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An optimal renewable energy model for various end-uses
Energy 25 (2000) 563–575 www.elsevier.com/locate/energy
An optimal renewable energy model for various end-uses S. Iniyan, K. Sumathy
*
Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road, Hong Kong Received 16 July 1998
Abstract Renewable energy sources are likely to play a significant role in meeting the future energy requirement of a developing country like India. The effect of introducing renewable energy sources on the commercial energy scene may have to be analyzed carefully. In this paper, we present an Optimal Renewable Energy Model that minimizes the cost/efficiency ratio and determines the optimum allocation of different renewable energy sources for various end-uses. The potential of renewable energy sources, energy demand, reliability of renewable energy systems and their acceptance level will determine the pattern of renewable energy distribution and are used as constraints in the model. The model allocates the renewable energy distribution pattern for the year 2020–21 in India, which would be helpful for policy makers in commercializing the renewable energy sources to the greatest extent. The results indicate that solar energy systems can be utilized for lighting, pumping, heating and cooling to an extent of 6%, 16%, 2%, and 12% of total renewable energy demand in India, respectively. Similarly, the bio-energy systems can be utilized 9% of lighting, 18% of cooking, 1% of pumping, 17% of heating, and 14% of transportation of total renewable energy demand. It is also observed that wind energy can be utilized for pumping end-use to an extent of 4% of total renewable energy demand. The scenario for different potential limits is presented in this paper. A sensitivity analysis has been also carried out to validate the model. 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction The area of renewable energy sources is expanding rapidly and numerous innovations and applications are taking place. The decentralized renewable energy systems concept has been recognized as an answer to meeting the energy demands both in the household and in the agro–industrial sector. The depletion of natural resources and the accelerated demand of conventional energy have forced planners and policy makers to look for alternate sources. India has a huge potential * Corresponding author. Tel.: +852-2859-2632; fax: +852-2858-5415. E-mail address: [email protected] (K. Sumathy).
0360-5442/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 6 0 - 5 4 4 2 ( 9 9 ) 0 0 0 9 0 - 0
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in renewable energy sources (Table 1). Significant amounts of electric power, heat, mechanical energy and, in some cases, transportation energy are already being supplied by renewable energy sources. An energy model would facilitate the effective utilization of renewable energy sources in India. Presently, even though commercial energy sources like coal, oil and electricity are being utilized to a large extent, renewable sources of energy are slowly gaining importance. Natarajan [1] has discussed several important issues in energy policy and planning for the future with particular reference to developing countries. Rong-Hwa Wu and Chia-Yon Chen [2] have analyzed energy issues in the short-run for Taiwan using a static input–output (I–O) framework. Also, a multicriteria evaluation method [3] was employed to comprehensively evaluate the alternatives for new energy system development in Taiwan. Alam et al. [4] have discussed the physical quality of life as a function of per capita electrical energy consumption in their mathematical model. In Ethiopia, energy utilization patterns in three factories—cement production, textile manufacturing and food processing—were discussed by Wolde-Ghiorgis [5] using the Industrial Energy Utilization Model. Edmonds and Reilly [6] developed a long-term global energy-economic model of carbon dioxide (CO2) release from fossil fuel utilization. Subsequently, they have analyzed the impact on alternative energy evolutions (other than commercial) over the next 100 years [7]. The impact of atmospheric CO2 emission-reduction strategies was estimated using the coupled climate–carbon cycle model [8]. Energy systems, which significantly reduce emissions of acidifying gases and CO2 from non-mobile sources, were identified for Western Scania using an end-use accounting model [9]. The continuing depletion of fossil fuels and the environmental hazards posed by the needs of future development are gradually shifting the path of development towards sustainability, better sociability, and environmental responsibility, which in turn emphasizes the need for renewable energy sources. The 1980s witnessed changes in industrial policy which led to an unprecedented rise in the commercial energy consumption. Changes in foreign policies (e.g. the Gulf war and the breakdown of the Soviet Union) were seen to have affected the energy situation in India. Oil prices are currently very high. In order to reduce the continuous drain of India’s foreign exchange reserve due to oil import, the prospect of partial substitution of the country’s conventional energy by renewable energy should be analysed and discussed. In this paper, we present an Optimal Renewable Energy Model (OREM) to give an optimum allocation of renewable energy sources in India for the year 2020–21. We propose and discuss different scenarios to best use the available resources to reduce the emission levels, and to improve the quality of life in India. Table 1 Potential of renewable energy in India Sources
Potential
Solar energy Wind energy Biomass electricity Fuel wood Biogas Ethanol (sugarcane molasses) Ethanol (rice straw)
5×1015 kWhr/year 20000 MW 17000 MW 218.5 Mt/year 89 million m3 3000 million l/year 22.5 Mt
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2. Optimal renewable energy model The renewable energy flow diagram (Fig. 1) shows the various sources and the corresponding renewable energy systems for different end-uses such as lighting, cooking, pumping, heating, cooling and transportation. Thirty-eight possible energy options were considered in the analysis. The schematic representation of the Optimal Renewable Energy Model (OREM) is shown in Fig. 2. A Delphi study was conducted to identify the critical factors in renewable energy utilization in the Indian context. The study revealed that cost and efficiency are highly critical factors in the utilization of renewable energy sources, and that factors such as technology, availability and reliability should be considered in order to select the appropriate renewable energy systems for different end-uses. For optimization, the cost/efficiency ratio was chosen as the objective function and minimization of the function was carried out. The ultimate aim was to select systems with low cost and high efficiency. The following are the unit costs of energy used in the optimization
Fig. 1. Schematic representation of renewable energy options for different end-uses.
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Fig. 2. Schematic representation of optimal renewable energy model (OREM).
model. The costs of solar thermal electric conversion and solar photovoltaic electric conversion were estimated to be Rs. 11.92/kWhr and Rs. 11.70/kWhr, respectively [10]; the approved costs of solar cookers were in the range of Rs. 800–1000 and the installed cost of hot water systems (delivering water at about 60°C) was about Rs. 65–75 litre/day [11]; the cost of wind energy electric conversion was estimated to be Rs. 1.75–2.25/kWhr [12]; and the costs of biomass gasifier electric conversion and biogas electric conversion systems, widely installed in India by the Ministry of Non-Conventional Energy Sources, were estimated to be Rs. 2.50/kWhr and Rs. 1.25/kWhr, respectively. The different efficiencies of renewable energy systems substituted in the model are: 14% for solar thermal electric conversion, 13–15% for solar photovoltaic system, 35–45% for solar direct thermal, 25–30% for wind energy electric conversion system [13], 18% for biomass gasifier electric conversion system, 22–26% for biogas electric conversion system, 12–14% for biomass direct combustion, and 33–35% for solar cooker [14]. The social acceptance factor for each renewable energy option was obtained from Delphi results. The Delphi questionnaire was analyzed to determine (i) change in the responses from the first round to the second round, (ii) decrease in uncertainty, and (iii) arrival at a consensus. Mean, Standard Deviation, Confidence, and Stability of the participants in answering the questions were determined. In 97.9% of the cases, a consensus was arrived. Similarly, the group stability and individual stability were found in 100% and 95% of the cases, respectively. This shows that the respondents did not vary much in answering the questionnaire and hence the results directly reflect the realistic scenario for future energy utilization pattern in India. The respondents opinion indicated that solar, wind, biomass, and commercial energy sources would be utilized to an extent of 7.12, 7.9, 10.49 and 74.49% of the total, respectively. Detailed analysis of Delphi study was given in an earlier paper [15]. Social acceptance was used as one of the constraints in the OREM. The energy demand was predicted for the years 2020–21 by using the two-stage least square
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forecasting method. The renewable energy demand for different end-uses was determined by comparing it against the social acceptance level and was used as demand constraint in the OREM. Even though the potential sources of solar, wind, and biomass energy are abundant in India, factors such as the quality of the resources, intermittent nature, and technical feasibility would determine the amount of renewable energy to be used. In the OREM, these factors are considered potential constraints. A failure analysis was done to find out the reliability factor of the renewable energy system and the factor was taken as reliability constraint in the OREM for reliable power supply. The reliability factor of 0.1 at 10,000 hours for solar photovoltaic system, 0.5 at 10,000 hours for wind energy system, and 0.9 at 10,000 hours for biomass energy system were used in the model. A detailed analysis of reliability analysis was given in an earlier paper [16]. The calculated reliability index for renewable energy systems was used to formulate the reliability constraint equation. The mathematical representation of the OREM is given in the following equations:
冘冘 6
Minimize
l
(Cij /hij )Xij
(1)
冘 冋冘
(2)
(Xij )ⱕDj
(3)
j⫽1i⫽1
Subject to
6
l
(Xij /Sij )ⱕDj
Social acceptance
j⫽1 i⫽1
冘冋冘 冘冋冘 冘冋 冘 6
Demand
l
j⫽1 i⫽1 6
m
(Xik)ⱕPk
Potential limit
(4)
k⫽1 i⫽1
3
Reliability
m
(1/Rk)
k⫽1
(Xik)ⱕPk
(5)
i⫽1
where, C=unit cost of the system, h=efficiency of the system, X=quantum of renewable energy, D=renewable energy demand, S=social acceptance level, P=potential, i=renewable energy system, j=end-use, k=resource, l=number of systems in respective end-use (l=5 for lighting, l=7 for cooking, l=7 for pumping, l=6 for heating, l=6 for cooling, l=7 for transportation), m=number of systems in respective resource (m=16 for solar, m=6 for wind, m=6 for biomass, m=2 for fuelwood, m=6 for biogas, m=2 for ethanol). The model has been run for the renewable energy distribution in India for the year 2020–21 for different end-uses. 3. Results and discussion The OREM gives the optimum allocation for renewable energy sources in India for the year 2020–21 (Fig. 3). It predicts that approximately a quarter of India’s total energy consumption
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Fig. 3. Optimal renewable energy distribution pattern for year 2020–21 in India.
(2257.5 billion units of electricity equivalent) would come from renewable energy sources. At the optimal condition, for lighting, solar PV and biogas electricity conversion amount to 0.52×1015 kJ (6% of total renewable energy demand) and 0.75×1015 kJ (9% of total renewable energy demand), respectively. For cooking, biomass direct combustion through improved chulas could provide 1.49×1015 kJ (18% of the total renewable energy demand); for pumping, solar PV, wind energy, and biomass gasifier could supply 1.34×1015 kJ (16%), 0.31×1015 kJ (4%), and 0.05×1015 kJ (1%), respectively; for heating solar thermal and biomass direct combustion could produce 0.11×1015 kJ (2%), and 1.38×1015 kJ (17%), respectively; for cooling, solar thermal could account 1.02×1015 kJ (12%); and for transportation, biomass gasifier and ethanol fueled engine could generate 0.48×1015 kJ (6%), and 0.67×1015 kJ (8%), respectively. 3.1. Effect of the variation of renewable energy potentials The renewable energy potentials could not be fully commercialized in the immediate future. Scenarios of profitable exploition of these sources were developed in our study. The utilization rate of the wind energy potential was varied from 25%, 50%, 75% to 100% in the OREM and its distribution pattern is shown in Fig. 4. Even though the wind energy system is selected only for the end-use of pumping in the model, the variation of wind energy potential affected the distribution patterns of other end-uses such as lighting and transportation. For example, for the end-use of lighting, if 25%, 50% or 75% of wind energy potential is considered in the model, then solar PV, biomass gasifier, and biogas conversion could be used to the extent of 0.65×1015 kJ, 0.49×1015 kJ and 0.14×1015 kJ, respectively; but if 100% of wind energy potential is considered, solar PV and biogas electricity conversion alone could be used to an extent of 0.52×1015 kJ and 0.75×1015 kJ for lighting. Similarly, for the end-use of pumping, when 25% of wind energy is considered in the model, the energy demand could be shared by solar PV (1.63×1015 kJ) and wind energy (0.08×1015 kJ); if 50% of wind energy is used, its contribution would increase to
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Fig. 4.
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Renewable energy distribution pattern for various end-uses for year 2020–21 by varying wind energy potential.
0.16×1015 kJ (i.e., by 100%) and the solar PV contribution would decrease to 1.55×1015 kJ (i.e., by 5%); if 75% of wind energy is used, then its contribution would be 0.24×1015 kJ (i.e., a 200% increase) and solar PV contribution would be 1.48×1015 kJ (i.e., a 10% decrease). In the case of transportation, biogas and biomass gasifiers could be used to supply 0.54×1015 kJ and 0.61×1015 kJ, respectively, for the variation of 25%, 50% and 75% potential of wind energy. If 100% wind energy potential is considered in the model the biomass gasifier and ethanol could be used to an extent of 0.48×1015 kJ and 0.67×1015 kJ, respectively. The utilization rate of the biomass electric generation potential was varied in the OREM from 25% to 100% and the distribution pattern is shown in Fig. 5. In the case of lighting, the contribution of biomass gasifier electricity generation would be 0.12×1015 kJ, 0.24×1015 kJ and 0.37×1015 kJ when 25%, 50% and 75% of biomass electric generation potential is considered; when this potential is increased from 25% to 50% and 75%, the contribution of solar PV would decrease by 12% and 24%, respectively. In the case of pumping and transportation, when 100% of biomass electric generation potential is considered, the biomass gasifier could be introduced to an extent of 0.05×1015 kJ and 0.483×1015 kJ, respectively. Also, the introduction of 100% biomass potential would reduce solar PV by 4% for pumping and eliminate the necessity of using biogas for transportation. The biogas energy potential is varied from 25% to 100% and the distribution pattern is shown in Fig. 6. Variations in distribution pattern were found in lighting, pumping, and transportation. In the case of lighting, biogas could be used to an extent of 0.75×1015 kJ if 100% of its potential
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Fig. 5. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying potential of biomass electricity generation.
is considered in the model. Even though biogas is not preferred for the end-use of pumping, the variation of biogas potential affects the distribution pattern of pumping; Solar PV and wind energy system could be used to an extent of 1.4×1015 kJ and 0.32×1015 kJ, respectively, when 75% of biogas potential is considered; and biomass gasifier could be used to an extent of 0.05×1015 kJ when 100% of biogas potential is considered. For transportation, when the potential of biogas is increased from 25% to 50% and 75%, the contribution of biogas would vary from 0.17×1015kJ to 0.37×1015 kJ and 0.51×1015 kJ, respectively, and the contribution of biomass gasifier system would decrease by 55% and 91%, respectively. The firewood potential is varied from 25% to 100% and the renewable energy distribution pattern is shown in Fig. 7. In the case of lighting, solar PV, biomass gasifier electric conversion, and biogas electric conversion could be used to an extent of 0.65×1015 kJ, 0.49×1015 kJ and 0.14×1015 kJ, respectively, and this distribution pattern does not change for up to 75% of firewood potential. However, when 100% of firewood potential is used, biogas could be used to an extent of 0.75×1015 kJ, which reduces the contribution of solar PV by 20% and eliminates the entire biomass gasifier utilization. In the case of cooking, when 25% of firewood potential is utilized, the energy demand would be shared by firewood and solar cooker to an extent of 0.99×1015 kJ and 0.5×1015 kJ, respectively; if 50% of firewood is used, the entire energy demand would be met by firewood alone. In the case of heating, if 25% of firewood is considered, then solar thermal would be used to an extent of 1.49×1015 kJ; if 50% is considered, which means that firewood
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Fig. 6. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying biogas potential.
could be used to an extent of 0.5×1015 kJ, then the use of solar thermal would be reduced by 34%; if 75% of firewood potential is used, then the entire energy demand for heating could be met solely by firewood. The potential of ethanol is varied from 25% to 100% in the OREM and the effect in the distribution is shown in Fig. 8. Even though ethanol is preferred in transportation, it affects the distribution patterns of lighting and pumping end-uses. When ethanol potential is varied up to 75%, solar PV and biomass gasifier would meet the lighting demand, but when it is varied to 100%, the biogas system would replace the biomass gasifier system. In the case of pumping, solar PV and wind energy system contribute when up to 75% of ethanol potential is considered; but when 100% ethanol is considered, biomass gasifier system could be introduced to an extent of 0.05×1015 kJ, which would reduce the solar PV by 4%. In the case of transportation, the ethanol could be used to an extent of 0.15×1015 kJ, 0.31×1015 kJ and 0.46×1015 kJ when 25%, 50% and 75% of ethanol potential is considered. The increase of ethanol potential from 25% to 50% and 75% would reduce the biomass gasifier utilization by 47% and 95%, respectively; the utilization of 100% ethanol potential would eliminate the need to use biogas. 3.2. Effect of absence of potential constraint in different end-uses The effect of the removal of potential constraint from the OREM was analyzed and the results are shown in Fig. 9. In the case of lighting, wind energy electric conversion would be used to
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Fig. 7. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying firewood potential.
an extent of 0.32×1015 kJ instead of solar PV system; and the contribution from biogas electric conversion system would be increased to by 27% to 0.95×1015 kJ. In the case of cooking, energy demand would be met by solar cooker instead of biomass direct combustion, to an extent of 1.5×1015 kJ. For pumping, ethanol fueled system, instead of solar PV, wind energy system or biomass gasifier systems, would be used to an extent of 1.71×1015 kJ. In the case of heating, solar thermal alone would meet the energy requirement of 1.48×1015 kJ. For cooling, biogas electric generation would replace solar thermal, providing 1.00×1015 kJ. For transportation, ethanol-fueled system would meet the entire energy requirement, supplying 1.15×1015 kJ. Our results reveal that the utilization of ethanol and biogas would exceed their potential limits, if the potential constraints in the model were excluded. Therefore it is necessary to include the potential constraint in the OREM to obtain a realistic distribution pattern. 3.3. Sensitivity analysis of OREM by varying potential Sensitivity analysis was also carried out to validate the OREM. The potential of renewable energy sources was increased by 1%, 5% and 10% to test the sensitiveness of the model. When the potential of all renewables was increased by 1%, the coefficient of sensitivity was 0.0067 (0.67%). When each of potentials of wind, biomass, biogas, firewood and ethanol was raised one at a time by 1%, each respectively registered a value of around 0.0016 (0.16%). It reveals that OREM is sensitive even with a 1% variation of potential. When the potential was further increased
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Fig. 8. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying ethanol potential.
by 5% for all renewables, the coefficient of sensitivity became 0.0402 (4.02%). When the potentials of wind, biomass, biogas, and ethanol were raised individually by 5%, the coefficient of sensitivity for each was 0.005 (0.5%), 0.0083 (0.83%), 0.0134 (1.34%), and 0.0117 (1.17%), respectively. In the case of 10% increase in all potentials, the coefficient of sensitivity was 0.0805 (8.05%). A 10% increase in wind and biomass potential yielded sensitivity coefficients of 0.01 (1%) and 0.0158 (1.58%), respectively. With a 10% increase in biogas and ethanol potentials, the corresponding sensitivity coefficients would be the same, 0.0252 (2.52%). Similarly, the sensitivity analysis was also carried out and the coefficients obtained when the potentials were decreased by of 1%, 5% and 10%. The analysis reveals that the OREM is very sensitive to variations in the potentials of renewable energy sources. 4. Conclusions The OREM was developed for renewable energy allocation in India for the year 2020–21. The model is formulated with the objective of minimizing the cost/efficiency ratio and the constraints of social acceptance, the reliability factor of renewable energy systems, potentials of renewable energy and energy demand for different end-uses. The renewable energy contribution would be 8.13×1015 kJ, about 25% of the total energy demand of India. The cost, efficiency, social acceptance, reliability, demand, and potential factors were used as input in the model and the results
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Fig. 9. Effect of absence of potential constraint for various end-uses.
were analyzed and discussed. The potentials of wind, biomass, biogas, firewood, and ethanol were varied from 25% to 100% in the model; in particular, the use of wind energy potential to a value of 50% would meet the growing demand of energy for pumping (0.16×1015 kJ). When the biomass potential is fully utilized, the biomass gasifier could be introduced for pumping and transportation end-uses to an extent of 0.05×1015 kJ and 0.48×1015 kJ, respectively. Similarly, for transportation, the increased use of the biogas potential from 25% to 50% would provide 0.17×1015kJ to 0.37×1015 kJ; and ethanol also plays a major role in meeting the future energy demand. The potential constraint in the OREM is essential for the optimal distribution pattern. If it is not considered in the model, the utilization of ethanol and biogas would exceed their potential limits. The stability and consensus analysis proved the validity of Delphi study, which is conducted to find out the social acceptance in the utilization of renewable energy sources. Our study shows that the OREM would facilitate effective utilization of the renewable energy sources in India. It is hoped that the model will help policy makers in the formulation and implementation of strategies of renewable sources for the next two decades. The sensitivity analysis proved the model to be highly sensitive to variation in the potentials of renewable energy sources. Other renewable energy sources, such as hydrogen, fuel cells, and geothermal, were not
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considered in the model because of the lack of data. Further research and development, cost reduction, and advanced technologies must be implemented in order to realize this projected renewable energy utilization scheme for the year 2020–21. References [1] Natarajan R. Some essential considerations in energy policy and planning for the future. Urja 1990;27(1):39–50. [2] Wu Rong-Hwa, Chen Chia-Yon. On the application of input–output analysis to energy issues. Energy Economics 1990:January:71–76. [3] Tzeng G-H, Shiau T-A, Lin C-Y. Application of multicriteria decision making to the evaluation of new energy system development in Taiwan. Energy 1992;17(10):983–92. [4] Alam MS, Bala BK, Huq AM, Matin MA. A model for the quality of life as a function of electrical energy consumption. Energy 1991;16(4):739–45. [5] Wolde-Ghiorgis W. Industrical energy utilization patterns in a developing country: A case study of selected industries in Ethiopia. Energy Convers Mgmt 1991;31(3):285–93. [6] Edmonds J, Reilly J. A long-term global energy-economic model of carbon dioxide release from fossil use. Energy Economics 1983:April:74-88. [7] Reilly J, Edmonds J. Global energy and carbon dioxide. IMACS 1985:245-250. [8] Danny HLD. Managing atmospheric CO2: Policy implications. Energy 1990;15(2):91–104. [9] Gustavsson L, Johansson B, Bulow-Hube H. An environmentally benign energy future for Western Scania, Sweden. Energy 1992;17(9):809–22. [10] Chandra Shekhar S. Renewable energy programmes in India. Natural Resources Forum 1994;18(3):213–24. [11] TERI. Fuelish trends and wise choices: Options for the future. New Delhi, India: Tata Energy Research Institute, 1992. [12] Gupta AK. Wind power development in India. Energy and Environment Monitor 1995;11(1):59–64. [13] WEC. Energy for tommorrow’s world. London: World Energy Council, St. Martins Press, 1993. [14] Rangarajan S. Wind energy potential in India. Energy and Environment Monitor 1995;11(1):1–12. [15] Iniyan S, Jagadeesan TR. A comparative study of critical factors influencing the renewable energy systems use in the Indian context. Renewable Energy 1997;11(3):299–317. [16] Iniyan S, Suganthi L, Jagadeesan TR. Critical analysis of wind farms for sustainable generation. Solar World Congress, Korea, 1997.
An optimal renewable energy model for various end-uses S. Iniyan, K. Sumathy
*
Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road, Hong Kong Received 16 July 1998
Abstract Renewable energy sources are likely to play a significant role in meeting the future energy requirement of a developing country like India. The effect of introducing renewable energy sources on the commercial energy scene may have to be analyzed carefully. In this paper, we present an Optimal Renewable Energy Model that minimizes the cost/efficiency ratio and determines the optimum allocation of different renewable energy sources for various end-uses. The potential of renewable energy sources, energy demand, reliability of renewable energy systems and their acceptance level will determine the pattern of renewable energy distribution and are used as constraints in the model. The model allocates the renewable energy distribution pattern for the year 2020–21 in India, which would be helpful for policy makers in commercializing the renewable energy sources to the greatest extent. The results indicate that solar energy systems can be utilized for lighting, pumping, heating and cooling to an extent of 6%, 16%, 2%, and 12% of total renewable energy demand in India, respectively. Similarly, the bio-energy systems can be utilized 9% of lighting, 18% of cooking, 1% of pumping, 17% of heating, and 14% of transportation of total renewable energy demand. It is also observed that wind energy can be utilized for pumping end-use to an extent of 4% of total renewable energy demand. The scenario for different potential limits is presented in this paper. A sensitivity analysis has been also carried out to validate the model. 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction The area of renewable energy sources is expanding rapidly and numerous innovations and applications are taking place. The decentralized renewable energy systems concept has been recognized as an answer to meeting the energy demands both in the household and in the agro–industrial sector. The depletion of natural resources and the accelerated demand of conventional energy have forced planners and policy makers to look for alternate sources. India has a huge potential * Corresponding author. Tel.: +852-2859-2632; fax: +852-2858-5415. E-mail address: [email protected] (K. Sumathy).
0360-5442/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 6 0 - 5 4 4 2 ( 9 9 ) 0 0 0 9 0 - 0
564
S. Iniyan, K. Sumathy / Energy 25 (2000) 563–575
in renewable energy sources (Table 1). Significant amounts of electric power, heat, mechanical energy and, in some cases, transportation energy are already being supplied by renewable energy sources. An energy model would facilitate the effective utilization of renewable energy sources in India. Presently, even though commercial energy sources like coal, oil and electricity are being utilized to a large extent, renewable sources of energy are slowly gaining importance. Natarajan [1] has discussed several important issues in energy policy and planning for the future with particular reference to developing countries. Rong-Hwa Wu and Chia-Yon Chen [2] have analyzed energy issues in the short-run for Taiwan using a static input–output (I–O) framework. Also, a multicriteria evaluation method [3] was employed to comprehensively evaluate the alternatives for new energy system development in Taiwan. Alam et al. [4] have discussed the physical quality of life as a function of per capita electrical energy consumption in their mathematical model. In Ethiopia, energy utilization patterns in three factories—cement production, textile manufacturing and food processing—were discussed by Wolde-Ghiorgis [5] using the Industrial Energy Utilization Model. Edmonds and Reilly [6] developed a long-term global energy-economic model of carbon dioxide (CO2) release from fossil fuel utilization. Subsequently, they have analyzed the impact on alternative energy evolutions (other than commercial) over the next 100 years [7]. The impact of atmospheric CO2 emission-reduction strategies was estimated using the coupled climate–carbon cycle model [8]. Energy systems, which significantly reduce emissions of acidifying gases and CO2 from non-mobile sources, were identified for Western Scania using an end-use accounting model [9]. The continuing depletion of fossil fuels and the environmental hazards posed by the needs of future development are gradually shifting the path of development towards sustainability, better sociability, and environmental responsibility, which in turn emphasizes the need for renewable energy sources. The 1980s witnessed changes in industrial policy which led to an unprecedented rise in the commercial energy consumption. Changes in foreign policies (e.g. the Gulf war and the breakdown of the Soviet Union) were seen to have affected the energy situation in India. Oil prices are currently very high. In order to reduce the continuous drain of India’s foreign exchange reserve due to oil import, the prospect of partial substitution of the country’s conventional energy by renewable energy should be analysed and discussed. In this paper, we present an Optimal Renewable Energy Model (OREM) to give an optimum allocation of renewable energy sources in India for the year 2020–21. We propose and discuss different scenarios to best use the available resources to reduce the emission levels, and to improve the quality of life in India. Table 1 Potential of renewable energy in India Sources
Potential
Solar energy Wind energy Biomass electricity Fuel wood Biogas Ethanol (sugarcane molasses) Ethanol (rice straw)
5×1015 kWhr/year 20000 MW 17000 MW 218.5 Mt/year 89 million m3 3000 million l/year 22.5 Mt
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2. Optimal renewable energy model The renewable energy flow diagram (Fig. 1) shows the various sources and the corresponding renewable energy systems for different end-uses such as lighting, cooking, pumping, heating, cooling and transportation. Thirty-eight possible energy options were considered in the analysis. The schematic representation of the Optimal Renewable Energy Model (OREM) is shown in Fig. 2. A Delphi study was conducted to identify the critical factors in renewable energy utilization in the Indian context. The study revealed that cost and efficiency are highly critical factors in the utilization of renewable energy sources, and that factors such as technology, availability and reliability should be considered in order to select the appropriate renewable energy systems for different end-uses. For optimization, the cost/efficiency ratio was chosen as the objective function and minimization of the function was carried out. The ultimate aim was to select systems with low cost and high efficiency. The following are the unit costs of energy used in the optimization
Fig. 1. Schematic representation of renewable energy options for different end-uses.
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Fig. 2. Schematic representation of optimal renewable energy model (OREM).
model. The costs of solar thermal electric conversion and solar photovoltaic electric conversion were estimated to be Rs. 11.92/kWhr and Rs. 11.70/kWhr, respectively [10]; the approved costs of solar cookers were in the range of Rs. 800–1000 and the installed cost of hot water systems (delivering water at about 60°C) was about Rs. 65–75 litre/day [11]; the cost of wind energy electric conversion was estimated to be Rs. 1.75–2.25/kWhr [12]; and the costs of biomass gasifier electric conversion and biogas electric conversion systems, widely installed in India by the Ministry of Non-Conventional Energy Sources, were estimated to be Rs. 2.50/kWhr and Rs. 1.25/kWhr, respectively. The different efficiencies of renewable energy systems substituted in the model are: 14% for solar thermal electric conversion, 13–15% for solar photovoltaic system, 35–45% for solar direct thermal, 25–30% for wind energy electric conversion system [13], 18% for biomass gasifier electric conversion system, 22–26% for biogas electric conversion system, 12–14% for biomass direct combustion, and 33–35% for solar cooker [14]. The social acceptance factor for each renewable energy option was obtained from Delphi results. The Delphi questionnaire was analyzed to determine (i) change in the responses from the first round to the second round, (ii) decrease in uncertainty, and (iii) arrival at a consensus. Mean, Standard Deviation, Confidence, and Stability of the participants in answering the questions were determined. In 97.9% of the cases, a consensus was arrived. Similarly, the group stability and individual stability were found in 100% and 95% of the cases, respectively. This shows that the respondents did not vary much in answering the questionnaire and hence the results directly reflect the realistic scenario for future energy utilization pattern in India. The respondents opinion indicated that solar, wind, biomass, and commercial energy sources would be utilized to an extent of 7.12, 7.9, 10.49 and 74.49% of the total, respectively. Detailed analysis of Delphi study was given in an earlier paper [15]. Social acceptance was used as one of the constraints in the OREM. The energy demand was predicted for the years 2020–21 by using the two-stage least square
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forecasting method. The renewable energy demand for different end-uses was determined by comparing it against the social acceptance level and was used as demand constraint in the OREM. Even though the potential sources of solar, wind, and biomass energy are abundant in India, factors such as the quality of the resources, intermittent nature, and technical feasibility would determine the amount of renewable energy to be used. In the OREM, these factors are considered potential constraints. A failure analysis was done to find out the reliability factor of the renewable energy system and the factor was taken as reliability constraint in the OREM for reliable power supply. The reliability factor of 0.1 at 10,000 hours for solar photovoltaic system, 0.5 at 10,000 hours for wind energy system, and 0.9 at 10,000 hours for biomass energy system were used in the model. A detailed analysis of reliability analysis was given in an earlier paper [16]. The calculated reliability index for renewable energy systems was used to formulate the reliability constraint equation. The mathematical representation of the OREM is given in the following equations:
冘冘 6
Minimize
l
(Cij /hij )Xij
(1)
冘 冋冘
(2)
(Xij )ⱕDj
(3)
j⫽1i⫽1
Subject to
6
l
(Xij /Sij )ⱕDj
Social acceptance
j⫽1 i⫽1
冘冋冘 冘冋冘 冘冋 冘 6
Demand
l
j⫽1 i⫽1 6
m
(Xik)ⱕPk
Potential limit
(4)
k⫽1 i⫽1
3
Reliability
m
(1/Rk)
k⫽1
(Xik)ⱕPk
(5)
i⫽1
where, C=unit cost of the system, h=efficiency of the system, X=quantum of renewable energy, D=renewable energy demand, S=social acceptance level, P=potential, i=renewable energy system, j=end-use, k=resource, l=number of systems in respective end-use (l=5 for lighting, l=7 for cooking, l=7 for pumping, l=6 for heating, l=6 for cooling, l=7 for transportation), m=number of systems in respective resource (m=16 for solar, m=6 for wind, m=6 for biomass, m=2 for fuelwood, m=6 for biogas, m=2 for ethanol). The model has been run for the renewable energy distribution in India for the year 2020–21 for different end-uses. 3. Results and discussion The OREM gives the optimum allocation for renewable energy sources in India for the year 2020–21 (Fig. 3). It predicts that approximately a quarter of India’s total energy consumption
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Fig. 3. Optimal renewable energy distribution pattern for year 2020–21 in India.
(2257.5 billion units of electricity equivalent) would come from renewable energy sources. At the optimal condition, for lighting, solar PV and biogas electricity conversion amount to 0.52×1015 kJ (6% of total renewable energy demand) and 0.75×1015 kJ (9% of total renewable energy demand), respectively. For cooking, biomass direct combustion through improved chulas could provide 1.49×1015 kJ (18% of the total renewable energy demand); for pumping, solar PV, wind energy, and biomass gasifier could supply 1.34×1015 kJ (16%), 0.31×1015 kJ (4%), and 0.05×1015 kJ (1%), respectively; for heating solar thermal and biomass direct combustion could produce 0.11×1015 kJ (2%), and 1.38×1015 kJ (17%), respectively; for cooling, solar thermal could account 1.02×1015 kJ (12%); and for transportation, biomass gasifier and ethanol fueled engine could generate 0.48×1015 kJ (6%), and 0.67×1015 kJ (8%), respectively. 3.1. Effect of the variation of renewable energy potentials The renewable energy potentials could not be fully commercialized in the immediate future. Scenarios of profitable exploition of these sources were developed in our study. The utilization rate of the wind energy potential was varied from 25%, 50%, 75% to 100% in the OREM and its distribution pattern is shown in Fig. 4. Even though the wind energy system is selected only for the end-use of pumping in the model, the variation of wind energy potential affected the distribution patterns of other end-uses such as lighting and transportation. For example, for the end-use of lighting, if 25%, 50% or 75% of wind energy potential is considered in the model, then solar PV, biomass gasifier, and biogas conversion could be used to the extent of 0.65×1015 kJ, 0.49×1015 kJ and 0.14×1015 kJ, respectively; but if 100% of wind energy potential is considered, solar PV and biogas electricity conversion alone could be used to an extent of 0.52×1015 kJ and 0.75×1015 kJ for lighting. Similarly, for the end-use of pumping, when 25% of wind energy is considered in the model, the energy demand could be shared by solar PV (1.63×1015 kJ) and wind energy (0.08×1015 kJ); if 50% of wind energy is used, its contribution would increase to
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Fig. 4.
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Renewable energy distribution pattern for various end-uses for year 2020–21 by varying wind energy potential.
0.16×1015 kJ (i.e., by 100%) and the solar PV contribution would decrease to 1.55×1015 kJ (i.e., by 5%); if 75% of wind energy is used, then its contribution would be 0.24×1015 kJ (i.e., a 200% increase) and solar PV contribution would be 1.48×1015 kJ (i.e., a 10% decrease). In the case of transportation, biogas and biomass gasifiers could be used to supply 0.54×1015 kJ and 0.61×1015 kJ, respectively, for the variation of 25%, 50% and 75% potential of wind energy. If 100% wind energy potential is considered in the model the biomass gasifier and ethanol could be used to an extent of 0.48×1015 kJ and 0.67×1015 kJ, respectively. The utilization rate of the biomass electric generation potential was varied in the OREM from 25% to 100% and the distribution pattern is shown in Fig. 5. In the case of lighting, the contribution of biomass gasifier electricity generation would be 0.12×1015 kJ, 0.24×1015 kJ and 0.37×1015 kJ when 25%, 50% and 75% of biomass electric generation potential is considered; when this potential is increased from 25% to 50% and 75%, the contribution of solar PV would decrease by 12% and 24%, respectively. In the case of pumping and transportation, when 100% of biomass electric generation potential is considered, the biomass gasifier could be introduced to an extent of 0.05×1015 kJ and 0.483×1015 kJ, respectively. Also, the introduction of 100% biomass potential would reduce solar PV by 4% for pumping and eliminate the necessity of using biogas for transportation. The biogas energy potential is varied from 25% to 100% and the distribution pattern is shown in Fig. 6. Variations in distribution pattern were found in lighting, pumping, and transportation. In the case of lighting, biogas could be used to an extent of 0.75×1015 kJ if 100% of its potential
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Fig. 5. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying potential of biomass electricity generation.
is considered in the model. Even though biogas is not preferred for the end-use of pumping, the variation of biogas potential affects the distribution pattern of pumping; Solar PV and wind energy system could be used to an extent of 1.4×1015 kJ and 0.32×1015 kJ, respectively, when 75% of biogas potential is considered; and biomass gasifier could be used to an extent of 0.05×1015 kJ when 100% of biogas potential is considered. For transportation, when the potential of biogas is increased from 25% to 50% and 75%, the contribution of biogas would vary from 0.17×1015kJ to 0.37×1015 kJ and 0.51×1015 kJ, respectively, and the contribution of biomass gasifier system would decrease by 55% and 91%, respectively. The firewood potential is varied from 25% to 100% and the renewable energy distribution pattern is shown in Fig. 7. In the case of lighting, solar PV, biomass gasifier electric conversion, and biogas electric conversion could be used to an extent of 0.65×1015 kJ, 0.49×1015 kJ and 0.14×1015 kJ, respectively, and this distribution pattern does not change for up to 75% of firewood potential. However, when 100% of firewood potential is used, biogas could be used to an extent of 0.75×1015 kJ, which reduces the contribution of solar PV by 20% and eliminates the entire biomass gasifier utilization. In the case of cooking, when 25% of firewood potential is utilized, the energy demand would be shared by firewood and solar cooker to an extent of 0.99×1015 kJ and 0.5×1015 kJ, respectively; if 50% of firewood is used, the entire energy demand would be met by firewood alone. In the case of heating, if 25% of firewood is considered, then solar thermal would be used to an extent of 1.49×1015 kJ; if 50% is considered, which means that firewood
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Fig. 6. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying biogas potential.
could be used to an extent of 0.5×1015 kJ, then the use of solar thermal would be reduced by 34%; if 75% of firewood potential is used, then the entire energy demand for heating could be met solely by firewood. The potential of ethanol is varied from 25% to 100% in the OREM and the effect in the distribution is shown in Fig. 8. Even though ethanol is preferred in transportation, it affects the distribution patterns of lighting and pumping end-uses. When ethanol potential is varied up to 75%, solar PV and biomass gasifier would meet the lighting demand, but when it is varied to 100%, the biogas system would replace the biomass gasifier system. In the case of pumping, solar PV and wind energy system contribute when up to 75% of ethanol potential is considered; but when 100% ethanol is considered, biomass gasifier system could be introduced to an extent of 0.05×1015 kJ, which would reduce the solar PV by 4%. In the case of transportation, the ethanol could be used to an extent of 0.15×1015 kJ, 0.31×1015 kJ and 0.46×1015 kJ when 25%, 50% and 75% of ethanol potential is considered. The increase of ethanol potential from 25% to 50% and 75% would reduce the biomass gasifier utilization by 47% and 95%, respectively; the utilization of 100% ethanol potential would eliminate the need to use biogas. 3.2. Effect of absence of potential constraint in different end-uses The effect of the removal of potential constraint from the OREM was analyzed and the results are shown in Fig. 9. In the case of lighting, wind energy electric conversion would be used to
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Fig. 7. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying firewood potential.
an extent of 0.32×1015 kJ instead of solar PV system; and the contribution from biogas electric conversion system would be increased to by 27% to 0.95×1015 kJ. In the case of cooking, energy demand would be met by solar cooker instead of biomass direct combustion, to an extent of 1.5×1015 kJ. For pumping, ethanol fueled system, instead of solar PV, wind energy system or biomass gasifier systems, would be used to an extent of 1.71×1015 kJ. In the case of heating, solar thermal alone would meet the energy requirement of 1.48×1015 kJ. For cooling, biogas electric generation would replace solar thermal, providing 1.00×1015 kJ. For transportation, ethanol-fueled system would meet the entire energy requirement, supplying 1.15×1015 kJ. Our results reveal that the utilization of ethanol and biogas would exceed their potential limits, if the potential constraints in the model were excluded. Therefore it is necessary to include the potential constraint in the OREM to obtain a realistic distribution pattern. 3.3. Sensitivity analysis of OREM by varying potential Sensitivity analysis was also carried out to validate the OREM. The potential of renewable energy sources was increased by 1%, 5% and 10% to test the sensitiveness of the model. When the potential of all renewables was increased by 1%, the coefficient of sensitivity was 0.0067 (0.67%). When each of potentials of wind, biomass, biogas, firewood and ethanol was raised one at a time by 1%, each respectively registered a value of around 0.0016 (0.16%). It reveals that OREM is sensitive even with a 1% variation of potential. When the potential was further increased
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Fig. 8. Renewable energy distribution pattern for various end-uses for year 2020–21 by varying ethanol potential.
by 5% for all renewables, the coefficient of sensitivity became 0.0402 (4.02%). When the potentials of wind, biomass, biogas, and ethanol were raised individually by 5%, the coefficient of sensitivity for each was 0.005 (0.5%), 0.0083 (0.83%), 0.0134 (1.34%), and 0.0117 (1.17%), respectively. In the case of 10% increase in all potentials, the coefficient of sensitivity was 0.0805 (8.05%). A 10% increase in wind and biomass potential yielded sensitivity coefficients of 0.01 (1%) and 0.0158 (1.58%), respectively. With a 10% increase in biogas and ethanol potentials, the corresponding sensitivity coefficients would be the same, 0.0252 (2.52%). Similarly, the sensitivity analysis was also carried out and the coefficients obtained when the potentials were decreased by of 1%, 5% and 10%. The analysis reveals that the OREM is very sensitive to variations in the potentials of renewable energy sources. 4. Conclusions The OREM was developed for renewable energy allocation in India for the year 2020–21. The model is formulated with the objective of minimizing the cost/efficiency ratio and the constraints of social acceptance, the reliability factor of renewable energy systems, potentials of renewable energy and energy demand for different end-uses. The renewable energy contribution would be 8.13×1015 kJ, about 25% of the total energy demand of India. The cost, efficiency, social acceptance, reliability, demand, and potential factors were used as input in the model and the results
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Fig. 9. Effect of absence of potential constraint for various end-uses.
were analyzed and discussed. The potentials of wind, biomass, biogas, firewood, and ethanol were varied from 25% to 100% in the model; in particular, the use of wind energy potential to a value of 50% would meet the growing demand of energy for pumping (0.16×1015 kJ). When the biomass potential is fully utilized, the biomass gasifier could be introduced for pumping and transportation end-uses to an extent of 0.05×1015 kJ and 0.48×1015 kJ, respectively. Similarly, for transportation, the increased use of the biogas potential from 25% to 50% would provide 0.17×1015kJ to 0.37×1015 kJ; and ethanol also plays a major role in meeting the future energy demand. The potential constraint in the OREM is essential for the optimal distribution pattern. If it is not considered in the model, the utilization of ethanol and biogas would exceed their potential limits. The stability and consensus analysis proved the validity of Delphi study, which is conducted to find out the social acceptance in the utilization of renewable energy sources. Our study shows that the OREM would facilitate effective utilization of the renewable energy sources in India. It is hoped that the model will help policy makers in the formulation and implementation of strategies of renewable sources for the next two decades. The sensitivity analysis proved the model to be highly sensitive to variation in the potentials of renewable energy sources. Other renewable energy sources, such as hydrogen, fuel cells, and geothermal, were not
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