Summary of network simulation results for the various cases.
\r\n\tCongenital hearing loss means hearing loss that is present at birth. I have managed children with hearing loss for many years, and the most touching thing is the light that blooms on the face while the hearing-impaired child heard his mother's voice at first time. The scene of "happy tears" impressed me so much. To hear the voice that has not been heard is so pleasant, as if this ordinary listening experience is a supreme listening enjoyment.
\r\n\r\n\tAge-related hearing loss means a progressive loss of ability to hear high frequencies with aging, also known as presbycusis. Among them are the influence of internal and external factors such as genes, drugs and noise exposure. The studies pointed out that the brain stimulation of the hearing-impaired person is greatly reduced compared with subjects with normal hearing. The connection of auditory cortex and other brain areas has declined a lot, which is probably one of the important causes of dementia or even depression in the elderly.
\r\n\r\n\tNoise-induced hearing loss is hearing impairment resulting from exposure to loud sound. There is actually continuous and endless noise in many workplaces, which may cause chronic and cumulative damage. Some young people often work hard but easily neglect to protect themselves. In addition, in recent years, entertainment noise (such as nightclubs, concerts, and personal listening devices) has caused hearing impairment in young people. These should be avoidable and preventable.
\r\n\r\n\tHearing Science is the study of impaired auditory perception, the technologies and other rehabilitation strategies for persons with hearing loss. Public health has been defined as "the science and art of preventing disease", improving quality of life through organized efforts. To avoid the “epidemic” of hearing loss, it is necessary to promote early screening, use hearing protection, and change public attitudes toward noise.
\r\n\r\n\tBased on these concepts, the book incorporates updated developments as well as future perspectives in the ever-expanding field of hearing loss. Besides, it is also a great reference for audiologists, otolaryngologists, neurologists, specialists in public health, basic and clinical researchers.
",isbn:"978-1-83968-678-8",printIsbn:"978-1-83968-677-1",pdfIsbn:"978-1-83968-679-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"a4b7dbb02ba00e7412422cd5dbffa029",bookSignature:"Dr. Tang-Chuan Wang",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10529.jpg",keywords:"Hidden Hearing Loss, Plasticity, Electrophysiology, Otoacoustic Emission, Newborn Hearing Screening, Genetics, Aging, Hearing Aids, Noise Exposure, Occupational Hearing Loss, Epidemiology, Prevention",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 3rd 2020",dateEndSecondStepPublish:"October 1st 2020",dateEndThirdStepPublish:"November 30th 2020",dateEndFourthStepPublish:"February 18th 2021",dateEndFifthStepPublish:"April 19th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Tang-Chuan Wang is an excellent otolaryngologist-head and neck surgeon in Taiwan; a research scholar of Harvard Medical School and University of Iowa Hospitals. He worked in the Hospital of the University of Pennsylvania, Boston Children's Hospital, and Massachusetts Eye and Ear. Due to his contribution to biomedical engineering, he was invited into the executive committee of HIWIN-CMU Joint R & D Center in Taiwan.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"201262",title:"Dr.",name:"Tang-Chuan",middleName:null,surname:"Wang",slug:"tang-chuan-wang",fullName:"Tang-Chuan Wang",profilePictureURL:"https://mts.intechopen.com/storage/users/201262/images/system/201262.gif",biography:'Dr. Tang-Chuan Wang is an excellent otolaryngologist – head and neck surgeon in Taiwan. He is also a research scholar of Harvard Medical School and University of Iowa Hospitals. During his substantial experience, he worked in Hospital of the University of Pennsylvania, Boston Children\'s Hospital and Massachusetts Eye and Ear. Besides, he is not only working hard on clinical & basic medicine but also launching out into public health in Taiwan. In recent years, he devotes himself to innovation. He always says that "in theoretical or practical aspects, no innovation is a step backward". 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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"}}]},chapter:{item:{type:"chapter",id:"18412",title:"C4 Plants Adaptation to High Levels of CO2 and to Drought Environments",doi:"10.5772/24936",slug:"c4-plants-adaptation-to-high-levels-of-co2-and-to-drought-environments",body:'\n\t\tAll plants use the Photosynthetic Carbon Reduction (PCR or Calvin-Benson) cycle for CO2 fixation in which Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) catalyzes the first step producing a three-carbon compound, phosphoglycerate (3-PGA). For this reason this process is referred to as the C3 cycle. Plants utilizing this pathway are often named as C3 species. A major problem with the C3 cycle is that the enzyme Rubisco catalyzes two competing reactions: carboxylation and oxygenation (Portis & Parry, 2007). The oxygenation reaction directs the flow of carbon through the photorespiratory pathway, and this can result in losses of between 25% and 30% of the carbon fixed. Environmental variables such as high temperature and drought can result in an increase in the oxygenase reaction. Therefore, reducing the Rubisco oxygenase reaction has the potential to increase carbon assimilation significantly and would represent a step change in photosynthesis (up to 100% depending on temperature; Long et al., 2006).
\n\t\t\t\tThe C4 photosynthesis is an adaptation of the C3 pathway that overcomes the limitation of the photorespiration, improving photosynthetic efficiency and minimizing the water loss in hot, dry environments (Edwards & Walker, 1983). Generally, C4 species originate from warmer climates than C3 species (Sage & Monson, 1999). Most C4 plants are native to the tropics and warm temperate zones with high light intensity and high temperature. Under these conditions, C4 plants exhibit higher photosynthetic and growth rates due to gains in the water, carbon and nitrogen efficiency uses. Indeed, the highest known productivity in natural vegetation is for a C4 perennial grass in the central Amazon, which achieves a net production of 100 t (dry matter) ha-1 year-1 (Piedade et al., 1991, Long, 1999). Some of the world´s most productive crops and pasture, such as maize (Zea mays), sugar cane (Saccharum officinarum), sorghum (Sorghum bicolor), amaranth, paspalums (Paspalum notatum and P. urvillei), bermudagrass (Cynodon dactylon), blue grama (Bouteloua gracilis) and rhodes grass (Chloris gayana) are C4 plants. In addition, the most troublesome weeds like nutgrass, crabgrass and barnyard, are also C4 species. Although C4 plants represent only a small portion of the world´s plant species, accounting for only 3 % of the vascular plants, they contribute about 20% to the global primary productivity because of highly productive C4-grass-lands (Ehleringer et al., 1997). Approximately half of the ˜10,000 grass and sedge species have C4 photosynthesis, but fewer than 2,000 of the dicotyledonous species exhibit C4 photosynthesis. Given their disproportionate influence on global productivity, C4 plants have attracted much attention by the ecophysiological and ecosystem communities (Sage & Monson, 1999).
\n\t\t\t\tIn C4 plants, the photorespiration is suppressed by elevating the CO2 concentration at the site of Rubisco though suppressing the oxygenase activity of the enzyme. This is achieved by a biochemical CO2 pump and relies on a spatial separation of the CO2 fixation and assimilation. In general, these species have a particular anatomy (Kranz anatomy), where mesophyll and bundle sheath cells cooperate to fix CO2 (Figure 1). Differentiation of these two cell types is essential for the operation of C4 photosynthesis, although special cases for the operation of the C4 cycle within only one type of photosynthetic cell have been found (Edwards et al., 2004, Lara et al., 2002, Lara & Andreo, 2005).
\n\t\t\t\tBasically, carboxylation of phosphoenolpyruvate (PEP) by the phosphoenolpyruvate carboxylase (PEP-carboxylase) produces four-carbon organic acids in the cytosol of mesophyll cells. This so-called C4 compounds are transported to the bundle sheath cells and decarboxylated to yield CO2 which is assimilated by Rubisco in the Photosynthetic Carbon Reduction (PCR) cycle (Hatch, 1987). The decarboxylation reaction also produces three-carbon organic acids (C3) that return to the mesophyll cells to regenerate PEP in a reaction catalyzed by the enzyme pyruvate orthophosphate dikinase (PPDK). This process called
\n\t\t\t\tSimplified scheme of carbon fixation pathways operating in C3 and C4 plants. Abbreviations: C3, three-carbon organic acids; C4, four-carbon organic acids; C5, ribulose-1,5-bisphosphate; PCR, Photosynthetic Carbon Reduction Cycle; PEPC, phosphoenolpyruvate carboxylase; Rubisco, Ribulose-1,5-bisphosphate carboxylase/oxygenase.
\n\t\t\t\t\tHatch-Slack pathway, after the first scientists that postulated the metabolic pathway. However, they used the name C4\n\t\t\t\t\tdicarboxylic acid pathway of photosynthesis. Due to current use, the name has been shortened to C4\n\t\t\t\t\tphotosynthesis, C4\n\t\t\t\t\tpathway, C4\n\t\t\t\t\tsyndrome or C4\n\t\t\t\t\tmetabolism. The plants that perform this type of photosynthesis are then called C4\n\t\t\t\t\tplants.
\n\t\t\t\tThis general scheme is common among the C4 species; however, there are variations to this basic pathway that include diverse decarboxylation enzymes as well as different transported metabolites. Thus, the decarboxylation process occurs in three diverse ways, mainly using one of the following enzymes: NADP-malic enzyme (NADP-ME), NAD-malic enzyme (NAD-ME) or phosphoenolpyruvate carboxykinase (PEP-CK). Therefore, C4 plants have been traditionally grouped into three biochemical subtypes depending on the major decarboxylase used (C4-NADP-ME subtype; C4-NAD-ME subtype or C4-PEP-CK subtype). Each C4 subgroup possesses particular structural features, biochemistry and physiology, and also differences in the mechanism used to regenerate phosphoenolpyruvate (PEP), the substrate of PEP-carboxylase in mesophyll cells. Nevertheless, it is now becoming apparent that, in several cases, more than one decarboxylase operates at the same time (Drincovich et al., 2011).
\n\t\t\tC4 species have evolved in a high CO2 environment. This increases both their nitrogen and water use efficiency compared to C3 species. C4 plants have greater rates of CO2 assimilation than C3 species for a given leaf nitrogen when both parameters are expressed either on a mass or an area basis (Ghannoum et al., 2011). Although the range in leaf nitrogen content per unit areas is less in C4 compared to C3 plants, the range in leaf nitrogen concentration per unit dry mass is similar for both C4 and C3 species. Even though leaf nitrogen is invested into photosynthetic components into the same fraction in both C3 and C4 species, C4 plants allocate less nitrogen to Rubisco protein and more to other soluble protein and thylakoids components. In C3 plants, the photosynthetic enzyme Rubisco accounts for up to 30% of the leaf nitrogen content (Lawlor et al., 1989), but accounts for only 4–21% of leaf nitrogen in C4 species (Evans & von Caemmerer, 2000, Sage et al., 1987). The lower nitrogen requirement of C4 plants results from their CO2-concentrating mechanism, which raises the bundle sheath CO2 concentration, saturating Rubisco in normal air and almost eliminating photorespiration. Without this mechanism, Rubisco in the C3 photosynthetic pathway operates at only 25% of its capacity (Sage et al., 1987) and loses ca. 25% of fixed carbon to photorespiration (Ludwig & Canvin, 1971). To attain comparable photosynthetic rates to those in C4 plants, C3 leaves must therefore invest more heavily in Rubisco and have a greater nitrogen requirement. Because the Rubisco specificity for CO2 decreases with increasing temperature (Long, 1991), this difference between the C3 and C4 photosynthetic nitrogen-use efficiency is greatest at high temperatures (Long, 1999). The high photosynthetic nitrogen-use efficiency of C4 plants is partially offset by the nitrogen-requirement for CO2-concentrating mechanism enzymes, but the high maximum catalytic rate of PEP-carboxylase means that these account for only ca. 5% of leaf nitrogen (Long, 1999). Improved leaf and plant water use efficiency in C4 plants is due to both higher photosynthetic rates per unit leaf area and lower stomatal conductance, with the greater CO2 assimilation contributing to a major extent (Ghannoum et al., 2011).
\n\t\t\t\tThe advantages of greater nitrogen use efficiency and water use efficiency of C4 relative to C3 photosynthesis are fully realized at high light and temperature, where oxygenase reaction of Rubisco is greatly increased. It is worth noting, although in C4 plants energy loss due to photorespiration is eliminated, and additional energy is required to operate the C4 cycle (2 ATPs per CO2 assimilated). In dim light, when photosynthesis is linearly dependent on the radiative flux, the rate of CO2 assimilation depends entirely on the energy requirements of carbon assimilation (Long, 1999). The additional ATP required for assimilation of one CO2 in C4 photosynthesis, compared with C3 photosynthesis, increases the energy requirement in C4 plants (Hatch, 1987). However, when the temperature of a C3 leaf exceeds ca. 25 ºC, the amount of light energy diverted into photorespiratory metabolism in C3 photosynthesis exceeds the additional energy required for CO2 assimilation in C4 photosynthesis (Hatch, 1992, Long, 1999). This is the reason why at temperatures below ca. 25–28 ºC, C4 photosynthesis is less efficient than C3 photosynthesis under light-limiting conditions. It is interesting to note, that while global distribution of C4 grasses is positively correlated with growing season temperature, the geographic distribution of the different C4 subtypes is strongly correlated with rainfall (Ghannoum et al., 2011).
\n\t\t\t\tOn the contrary, C4 plants are rare to absent in cold environments. Although there are examples of plants with C4 metabolisms that show cold adaptation, they still require warm periods during the day in order to exist in cold habitats (Sage et al., 2011). In consequence, C4 species are poorly competitive against C3 plants in cold climates (Sage & McKown, 2006, Sage & Pearce, 2000). The mechanisms explaining the lower performance of C4 plants under cold conditions have not been clarified (Sage et al., 2011). Among early plausible explanations were the low quantum yield of the C4 relative to the C3 pathway (Ehleringer et al., 1997), and enzyme lability in the C4 cycle, most notably around PEP metabolism (PEP-carboxylase and pyruvate orthophosphate dikinase) (Matsuba et al., 1997). Both hypothesis are insufficient since maximum quantum yield differences do not relate to conditions under which the vast majority of daily carbon is assimilated and there cold-adapted C4 species that have cold stabled forms of PEP-carboxylase and pyruvate orthophosphate dikinase, and synthesize sufficient quantity to overcome any short term limitation (Du et al., 1999, Hamel & Simon, 2000, Sage et al., 2011). The current hypothesis is that C4 photosynthesis is limited by Rubisco capacity at low temperatures. Even in cold-tolerant C4 species, Rubisco capacity becomes limiting at low temperature and imposes a ceiling on photosynthetic rate below20 ºC (Kubien et al., 2003, Pittermann & Sage, 2000, Sage,2002).
\n\t\t\tAccording to the Intergovernmental Panel on Climate Change (IPCC), the current atmospheric CO2 level of 384 μmol l-1 (800 Gt) is predicted to rise to 1000 Gt by the year 2050. Only this time humans are the drivers of these changes and not glacial-interglacial cycles. Human-caused increases in atmospheric CO2 concentration are thought to be largely responsible for recent increases in global mean surface temperatures and are projected to increase by 1.4 to over 5 ºC by 2100 (Intergovernmental Panel on Climate Change, 2001, 2007). Increase in global average temperatures would further result in drastic shifts in the annual precipitation with a 20% reduction per year, and about 20% loss in soil moisture (Schiermeier, 2008). Regarding plants, higher atmospheric CO2 levels tend to reduce stomatal conductance and transpiration, thereby lowering latent heat loss and causing higher leaf temperatures (Bernacchi et al., 2007). Thus, in the future, plants will likely experience increases in acute heat and drought stress, which can impact ecosystem productivity (Cias et al., 2005) and biodiversity (Thomas et al., 2004). The sensitivity of photosynthesis to each of the environmental variables including high temperature, low water availability, vapor pressure deficit and soil salinity, associated with the inevitable rise in atmospheric CO2, has not been well documented in assessing plant responses to the new changing environment (Reddy et al., 2010). How plant growth responds to the rising CO2 concentration will not only affect ecosystem productivity in the future, but also the magnitude of C sequestration by plants and, consequently, the rate of CO2 increase in the atmosphere. C4 plants are directly affected by all major global change parameters, often in a manner that is distinct from that of C3 plants. In the present chapter, we will focus on the effect of increased CO2, and its relation to temperature and drought, on C4 plants. Understanding how plants have and will respond to the rapid change in CO2 concentration, together with developing knowledge about their capacity to adapt, is an essential initial step in understanding the full impact that the multiple interacting factors of global change (e.g. drought, temperature, ozone) will have on terrestrial ecosystems. These ecosystems produce services upon which we are dependent for food, fuel, fiber, clean air, and fresh water (Leakey et al., 2009).
\n\t\tIn theory, increases in atmospheric levels of CO2 above current levels can increase photosynthesis by decreasing photorespiration (fixation of O2 rather than CO2 by Rubisco), which increases with temperature and is higher in C3 than C4 and crassulacean acid metabolism (CAM) plants (Sage & Monson, 1999). In addition, rising CO2 generally stimulates C3 photosynthesis more than C4. Doubling of the current ambient CO2 concentration stimulated the growth of C4 plants to the tune of 10–20% whereas that in C3 plants was about 40–45% (Ghannoum et al., 2000).
\n\t\t\tC3 photosynthesis is known to operate at less than optimal CO2 levels and can show dramatic increase in carbon assimilation, growth and yields. As Rubisco is substrate-limited by the current atmospheric CO2 levels, this enzyme has the potential to respond to increases in CO2 concentration; and have a metabolic control to alter the CO2 flux during carbon assimilation (Bernacchi et al., 2003, Long et al., 2004). On the contrary, photosynthetic carbon assimilation in the C4 species is saturated or almost CO2-saturated a low ambient pCO2. The reason is that PEP-carboxylase utilizes HCO3\n\t\t\t\t- as substrate rather than CO2; in consequence, the enzyme is insensitive to changes in the ratio of CO2: O2 due to lack of binding of O2 to the catalytic site of PEP-carboxylase. Therefore, if plants were grown under elevated CO2, carbon fixation would be little affected. This assumption that the inherent CO2 concentrating mechanism in C4 plants renders these plants insensitive to elevated CO2 atmosphere is reflected in the lack of interest that it has been attributed to the study of the C4 plants response to elevated CO2 levels. To show this, Reddy et al. (2010) performed an exhaustive fifteen year- literature survey on the influence of elevated CO2 among certain C3, C4 and CAM species. The authors provided information for forty C3 plants and for only two C4 species and three CAM plants. Most of the C3 plants presented a significant positive response to photosynthetic acclimation, Sorghum and Panicum (C4 plants) exhibited negative response, whereas Ananas, Agave and Kalanchoe (CAM plants) showed positive responses to increased CO2 concentration during growth. In view of this survey, it is then evident, that responses to elevated CO2 have been little investigated in C4 species. Moreover, conflicting reports on plant responses to elevated CO2, and several such differential photosynthetic responses, could be attributed to differences in experimental technologies, plant species used for the experiments, age of the plant as well as duration of the treatment (Sage, 2002). Nevertheless, C4 species still exhibit positive responses (Fig. 2), particularly at elevated temperature and arid conditions where they are currently common and under nutrient-limited situations as well (Ghannoum et al., 2000, Sage & Kubien, 2003). High CO2 aggravates nitrogen limitations and in doing so may favor C4 species, which have greater photosynthetic nitrogen use efficiency (Sage & Kubien, 2003). On the other hand, elevated CO2 can also increase water use efficiency, in part by decreasing stomatal conductance and transpiration (Ainsworth et al., 2002). The irradiance is also a paramount factor; enhanced photosynthesis under elevated CO2 conditions was observed in C4 plants grown under high irradiance, while there was not much response when grown under low irradiance (Ghannoum et al., 2000).
\n\t\t\tDifferences in the conductance of the bundle sheath cells to CO2 (varying with the decarboxylating subtype and also associated with changes in the ratio of Rubisco:PEP-carboxylase activity) were proposed to be responsible for different rates of CO2 leakage (Brown & Byrd, 1993, Ehleringer & Pearcy, 1983, Hattersley, 1982, Saliendra et al., 1996). Nevertheless, further studies showed that the stimulation of leaf photosynthesis at elevated CO2 was not associated with CO2 leak rates from the bundle sheath or with changes in the ratio of activities of PEP-carboxylase to Rubisco (Ziska et al., 1999).
\n\t\t\tAnother aspect of plant metabolism which may vary under exposure to increased CO2 is the respiration. As highlighted by Reddy and colleagues (2010) in C4 plants little is known about the impact of elevated CO2 on the respiratory rates, which are reduced in C3 species and thus, probably contributing to increase biomass yield.
\n\t\t\tNeither C3 nor C4 species show acclimation responses that are directly linked to CO2 level. Instead, the CO2 effect on the photosynthetic biochemistry is largely mediated by carbohydrate accumulation in leaves under conditions where carbon sinks in the plant are also experiencing high carbon supply (Sage & McKown, 2006). The effectiveness with which increases in CO2 can be translated into growth benefits is depending in the sink-source balance and is affected by various plant and environmental factors. Depending on the growing conditions, these changes may or not conduct to increases in leaf area (Ghannoum et al., 2001, Leakey et al., 2006, Morison & Lawlor, 1999). For plants grown under optimal growth conditions and elevated CO2, photosynthetic rates can be more than 50% higher than for plants grown under normal CO2 concentrations. This reduces to 40% higher for plants grown under the average of optimal and suboptimal conditions, and over the course of a full day, average photosynthetic enhancements under elevated CO2 are estimated to be about 30%. The 30% enhancement in photosynthesis is reported to increase relative growth rate by only about 10%. This discrepancy is probably due to enhanced carbohydrate availability exceeding many plants’ ability to fully utilize it due to nutrient or inherent internal growth limitations. Consequently, growth responses to elevated CO2 increase with a plant’s sink capacity and nutrient status (Kirschbaum, 2010).
\n\t\t\tGlobal circulation models have predicted that, together with increases in the CO2 concentration, in the future some regions will have increases in the frequency and severity of droughts.
\n\t\t\t\t\tLeaky et al. (2009) proposed that the potential for increased growth and yield of C4 plants at elevated CO2 concentrations relays on the decrease in water use and reduction of drought stress, and not by a direct effect of increased photosynthesis. In this respect, some C4 plants
\n\t\t\t\t\tSummary of the main factors involved in the response of plants to elevated CO2\n\t\t\t\t\t\t\t
grown under Free-Air Carbon dioxide Enrichment (FACE) exhibited increased photosynthetic rates only during drought or under the conditions of atmospheric vapor pressure deficits (Cousins, et al., 2002, Leakey et al., 2009). Elevated CO2 reduced midday stomatal conductance of FACE-grown sorghum by 32% with irrigation and by 37% under drought stress (Wall et al., 2001). The effect of elevated CO2 concentration on whole plant water use was smaller, but still significant (Conley et al., 2001). It is worth mentioning, that this indirect mechanism of enhanced carbon uptake by elevated CO2 concentration is not unique to C4 plants. Decreased stomatal conductance at elevated concentration of CO2 in a C3 soybean canopy also led to a significant reduction in canopy evapo-transpiration (Bernacchi et al., 2007). Therefore, interactive effects of CO2 and water availability may alter the relative performance of C3 and C4 species. At stated before, at current CO2 levels, C4 species (particularly dicots) generally require less water than C3 because of the higher CO2 uptakes rates and greater stomatal resistance to water loss (Ehleringer et al., 1997). Under conditions of drought and elevated CO2, based on comparative studies using model C3 and C4 plants, Ward et al. (1999) postulated that C3 species would be more competitive than C4 species as results of decreased water loss through transpirations and higher CO2 rates that would decrease the relative advantage of C4 plants under drought conditions.
\n\t\t\t\tGlobal increases in temperature and CO2 may have interactive effects on photosynthesis. On one hand, negative effects of heat stress on plants are well known, since photosynthesis is thought to be among the most thermosensitive aspects of plant function. Both the light (electron transport) and dark (Calvin cycle) reactions of photosynthesis have thermolabile components, especially photosystem II (PSII) in the light reactions (Berry & Björkman, 1980, Heckathorn et al., 1998, 2002, Santarius 1975, Weis & Berry, 1988) and Rubisco activase in the Calvin cycle (Crafts-Brandner & Salvucci, 2002). Therefore, limiting processes controlling photosynthesis at elevated temperature could be either declining capacity of electron transport to regenerate ribulose-1,5-bisphosphate, or reductions in the capacity of Rubisco activase to maintain Rubisco in an active configuration (Sage et al., 2008).
\n\t\t\t\t\tSince, studies examining the effects of elevated CO2 and increased growth temperature (typically 3–5 ◦C) had yield positive (Faria et al., 1996, 1999, Ferris et al.,1998, Huxman et al., 1998, Taub et al., 2000), negative (Bassow et al., 1994, Roden & Ball, 1996), and no effects (Coleman et al., 1991) on photosynthetic and plant tolerance to acute heat stress. Again, growing conditions and type of carbon assimilation pathways are need to be discriminated. General effects of elevated CO2 on photosynthetic heat tolerance were recently investigated in a comparative study including C3 and C4 species and they can be summarized as follows: (i) in C3 species, elevated CO2 typically increases heat tolerance of photosynthesis, except for plants grown at supra-optimal growing temperature, then elevated CO2 may provide no benefit or even decrease photosynthesis; (ii) in C4 species, elevated CO2 frequently decreases photosynthetic thermotolerance, at near-optimal growing temperature as well as supra-optimal growing temperature (Wang et al. 2008; Hamilton et al., 2008). Although both C3 and C4 plants experience reductions of similar magnitude in stomatal conductance with increasing CO2 (e.g., 20%–50% with a doubling of CO2) (Sage, 1994; Reich et al., 2001; Wang et al., 2008), the lower stomatal conductance of C4 plants at any given CO2 level means lower average transpiration and higher leaf temperatures in C4 plants, which may increase heat related damage in C4 plants compared with C3 plants in the same habitat. On the other hand, elevated CO2 increases leaf size (Morison & Lawlor, 1999), and this should increase leaf temperatures during heat stress more in C3 than C4 species, given the greater average stimulation of growth in elevated CO2 in C3 species (Poorter & Navas, 2003).
\n\t\t\t\tFinally, to have a deeply understanding of the performance of C4 plants under increased CO2 conditions other factors besides water availability, soil nutrition and temperature, should be considered. One aspect to be included in the analysis should be pests and diseases.
\n\t\t\t\t\tChanges in the ratio of CO2/O2 in the atmosphere affects plant metabolism in ways that ultimately influence the quality of leaves as a food resource for animals. To herbivores, the decreased leaf protein contents and increased carbon/nitrogen ratios common to all leaves under elevated atmospheric carbon dioxide imply a reduction in food quality. Stiling and Cornelissen (2007) analyzed plant-herbivore interactions using C3 species and found that plants grown under elevated CO2 usually had lower nutrient concentrations, which reduced the growth rate of herbivores feeding on that plant material. Contrasting C4 and C3 species, C4 grasses are a less nutritious food resource than C3 grasses, both in terms of reduced protein content and increased carbon/nitrogen ratios. The abundance of C3 and C4 plants (particularly grasses) are affected by atmospheric carbon dioxide. There is an indication that as C4-dominated ecosystems expanded 6–8 Ma b.p., there were significant species-level changes in mammalian grazers. Today there is evidence that mammalian herbivores differ in their preference for C3 versus C4 food resources, although the factors contributing to these patterns are not clear. Elevated carbon dioxide levels will likely alter food quality to grazers both in terms of fine-scale (protein content, carbon/nitrogen ratio) and coarse-scale (C3 versus C4) changes (Ehleringer et al., 2002).
\n\t\t\t\t\tRegarding plant-plant interactions using C3 species, Wang (2007) showed that the growth response of mixed-species communities to elevated CO2 was less than the response of single-species populations. In addition, the relative importance of these and other factors should be established for C4 species grown under elevated CO2.
\n\t\t\t\tC4 plants are directly affected by all major global change parameters, often in a manner that is distinct from that of C3 plants. Although an ongoing effort has been dedicated to the study of the response of C4 plants to CO2 enrichment, the literature regarding the response of C4 plants is still under-represented when comparing to that of C3 species. An understanding of C4 plants responses to ambient variables such as temperature, CO2, nutrients and water is essential for predictions of how agricultural and wild C4 populations will respond to climate variations such as those predicted to occur with global climate change (Intergovernmental Panel on Climate Change, IPCC, 2001).
\n\t\tThis work was funded by a grant from Agencia Nacional de Promoción Científica y Tecnológica (PICT Nº 2008-2164) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, PIP Nº0679). CSA and MVL and are members of the Researcher Career of CONICET.
\n\t\tNon-conventional or clean production is increasing rapidly around the world, thanks to it’s advantages such as reduced environmental impact, small size and it is a renewable energy [1]. The traditional power system is characterized by the unidirectional of the power flow because the energy comes from centralized sources [2]. These sources are generally based on fossil resources that are exhaustible and polluting for the environment.
\nThe transmission power system spreads on long distances, which leads to losses in the lines by Joule effect on the one hand, and on the other hand, it requires huge investments and waste lands with long achievement times, which has led researchers to think about other solutions to face these problems [3].
\nAmong these solutions is to have sources of electrical energy close to the consumers (local production). With the liberalization of the electricity market and the evolution of decentralized source (DS) technology in recent years, an increased trend towards their use has emerged. DS is defined as small producers based on renewable or conventional sources installed at different points in the power system, either at the transmission or distribution level [4]. The rate of DS integration tends to increase progressively in several countries [5].
\nIn spite of the various advantages of DS, its sources present disadvantages when they are inserted into the power system, such as frequency instability caused by the intermittency of some renewable sources (e.g. photovoltaic and wind power) [6]. They also present some constraints, such as exceeding the thermal limits of power lines, increased Joule effect losses and the dysfunctioning of electrical protection devices and the exceeding of voltages at connection points. These constraints are due to the wrong choice of size (maximum power) and site of the DS, which requires the search for the best sites and adequate sizes [7].
\nA number of researchers have studied the problem of optimal site and size of DS to distribution networks [4, 8]. Example, in researchers [9] presented a technique a review of optimal DS placement in distribution network. DS site and size search techniques can be based on artificial intelligence techniques, metaheuristic techniques or deterministic techniques [10]. While most papers deal with this problem as a single-objective problem and some papers have focused on the problem of multi-objective optimizations [11]; these studies have considered a variety of objective functions including voltage profile improvement, losses minimization, reliability index minimization (SAIFI, SAIDI and END), fuel cost minimization, greenhouse gas emission minimization, maximization of DS penetration rate and maximization of voltage stability [12, 13, 14, 15, 16, 17, 18]. Some researchers have examined the problem of optimal sitting and size of DS to transmission networks [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29].
\nBased on the literature search carried out, it was noted that the optimal integration of DS into distribution networks is largely discussed, however, there is less literature on the integration of DS into transmission networks.
\nThe objective of this chapter is to study the problem of determining the Optimal Sites and Sizes of DS (OSSDS) in the transmission power system while taking into account the various constraints of the system (technical and security constraints). This is achieved by using a metaheuristic optimization technique such as the Genetic Algorithm (GA) technique. The objective functions considered in this work are the minimization of active power losses and the voltage stability improvement. These objective functions are treated as mono-objective and multi-objective (the objective functions are combined to a single objective function via weighting factors). This study has been applied on the IEEE 30 bus network under MATLAB code. For this reason, this chapter is organized according to the following plan: Section 2 will present the mathematical formulas of the OSSDS problem, i.e. the objective functions, the different constraints. Section 3 will present definitions on the method used (GA) and their application on OSSDS. Section 4 will give the description of the IEEE 30 node network and the limitations of the study framework. Section 5 will present the simulation, interpretation and analysis of the results obtained. Finally, the conclusion of the chapter and some perspectives.
\nThe objective of this work is to research what is the optimal power to be injected by DS and the bus of their insertions that gives us the best performance of the power system considering the imposed constraints. To reach this objective we must define the fitness function and the constraints of equality and inequality which will be detailed in the following sections [30, 31].
\nThe OSSDS is formulated as a single and multi-objective problem using two objective functions.
\nThe first concern of the power system operator is to minimize active power losses in the line, this is expressed by the following equation:
\nwhere \n
To assure that the operating point of the power system is far from the voltage collapse point, the voltage stability index must be improved. Among the effective indices that have been proposed in the literature is the Line Voltage Stability Index (LVSI) [32]. The LVSI of the line between bus \n
where \n
The line which represents an LVSI value close to 1 will be considered, the most critical line and may lead to the network collapse.
\nFor multiobjective optimization, the two objectives functions are combined in a single linear function using weighting factors. Mathematically, this is given in the following form:
\nwhere \n
Equality constraints represent power balance equations between generation and demand. For a transmission power system in presence of DS, the active and reactive power equality constraints can be expressed as follows [33]:
\nwhere \n
These constraints represent the physical limits of the lines, conventional generators and DS as also the security limits of the voltages of the network busses. They are expressed by the following equations:
\nwhere \n
For reasons of power system security, the network operator has limited the penetration rate (\n
To optimize the site and size of the DS, it is important to mention that the control variables are generated within their allowable limits using a random strategy using a metaheuristic technique. During the optimization process, it is possible to come across solutions that are unfeasible due to exceeding the voltage limit, the thermal limit of the lines or the power limit of the reference generator, in this case the OF is penalized. This OF is reformulated as follows [33]:
\nwhere \n
The limits of the various variables are determined by the following equation:
\n\n\n
The values of the penalty factors are determined by empirical means. After several tests it is decided that, \n
From the above mathematical formulation, it can be noted that the use of such a model to solve real size problems is practically not possible with a classical approach. Consequently, in order to solve practical problems, it is necessary to use metaheuristic methods, that is, the genetic algorithms method.
\nGA is a metaheuristic optimization technique inspired by natural selection, and genetics developed by Holland-John, who conceived and realized an idea on how to transform the characteristics of natural evolution into a computer program [35]. The algorithm is based on a set of possible solutions randomly initialized in the search space. Individuals are represented by their design variables or by chromosome coding. Some solutions from the first population are used to form a new population based on genetic operators (crossover, mutation and selection). The goal is for the new population to be better than the previous one. The solutions that will be used to form new solutions are randomly selected according to their merit represented by an objective function specific to the problem posed, which should be minimized or maximized, so the better the individual, the greater his chances of surviving and reproducing, until the stop criterion is satisfied.
\nThe goal of the GA method is to determine the optimal site and size of the DS to be integrated into the power system while minimizing the objective function under imposed constraints. Initially the vector of state variables and the vector of control variables are expressed as below:
\nThe vector \n
The vector \n
\nFigure 1 indicates the chromosome structure employed in this study.
\nControl variable vector structure.
Step 1: In determining the site and size of DS, the genetic algorithm method was suggested. The main steps for researching the site and size of DS are as follows:
\nStep 2: Run a power flow and determine the various network parameters in the absence and presence of DS, using the Newton Raphson method.
\nStep 3: Select the GA parameters (number of generations, population size, crossover and mutation probability) and randomly generate the values of the sites and sizes between their limits using Eqs. E16, E17 and E18 (creation of the initial population \n
where \n
Start the iteration meter \n
Step 4: Run the power flow in the presence of the DS and evaluate the objective function for each individual.
\nStep 5: Examine all network constraints using Eqs. E4 to E8. If the latter are satisfied, go to the next step. If not, penalize the OF using Eq. E12 and go to the next step.
\nStep 6: Generate new populations following the laws of GA (crossover, mutation and selection), increase the generation meter \n
Step 7: Extract the best individual and show the results (site, size and various network parameters).
\n\nFigure 2 shows the flowchart of the search for the site and size of DS to be incorporated into the power system.
\nDS site and size search flowchart.
The IEEE 30 bus network is used by power system researchers to test the effectiveness of their programs simulation. This system is composed of 30 buss, 41 transmission lines, 04 transformers, 06 conventional generators with a total generating capacity of 435 MW with a reactive capacity of −95 MVAr to 520 MVAr and 21 loads with a total power demand of 283.4 MW and 126.2 MVAr [35]. Bus No. 1 is taken as the reference (slack bus). Figure 3 shows the single line diagram of the network studied.
\nSchematic diagram of the IEEE 30 bus network.
Before beginning the presentation of the simulation results, it is necessary to cite the limitations of the study framework under consideration. The Newton Raphson method is used via the MATPOWER software to calculate the power flow. For the GA method the existing Toolbox in the MATLAB library (GA function) will be used. The voltage limits of the network busses are limited between 0.95 pu- 1.1 pu. DS are considered as sources capable of delivering active and reactive power, using a power factor of 0.8. DS integration are modeled as PQ busses (negative loads). It is important to note that all load nodes are considered candidate busses for DS sites. It is important to note that the number of DS to be integrated into the network is chosen in advance. After integration of a DS up to 10 DS, it is observed that, the best number from the viewpoint of improving the network parameters is 05 DS. For this reason, this study considers the insertion of five DS. The limits of the active power delivered by each DS are between 0 and 100 MW. And the penetration level limit of the total power delivered by the DS is \n
After the execution of the elaborated program, the simulation results achieved are shown bellow. Table 1, represents all the simulation results obtained for the five cases studied, as also the physical power limits of conventional generators. Figure 4, shows the situation of apparent power transmitted via transmission lines in all the cases studied. Figure 5 shows the voltage profile of several different cases.
\nParameters | \nLimits | \nCase 01 | \nCase 02 | \nCase 03 | \nCase 04 | \n|
---|---|---|---|---|---|---|
Max | \nMin | \n|||||
PG1(MW) | \n200 | \n50 | \n182.64 | \n52.47 | \n60.61 | \n50.46 | \n
PG2(MW) | \n80 | \n20 | \n50.58 | \n50.58 | \n50.58 | \n50.58 | \n
PG3(MW) | \n50 | \n15 | \n18.52 | \n18.52 | \n18.52 | \n18.52 | \n
PG4(MW) | \n35 | \n10 | \n18.09 | \n18.09 | \n18.09 | \n18.09 | \n
PG5(MW) | \n30 | \n10 | \n10.40 | \n10.40 | \n10.40 | \n10.40 | \n
PG6(MW) | \n40 | \n10 | \n13.26 | \n13.26 | \n13.26 | \n13.26 | \n
QG1(MVAr) | \n200 | \n−20 | \n−8.56 | \n16.94 | \n6.16 | \n17.97 | \n
QG2(MVAr) | \n100 | \n−20 | \n23.47 | \n−8.88 | \n−15.30 | \n−8.98 | \n
QG3(MVAr) | \n50 | \n−15 | \n32.04 | \n3.22 | \n23.75 | \n−5.10 | \n
QG4(MVAr) | \n60 | \n−15 | \n49.29 | \n−5.34 | \n−2.80 | \n−4.82 | \n
QG5(MVAr) | \n50 | \n−10 | \n5.39 | \n−10.00 | \n−6.98 | \n−7.92 | \n
QG6(MVAr) | \n60 | \n−15 | \n2.74 | \n−14.26 | \n−15.00 | \n−12.29 | \n
Total Conventional Active Production | \n435 | \n115 | \n293.49 | \n163.33 | \n171.46 | \n161.31 | \n
Total Conventional Reactive Production | \n520 | \n−15 | \n104.37 | \n−18.32 | \n−10.16 | \n−21.14 | \n
Total Load (MW, MVAr) | \n(283.40, 126.20) | \n|||||
DS Site(Best Location) | \n** | \n** | \n** | \n7 | \n4 | \n7 | \n
9 | \n12 | \n17 | \n||||
12 | \n20 | \n20 | \n||||
18 | \n23 | \n23 | \n||||
30 | \n29 | \n30 | \n||||
DS Size (MW, MVAr) | \n** | \n(46.96, 35.22) | \n(36.40, 27.30) | \n(67.52, 50.64) | \n||
(38.58, 28.93) | \n(52.97, 39.73) | \n(27.48, 20.61) | \n||||
(5.63, 4.22) | \n(6.07, 4.55) | \n(10.36, 7.77) | \n||||
(18.09, 13.56) | \n(11.37, 8.52) | \n(4.60, 3.45) | \n||||
(13.60, 10.20) | \n(9.73, 7.30) | \n(15.05, 11.28) | \n||||
TPLI (MW) | \n10.89 | \n2.798 | \n4.620 | \n2.945 | \n||
LVSI | \n0.181 | \n0.158 | \n0.067 | \n0.149 | \n||
Loading Parameter (pu) | \n2.1 | \n4.4 | \n5.7 | \n5.2 | \n
Summary of network simulation results for the various cases.
** without DS integration.
Apparent power of the transmission lines for several cases.
Voltage values of the network bus for several cases.
From the results obtained by running the optimal power flow without DS (case 01), it is found that the total active losses are 10.89 MW, the LVSI is 0.181 and loading parameter (LP) is 2.1 pu.
\nIn the case of the minimization the TPLI only (case 2), it can be seen that the total active losses are reduced to 2.798 MW, which represents a reduction rate of 74.3%, the LVSI has been improved with 12.7% and loading parameter represents a rate of 52.27%, that is after the integration of 05 DS with a total active power of 122.86 MW and reactive power of 92.13 MVAr distributed to the different sites (busses 7, 9, 12, 18 and 30) respecting all the constraints of the network.
\nWhen improving the LVSI only (case 3), the simulation results show that busses 4, 12, 20, 23 and 29 are selected as the best sites for DS power installations producing a total power of 116.54 MW and 87.4 MVAr. This implementation has enhanced the LVSI parameter from the value 0.181 to 0.067, which represents a rate of 62.9%, the losses are reduced to 4.62 MW, which is 57.5% and a loading parameter improvement rate of 63.15%.
\nThe multiobjective optimization (case 04) proved that the results give a compromise between the different values of the minimized objective functions. In this case, the TPLI is improved by 72.9%, LVSI has been minimized by 17.67% and loading parameter has been increased by 59.61%. The advantage of Multi-objective optimization consists in improving several parameters of the electrical network and having the best compromise.
\n\nFigure 4 shows that all line powers are below the thermal limit power, which demonstrates the consideration of line stresses.
\n\nFigure 5 shows that all the bus voltages are included between the limits 0.95 pu and 1.1 pu. Figure 6 illustrates the PV curves before and after the integration of the DS for the three cases.
\n(a) LP in case 2, (b) LP in case 3 (c) LP in case 4.
\nFigure 6 shows the positive influence of DS integration on the voltage stability margin of the IEEE 30 bus network. This figure also shows that case 3 represents the best value of LM, because the objective function only maximizes the voltage stability.
\nThis chapter solves the problem of searching for the optimal location and size of DS in the transmission power system considering the required constraints, using the GA technique. In this work, various objective functions are targeted, minimizing active losses and voltage stability improvement. The last ones are treated as a mono and multi objective problem. The simulations carried out have shown that the results are dependent on the minimized objective function. The results achieved show the efficiency of the GA method. The perspective of this study is to include the constraint related to the transient stability of the rotor angles of conventional generators, in order to maintain the stability of the system during DS disconnection.
\nI thank very sincerely the members of the laboratory of LACoSERE, Electrical Department of the Amar Telidji University, Laghouat, Algeria and laboratory of LAADI, Electrical Department of Djelfa University, Djelfa, Alegria for their participations in the realization of this work.
\nThe data (lines, consumptions, generators) of the IEEE 30 bus network used in this study are shown below [36]. Table 2 shows the bus data of the IEEE network studied. Table 3 represents the line data. Table 4 shows the data of the conventional generators.
\nBus i | \nType | \nPd(MW) | \nQd(MVAr) | \nGs\n | \nBs\n | \nArea | \nVm\n | \nVa\n | \nBaseKV | \nZone | \nVmax\n | \nVmin\n | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|
1* | \n3 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.06 | \n0 | \n132 | \n1 | \n1.1 | \n0.95 | \n
2 | \n2 | \n21.7 | \n12.7 | \n0 | \n0 | \n1 | \n1.043 | \n−5.48 | \n132 | \n1 | \n1.1 | \n0.95 | \n
3 | \n1 | \n2.4 | \n1.2 | \n0 | \n0 | \n1 | \n1.021 | \n−7.96 | \n132 | \n1 | \n1.1 | \n0.95 | \n
4 | \n1 | \n7.6 | \n1.6 | \n0 | \n0 | \n1 | \n1.012 | \n−9.62 | \n132 | \n1 | \n1.1 | \n0.95 | \n
5 | \n2 | \n94.2 | \n19 | \n0 | \n0 | \n1 | \n1.01 | \n−14.37 | \n132 | \n1 | \n1.1 | \n0.95 | \n
6 | \n1 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.01 | \n−11.34 | \n132 | \n1 | \n1.1 | \n0.95 | \n
7 | \n1 | \n22.8 | \n10.9 | \n0 | \n0 | \n1 | \n1.002 | \n−13.12 | \n132 | \n1 | \n1.1 | \n0.95 | \n
8 | \n2 | \n30 | \n30 | \n0 | \n0 | \n1 | \n1.01 | \n−12.1 | \n132 | \n1 | \n1.1 | \n0.95 | \n
9 | \n1 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.051 | \n−14.38 | \n1 | \n1 | \n1.1 | \n0.95 | \n
10 | \n1 | \n5.8 | \n2 | \n0 | \n19 | \n1 | \n1.045 | \n−15.97 | \n33 | \n1 | \n1.1 | \n0.95 | \n
11 | \n2 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.082 | \n−14.39 | \n11 | \n1 | \n1.1 | \n0.95 | \n
12 | \n1 | \n11.2 | \n7.5 | \n0 | \n0 | \n1 | \n1.057 | \n−15.24 | \n33 | \n1 | \n1.1 | \n0.95 | \n
13 | \n2 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.071 | \n−15.24 | \n11 | \n1 | \n1.1 | \n0.95 | \n
14 | \n1 | \n6.2 | \n1.6 | \n0 | \n0 | \n1 | \n1.042 | \n−16.13 | \n33 | \n1 | \n1.1 | \n0.95 | \n
15 | \n1 | \n8.2 | \n2.5 | \n0 | \n0 | \n1 | \n1.038 | \n−16.22 | \n33 | \n1 | \n1.1 | \n0.95 | \n
16 | \n1 | \n3.5 | \n1.8 | \n0 | \n0 | \n1 | \n1.045 | \n−15.83 | \n33 | \n1 | \n1.1 | \n0.95 | \n
17 | \n1 | \n9 | \n5.8 | \n0 | \n0 | \n1 | \n1.04 | \n−16.14 | \n33 | \n1 | \n1.1 | \n0.95 | \n
18 | \n1 | \n3.2 | \n0.9 | \n0 | \n0 | \n1 | \n1.028 | \n−16.82 | \n33 | \n1 | \n1.1 | \n0.95 | \n
19 | \n1 | \n9.5 | \n3.4 | \n0 | \n0 | \n1 | \n1.026 | \n−17 | \n33 | \n1 | \n1.1 | \n0.95 | \n
20 | \n1 | \n2.2 | \n0.7 | \n0 | \n0 | \n1 | \n1.03 | \n−16.8 | \n33 | \n1 | \n1.1 | \n0.95 | \n
21 | \n1 | \n17.5 | \n11.2 | \n0 | \n1 | \n1 | \n1.033 | \n−16.42 | \n33 | \n1 | \n1.1 | \n0.95 | \n
22 | \n1 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.033 | \n−16.41 | \n33 | \n1 | \n1.1 | \n0.95 | \n
23 | \n1 | \n3.2 | \n1.6 | \n0 | \n0 | \n1 | \n1.027 | \n−16.61 | \n33 | \n1 | \n1.1 | \n0.95 | \n
24 | \n1 | \n8.7 | \n6.7 | \n0 | \n4.3 | \n1 | \n1.021 | \n−16.78 | \n33 | \n1 | \n1.1 | \n0.95 | \n
25 | \n1 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.017 | \n−16.35 | \n33 | \n1 | \n1.1 | \n0.95 | \n
26 | \n1 | \n3.5 | \n2.3 | \n0 | \n0 | \n1 | \n1 | \n−16.77 | \n33 | \n1 | \n1.1 | \n0.95 | \n
27 | \n1 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.023 | \n−15.82 | \n33 | \n1 | \n1.1 | \n0.95 | \n
28 | \n1 | \n0 | \n0 | \n0 | \n0 | \n1 | \n1.007 | \n−11.97 | \n132 | \n1 | \n1.1 | \n0.95 | \n
29 | \n1 | \n2.4 | \n0.9 | \n0 | \n0 | \n1 | \n1.003 | \n−17.06 | \n33 | \n1 | \n1.1 | \n0.95 | \n
30 | \n1 | \n10.6 | \n1.9 | \n0 | \n0 | \n1 | \n0.992 | \n−17.94 | \n33 | \n1 | \n1.1 | \n0.95 | \n
Bus data of IEEE 30 bus power system.
Base MVA = 100
Busi\n | \nBusj\n | \nR(pu) | \nx(pu) | \nb(pu) | \nRateA (Slim MVA) | \nRateB | \nRateC | \nRatio | \nAngle | \nStatus | \n
---|---|---|---|---|---|---|---|---|---|---|
1 | \n2 | \n0.0192 | \n0.0575 | \n0.0528 | \n130 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
1 | \n3 | \n0.0452 | \n0.1652 | \n0.0408 | \n130 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
2 | \n4 | \n0.057 | \n0.1737 | \n0.0368 | \n65 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
3 | \n4 | \n0.0132 | \n0.0379 | \n0.0084 | \n130 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
2 | \n5 | \n0.0472 | \n0.1983 | \n0.0418 | \n130 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
2 | \n6 | \n0.0581 | \n0.1763 | \n0.0374 | \n65 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
4 | \n6 | \n0.0119 | \n0.0414 | \n0.009 | \n90 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
5 | \n7 | \n0.046 | \n0.116 | \n0.0204 | \n70 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
6 | \n7 | \n0.0267 | \n0.082 | \n0.017 | \n130 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
6 | \n8 | \n0.012 | \n0.042 | \n0.009 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
6 | \n9 | \n0 | \n0.208 | \n0 | \n65 | \n0 | \n0 | \n0.978 | \n0 | \n1 | \n
6 | \n10 | \n0 | \n0.556 | \n0 | \n32 | \n0 | \n0 | \n0.969 | \n0 | \n1 | \n
9 | \n11 | \n0 | \n0.208 | \n0 | \n65 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
9 | \n10 | \n0 | \n0.11 | \n0 | \n65 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
4 | \n12 | \n0 | \n0.256 | \n0 | \n65 | \n0 | \n0 | \n0.932 | \n0 | \n1 | \n
12 | \n13 | \n0 | \n0.14 | \n0 | \n65 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
12 | \n14 | \n0.1231 | \n0.2559 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
12 | \n15 | \n0.0662 | \n0.1304 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
12 | \n16 | \n0.0945 | \n0.1987 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
14 | \n15 | \n0.221 | \n0.1997 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
16 | \n17 | \n0.0524 | \n0.1923 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
15 | \n18 | \n0.1073 | \n0.2185 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
18 | \n19 | \n0.0639 | \n0.1292 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
19 | \n20 | \n0.034 | \n0.068 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
10 | \n20 | \n0.0936 | \n0.209 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
10 | \n17 | \n0.0324 | \n0.0845 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
10 | \n21 | \n0.0348 | \n0.0749 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
10 | \n22 | \n0.0727 | \n0.1499 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
21 | \n22 | \n0.0116 | \n0.0236 | \n0 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
15 | \n23 | \n0.1 | \n0.202 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
22 | \n24 | \n0.115 | \n0.179 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
23 | \n24 | \n0.132 | \n0.27 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
24 | \n25 | \n0.1885 | \n0.3292 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
25 | \n26 | \n0.2544 | \n0.38 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
25 | \n27 | \n0.1093 | \n0.2087 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
28 | \n27 | \n0 | \n0.396 | \n0 | \n65 | \n0 | \n0 | \n0.968 | \n0 | \n1 | \n
27 | \n29 | \n0.2198 | \n0.4153 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
27 | \n30 | \n0.3202 | \n0.6027 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
29 | \n30 | \n0.2399 | \n0.4533 | \n0 | \n16 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
8 | \n28 | \n0.0636 | \n0.2 | \n0.0428 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
6 | \n28 | \n0.0169 | \n0.0599 | \n0.013 | \n32 | \n0 | \n0 | \n0 | \n0 | \n1 | \n
Line data of IEEE 30 bus power system.
Bus | \nPg\n (MW) | \nQg\n (MVAr) | \nQmax\n (MVAr) | \nQmin (MVAr) | \nVg\n | \nS Base\n | \nStatus | \nPmax (MW) | \nPmin (MW) | \n
---|---|---|---|---|---|---|---|---|---|
1 | \n0 | \n0 | \n200 | \n−20 | \n1.06 | \n100 | \n1 | \n200 | \n50 | \n
2 | \n50.5846 | \n0 | \n100 | \n−20 | \n1.045 | \n100 | \n1 | \n80 | \n20 | \n
5 | \n18.5227 | \n0 | \n50 | \n−15 | \n1.02 | \n100 | \n1 | \n50 | \n15 | \n
8 | \n18.0865 | \n0 | \n60 | \n−15 | \n1.029 | \n100 | \n1 | \n35 | \n10 | \n
11 | \n10.4038 | \n0 | \n50 | \n−10 | \n1.06 | \n100 | \n1 | \n30 | \n10 | \n
13 | \n13.2553 | \n0 | \n60 | \n−15 | \n1.06 | \n100 | \n1 | \n40 | \n12 | \n
Generator data of IEEE 30 bus power system.
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