap≤PBt≤0.8BcapE3
When charging or discharging the battery, the maximum amount of energy that can be removed during a 1-hour interval is BHR. This limit is expressed by the following inequality.
PBt−PBt−1≤BHRE4
Both the battery capacity and the hourly charge/discharge limit are parameters for the microgrid.
For every hour in the simulation of the microgrid, there is a decision made to charge or discharge the battery. This decision is determined in part by the charge level of the battery. If the current charge level is in the range of 20–80% of BCAP, then the battery can be charged or discharged. If the current charge level is less than 20% of BCAP, the battery can only be charged. If the current charge level is more than 80% of BCAP, the battery can only be discharged.
3.4. Diesel generator model
The diesel generator supports the microgrid by supplying power PDLt directly to the load and power PDBt to the battery. The sum of these two must be less than the maximum power output Dmax from the generator, such that.
0≤PDBt+PDLt≤DmaxE5
The conditions for activating the generator at each hour are determined by the current load and RER output, the state of the battery’s charge, and whether or not the generator was running the previous hour. If the RER and battery cannot meet the load, the generator is activated. If the generator was running the previous hour and the battery can take more charge, then the generator is allowed to run during the current hour.
4. Dynamic microgrid modeling
4.1. Isolated RER dispatch algorithm
At each hour of the simulation for the grid-isolated system illustrated in Figure 6, the RER power Pt is determined using the TMY data. In addition, the load PLt and the charge level of the battery at the previous hour PBt−1 are known. From this information, the graph in Figure 8 is used to determine all other quantities. The graph illustrates the process of updating these quantities each hour. The following characteristics are implemented in this graph.
If the RER output is less than the load at a given hour, then all of the available RER output is sent to the load (i.e., no battery charging at this hour), and the battery satisfies the remainder of the load.
If the RER output is greater than the load, then the load is completely satisfied by the RER, and no battery power is used for the load. As much charging power as possible is transmitted to the battery, if its charge level is less than the upper threshold. Any remaining power from the RER is sent back to local grid.
There is a possibility of power outage if the combined RER and battery outputs cannot meet the load. Increasing the size of the system reduces this possibility.
Figure 8.
Isolated RER dispatch algorithm.
4.2. Diesel generator RER dispatch algorithm
At each hour of the simulation for the RER with distributed generation (DG) system illustrated in Figure 7, the RER power Pt is determined using the TMY data. In addition, the load PLt and the charge level of the battery at the previous hour PBt−1 are known. From this information, the graph in Figure 9 is used to determine all other quantities and to determine when the DG is turned on/off. The graph illustrates the process of updating these quantities each hour. The following characteristics are implemented in this graph.
If the RER output is less than the load at a given hour, then all of the available RER output is sent to the load (i.e., no battery charging at this hour), and as much power as possible from the battery is used to meet the load if its charge level is above the minimum threshold. If this is not sufficient to meet the load, the generator is used to make up the difference.
If the RER output is greater than the load, then the load is completely satisfied by the RER and no battery power is used for the load. As much charging power as possible is transmitted to the battery if its charge level is less than the upper threshold. Any remaining power from the RER is sent back to the local grid.
The generator is turned on if the combined RER and battery power cannot meet the load. If it is already running, then it will remain on until the battery is fully charged.
There is a possibility of power outage if the combined RER, battery, and DG outputs cannot meet the load. Increasing the size of the system reduces this possibility.
Figure 9.
Diesel generator RER dispatch algorithm.
5. Annual cost model
The annual cost of the system, ACS, is found with the following equation.
ACS=ACC+ARC+AOC+AGCE6
where ACC=capital cost, ARC=replacement costs, AFC=fuel cost, and AOC=operating costs.
Each of these categories is described in the following subsections.
5.1. Annual capital cost
The capital cost is found from the initial costs for the PV array, MT units, BESS, and DG. The PV capital cost is $1.8 per peak watt of PV power. It is assumed that each solar panel in the array is 2 m2 with a 20% efficiency, and that the peak radiation intensity is 1000 W/m2. This leads to a per-panel capital cost of $720, with Ns panels in the entire array. Each MT unit has an up-front cost, including installation, of $22,000 (about $2 per peak watt of wind power) [11, 12]. There are NW of these units installed. Each kWh of BESS capacity has a cost of $300 [11, 12], and the DG cost is approximated by a linear function of the capacity Dmax, from $7000 for a 5 kW capacity to $14,000 for a 40 kW capacity, based on advertised prices [12]. The DG cost formula is therefore
CDG=Dmax−5kW$700035kW+$7000E7
The total up-front capital cost is then the sum of all four terms.
CCAP=$720·Ns+$22,000·NW+$300·BCap+CDGE8
Standard amortization is applied to this capital cost, using an interest rate of i=6% and a project lifetime of N=20 years. The amortization factor is.
CRF=i1+iN1+iN−1E9
This factor is applied to the capital cost to find the annual capital cost as.
ACC=CRF·CCAPE10
5.2. Annual replacement cost
The PV and MT components last the full lifetime of the system, but the BESS and DG have shorter lifetimes and therefore need periodic replacement. Replacement costs are found with a sinking fund factor, which computes the amount of money that needs to be annually set aside to pay for periodic replacement of the BESS and DG. The formula for the sinking fund factor is.
SFFNL=i1+iNL−1E11
The lifetime of the component in question is NL and the interest rate i is the same as that used in the capital recovery factor. The BESS lifetime is 7 years, and the DG lifetime is 15 years. The annual replacement cost is therefore given by.
ARC=$300·BCap·SFF7+CDG·SFF15E12
5.3. Annual operating costs
The only component that has significant operating costs is the DG, which requires fuel, oil for lubrication, and periodic maintenance. Each kWh of output from the DG requires 0.13 gallons of diesel fuel at a cost of $2 per gallon, with an additional maintenance cost of $0.05/kWh. The simulation produces the hourly output from the DG as PDt, and summing this quantity gives the yearly energy output. For the entire plant lifetime, the total operating cost TOC can therefore be given as follows.
TOC=N∑PDt0.13galKWh2$gal+0.05$KWhE13
This quantity can be amortized using the same amortization factor that was applied to the capital cost. This gives an annual operating cost of.
AOC=TOC·CRFE14
6. Optimization problem formulation
In this section, the objective function is the total annual microgrid cost ACS as described in the previous section. This total annual cost is a nonlinear function of the parameters Ns, Nw, and BCAP, and evaluating this function requires execution of the dynamic model.
The nonlinear minimization is achieved with either the particle-swarm optimization (PSO) algorithm or the genetic algorithm (GA). PSO is used in situations in which no nonlinear constraints are needed, while GA is used if there are constraints.
Some of the constraints are simply bounds on the variables. The minimum values for each number cannot be negative, for example, and the upper bounds are chosen to be large enough for the RER to meet a required percentage of the load. The percentage of the annual load that is met by the wind and solar energy is called the renewable energy penetration formed with the following equation:
PEN=∑P1t+P2t∑Lt×100%E15
where P1 is Power from RER sources, P2 is Power from battery, and L is total load.
The system with DG backup has its own dispatch algorithm, which behaves differently than the grid-connected. This is because the DG on/off cycling incurs a maintenance cost. To avoid this, the rules for determining when to turn on and off the DG are designed to minimize the number of DG cycles. To ensure that there are no power losses, the minimum DG size is restricted to be equal to the peak load, such that the DG is capable of supplying the entire load with no RER assistance, if necessary. A nonlinear constraint is used to enforce a minimum RER penetration. The optimization requires the GA, and it is summarized as follows:
where Ns is the number of solar panels, NW is number of wind turbines, and BCAP is battery capacity and diesel generation capacity.
The grid-isolated system with no backup is similarly optimized, except that the renewable energy penetration is naturally 100% for this system, since there is no diesel generator backup power used. A modified version of the backup code is used to model the grid-isolated system with no backup. To ensure that there are no power losses, the cost function includes a large cost penalty for each hour of power shortage. With this arrangement, there is no nonlinear constraint function, such that the PSO algorithm can be used. The optimization algorithm chooses a sufficiently large RER system in order to avoid the high cost penalty on the power losses.
7. Simulation results
The MATLAB simulation results for the optimization are presented in this section, for two versions of the microgrid: grid-isolated with no backup, and grid-isolated with diesel backup. The hourly RER power is generated in the same way for two cases, using hourly TMY profiles for solar radiation and wind speed. Figure 10 shows an example of the RER power, for 10 wind turbines and 100 solar panels, relative to the combined commercial and residential load. The upper plot in this figure shows hourly RER power produced for the entire year, and the lower figure compares the RER power to the load for 1 week. When RER power output is greater than the load, the excess production can be passed to the battery. When the RER power is less than the load, the battery must make up the deficit completely for the grid-isolated with no backup scenario. If the backup is available, then these elements can be used in addition to the battery.
Figure 10.
Combined PV and MT output for Ns=100 and Nw=10 (top). Comparison between total PV and MT output and the combined load for 1 week (bottom).
7.1. Isolated microgrid with no backup
The dynamic modeling results for the isolated microgrid with no backup are illustrated in Figure 11. This figure shows the hourly flows of power from the RER directly to the load, along with the power from the battery to the load. The RER and battery sizes must be large enough to meet the load each hour, and the figure illustrates that the combined RER and battery flows are equal to the load. This constraint forces the RER and battery sizes to be large enough to meet the highest demand in the year, which means that during most of the year the battery is not fully utilized. This fact is demonstrated by Figure 12, which shows the battery charge level. The charge level remains high for most of the year, dipping down to low values only during a few times of peak demand.
Figure 11.
Power dispatching for 48 h in January (grid-isolated no backup).
Figure 12.
Battery storage level for isolated microgrid without backup.
Table 1 summarizes the results that optimize total microgrid annual cost for the residential load alone, the commercial load alone, and the mixture of the two. For comparison, the sum of the results for the residential and commercial loads is included.
Load
Ns
Nw
BCAP (kWh)
cost (×1000 $)
%RER output to the load
%RER Pen.
No backup
Residential
388
6
621
46.20
28
100
Commercial
428
7
487
45.12
24
100
Sum
816
13
1108
91.32
26
100
Mixed
790
13
959
86.00
27
100
Table 1.
Optimization results for the isolated microgrid with no backup power.
When added separately, the sum of the residential and commercial load annual costs is $91,320. If these loads are mixed, however, and are satisfied by a single, larger microgrid, then the annual cost decreases to $86,000. This illustrates the cost advantage of combining the two loads into a single mixed load. Separately supplying the two loads would require more solar panels and a significantly larger battery capacity than supplying the mixed load, although the number of MTs remains the same. Because this simulation does not have diesel generator backup, the RER penetration is 100%. For each case, only about 25% of the RER output is transmitted to the load. This low percentage is due to the fact that the RER size must be chosen significantly large to meet peak yearly demand. This means that there will be many hours of energy overproduction that cannot be utilized.
7.2. Microgrid with diesel generator backup
When the grid-isolated microgrid is augmented with a DG backup system, this allows the relatively few hours of peak demand to be partially met with DG power. When the system is cost optimized, this leads to several interesting features. The size of the RER shrinks significantly, the usage of the battery becomes more regular throughout the year, and the percentage of RER power that makes it to the load nearly doubles. The trade-off for these improvements is a reduced RER penetration.
Figure 13 illustrates the power dispatching for a one-week period in January, showing the intermittent operation of the DG. The DG operates during unusually high loads, which can be seen to occur as shown in Figure 14 when the battery energy level dips to low levels.
Figure 13.
Power dispatching for 160 h in January (grid-isolated with DG backup).
Figure 14.
Battery and DG operation for microgrid with backup.
The DG removes the burden of meeting the peak loads from the battery, such that the energy levels in the battery can swing more uniformly over its full range throughout the year. This is illustrated in Figure 14, which shows the battery energy level together with the DG operation for the entire year of the simulation. The pattern in the DG clearly shows a seasonal component, such that it operates more frequently during the heating and cooling seasons.
Table 2 summarizes the results that optimize total microgrid annual cost for the residential load alone, the commercial load alone, and the mixture of the two. The sum of the individual results for the residential and commercial loads is included as before, for comparison with the mixed-load results.
Load
Ns
Nw
BCAP (kWh)
DMAX (kW)
DG on time (hr)
DG Starts
cost (×1000 $)
% RER output to the load
% RER Pen.
Residential
214
2
228
35
588
67
21.00
49
82
Commercial
244
1
215
24
756
50
20.13
47
84
Sum
458
3
443
59
1344
117
41.13
48
83
Mixed
404
1
356
50
602
83
39.68
52
82
Table 2.
Optimization results for the isolated microgrid with DG backup power.
The largest effect of adding the DG backup to the microgrid is with the costs, which are about a fourth of the costs for the no-backup case. The optimization was made with the constraint of greater than 80% RER penetration, and it should be mentioned that the costs can be further reduced by lowering this constraint. Another large benefit in having the DG is the increase in the amount of RER power that makes it to the load, which doubles from roughly 25–50%. When comparing the sum of the costs of the residential and commercial loads to the cost of the mixed load, there is less of a difference than with the no-backup case. This indicates that the DG is reducing some of the benefit of mixing the loads.
8. Summary and discussion
To illustrate the effect of renewable energy penetration, the cost optimization is constrained to produce a result with a fixed penetration. This applies to the isolated grid with diesel backup. Figure 15 shows a plot of cost per kWh versus penetration, under the assumption that the microgrid’s non-renewable power component has a price of $0.2/kWh. Plots of the average cost per kWh for all three loads (residential, commercial, and mixed) are shown, where the average cost is computed by dividing the annual cost by the total annual load. As the renewable energy penetration approaches 100%, the cost of power from the microgrid becomes rapidly more expensive, approaching the case 1 result. As the renewable energy penetration decreases to zero, the cost of power approaches the price of nonrenewable energy, $0.2/kWh. The minimum cost per kWh occurs in the range of 30–40% of penetration.
Figure 15.
Average cost per kWh versus RER penetration.
Figure 15 also shows the interaction between load mixing and renewable energy penetration. Below 70% penetration, the mixed load cost is between the residential and commercial costs. Above this threshold, however, the mixed load cost is below both the residential and commercial costs.
9. Conclusion
The simulation results confirm that using a backup power source to support renewable energy reduces overall microgrid costs. For example, using a DG backup lowers the renewable energy penetration from 100 to 82%, but cuts the overall cost by more than a factor of two. However, the simulation results also indicate a clear benefit to mixing residential and commercial loads, such that the cost for satisfying the mixed load is less than the sum of the costs for the loads individually. Depending on the configuration, the cost for the mixed load is 3–8% less than the sum of the individual costs.
Microgrid designs for building applications involve determining the best mix of building loads for optimizing energy delivery. The result of the modeling work presented here is that combining loads allow for a measure of control over the microgrid costs. This concept is important for moving toward 100% renewable energy penetration.
\n',keywords:"renewable energy system, load profile, PV, wind turbine, battery, loads shared",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/60785.pdf",chapterXML:"https://mts.intechopen.com/source/xml/60785.xml",downloadPdfUrl:"/chapter/pdf-download/60785",previewPdfUrl:"/chapter/pdf-preview/60785",totalDownloads:868,totalViews:380,totalCrossrefCites:0,dateSubmitted:"November 12th 2017",dateReviewed:"February 26th 2018",datePrePublished:"November 5th 2018",datePublished:"July 11th 2018",dateFinished:null,readingETA:"0",abstract:"Renewable energy resource (RER) energy systems are becoming more cost-effective and this work investigates the effect of shared load on the optimal sizing of a renewable energy resource (RER) microgrid. The RER system consists of solar panels, wind turbines, battery storage, and a backup diesel generator, and it is isolated from conventional grid power. The building contains a restaurant and 12 residential apartments. Historical meter readings and restaurant modeling represent the apartments and restaurant, respectively. Weather data determines hourly RER power, and a dispatching algorithm predicts power flows between system elements. A genetic algorithm approach minimizes total annual cost over the number of PV and turbines, battery capacity, and generator size, with a constraint on the renewable penetration. Results indicate that load-mixing serves to reduce cost, and the reduction is largest if the diesel backup is removed from the system. This cost is optimized with a combination of particle swarm optimization with genetic-algorithm approach minimizes total annual cost over the number of solar panels and micro-turbines, battery capacity, and diesel generator size, with a constraint on the renewable penetration. Results indicate that load-mixing serves to reduce cost, and the reduction is largest if the diesel backup is removed from the system.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/60785",risUrl:"/chapter/ris/60785",signatures:"Ibrahim Aldaouab and Malcolm Daniels",book:{id:"6698",title:"Smart Microgrids",subtitle:null,fullTitle:"Smart Microgrids",slug:"smart-microgrids",publishedDate:"July 11th 2018",bookSignature:"Majid Nayeripour, Eberhard Waffenschmidt and Mostafa Kheshti",coverURL:"https://cdn.intechopen.com/books/images_new/6698.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"66929",title:"Prof.",name:"Majid",middleName:null,surname:"Nayeripour",slug:"majid-nayeripour",fullName:"Majid Nayeripour"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"233877",title:"Ph.D. Student",name:"Ibrahim",middleName:null,surname:"Aldaouab",fullName:"Ibrahim Aldaouab",slug:"ibrahim-aldaouab",email:"aldaouabi@ieee.org",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Distributed electricity generation",level:"2"},{id:"sec_2_2",title:"1.2. Motivation for expanding distributed generation",level:"2"},{id:"sec_3_2",title:"1.3. Problem statement",level:"2"},{id:"sec_5",title:"2. Building energy demand model",level:"1"},{id:"sec_5_2",title:"2.1. Historical residential demand",level:"2"},{id:"sec_6_2",title:"2.2. Commercial kitchen demand model",level:"2"},{id:"sec_7_2",title:"2.3. Controlling load profile characteristics",level:"2"},{id:"sec_9",title:"3. Proposed microgrid structure",level:"1"},{id:"sec_9_2",title:"3.1. Isolated RER",level:"2"},{id:"sec_10_2",title:"3.2. RER and diesel generator",level:"2"},{id:"sec_11_2",title:"3.3. RER and battery modeling",level:"2"},{id:"sec_11_3",title:"3.3.1. Photovoltaic model",level:"3"},{id:"sec_12_3",title:"3.3.2. Micro-wind turbine model",level:"3"},{id:"sec_13_3",title:"3.3.3. Battery energy storage model",level:"3"},{id:"sec_15_2",title:"3.4. Diesel generator model",level:"2"},{id:"sec_17",title:"4. Dynamic microgrid modeling",level:"1"},{id:"sec_17_2",title:"4.1. Isolated RER dispatch algorithm",level:"2"},{id:"sec_18_2",title:"4.2. Diesel generator RER dispatch algorithm",level:"2"},{id:"sec_20",title:"5. Annual cost model",level:"1"},{id:"sec_20_2",title:"5.1. Annual capital cost",level:"2"},{id:"sec_21_2",title:"5.2. Annual replacement cost",level:"2"},{id:"sec_22_2",title:"5.3. Annual operating costs",level:"2"},{id:"sec_24",title:"6. Optimization problem formulation",level:"1"},{id:"sec_25",title:"7. Simulation results",level:"1"},{id:"sec_25_2",title:"7.1. Isolated microgrid with no backup",level:"2"},{id:"sec_26_2",title:"7.2. Microgrid with diesel generator backup",level:"2"},{id:"sec_28",title:"8. Summary and discussion",level:"1"},{id:"sec_29",title:"9. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Distributed Generation. Definitions, benefits, technologies & challenges. International Journal of Science and Research (IJSR). 2016;5(7):1941-1948'},{id:"B2",body:'How Electricity Is Delivered To Consumers. Available from: https://www.eia.gov/energyexplained/index.cfm?page=electricity_delivery [Accessed: November 7, 2017]'},{id:"B3",body:'Obi M, Bass R. Trends and challenges of grid-connected photovoltaic systems—A review. Renewable and Sustainable Energy Reviews. 2016;58:1082-1094'},{id:"B4",body:'Kumar Y, Ringenberg J, Depuru S, Devabhaktuni V, Lee J, Nikolaidis E, Andersen B, Afjeh A. Wind energy: Trends and enabling technologies. Renewable and Sustainable Energy Reviews. 2016;53:209-224'},{id:"B5",body:'Diouf B, Pode R. Potential of lithium-ion batteries in renewable energy. Renewable Energy. 2015;76:375-380'},{id:"B6",body:'Erdinc O, Uzunoglu M. Optimum design of hybrid renewable energy systems: Overview of different approaches. Renewable and Sustainable Energy Reviews. 2012;16(3):1412-1425'},{id:"B7",body:'Shaahid SM, Elhadidy MA. Technical and economic assessment of grid-independent hybrid photovoltaic–diesel–battery power systems for commercial loads in desert environments. Renewable and Sustainable Energy Reviews. 2007;11(8):1794-1810'},{id:"B8",body:'Aldaouab I, Daniels M, Hallinan K. Microgrid Cost Optimization for a Mixed-Use Building. In: 2017 IEEE Texas Power and Energy Conference (TPEC). 2017'},{id:"B9",body:'Aldaouab I, Daniels M. Microgrid battery and thermal storage for improved renewable penetration and curtailment. In: IEEE International Energy & Sustainability Conference (IESC). 2017'},{id:"B10",body:'NSRDB Update—TMY3: Alphabetical List by State and City. Available from: http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/ [Accessed: December 1, 2017]'},{id:"B11",body:'Aldaouab I, Daniels M. Renewable energy dispatch control algorithms for a mixed-use building. In: 2017 IEEE Green Energy and Smart Systems Conference IGESSC. 2017'},{id:"B12",body:'Find Quality Manufacturers, Suppliers, Exporters, Importers, Buyers, Wholesalers, Products and Trade Leads from OurAward-winning International. Available from: https://www.alibaba.com [Accessed: February 2, 2017]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ibrahim Aldaouab",address:"aldaouabi1@udayton.edu",affiliation:'
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In the last years, he contributed to elucidate the mechanism of somatic hypermutation and class switch recombination process in Chronic Lymphocytic Leukemia and to identify new prognosis markers for this disease.",institutionString:null,institution:{name:"Institut Pasteur",institutionURL:null,country:{name:"France"}}},{id:"217429",title:"Prof.",name:"Andrew",surname:"Spencer",slug:"andrew-spencer",fullName:"Andrew Spencer",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"217533",title:"Dr.",name:"Vijaya",surname:"Pilli",slug:"vijaya-pilli",fullName:"Vijaya Pilli",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"217917",title:"Dr.",name:"Marcelo",surname:"Navarrete",slug:"marcelo-navarrete",fullName:"Marcelo Navarrete",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217917/images/14414_n.jpg",biography:null,institutionString:null,institution:{name:"University of Magallanes",institutionURL:null,country:{name:"Chile"}}},{id:"218159",title:"MSc.",name:"Julieta",surname:"Sepulveda",slug:"julieta-sepulveda",fullName:"Julieta Sepulveda",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",biography:null,institutionString:null,institution:{name:"Leiden University Medical Center",institutionURL:null,country:{name:"Netherlands"}}},{id:"218286",title:"Ph.D.",name:"Bhaskar",surname:"Kahali",slug:"bhaskar-kahali",fullName:"Bhaskar Kahali",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St. Jude Children's Research Hospital",institutionURL:null,country:{name:"United States of America"}}},{id:"219703",title:"Ph.D.",name:"Cecilia",surname:"Lantos",slug:"cecilia-lantos",fullName:"Cecilia Lantos",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"219705",title:"Dr.",name:"Steven M.",surname:"Kornblau",slug:"steven-m.-kornblau",fullName:"Steven M. 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He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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