introduction

Life cycle energy, cost and externalities estimation from Small Hydropower plant in IndiaSUPERVISOR- PRESENTED BY- DR. VARUN GOEL JANMEJAY KUMAR Roll no - 12M334

DEPARTMENT OF MECHANICAL ENGINEERINGNATIONAL INSTITUTE OF TECHNOLOGYHAMIRPUR (H.P.) 177005, INDIAContentsIntroductionSmall hydropower plant Literature reviewObjectivesMethodologyReferences

Introduction Most of the energy we use today comes from fossil fuels. Coal, oil and natural gas are all fossil fuels created several millions of years before by the decay of plants and animals. These fuels lie buried between layers of earth and rock. While fossil fuels are still being created today by underground heat and pressure, they are being consumed more rapidly than they are created. For that reason, fossil fuels are considered as non-renewable. Moreover burning fossil fuels leads to pollution and many environmental impacts.Introduction cont.. The increased generation and consumption of electricity from conventional sources are likely to result in adverse consequences for the environment and human health. So ,we need to use sources of energy that will last forever. These sources are called renewable, as they can be used repeatedly. Renewable energy systems use resources that are constantly replaced and are less polluting. All renewable energy sources solar, hydropower, biomass and wind have their origins in activities of the sun.Introduction cont Hydropower energy is mainly in competition with thermal as a source of energy. Because of falling prices on the electro-mechanical equipment during recent years, small hydropower plants are coming under increasing pressure for development. The contribution of small hydropower (SHP) in total hydropower installed capacity is about 5% with 34,000 MWSmall Hydropower Hydroelectric generation of electricity (hydropower) is commonly thought of as renewable , sustainable and lacking in of atmospheric pollutants. Small hydrois the development of hydroelectric power on a scale serving a small community or industrial plant. The criterion generally adopted to define the type of hydropower plant is based on installed capacity as follows . Class Station Capacity in (kW) Micro hydropower plants: Up to 100 Mini hydropower plants: 101 to 2000 Small hydropower plants: 2001 to 25000 Large hydropower plants: >25,000

Small hydropower plants can be classified according to their function and based on source of water asrun-of-river schemescanal-based schemes dam- toe schemes

Run -of river SHP schemes Run-of-river plants are those that utilize the instantaneous river flow, having no reservoir at its upstream head works . A weir is constructed across the river simply to raise the water level slightly and divert water into a conductor system for power generation. The difference between run-of-river and traditional hydro power generation is that a run-of-river project does not require a large reservoir .The main structure of a run-of-river plant is simply to redirect water flow from a weir towards the penstock , which feeds the water downhill to the power station. After this process, the water is redirected back to the natural flow of the riverCanal based SHP schemes

Canal-based small hydropower schemes are planned to generate power by utilizing the flow and fall in the canal. These schemes may be planned in the canal itself or in the by-pass channel. These are low head and high discharge schemes. These schemes are associated with advantages such as low gestation period, simple layout, no submergence and rehabilitation problems, and practically no environmental problems. Low-head hydropower stations involve handling of large quantity of water, thus the size of equipment is bigger and the costs are high.

Dam toe based SHP schemes Dam-based schemes are those in which water is store in the river by constructing a dam across the river and power is generated by controlled flow from the storage .In dam-toe schemes, the intake system forms part of the main dam and the powerhouse is constructed at the toe of the dam. Water is conveyed to the turbine through penstocks installed directly through the body of the dam. Such schemes are common in southern India. Such schemes utilize the head created by the dam.

Literature reviewS.K. Singal et al. (2008), In the present paper an attempt has been made to determine the correlations for the cost of different components of canal based SHP schemes. The cost based on the developed correlations, having different head and capacity, has been compared with the available cost data of the existing hydropower stations. It has been found that these correlations can be used reasonably for the estimation of cost of new canal-based SHP schemes.B. Ogayar et al (2009), in this paper Cost determination of the electro-mechanical equipment of a small hydro-power plant has been done and he shows that the obtained equations have been validated with data from real installations which have been subject to analysis by engineering companies working on the assembly and design of small plantsB. Ogayar et al (2009), in this paper Analysis of the cost for the refurbishment of small hydropower plants and a series of simple equations has been developed based on the economic optimization of the different elements. These equations can also be used for completely new hydropower plants. The result of this study will allow us to obtain quite approximate costs for the refurbishment of old hydropower plants, or the construction of new ones.G.A. Aggidis et al (2010),in this paper Empirical formulae to estimate the cost of electro-mechanical equipment and the costs of different types of turbines were developed through statistical analysis of cost data obtained from a range of turbine manufacturers . The derived results were compared to the results obtained from using other methodologies and were found to provide more realistic cost estimates.Varun et al.(2012), in this paper an attempt has been made to develop life cycle GHG emissions correlations for three different types of small hydropower schemes in India .It has been found out that GHG emissions depend on the head and capacity of the small hydropower projectA. Nishimura et al.(2010), In this study, the environmental load of photovoltaic power generation system (PV) during its life cycle and energy payback time (EPT) are evaluated by LCA scheme.L. Lu et al.(2010), This paper reports the investigation results of the energy payback time (EPBT) and greenhouse-gas payback time (GPBT) of a rooftop PV system are evaluated by LCA schemesD. Weibach et al.(2013), In this paper, The energy returned on invested (EROI), has been evaluated for typical power plants representing wind energy, photovoltaic, solar thermal, hydro, natural gas, biogas, coal and nuclear power.Ray Galvin et al.(2012), This paper offers a method for incorporating a factor for fuel price elasticity into models for assessing the net present value and payback time of thermal retrofits of existing homes.

Scott W. White et al.(2000), Birth to death analysis of the energy payback ratio and CO2 gas emission rates from coal, fission, wind, and DT-fusion electrical power plants

ObjectivesTo Estimation Life Cycle Acidification(SO2) and obtaining the correlations for SO2 gas emissions from Small Hydropower Schemes in IndiaTo calculate energy payback time and obtaining the correlation for Small Hydropower Schemes in India.To calculate the life cycle cost for small hydropower Schemes in India. To develop the correlation between life cycle cost and life cycle Green House Gases emissions for small hydropower plant in India.

MethodologyLife Cycle Assessment (LCA) is a tool for evaluating the environmental impacts of a product/system (project) through its entire life span, usually from raw material extraction to its final disposal (cradle to grave).An Economic input output (EIO) based model of US economy is maintained by the Green Design Institute at Carnegie Mellon University (CMU). The process model approach is used to account for various toxic releases and energy usage associated with the manufacturing of major construction materials and electro-mechanical equipment used in various projects. Since no EIO-LCA model has been developed for India hence the Carnegie Mellon EIO-LCA model (US Dept. of Commerce 2002 Industry Benchmark) is used in the present study to estimate acidification potential of SHP schemes in India.

The cost estimates of these projects pertain to different years. These costs are inflation adjusted and expressed in Indian Rupees for the year 2004-05. Further the costs are converted into equivalent US dollars in that year (2004). Further US dollar has been adjusted for the year 2002 by using the inflation index of US and this model has been used in various LCA studies

In this study, functional unit is considered to be 1kWh of net electricity produced (kWhe). Acidification potential are normalised to an equivalent of SO2 (g) emissions per kWh of net electricity production based general acidification equivalents expressed relative to SO2 .

Correlation development for run-of river SHP schemes The regression analysis is used to develop statistical correlation of the data obtained by using EIO-LCA methodology. Fig. 1 shows a plot of SO2 emissions (g-SO2eq/kWhe) as a function of capacity (kW) for head ranging from 10 to 90 m. The power law relation between SO2 emission and capacity is shown below SO2 emissions =A0C-0.39The coefficient A0 will depend on other parameters. The other parameter is considered in the study is head. The value of SO2 emissions /capacity -0.39 has been plotted against the head which is given in Fig. 2. Finally the following correlation for SO2 emissions (for head range 10-90 m) is obtained as; SO2 emissions/C-0.39=2.286H0.046 SO2 emissions =2.286H0.046C-0.39

Fig 1: Variation of SO2 emissions with capacity for head range 10 - 90 m for run-of river schemes

Fig 2: Variation of SO2 emissions/capacity-0.39 with head for run-of river SHP schemes

18A similar method is used for the development of SO2 emissions for head ranging from 90 to 430 m. Fig. 3 shows a plot of SO2 emissions (g-SO2eq/kWhe) as a function of capacity (kW) . The power law relation between SO2 emission and capacity is shown below.SO2 emissions =B0C-0.36 The coefficient B0 will depend on other parameter head of the system. The value of SO2 emissions /capacity -0.36 has been plotted against the head in Fig. 4 and obtained the resultant correlation as follows.SO2 emissions/C-0.36=1.628H0.03SO2 emissions =1.628H0.03C-0.36

Fig 3: Variation of SO2 emissions with capacity for head range 90 - 430 m for run-of river schemesFig 4: Variation of SO2 emissions /capacity -0.36 with head for run of river schemesCorrelation development for canal based SHP schemesThe regression analysis is used to developed statistical correlation of the data obtained by using EIO-LCA methodology. Fig. 5 shows a plot of SO2 emissions (g-SO2eq/ kWhe) as a function of capacity (kW) for head ranging from 1 to 7 m. The power law relation between SO2 emissions and capacity is given below, SO2 emissions =A0C-0.17The coefficient A0 will depend on any other parameters. The other parameter is considered as head. The value of SO2 emissions /capacity -0.17 has been plotted against the head which is shown in Fig. 6. SO2 emissions/C-0.17=0.495H-0.17 SO2 emissions =0.495H-0.17C-0.17

Fig 5: Variation of SO2 emissions with capacity for head range 1 -7 m for canal based SHP schemesFig 6: Variation of SO2 emissions /capacity-0.17 with head for canal based SHP schemesA similar method is used for the development of SO2 emissions for head ranging from 7 to 25 m. Fig. 7 shows a plot of SO2 emissions (g-SO2eq/ kWhe) as a function of capacity (kW). The power law relation between SO2 emissions and capacity is shown belowSO2 emissions =B0C-0.14The coefficient B0 will depend on any other parameter. The other parameter is considered as head. The value of SO2 emissions / capacity -0.14 has been plotted against the head in Fig. 8.SO2 emissions/C-0.14=0.231H0.061SO2 emissions =0.231H0.061C-0.14

Fig 7: Variation of SO2 emissions with capacity for head range 7 -25 m for canal based SHP schemesFig 8: Variation of SO2 emissions/capacity-0.14 with head for canal based SHP schemes

The regression analysis is used to develop correlations for predicting life cycle SO2 emissions from dam-toe SHP schemes using EIO-LCA methodology. Fig. 9 shows a plot of SO2 emissions (g-SO2eq / kWhe) as a function of capacity (kW) for head ranging from 10 to 40 m. The power law relation between SO2 emissions and capacity is shown below SO2 emissions =A0C-0.19The coefficient A0 will depend on any other parameter. The other parameter is considered as head. The value of SO2 emissions /capacity -0.19 has been plotted against the head in Fig. 10.

SO2 emissions/C-0.19=0.554H-0.09SO2 emissions =0.554H-0.09C-0.19

Fig 9: Variation of SO2 emissions with capacity for head range 10 - 40 m for dam-toe SHP schemesFig 10: Variation of SO2 emissions /capacity-0.19 with head for dam-toe SHP schemesA similar method is used for the development of SO2 emissions for head ranging from 40 to 258 m. Fig. 11 shows a plot of SO2 emissions (g-SO2eq / kWhe) as a function of capacity (kW). The power law relation between SO2 emissions and capacity is shown below.SO2 emissions =B0C-0.14The coefficient B0 will depend on any other parameter. The other parameter is considered as head. The value of SO2 emissions /capacity -0.14 has been plotted against the head in Fig. 12. Finally the following correlation for SO2 emissions (for head range 40-258 m) is obtained as SO2 emissions/C-0.14=0.454H0.033SO2 emissions =0.454H0.033C-0.14

Fig 11: Variation of SO2 emissions with capacity for head range 40-300 m for dam-toe schemesFig 12: Variation of SO2 emissions/capacity-0.14 with head for dam-toe schemes.Discussion and comparisonFrom the correlations it is clear that the life cycle SO2 emissions (g-SO2eq/kWhe) decrease with the increase in capacity for all type (run-off river, canal based and dam-toe) of SHP schemes. It is due to the fact that with the increase in capacity of SHP plant all the resource requirement for the generation of 1 kWh of electricity is reduced in comparison with the SHP plant which is having small capacity. It is similar to the generation cost of 1 kWh of electricity that is more in small capacity plants as compared with big capacity plants. However, SO2 gas emissions also depends upon capacity and the type of SHP schemes as well as the range of head. For all the type of SHP schemes at high head, with the increase in head, SO2 emission increases. However, for lower head schemes of canal based and dam-toe, the SO2 emissions decrease with the increase in head. But for run-of river schemes, the SO2 emission increases with the increase in head. This is due to the fact that with the increase in head, the civil work component increases; and civil work components (penstock, construction etc.) being highly energy intensive cause an increase in SO2 emission. Energy Payback TimeEnergy payback timemeans the length of time that device will take to produce that same amount of energy that was used to make it and is defined as EPBT = Einput/Esaved where Einputis the energy input during the module life cycle (which includes the energy requirement for manufacturing, installation, energy use during operation, and energy needed for decommissioning) and Esavedthe annual energy savings due to electricity generated by the Hydropower Schemes. Here thermal energy must be converted into electrical energy and hence conversion efficiency is used.

The regression analysis is used to developed statistical correlation of the data obtained by using EIO-LCA methodology. Fig. 13 shows a plot of Energy payback time(EPBT) as a function of capacity (kW) for head ranging from 10 to 90 m. The power law relation between Energy payback time and capacity is shown below Energy payback time =A0C-0.10The coefficient A0 will depend on other parameters. The other parameter is considered in the study is head. The value of EPBT /capacity -0.10 has been plotted against the head which is given in Fig. 14. Finally the following correlation for EPBT (for head range 10-90 m) is obtained as; EPBT/C-0.10=3.905H0.053 EPBT =3.905H0.053C-0.10Correlation development for run-of river SHP schemesFig 13: Variation of EPBT with capacity for Run of river schemes for head ranging (10-90m)Fig 14: Variation of EPBT /capacity-0.10 with head for run of river schemesA similar method is used for the development of EPBT for head ranging from 90 to 430 m. Fig. 15 shows a plot of EPBT as a function of capacity (kW) . The power law relation between EPBT and capacity is shown below.Energy Payback Time =B0C-0.08 The coefficient B0 will depend on other parameter head of the system. The value of EPBT /capacity -0.08 has been plotted against the head in Fig. 16 and obtained the resultant correlation as follows. Energy Payback Time /C-0.08=4.985H-0.02

Energy Payback Time =4.985H-0.02C-0.08

Fig 15: Variation of EPBT with capacity for Run of river schemes for head ranging (90-430m)Fig 16: Variation of EPBT /capacity-0.08 with head for run of river schemesCorrelation development for Canal based SHP schemesThe regression analysis is used to developed statistical correlation of the data obtained by using EIO-LCA methodology. Fig. 17 shows a plot of Energy payback time(EPBT) as a function of capacity (kW) for head ranging from 1 to 7 m. The power law relation between Energy payback time and capacity is shown below Energy payback time =A0C0.773The coefficient A0 will depend on other parameters. The other parameter is considered in the study is head. The value of EPBT /capacity 0.773 has been plotted against the head which is given in Fig. 18. Finally the following correlation for EPBT (for head range 10-90 m) is obtained as; EPBT/C0.773=0.008H-0.05 EPBT =0.008H-0.05C0.773Fig 17: Variation of EPBT with capacity for Canal based schemes for head ranging (1-7m)Fig 18: Variation of EPBT /capacity0.773 with head for Canal based schemesA similar method is used for the development of EPBT for head ranging from 7 to 25 m. Fig. 19 shows a plot of EPBT as a function of capacity (kW) . The power law relation between EPBT and capacity is shown below.Energy Payback Time =B0C0.832The coefficient B0 will depend on other parameter head of the system. The value of EPBT /capacity 0.832 has been plotted against the head in Fig. 20 and obtained the resultant correlation as follows. Energy Payback Time /C0.832=0.002H0.083 Energy Payback Time =0.002H0.083C0.832

Fig 19: Variation of EPBT with capacity for Canal based schemes for head ranging (7-25m)Fig 20: Variation of EPBT /capacity0.832 with head for Canal based schemesCorrelation development for Dam toe SHP schemesThe regression analysis is used to developed statistical correlation of the data obtained by using EIO-LCA methodology. Fig. 21 shows a plot of Energy payback time(EPBT) as a function of capacity (kW) for head ranging from 10 to 40 m. The power law relation between Energy payback time and capacity is shown below Energy payback time =A0C-0.19The coefficient A0 will depend on other parameters. The other parameter is considered in the study is head. The value of EPBT /capacity-0.19 has been plotted against the head which is given in Fig. 22. Finally the following correlation for EPBT (for head range 10-40 m) is obtained as; EPBT/C-0.19=4.169H-0.04 EPBT =4.169H-0.04C-0.19Fig 21: Variation of EPBT with capacity for Dam-toe schemes for head ranging (10-40m)Fig 22: Variation of EPBT /capacity-0.19 with head for Dam-toe schemesA similar method is used for the development of EPBT for head ranging from 40 to 258 m. Fig. 23 shows a plot of EPBT as a function of capacity (kW) . The power law relation between EPBT and capacity is shown below.Energy Payback Time =B0C-0.02The coefficient B0 will depend on other parameter head of the system. The value of EPBT /capacity-0.02 has been plotted against the head in Fig. 24 and obtained the resultant correlation as follows. Energy Payback Time /C-0.02=1.841H0.088 Energy Payback Time =1.841H0.088C-0.02

Fig 21: Variation of EPBT with capacity for Dam-toe schemes for head ranging (40-258m)Fig 24: Variation of EPBT /capacity-0.02 with head forDam-toe based schemesRefrences1.Central Electricity Authority, 1982. Guidelines for development of small hydro-electric schemes, Government of India, New Delhi. Berresheim H, Wine PH, and Davies DD (1995). "Sulfur in the Atmosphere". In Composition, Chemistry and Climate of the Atmophere, ed. H.B. Singh. Van Nostrand Rheingold3.Zhang Q, Karney B, Maclean HL, Feng J. Life-cycle inventory of energy use and greenhouse gas emissions for two hydropower projects in China. Journal of Infrastructure Systems ASCE 2007;13(4):271-9.4.S.K. Singal , R.P. Saini, Analytical approach for development of correlations for cost of canal-based SHP schemes, Renewable Energy 33 (2008) 2549 2558 , Roorkee5.Yang YH, Lin SJ, Lewis C, Reduction of acidification from electricity - Generating industries in Taiwan by Life Cycle Assessment and Monte Carlo optimization, Ecological Economics, 68 (2009) 15751582.6.B. Ogayar, P.G. Vidal, Cost determination of the electro-mechanical equipment of a small hydro-power plant, Renewable Energy 34 (2009) 613,spainRefrences7.B. Ogayar, P.G. Vidal, J.C. Hernandez, Analysis of the cost for the refurbishment of small hydropower plants, Renewable Energy 34 (2009) 25012509, spain 8.G.A. Aggidis , E. Luchinskaya , R. Rothschild , D.C. Howard, The costs of small-scale hydro power production: Impact on the development of existing potential, Renewable Energy 35 (2010) 2632-2638, Lancaster9. Tang Y, Ma X, Lai Z, Zhou D, Lin H, Chen Y, NOx and SO2 emissions from municipal solid waste (MSW) combustion in CO2/O2 atmosphere, Energy 2012;40:300-306.Nazari S, Shahhoseini O, Sohrabi-Kashani A, Davari S, Sahabi H, Rezaeian A, SO2 pollution of heavy oil-fired steam power plants in Iran, Energy Policy 2012;43:456465.Varun ,Ravi prakash ,I.K.Bhat,life cycle greenhouse gas emissions estimation for small hydropower schemes in india, Energy 44(2012)498-50812.Carnegie Mellon University Green Design Institute (CMU-Green Design Institute). Economic input-output life cycle assessment (EIO-LCA) model, June 2012.

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