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Optimal use of coal for power generation in India
Energy Policy 31 (2003) 319–331
Optimal use of coal for power generation in India$ Ritu Mathura,*, Sharat Chanda, Tetsuo Tezukab a
Tata Energy Research Institute, Darbari Seth Block, Habitat Place, Lodhi Road, New Delhi 110 003, India Graduate School of Energy Science, Kyoto University, Yoshida-honmachi, Sakyo, Kyoto 606-8501, Japan
b
Abstract There is growing consensus among energy planners that electricity requirements in India would increase rapidly in the next couple of decades, and that coal would continue to dominate the generating capacity mix. Comparatively high levels of ash in Indian coal causes concern both in terms of the high costs of coal movement and the associated environmental impacts. As per the notification of September 1997, all power plants located in sensitive areas, metropolitan cities and in areas distant from the coalfields, must use coal with o34% ash. However, little progress has been made towards coal beneficiation and some consumers have already started to import non-coking coal for blending in order to comply with environmental requirements. The importance of planning for optimal utilization and transportation of thermal coal cannot be underestimated, especially at a juncture where the Indian coal industry is already facing competition from rising imports of non-coking coal. This paper assesses the optimality of the current patterns of coal movement and examines the economics of beneficiating thermal coals. A linear programming model has been developed based on the framework of the general transportation problem. The authors conclude that the washery is not economically attractive given the current costs, beneficiation technique and quality of Indian non-coking coal. Model simulations have been attempted to assess the possibility of coal beneficiation based on techno-economic considerations rather than political or other considerations. The paper also stresses the possibility of overall gains to the economy by modifying the current patterns of coal movement. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Optimal coal utilization; Coal linkage model; India
1. Introduction
1.2. Role of coal-based power generation in India
1.1. Background
Over the last decade, the demand for power in India has been increasing at an annual compounded growth rate of about 9%, which ranks among the highest in the world (Fourth National Power Plan, 1997–2012). With the liberalization of the economy and the current trends in economic growth, it is expected that the demand for power would continue growing at a rapid pace in the future. Moreover, because of many factors, such as large gestation periods and re-location problems associated with hydropower development, the share of thermal energy and especially of coal-based generation has been rising. With a coal-based power generating capacity of about 70% of total installed capacity, coal consumption by power utilities in 1996/97 was 199.6 million tons (Mt) (TEDDY, 2000/2001; Tata Energy Research Institute, 2000; New Delhi, India), which is estimated to have increased to around 250 Mt in 2000/01. According to estimates of the Ministry of Coal, the demand for thermal coal (coal used by power plants) is expected to
The growth of an economy is closely related with growth in its energy consumption, particularly in the case of developing countries like India. Electricity, by virtue of being a relatively clean, efficient and convenient form of energy has a vital role to play in the socio-economic development of the country. A good understanding of the techno-economical and environmental concerns in India’s power sector is a crucial first step towards correct energy planning and policy making.
$
This study has been financially supported partly by Core Research for Evolutional Science and Technology (CREST) of Japan Science Technology Corporation. *Corresponding author. Tel.: +91-468-2100; fax: +91-468-2145. E-mail address: [email protected] (R. Mathur).
0301-4215/03/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 1 - 4 2 1 5 ( 0 2 ) 0 0 0 6 7 - 8
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increase to about 415 Mt by 2011/12. Although estimates of future coal requirement also vary based on different assumptions regarding the fuel and technology mix in future generating capacity, the fact remains that coal-based power generation and the consequent need for thermal coal in the country is bound to increase at a rapid rate. 1.3. Concerns related with quality of coal Indian non-coking coals are classified as grades A–G, with grades A–C representing the superior grades, while thermal coal is generally understood to represent coal of grades D–G. The quality of thermal coal has deteriorated over the years and power plants mainly receive grades E, F and G containing high levels of ash (often ranging between 35% and 45%) and shale. Many power plants in the country are located more than 1000 km away from the coalfields that are concentrated mostly in Eastern and Central India. Consequently, the additional freight charges that need to be borne in transporting high-ash coal generate much concern in addition to the problems faced at the power plants in terms of emissions and associated problems of ash handling and disposal. 1.4. Coal imports Environmental concerns have led to the enforcement of a recent directive that restricts use of high-ash coal in certain power plants. According to the Gazette of India notification, dated 19.09.97 (modified in June 2001), all power plants situated 1000 km or more from the mining source and those in urban, sensitive and critically polluted areas (excluding pithead plants), would be required to use coal with a maximum of 34% ash level. Although some consumers switched to blending after the regulation came in, a few had already attempted blending in anticipation of the change and on account of various other factors such as the inability of the national coal industry to supply appropriate quality of coal and high delivered costs of domestic coal due to high freight. Apart from raising concerns of energy security, the shift towards imported coal provides an indication of the emerging competition that the domestic coal industry is likely to face. It is therefore critical for coal producers, suppliers and policy makers to examine the possibilities of utilizing indigenous coal in a more efficient manner and plan judiciously for supplying adequate quantity and quality of thermal coal to the industry. 1.5. Coal movement Historically, most of the coal was available in Eastern and Central India, and the movement of this coal to various consumers was restricted to specific rail corri-
dors running Northwest or down South. The Standing Committee on Coal Linkages was instrumental in deciding and setting quarterly coal linkages. Linkage Committees appointed by the Government of India apportion coal production of each coalfield to all major consumers based on requirements stated by them and in mutual agreement with the supplying coalfields and Railway Authorities. Over time, many consumers continue to be linked with the same coalfields purely due to the associations developed between the suppliers and consumers. Recently, the system of setting linkages has been done away with on a formal basis, although the parties continue to meet every quarter to decide the linkages. However, it is contentious whether the linkages assigned are most optimal for the economy or not. 1.6. Aim of the study In view of the poor quality of coal supplied to power plants, large distances for movement of coal to consumers and the notification of September 1997, there has been a lot of debate about the benefits that could accrue by beneficiation of Indian thermal coal. Experts have been trying to convince decision-makers that washing of Indian thermal coal is beneficial on many counts—it would lower costs of coal handling and transportation, reduce air pollution and lead to efficiency improvements at the power plants (Reuben, 1998). This paper examines the economic attractiveness of setting up washeries and beneficiating (washing) indigenous coal, using a modeling approach. Moreover, the optimality of the existing linkages is examined based on scenario analysis to ascertain the scope for enhancing overall benefits to the system.
2. COLINK model 2.1. Choice of methodological framework The analysis for this study has been carried out using the COLINK model written in Generalized Algebraic Modeling Systems (GAMS). The COal LINKage (COLINK) model is a linear programming optimization model and has been developed jointly by the Tata Energy Research Institute (TERI) and Kyoto University. The model is structured around the framework of a general transportation problem. The modeling framework adopted in this study enables a comparative assessment of changes in delivered costs for coal as well as overall system costs as a result of changes in various constraints in the model. Scenario analysis based on the model provides useful policy guidelines for the sector. It must, however, be kept in mind that results of the model are only indicative and a detailed assessment of
R. Mathur et al. / Energy Policy 31 (2003) 319–331
individual projects should be undertaken before taking major policy decisions. 2.2. Scope of the model The COLINK model is used to examine the patterns of coal supply under various scenarios for meeting electricity requirements of the country in various years. The model is static in nature and has been set up for three time periods representing the terminal years of the Five-year plans of the country. While the year 1996/97 represents the base year, scenario analysis is carried out for 2001/02 and 2006/07. Scenarios are designed to allow alternative linkages for coal movement, include the possibility of introducing washeries, and charge differential freight rates for transportation of clean coal in future. An analysis of differences in system costs, reduction in emission levels and variations in coal production and supply patterns in the alternative cases as compared to the base case is used to examine the role of various influencing factors. 2.3. Description of the COLINK model In its most simplistic form, the COLINK model (Fig. 1) is set up as a general transportation problem
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where coalfields and ports represent the supply nodes while coal-based power plants represent the demand nodes. The model structure is designed such that it can include T&D flows between power plant nodes and can be used for a larger power planning exercise based on regional power demands. However, in this study the focus was restricted to examining the optimality of coal linkages and the attractiveness of the coal washery. Accordingly, the model used in this study was driven by the energy requirement at each of the power plants based on planned capacities, PLF and heat rates. The model then makes choices about the source and routing of coal from various coalfields and washeries to meet the generation requirements at each power plant for each time period such that the total system cost is minimized. The model then makes choices about the source and routing of coal from various coalfields and washeries to meet the power requirements in each time period such that the total system cost is minimized. Accordingly, the quantity of coal selected for washing is also determined by the model based on the relative cost effectiveness of the washery under various scenarios. Balance equations as well as process control equations in the model ensure that coal which is mined from the coalfields is equal to that transported to the washeries and power plants, power generated from all plants in a
STRUCTURE OF THE COLINK MODEL movement of unwashed coal to plants COAL MINING
movement of unwashed COAL coal to washery WASHING
movement of washed coal to plants
COAL UTILIZATION transmission of FOR POWER power to region GENERATION
PLANT 1
MINE 1 D grade coal E grade coal F grade coal
ELECTRICITY SUPPLY
REGIONAL DEMAND 1
PLANT 2 MINE 2 F grade coal G grade coal
PLANT 3 T&D LINES
RAIL NETWORK COAL FIELDS
REGIONAL DEMAND 2
WASHERY
WASHERIES
THERMAL PLANTS
EMISSIONS
Influencing Characteristics
Gradewise calorific value Pithead price by grade Ash & moisture content
input & output efficiency cost
costs PLF availability capacity heat rate pollution control equipment
Fig. 1. Structure of COLINK model.
TOTAL POWER DEMAND TOTAL SYSTEMCOST
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region is equal to the sum of the regional power demand and transmission loss, quantities output from each washery relate to the input quantities, washery yields, as well as other characteristics such as calorific value, ash and moisture content of input coal.
3. Basic equations of the COLINK model Coal supply balance: X F2W ¼ qF2P fpg þ qfg ;
qMINED fg
p
qMINED pQCAP fg fg ; : quantity of grade g coal produced at coalfield qMINED fg f; qF2P fpg : quantity of grade g coal transported from coalfield f to power plant p; qF2W fg : quantity of grade g coal, which is to be washed at coalfield f ; QCAP fg : maximum capacity of coal production of g grade coal at coalfield f : Coal washing balance: X CFfg qF2W ¼ qW2P fg fpg ; p
X
p
X
WSH qW2P Þ; fpg CALfg
fg
genp : power generated at power plant p; : capacity of power plant p; capPOW p gp : generation efficiency of power plant p; PLFp : plant load factor of power plant p; CALfg : calorific value of grade g coal at coalfield f ; : calorific value of washed grade g coal at CALWSH fg coalfield f : Power supply balance: X X bpp0 tmpp0 tmpp0 ; DDp pgenp þ p0
capnewpow p : newly added capacity at power plant p; capnewWSH : newly added capacity of washery at f coalfield f ; : existing capacity at power plant p; CAPOLDPOW p CAPOLDWSH : existing capacity of washery at coalf field f ; Objective function: X C¼ qF2P fpg ðfreight chargeÞfp fpg
þ
X
qW2P fpg ðfreight chargeÞfp
fpg
þ
X
qMINED ðCoal priceÞfg fg
fg
þ
X
capnewPOW ðinvestment cost of power plantÞp p
p
þ
X
genp ðoperating cost of power plantÞp
p
þ
X
f
qW2P fpg : quantity of washed coal transported to power plant p; capWSH : capacity of washery at coalfield f ; f CFfg : conversion factor for washing g grade coal at coalfield f : Power generation balance: X capPOW PLFp 8760; genp p p qF2P fpg CALfg þ
¼ CAPOLDWSH þ capnewWSH ; capWSH f f f
capnewWSH ðinvestment cost of washeryÞf f
f
g
X
¼ CAPOLDPOW þ capnewPOW ; capPOW p p p
XX þ ð qWSH fpg Þ ðoperating cost of washeryÞf :
WSH qF2W ; fg pcapf
genp ¼ gp ð
Plant capacity balance:
p0
tmpp0 : power transmitted from power plant p to power plant p0 ; bpp0 : transmission loss coefficient between power plant p and p0 ; DDp : power demand at power plant p:
pg
4. Data and assumptions All data used in the study is based on secondary information published by the Coal India Limited (CIL), the Planning Commission, the Central Electricity Authority (CEA), the Indian Railways and other related Government agencies. Discussions with both suppliers and consumers were conducted to gain insights of the main problems faced by the sector and to thereby formulate and analyze meaningful scenarios. 4.1. Power supply and demand In this study, we assumed that all planned additions to coal-based generating capacity fructify to meet future electricity demands. Capacity and PLF as specified by the CEA were used for the existing power plants, while a PLF of 64% (based on national average in the base year) was assumed for power plants for which data was not available. Accordingly, the total coal-based power demand was estimated to increase from 269 Twh in 1996/97 to 353 Twh in 2001/02 and 418 Twh in 2006/07. Each of the coal-based thermal power plants included in the model was assigned to one of the five power planning regions of the country (North, South, East, West and Northeast) and was characterized by its
R. Mathur et al. / Energy Policy 31 (2003) 319–331
installed capacity, plant efficiency, heat-rate, costs, etc. The level of utilization of each of the power plants was included as a variable in the model. 4.2. Coal supply The model is provided with the option to choose coal of various grades from each of the supply nodes (coalfields and ports). Maximum levels of grade-wise availability of coal at each supply node were fixed based on existing data for 1996/97 and planned production levels for the future years. The actual off take from a coalfield was included as a variable so that the model determines the quantity of coal chosen from each source such that the least-cost accrues to the system as a whole. Upper and lower bounds on the grade-wise quantity that can be mined from each of the coalfields were used to fix realistic levels of operations where necessary. Representative levels of ash, moisture and calorific value for each grade of coal at each of the coalfields were provided to represent the coal characteristics at each of the supply nodes. The data on coal characteristics was compiled based on the colliery-wise documentation of coal properties (Classification of Non-coking Coals of India for the Ministry of Coal) and the detailed coal production data for 1996/97 (Coal Controller’s Directory. 1996/97). The data thus compiled indicated that there was substantial availability of good quality coal, and an initial model run based on this data indicated that the washery was not at all attractive. Discussions with consumers indicated that coal received by them was generally 1–2 grades lower in quality than what the suppliers claimed. Accordingly, a grade slippage of 1–2 grades was incorporated in the data before evaluating the feasibility of introducing washeries under alternative scenarios in this study. Various ports were included in the model to allow for imports of coal and total supply of imported coal was constrained at the 1996/97 level for the base year. 4.3. Washery operations The alternative scenarios examined for the future include the option of investing in washeries located at each coalfield node, to enable beneficiation of coal (if economical) before transporting it to the power plants. The model chooses to construct washery capacity based on the requirements for ash reduction, washery costs and the level of yield from the washeries. The input–output relationship for coal in the washery process is modeled based on a study on washability characteristics of Indian thermal grade coal. The yield ash data have been analyzed in this study for thermal coal from Piparwar and Talcher coalfields. Based on this study, the percentage of yield and combustible recovery
323
is a function of a single constant m; as described below. Yield ¼ 100ða=af Þm ;
ð1Þ
Recovery ¼ 100ða=af Þm ð100 aÞ=ð100 af Þ;
ð2Þ
where, ‘‘a’’ is the percentage of ash in the washed coal, ‘‘af ’’ is the percentage of ash in the feed (input for washery) and the yield decreases as the value of ‘‘m’’ increases (Narasimhan et al., 1997). The washery is modeled such that high-ash coal (>34% ash) can be washed to yield coal output with 34% or lower ash levels. Higher levels of ash reduction can be specified in subsequent scenario runs. Apart from reducing the amount of coal that needs to be transported, the benefits of coal washing were also modeled to include improved efficiency of power plants. For every 10% reduction in ash, the heat rate is assumed to improve by 1%, the PLF increases by 5% and the maintenance cost of the boiler decreases by 20% (Sinha, 1998). 4.4. Linkages Historically power plants were allocated quarterly linkages with certain coalfields based on coal requirements as stated by the plants, availability of coal at the coalfields and the capacity of the Railways to transport the coal between the supply and demand points. Accordingly, linkages were allowed based on information as available from the mid-term appraisal of the Eighth Five Year Plan (Coal demand and linkages, 94– 95 to 96–97 and 2001–02). Pithead power plants were provided fixed linkages from coalfields where they are located since alternative options would be irrational in such cases. Based on discussions with Railway Authorities, and their statements during the linkage committee meetings, the Railways did not indicate capacity constraints in moving coal in the short term. Also, coal is a high priority good in railway movement implying that capacity would be spared (by shifting other commodities to alternative routes) for its movement if required. Therefore, capacity constraints in rail movement are not included in the model. The availability of linkages (based on actual and planned data) is provided as a zero-one matrix for each year under consideration. 4.5. Cost parameters The system costs that are minimized include various costs provided to the model. The grade-wise price of coal at each coalfield is provided to the model enabling choice on the basis of the price and the calorific value of coal at each location. Further, freight charges are included between each demand and supply node based on distances between the nodes and the freight rates for
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coal as existing in 1996/97. Costs of setting up of new washery capacity were based on costs of the ST-BSES washery, which amounted to $ 15 million for a 2.5 million tons per annum (MtPA) washery (Shahi, 1998). Investment costs for new generating capacity were considered at Rs. 40 million/MW, while operating and maintenance costs were taken to be 3% of the investment costs. Moreover, a cost of Rs.120/ton is assumed for ash handling and disposal at the power plants. 4.6. Environmental constraints Constraints on levels of SPM and CO2 can be included in the model to enable a restriction of emission levels from the sector. In the baseline, the levels of SPM and CO2 are generated by the model based on the quantity and quality of coal used and the power generation technologies adopted in the country. The alternative scenarios can, however, consider restrictions on emission levels so as to examine other possibilities of coal utilization that abate the pollution levels. The levels of SPM emissions depend on the efficiency of the electrostatic precipitators (ESPs) at the power plants. Although it is often stated that the efficiency of ESPs are as high as 99%, discussions with experts revealed that efficiencies were often much lower due to improper maintenance. Accordingly, an efficiency of 95% was considered in the model. 4.7. Description of scenarios Various scenarios have been set up in the study with a view to examine the attractiveness of washeries in the system and to examine the potential for enhancing benefits by adopting alternative linkages for coal supply. The base year for the study is 1996/97 and the base case is examined to test the realistic working of the model for this year. The alternative scenarios are examined for 2001/02 and 2006/07. BAU scenario: The base case was developed for 1996/ 97 using documented data of coal supply and power generation and based on the existing set of coal linkages. The model was validated to ensure realistic working for this year. The business-as-usual (BAU) scenario envisages an extension of the base case assumptions for the years 2001/02 and 2006/07. Coal supply was assumed to increase as per production plans of the supplying agencies and the existing coal linkages were expected to continue. It was also assumed that there would be no beneficiation of coal by washing in the BAU scenario. Free linkage scenario: The authors hypothesize that the existing linkages for coal supply to power plants may not be most economical for the entire system. Discussions with Railway authorities indicated that track capacity is not a limiting factor and larger
quantities of coal could be moved between the demand and supply nodes even in the immediate short term without any additional capacity augmentation. Against this background, a free linkage (FL) scenario was set up for the immediate short term (2001/02) to examine whether overall system costs could be reduced if alternative linkages were allowed. As against the BAU scenario, the free linkage scenario allows movement of coal between all coalfields and power plants. The model then has an option to choose a set of linkages that may result in lower overall system costs as compared with the BAU scenario. Scenario for washery with single and multiple ash reduction targets: In light of the notification of September 1997, a washery scenario was set up to examine if utilization of washed coal was an economically preferable option. Accordingly, the model was provided with the option of introducing washeries to wash coal to 34% ash level in the single target scenario and to 34%, 30% and 25% ash levels under the multiple target scenario. The single and multiple target washery scenarios were evaluated for the year 2006/07, and it was assumed that a maximum of 4 Mt of washery capacity could exist at each of the coal supply nodes by this year. Dual price scenario: Given that there has not been much progress in India with regard to the setting up of washeries for thermal coal, a scenario was developed to examine whether beneficiation of thermal coals could be promoted by adopting a differential freight structure for transporting washed and unwashed coal. The dual price scenario attempts a sensitivity analysis of thermal coal beneficiation with progressively higher levels of discount to freight charges for washed coal. This scenario was examined for the year 2006/07. Technological improvement scenario: The technological improvement scenario was developed with a view to examine the competitiveness of alternative power generation technologies vis-a" -vis the option of coal washing. Along with the option of introducing coal washeries, this scenario allows the introduction of more efficient generation technologies such as integrated coal gasification combined cycle (IGCC) and super critical technologies against the existing sub-critical technology. This scenario would indicate the preferred technology for investment in terms of the least-cost option in moving towards cleaner coal-based power generation.
5. Results and analysis 5.1. BAU scenario The requirement of coal by power plants as per the model was 196.5 Mt in 1996/97 as compared with an actual level of 199.6 Mt (Coal Directory of India, 1997).
R. Mathur et al. / Energy Policy 31 (2003) 319–331
Moreover, the model indicated that 2.4 Mt of SPM and 78.2 Mt of CO2 were emitted in 1996/97. The total system cost was Rs. 638.9 billion of which freight costs were Rs. 80.3 billion, coal costs were Rs. 104.3 billion, and plant costs were Rs. 447.4 billion while costs of ash disposal were Rs. 6.9 billion. The cost of coal and its movement comprise the operational costs of supplying coal to the power plants, while the other components relate to investment costs at the power plants and washeries. It is seen that the cost of coal movement constitutes nearly 44% of the total operational cost of supplying coal to the power plants, while the cost of coal is 56%. An analysis of the future years shows that the coal requirement increases from 196.5 Mt in 1996/97 to 234.7 Mt in 2001 and 291 Mt by 2006/07. The total system cost increases from Rs. 638.9 billion in 1996/97 to Rs. 818.3 billion in 2001/02 and Rs. 1023.2 billion in 2006/07. Moreover, emissions of SPM increase from 2.4 Mt in 1996/97 to 3.9 Mt in 2006/07 (an increase of 38%) while CO2 emissions increase from a level of 78.2 Mt in 1996/ 97 to 133.4 Mt in 2006/07 (an increase of 41%).
5.2. Free linkage scenario An analysis of the system costs under the free linkages scenario for 2001/02 indicates that although the quantity of coal transported is the same as that under the BAU, re-allocation of linkages can result in a reduction of as much as 27% of the freight charges for coal transportation incurred in the BAU scenario (Table 1). In 2001/02, coal movement decreases from 117.2 billion tons kilometer (Btkm) under the BAU to 81.5 Btkm under the free linkage case (a reduction of approximately 30% over the BAU). Moreover, in the BAU scenario, coal is transported up to a maximum distance of 2000 km while the model chooses to move coal only up to a maximum distance of 1700 km in the free linkage scenario (Fig. 2). The results of this scenario stress upon the need to revisit the historically set linkages to review the possibility of changes by diverting or managing routing of traffic on alternative routes.
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It must be noted however, that the model attempts to minimize total system costs and may thereby decrease freight between some nodes at the cost of increasing freight between other nodes. However, the process of linkage setting in reality would attempt to minimize costs of individual consumers and may even be based on individual consumers0 preferences that may not necessarily be a function of the cost of coal movement alone. Factors such as reliability and past associations are likely to play an important role in the decision-making with regard to linkage setting. The model can therefore at best provide indicative results in this regard and any changes in specific linkages must be examined more specifically keeping in mind other factors such as capacity constraints of the Indian Railways, priorities of movements along specific corridors, past relationships between suppliers and consumers, etc. 5.3. Scenarios for washery with single and multiple ash reduction targets As discussed earlier, the initial model runs with the option of introducing washeries using data on coal availability and quality as per published documents and literature indicated that the washery was not at all attractive. The data was therefore modified to account for grade slippages (based on discussions with consumers) before conducting the washery analysis. 5.3.1. Washery scenario with single target of ash reduction to 34%: Despite providing the model with a larger quantity of inferior grade coal, it was observed that although the washery became attractive, a total of only 5.5 Mt of F grade coal from Talcher, South Karanpura and Ib Valley coalfields was selected for washing in 2006/07 (Table 2). Since the model chooses only a small quantity of coal for washing, the reduction in system costs over the BAU was also marginal. The decrease in costs is attributable to marginal decreases in the costs of coal movement, reduced costs (O&M) at the power plants and some decreases with respect to ash handling and disposal costs at the power plants. However, the increase in cost of coal and the cost of constructing the washery
Table 1 Break-up of system costs (Million Rs.): 2001/02
Freight charges for coal transportation Pithead price of coal Power plant costs Cost of ash handling and disposal Total system costs
BAU scenario
Free linkages scenario
Change over BAU
87,624 136,641 589,922 8095 822,281
64,388 136,171 584,280 8078 792,917
23236(26.5%) 470(0.3%) 5641(0.9%) 16(0.1%) 29364(3.5%)
R. Mathur et al. / Energy Policy 31 (2003) 319–331
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Coa l m ove m e nt- BAU & Fre e linka ge (2001/02) 14 0
10 0%
80 %
Qu an tity (M t)
10 0
70 % 60 %
80
50 % 60
40 % 30 %
40
20 % 20
10 %
0
0
50 -2 01
24
22
01
-2
-2
10
30
0
0 90 01
-1
70 01
-1 18
01 16
01 14
20
0 50 -1
-1
30
10 01 12
01
-1
110
80
0
0
0 90
70
0 60
140
1-
50
0 30
0 1-
10 20
0
0% 0
0 0-
C u m u lative q u an tity m o ve d (%)
90 %
12 0
Dis tan ce o f tr an s p o r tatio n (k m ) Q uan tity of c oal tr ans porte d - B A U (Mt)
Q uan tity of c oal tr ans porte d - Fre e L inkag e ( Mt)
Cumulativ e quan tity -B A U (% )
Cumulativ e quan tity -Free Lin ka ge (% )
Fig. 2. Frequency distribution of coal movement (BAU vs free linkage—2001/02).
Table 3 Break-up of costs (Million Rs.): 2006/07
Table 2 Quantity of coal to be washed (by coalfield) Coalfield
Grade
Quantity (Mt)
Talcher South Karanpura Ib Valley
F F F
4.00 1.15 0.37
substantially offset the cost benefits (reductions) that accrue to the system (Table 3). It is also argued that there may not be substantial benefits in terms of decreased costs of coal movement, since the reduction in weight of extraneous matter may be offset to some extent by the addition of surface moisture during the process of washing. It is also observed that with the introduction of the washery, emissions of SPM and CO2 reduce by approximately 42,000 and 100,000 tons, respectively, as compared with the BAU levels in 2006/07. Further, a sensitivity analysis was carried out to examine the breakeven costs for the washery process in the above scenario. The coalfield-wise quantum of coal selected by the model for washing was examined for every 10% increase in beneficiation costs over the baseline (Fig. 3). It was observed that the coal at Talcher was the most sensitive to increases in washery costs and although the quantity of coal washed reduced even with a 30% increase in washery costs, all coal ceases to be taken up for washing with a 40% increase in costs over the baseline. Coal from the Ib Valley is insensitive to
Cost of coal transportation Cost of coal Cost of power plant Cost of washery Ash handling costs Total system costs
BAU scenario
Single target washery scenario
Change over BAU
112,249
111,986
263 (0.2%)
203,254 616,958
203,477 616,546
+223 (+0.1%) 412 (0.1%)
0 11,068
351 10,999
+351 (N.A.) 69 (0.6%)
943,529
943,360
169 (0.02%)
changes in washing costs till a 40% increase over the base costs beyond which the washery is not economically attractive. The coal from South Karanpura was observed to be highly insensitive to changes in washery costs and was not considered attractive for beneficiation unless washing costs more than doubled over those in the base case. Although the model results show that poor grade coal from South Karanpura, Ib Valley and Talcher coalfields are chosen for washing, it is clear that the economic benefits as such are not very substantial under a scenario of coal washing possibilities. It is therefore unlikely that washeries would be set up for beneficiation of noncoking coal supplied to thermal power plants in the absence of any stringent policy directives with regard to the environment or in terms of supply or movement of high-ash coal.
R. Mathur et al. / Energy Policy 31 (2003) 319–331
327
S e nsitiv ity of coal be ne ficiation to incre ase in washe ry costs 6
Q uantity w as hed (M t)
5 4 Talc her (F grade) 3 2 Ib V alley (F grade) 1
S outh K aranpura (F grade)
0 1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
W as hing Cos t (inc reas e over bas eline)
Fig. 3. Sensitivity analysis of coal beneficiation with cost increases.
Table 4 Cost comparison—BAU and multiple target washery scenarios (Million Rs.): 2006/07
Cost of coal transportation Cost of coal Cost of power plant Cost of washery Ash handling costs Total system costs
BAU scenario
Multiple target washery scenario
Change over BAU
112,249
112,008
241 (0.2%)
203,254 616,958
203,652 616,279
+398 (+0.2%) 679 (0.1%)
11,068
0 363 10,985
+363 (N.A.) 83 (0.7%)
943,529
943,288
241 (0.02%)
5.3.2. Multiple washing targets The washery scenario with multiple targets of coal washing indicates marginal benefits in coal transportation costs as well as overall system costs as compared to the BAU in 2006.07 (Table 4). It is interesting to note that even with constant costs, the washery becomes increasingly unattractive, as we desire higher levels of ash reduction (Fig. 4). With a target of 25% ash coal, a minor quantity of coal is selected for washing, which is highly sensitive to increase in washery costs. Increases of even 20% beyond the existing washing costs render the washery process ineffective in the model for high levels of ash reduction. With a target of washing coal to 34% and 30% ash levels, a gradually increasing disincentive to wash coal is noticed as washery costs increase 60% above the existing costs, after which the model rejects washing of coal.
There is still much debate among experts about the yield of washeries for Indian thermal coal. It is felt that the yield of washeries could be much lower than that used in our model due to the specific nature of Indian coals. Indian coals have high levels of ‘‘near gravity material’’ that make coal washing unsuitable using current techniques. Improving the efficiency of the washery process is critical before beneficiation of thermal coals can be exploited to a greater extent in India. There is therefore a pressing need for developing and/or adapting appropriate technologies suitable for beneficiating Indian noncoking coal. 5.4. Dual price scenario The results of the dual price scenario are reflected in Fig. 5. It can be seen that as the transportation cost for washed coal decreases, it becomes more and more economical to wash coal from various coalfields. Although all the F grade coal from South Karanpura and Talcher coalfields are necessarily washed in the model even at the existing freight rates considered for washed coal, it is noticed that higher levels of coal are washed from other coalfields when freight rates are decreased. At the existing freight rates only 0.37 Mt of F grade coal from the Ib Valley coalfield was selected for washing, which increases to 3.26 Mt with a 10% discount in freight rate and further to 4 Mt when freight rate is decreased by more than 12.5% over the base rate. F grade coal from Korba coalfield also becomes economical to wash and transport when freight rates decrease by more than 5% over the base freights. Minor amounts of E grade coal from Jharia and F grade coal from Pench-Kanhan are selected for washing when freight rates for transporting washed coal are reduced by
R. Mathur et al. / Energy Policy 31 (2003) 319–331
328
Sensitivity of coal beneficiation w ith increasing levels of ash reduction
Quantity to be washed (Mt)
6.0 5.0 4.0 34%
3.0
30%
2.0
25%
1.0 0.0 1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
Increase in washing cost over baseline Fig. 4. Sensitivity of coal beneficiation with increasing levels of ash reduction.
S ensitivity of coal w ashing un der differential freigh t policy 1 6 .0
P enc h-K anhan (F grade) Jharia (E grade)
Q uantity to be was hed (M t)
1 4 .0 1 2 .0
K orba (F grade) 1 0 .0 8 .0
Ib V alley (F grade) 6 .0 4 .0
Talc her (F grade)
S outh K aranpura (F grade)
2 .0
%
%
%
%
% .0 40
.5 37
.0 35
.5 32
.0
.5
%
%
30
27
% .5
% .0
% .5
% .0
.0 25
22
20
17
%
%
15
.5 12
5%
.0 10
7.
0% 5.
5% 2.
0.
0%
0 .0
Dis c ount rate for freight of was hed c oal (% )
Fig. 5. Sensitivity of coal beneficiation with decrease in freight rates for washed coal.
more than 20% against baseline rates. This clearly indicates that the quantity of coal to be washed is sensitive to freight rates. The total quantity of coal that is washed increases from 5.5 Mt with the existing freight rates for washed coal to 14.2 Mt when the rates are reduced by 40%. It is seen that even with a 10% reduction in freight rate, the quantity of coal selected for washing increases to about 12 Mt from the base level of about 5.5 Mt. This indicates that the introduction of monetary incentives for the movement of clean coal could provide an impetus to beneficiate thermal coals in India. Such a policy perspective is also recommended in terms of the benefits that accrue in terms of emission reductions from
washing. An examination of the levels of SPM emissions against the reductions in freight rates and the consequent levels of coal washed reflect the environmental benefits of coal washing (Fig. 6). 5.5. Technological improvement scenario A sensitivity run was conducted with progressively increasing constraints on the levels of SPM (0–10% of the base level emissions) under a scenario where coal washing technology could compete with alternative power generation technologies for the year 2006/07. The marginal costs of emission reduction under the three alternative options were examined (Fig. 7).
R. Mathur et al. / Energy Policy 31 (2003) 319–331
329
99.5%
SPM emission (compared to BAU)
99.4%
99.3%
99.2%
99.1%
99.0%
98.9%
98.8% 0%
5%
10%
15%
20%
25%
30%
35%
40%
Discount Rate for freight of washed coal (%)
Marginal cost of Emission Reduction ($/ton)
Fig. 6. Decrease of SPM emissions with reductions in freight rate of washed coal.
12000 10000 8000 6000 4000 2000 0 0%
2%
4%
6%
8%
10%
Reduction of SPM Emission (%) Sub critical & Coal Washery
IGCC & No Washery
Supercritical & No Washery
Fig. 7. Marginal cost of emission reduction.
It is seen that the marginal costs for SPM emission reductions by the washery process are higher than that by adopting the efficient power generation technologies. This indicates that even under a policy regime of environmental improvement, it is economically preferred to invest in alternative coal-based power generation technologies (super critical or IGCC) rather than investing in coal washing technology. The model results indicate that alternative technological options for power generation or management
measures such as the adoption of alternative linkages are economically more attractive than coal washing. However, unless power plants based on the alternative technologies fructify or linkages are rationally apportioned, it may be imperative for power plants located in some areas to use washed coal or import coal for blending in order to comply with environmental regulations. Further, in order to continue operation of existing power plants, washing may be necessary even at higher costs in the long term.
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6. Conclusion The highlights emanating from this study are briefly discussed below. Firstly, if the data on coal quality as published by the suppliers is valid, the model indicates adequate availability of good quality coal and the washery is not found to be attractive at all. Significant variation was observed in data regarding thermal coal quality as reported by the suppliers and consumers. This is a cause for concern since it can change the very finding of the study. Based on discussions carried out during the study, a situation of ‘‘passing the buck’’ was clearly observed between the Railway authorities, the coal suppliers and the electricity consumers. Although none of the parties accepted the existence of constraints in the availability or movement of coal, reality does indicate clearly the lack of availability of adequate quantity and quality of indigenous non-coking coal and the consequent increase in use of imported coal in the country. It is therefore more important to first recognize and address the fallacies and data gaps that exist in the Indian coal sector before a correct assessment can be made regarding the need for beneficiation technologies. The results of the initial model run also stress on the need to focus on identifying and supplying good quality coal reserves which can be achieved by adopting better mining techniques in order to produce coal of desirable quality and quantity in the future. With assumptions of grade slippage, the model chooses to introduce washery capacity for beneficiating a small quantity of inferior grade coal (F and G grades). It must be noted however, that the operational and environmental benefits are not very substantial, and washery costs nearly offset these benefits. The model thereby indicates little profit motive and investors are therefore unlikely to be lured into setting up washeries on their own. Moreover, it is argued that washery yields for Indian thermal coal could even be much lower than those considered in this study. In the short term, therefore, the introduction of additional washery capacity using current techniques are likely to be driven only by the enforcement of stringent policy directives with regard to the environment or in terms of restricting supply or movement of high-ash coal. In the long term, it is however, important to focus research and developmental (R&D) activities towards enhancing the efficiency of the washery technology for Indian thermal coal, especially in light of the recent competition from coal imports and given that the capacity of coal-based power generation plants is expected to play a dominant role in India. Investment should be directed towards technological development and adaptation for beneficiation techniques suited to Indian coal. In light of the existing environment, it is felt
that third parties should be encouraged to set up washeries if the consumers give a firm commitment for the off-take of washed coal at higher prices. The possibility of joint ventures and technology transfer should also be explored towards this end. Collaborations with experienced nations would be especially helpful in this regard—these may be both in terms of technology transfer, in terms of monetary support for R&D and/or in terms of support by way of infrastructure and hardware capabilities. In the same context, it must also be realized that coal washing is not the only option for beneficiating power grade coal. Other alternatives such as the options of blending of thermal grade coals with better quality of coal (imported/indigenous) must be examined. Power plants faced with environmental restrictions have already started to resort to this option over the last few years. The option of cleaner technologies and alternative generation technologies such as supercritical and IGCC technologies should be evaluated for the future. The study indicates that a reduction in freight rate for movement of washed coal significantly increases the attractiveness of the washery. The maximum sensitivity is observed at a freight reduction of about 10% as compared to the BAU levels. Accordingly, the introduction of a differential freight rate policy where charges for transporting washed coal are lower, could be instrumental in enhancing the adoption of thermal coal washing. On the contrary, a policy of fixing higher freight rates for the movement of washed coal (as was tried out initially in the country) would be expected to function against the objective of promoting washeries. Another point that emanates clearly from the study is the need for re-examining the optimality of the existing linkages for the movement of coal to power plants. The free linkage scenario indicates that costs of coal movement could be reduced by as much as 27% compared to the BAU costs in 2001/02 through a mere re-allocation of linkages for coal movement. The assignment of linkages in reality may be influenced by many extraneous factors and it may not be possible to reallocate all movement as indicated by the model. However, given the substantial benefits as indicated by the model, it would still be worthwhile to re-assess possibilities for alternative supply sources and routing of coal. In essence, this study indicates that one of the most basic requirements of the coal sector is to develop a system of accountability and transparency with regard to coal quality. In the short term, measures such as charging of differential freight rates for washed coal and re-allocation of linkages of coal movement should be adopted to realize overall gains to the economy. Coal suppliers should ensure a reliable supply of relatively better quality of coal to power plants located in sensitive areas and distant from the coalfields. This is especially important to protect the domestic coal industry against
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imports in the long run. Investments should be directed towards promoting further research for the development or adaptation of appropriate coal production and utilization techniques for Indian thermal coal.
References Classification of Non-coking Coals of India for the Ministry of Coal, Government of India. Central Fuel Research Institute. Council for Scientific & Industrial Research. Ministry of Science & Technology. Government of India, Dhanbad, Bihar, India, March 1998. Coal demand and linkages 1994/95 to 1996/97 and 2001/02—mid term appraisal of Eighth Five Year Plan. August 1994. Coal Directory of India, 1996–97. Calcutta: Ministry of Coal. Coal Controller’s Organization, 1997, 128pp.
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Fourth National Power Plan, 1997–2012. Central Electricity Authority, Government of India, 1997. Narasimhan, K.S., Bhoumik, S.K., Reddy, P.S.R., Prakash, S., Biswal, S.K., 1997. Washability characteristics of Indian coals. Journal of Mines, Metals and Fuels. 45, 233–237. Reuben, B.S., 1998. Advantages of coal beneficiation—AEC’S perspective. Coal Prep India ‘98: Proceedings of the International Conference on Coal Beneficiation, January 12–13, 1998, New Delhi, India. Sinha, A., 1998. Indian coal perspectives problems and solutions with focus on thermal coal. Coal Prep India ‘98: Proceedings of the International Conference on coal Beneficiation, January 12–13, 1998, New Delhi, India. Shahi, R.V., 1998. Need for environment friendly use of coal for power generation. Coal Prep India ‘98: Proceedings of the International Conference on Coal Beneficiation, January 12–13, 1998, New Delhi, India.
Optimal use of coal for power generation in India$ Ritu Mathura,*, Sharat Chanda, Tetsuo Tezukab a
Tata Energy Research Institute, Darbari Seth Block, Habitat Place, Lodhi Road, New Delhi 110 003, India Graduate School of Energy Science, Kyoto University, Yoshida-honmachi, Sakyo, Kyoto 606-8501, Japan
b
Abstract There is growing consensus among energy planners that electricity requirements in India would increase rapidly in the next couple of decades, and that coal would continue to dominate the generating capacity mix. Comparatively high levels of ash in Indian coal causes concern both in terms of the high costs of coal movement and the associated environmental impacts. As per the notification of September 1997, all power plants located in sensitive areas, metropolitan cities and in areas distant from the coalfields, must use coal with o34% ash. However, little progress has been made towards coal beneficiation and some consumers have already started to import non-coking coal for blending in order to comply with environmental requirements. The importance of planning for optimal utilization and transportation of thermal coal cannot be underestimated, especially at a juncture where the Indian coal industry is already facing competition from rising imports of non-coking coal. This paper assesses the optimality of the current patterns of coal movement and examines the economics of beneficiating thermal coals. A linear programming model has been developed based on the framework of the general transportation problem. The authors conclude that the washery is not economically attractive given the current costs, beneficiation technique and quality of Indian non-coking coal. Model simulations have been attempted to assess the possibility of coal beneficiation based on techno-economic considerations rather than political or other considerations. The paper also stresses the possibility of overall gains to the economy by modifying the current patterns of coal movement. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Optimal coal utilization; Coal linkage model; India
1. Introduction
1.2. Role of coal-based power generation in India
1.1. Background
Over the last decade, the demand for power in India has been increasing at an annual compounded growth rate of about 9%, which ranks among the highest in the world (Fourth National Power Plan, 1997–2012). With the liberalization of the economy and the current trends in economic growth, it is expected that the demand for power would continue growing at a rapid pace in the future. Moreover, because of many factors, such as large gestation periods and re-location problems associated with hydropower development, the share of thermal energy and especially of coal-based generation has been rising. With a coal-based power generating capacity of about 70% of total installed capacity, coal consumption by power utilities in 1996/97 was 199.6 million tons (Mt) (TEDDY, 2000/2001; Tata Energy Research Institute, 2000; New Delhi, India), which is estimated to have increased to around 250 Mt in 2000/01. According to estimates of the Ministry of Coal, the demand for thermal coal (coal used by power plants) is expected to
The growth of an economy is closely related with growth in its energy consumption, particularly in the case of developing countries like India. Electricity, by virtue of being a relatively clean, efficient and convenient form of energy has a vital role to play in the socio-economic development of the country. A good understanding of the techno-economical and environmental concerns in India’s power sector is a crucial first step towards correct energy planning and policy making.
$
This study has been financially supported partly by Core Research for Evolutional Science and Technology (CREST) of Japan Science Technology Corporation. *Corresponding author. Tel.: +91-468-2100; fax: +91-468-2145. E-mail address: [email protected] (R. Mathur).
0301-4215/03/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 1 - 4 2 1 5 ( 0 2 ) 0 0 0 6 7 - 8
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increase to about 415 Mt by 2011/12. Although estimates of future coal requirement also vary based on different assumptions regarding the fuel and technology mix in future generating capacity, the fact remains that coal-based power generation and the consequent need for thermal coal in the country is bound to increase at a rapid rate. 1.3. Concerns related with quality of coal Indian non-coking coals are classified as grades A–G, with grades A–C representing the superior grades, while thermal coal is generally understood to represent coal of grades D–G. The quality of thermal coal has deteriorated over the years and power plants mainly receive grades E, F and G containing high levels of ash (often ranging between 35% and 45%) and shale. Many power plants in the country are located more than 1000 km away from the coalfields that are concentrated mostly in Eastern and Central India. Consequently, the additional freight charges that need to be borne in transporting high-ash coal generate much concern in addition to the problems faced at the power plants in terms of emissions and associated problems of ash handling and disposal. 1.4. Coal imports Environmental concerns have led to the enforcement of a recent directive that restricts use of high-ash coal in certain power plants. According to the Gazette of India notification, dated 19.09.97 (modified in June 2001), all power plants situated 1000 km or more from the mining source and those in urban, sensitive and critically polluted areas (excluding pithead plants), would be required to use coal with a maximum of 34% ash level. Although some consumers switched to blending after the regulation came in, a few had already attempted blending in anticipation of the change and on account of various other factors such as the inability of the national coal industry to supply appropriate quality of coal and high delivered costs of domestic coal due to high freight. Apart from raising concerns of energy security, the shift towards imported coal provides an indication of the emerging competition that the domestic coal industry is likely to face. It is therefore critical for coal producers, suppliers and policy makers to examine the possibilities of utilizing indigenous coal in a more efficient manner and plan judiciously for supplying adequate quantity and quality of thermal coal to the industry. 1.5. Coal movement Historically, most of the coal was available in Eastern and Central India, and the movement of this coal to various consumers was restricted to specific rail corri-
dors running Northwest or down South. The Standing Committee on Coal Linkages was instrumental in deciding and setting quarterly coal linkages. Linkage Committees appointed by the Government of India apportion coal production of each coalfield to all major consumers based on requirements stated by them and in mutual agreement with the supplying coalfields and Railway Authorities. Over time, many consumers continue to be linked with the same coalfields purely due to the associations developed between the suppliers and consumers. Recently, the system of setting linkages has been done away with on a formal basis, although the parties continue to meet every quarter to decide the linkages. However, it is contentious whether the linkages assigned are most optimal for the economy or not. 1.6. Aim of the study In view of the poor quality of coal supplied to power plants, large distances for movement of coal to consumers and the notification of September 1997, there has been a lot of debate about the benefits that could accrue by beneficiation of Indian thermal coal. Experts have been trying to convince decision-makers that washing of Indian thermal coal is beneficial on many counts—it would lower costs of coal handling and transportation, reduce air pollution and lead to efficiency improvements at the power plants (Reuben, 1998). This paper examines the economic attractiveness of setting up washeries and beneficiating (washing) indigenous coal, using a modeling approach. Moreover, the optimality of the existing linkages is examined based on scenario analysis to ascertain the scope for enhancing overall benefits to the system.
2. COLINK model 2.1. Choice of methodological framework The analysis for this study has been carried out using the COLINK model written in Generalized Algebraic Modeling Systems (GAMS). The COal LINKage (COLINK) model is a linear programming optimization model and has been developed jointly by the Tata Energy Research Institute (TERI) and Kyoto University. The model is structured around the framework of a general transportation problem. The modeling framework adopted in this study enables a comparative assessment of changes in delivered costs for coal as well as overall system costs as a result of changes in various constraints in the model. Scenario analysis based on the model provides useful policy guidelines for the sector. It must, however, be kept in mind that results of the model are only indicative and a detailed assessment of
R. Mathur et al. / Energy Policy 31 (2003) 319–331
individual projects should be undertaken before taking major policy decisions. 2.2. Scope of the model The COLINK model is used to examine the patterns of coal supply under various scenarios for meeting electricity requirements of the country in various years. The model is static in nature and has been set up for three time periods representing the terminal years of the Five-year plans of the country. While the year 1996/97 represents the base year, scenario analysis is carried out for 2001/02 and 2006/07. Scenarios are designed to allow alternative linkages for coal movement, include the possibility of introducing washeries, and charge differential freight rates for transportation of clean coal in future. An analysis of differences in system costs, reduction in emission levels and variations in coal production and supply patterns in the alternative cases as compared to the base case is used to examine the role of various influencing factors. 2.3. Description of the COLINK model In its most simplistic form, the COLINK model (Fig. 1) is set up as a general transportation problem
321
where coalfields and ports represent the supply nodes while coal-based power plants represent the demand nodes. The model structure is designed such that it can include T&D flows between power plant nodes and can be used for a larger power planning exercise based on regional power demands. However, in this study the focus was restricted to examining the optimality of coal linkages and the attractiveness of the coal washery. Accordingly, the model used in this study was driven by the energy requirement at each of the power plants based on planned capacities, PLF and heat rates. The model then makes choices about the source and routing of coal from various coalfields and washeries to meet the generation requirements at each power plant for each time period such that the total system cost is minimized. The model then makes choices about the source and routing of coal from various coalfields and washeries to meet the power requirements in each time period such that the total system cost is minimized. Accordingly, the quantity of coal selected for washing is also determined by the model based on the relative cost effectiveness of the washery under various scenarios. Balance equations as well as process control equations in the model ensure that coal which is mined from the coalfields is equal to that transported to the washeries and power plants, power generated from all plants in a
STRUCTURE OF THE COLINK MODEL movement of unwashed coal to plants COAL MINING
movement of unwashed COAL coal to washery WASHING
movement of washed coal to plants
COAL UTILIZATION transmission of FOR POWER power to region GENERATION
PLANT 1
MINE 1 D grade coal E grade coal F grade coal
ELECTRICITY SUPPLY
REGIONAL DEMAND 1
PLANT 2 MINE 2 F grade coal G grade coal
PLANT 3 T&D LINES
RAIL NETWORK COAL FIELDS
REGIONAL DEMAND 2
WASHERY
WASHERIES
THERMAL PLANTS
EMISSIONS
Influencing Characteristics
Gradewise calorific value Pithead price by grade Ash & moisture content
input & output efficiency cost
costs PLF availability capacity heat rate pollution control equipment
Fig. 1. Structure of COLINK model.
TOTAL POWER DEMAND TOTAL SYSTEMCOST
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region is equal to the sum of the regional power demand and transmission loss, quantities output from each washery relate to the input quantities, washery yields, as well as other characteristics such as calorific value, ash and moisture content of input coal.
3. Basic equations of the COLINK model Coal supply balance: X F2W ¼ qF2P fpg þ qfg ;
qMINED fg
p
qMINED pQCAP fg fg ; : quantity of grade g coal produced at coalfield qMINED fg f; qF2P fpg : quantity of grade g coal transported from coalfield f to power plant p; qF2W fg : quantity of grade g coal, which is to be washed at coalfield f ; QCAP fg : maximum capacity of coal production of g grade coal at coalfield f : Coal washing balance: X CFfg qF2W ¼ qW2P fg fpg ; p
X
p
X
WSH qW2P Þ; fpg CALfg
fg
genp : power generated at power plant p; : capacity of power plant p; capPOW p gp : generation efficiency of power plant p; PLFp : plant load factor of power plant p; CALfg : calorific value of grade g coal at coalfield f ; : calorific value of washed grade g coal at CALWSH fg coalfield f : Power supply balance: X X bpp0 tmpp0 tmpp0 ; DDp pgenp þ p0
capnewpow p : newly added capacity at power plant p; capnewWSH : newly added capacity of washery at f coalfield f ; : existing capacity at power plant p; CAPOLDPOW p CAPOLDWSH : existing capacity of washery at coalf field f ; Objective function: X C¼ qF2P fpg ðfreight chargeÞfp fpg
þ
X
qW2P fpg ðfreight chargeÞfp
fpg
þ
X
qMINED ðCoal priceÞfg fg
fg
þ
X
capnewPOW ðinvestment cost of power plantÞp p
p
þ
X
genp ðoperating cost of power plantÞp
p
þ
X
f
qW2P fpg : quantity of washed coal transported to power plant p; capWSH : capacity of washery at coalfield f ; f CFfg : conversion factor for washing g grade coal at coalfield f : Power generation balance: X capPOW PLFp 8760; genp p p qF2P fpg CALfg þ
¼ CAPOLDWSH þ capnewWSH ; capWSH f f f
capnewWSH ðinvestment cost of washeryÞf f
f
g
X
¼ CAPOLDPOW þ capnewPOW ; capPOW p p p
XX þ ð qWSH fpg Þ ðoperating cost of washeryÞf :
WSH qF2W ; fg pcapf
genp ¼ gp ð
Plant capacity balance:
p0
tmpp0 : power transmitted from power plant p to power plant p0 ; bpp0 : transmission loss coefficient between power plant p and p0 ; DDp : power demand at power plant p:
pg
4. Data and assumptions All data used in the study is based on secondary information published by the Coal India Limited (CIL), the Planning Commission, the Central Electricity Authority (CEA), the Indian Railways and other related Government agencies. Discussions with both suppliers and consumers were conducted to gain insights of the main problems faced by the sector and to thereby formulate and analyze meaningful scenarios. 4.1. Power supply and demand In this study, we assumed that all planned additions to coal-based generating capacity fructify to meet future electricity demands. Capacity and PLF as specified by the CEA were used for the existing power plants, while a PLF of 64% (based on national average in the base year) was assumed for power plants for which data was not available. Accordingly, the total coal-based power demand was estimated to increase from 269 Twh in 1996/97 to 353 Twh in 2001/02 and 418 Twh in 2006/07. Each of the coal-based thermal power plants included in the model was assigned to one of the five power planning regions of the country (North, South, East, West and Northeast) and was characterized by its
R. Mathur et al. / Energy Policy 31 (2003) 319–331
installed capacity, plant efficiency, heat-rate, costs, etc. The level of utilization of each of the power plants was included as a variable in the model. 4.2. Coal supply The model is provided with the option to choose coal of various grades from each of the supply nodes (coalfields and ports). Maximum levels of grade-wise availability of coal at each supply node were fixed based on existing data for 1996/97 and planned production levels for the future years. The actual off take from a coalfield was included as a variable so that the model determines the quantity of coal chosen from each source such that the least-cost accrues to the system as a whole. Upper and lower bounds on the grade-wise quantity that can be mined from each of the coalfields were used to fix realistic levels of operations where necessary. Representative levels of ash, moisture and calorific value for each grade of coal at each of the coalfields were provided to represent the coal characteristics at each of the supply nodes. The data on coal characteristics was compiled based on the colliery-wise documentation of coal properties (Classification of Non-coking Coals of India for the Ministry of Coal) and the detailed coal production data for 1996/97 (Coal Controller’s Directory. 1996/97). The data thus compiled indicated that there was substantial availability of good quality coal, and an initial model run based on this data indicated that the washery was not at all attractive. Discussions with consumers indicated that coal received by them was generally 1–2 grades lower in quality than what the suppliers claimed. Accordingly, a grade slippage of 1–2 grades was incorporated in the data before evaluating the feasibility of introducing washeries under alternative scenarios in this study. Various ports were included in the model to allow for imports of coal and total supply of imported coal was constrained at the 1996/97 level for the base year. 4.3. Washery operations The alternative scenarios examined for the future include the option of investing in washeries located at each coalfield node, to enable beneficiation of coal (if economical) before transporting it to the power plants. The model chooses to construct washery capacity based on the requirements for ash reduction, washery costs and the level of yield from the washeries. The input–output relationship for coal in the washery process is modeled based on a study on washability characteristics of Indian thermal grade coal. The yield ash data have been analyzed in this study for thermal coal from Piparwar and Talcher coalfields. Based on this study, the percentage of yield and combustible recovery
323
is a function of a single constant m; as described below. Yield ¼ 100ða=af Þm ;
ð1Þ
Recovery ¼ 100ða=af Þm ð100 aÞ=ð100 af Þ;
ð2Þ
where, ‘‘a’’ is the percentage of ash in the washed coal, ‘‘af ’’ is the percentage of ash in the feed (input for washery) and the yield decreases as the value of ‘‘m’’ increases (Narasimhan et al., 1997). The washery is modeled such that high-ash coal (>34% ash) can be washed to yield coal output with 34% or lower ash levels. Higher levels of ash reduction can be specified in subsequent scenario runs. Apart from reducing the amount of coal that needs to be transported, the benefits of coal washing were also modeled to include improved efficiency of power plants. For every 10% reduction in ash, the heat rate is assumed to improve by 1%, the PLF increases by 5% and the maintenance cost of the boiler decreases by 20% (Sinha, 1998). 4.4. Linkages Historically power plants were allocated quarterly linkages with certain coalfields based on coal requirements as stated by the plants, availability of coal at the coalfields and the capacity of the Railways to transport the coal between the supply and demand points. Accordingly, linkages were allowed based on information as available from the mid-term appraisal of the Eighth Five Year Plan (Coal demand and linkages, 94– 95 to 96–97 and 2001–02). Pithead power plants were provided fixed linkages from coalfields where they are located since alternative options would be irrational in such cases. Based on discussions with Railway Authorities, and their statements during the linkage committee meetings, the Railways did not indicate capacity constraints in moving coal in the short term. Also, coal is a high priority good in railway movement implying that capacity would be spared (by shifting other commodities to alternative routes) for its movement if required. Therefore, capacity constraints in rail movement are not included in the model. The availability of linkages (based on actual and planned data) is provided as a zero-one matrix for each year under consideration. 4.5. Cost parameters The system costs that are minimized include various costs provided to the model. The grade-wise price of coal at each coalfield is provided to the model enabling choice on the basis of the price and the calorific value of coal at each location. Further, freight charges are included between each demand and supply node based on distances between the nodes and the freight rates for
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coal as existing in 1996/97. Costs of setting up of new washery capacity were based on costs of the ST-BSES washery, which amounted to $ 15 million for a 2.5 million tons per annum (MtPA) washery (Shahi, 1998). Investment costs for new generating capacity were considered at Rs. 40 million/MW, while operating and maintenance costs were taken to be 3% of the investment costs. Moreover, a cost of Rs.120/ton is assumed for ash handling and disposal at the power plants. 4.6. Environmental constraints Constraints on levels of SPM and CO2 can be included in the model to enable a restriction of emission levels from the sector. In the baseline, the levels of SPM and CO2 are generated by the model based on the quantity and quality of coal used and the power generation technologies adopted in the country. The alternative scenarios can, however, consider restrictions on emission levels so as to examine other possibilities of coal utilization that abate the pollution levels. The levels of SPM emissions depend on the efficiency of the electrostatic precipitators (ESPs) at the power plants. Although it is often stated that the efficiency of ESPs are as high as 99%, discussions with experts revealed that efficiencies were often much lower due to improper maintenance. Accordingly, an efficiency of 95% was considered in the model. 4.7. Description of scenarios Various scenarios have been set up in the study with a view to examine the attractiveness of washeries in the system and to examine the potential for enhancing benefits by adopting alternative linkages for coal supply. The base year for the study is 1996/97 and the base case is examined to test the realistic working of the model for this year. The alternative scenarios are examined for 2001/02 and 2006/07. BAU scenario: The base case was developed for 1996/ 97 using documented data of coal supply and power generation and based on the existing set of coal linkages. The model was validated to ensure realistic working for this year. The business-as-usual (BAU) scenario envisages an extension of the base case assumptions for the years 2001/02 and 2006/07. Coal supply was assumed to increase as per production plans of the supplying agencies and the existing coal linkages were expected to continue. It was also assumed that there would be no beneficiation of coal by washing in the BAU scenario. Free linkage scenario: The authors hypothesize that the existing linkages for coal supply to power plants may not be most economical for the entire system. Discussions with Railway authorities indicated that track capacity is not a limiting factor and larger
quantities of coal could be moved between the demand and supply nodes even in the immediate short term without any additional capacity augmentation. Against this background, a free linkage (FL) scenario was set up for the immediate short term (2001/02) to examine whether overall system costs could be reduced if alternative linkages were allowed. As against the BAU scenario, the free linkage scenario allows movement of coal between all coalfields and power plants. The model then has an option to choose a set of linkages that may result in lower overall system costs as compared with the BAU scenario. Scenario for washery with single and multiple ash reduction targets: In light of the notification of September 1997, a washery scenario was set up to examine if utilization of washed coal was an economically preferable option. Accordingly, the model was provided with the option of introducing washeries to wash coal to 34% ash level in the single target scenario and to 34%, 30% and 25% ash levels under the multiple target scenario. The single and multiple target washery scenarios were evaluated for the year 2006/07, and it was assumed that a maximum of 4 Mt of washery capacity could exist at each of the coal supply nodes by this year. Dual price scenario: Given that there has not been much progress in India with regard to the setting up of washeries for thermal coal, a scenario was developed to examine whether beneficiation of thermal coals could be promoted by adopting a differential freight structure for transporting washed and unwashed coal. The dual price scenario attempts a sensitivity analysis of thermal coal beneficiation with progressively higher levels of discount to freight charges for washed coal. This scenario was examined for the year 2006/07. Technological improvement scenario: The technological improvement scenario was developed with a view to examine the competitiveness of alternative power generation technologies vis-a" -vis the option of coal washing. Along with the option of introducing coal washeries, this scenario allows the introduction of more efficient generation technologies such as integrated coal gasification combined cycle (IGCC) and super critical technologies against the existing sub-critical technology. This scenario would indicate the preferred technology for investment in terms of the least-cost option in moving towards cleaner coal-based power generation.
5. Results and analysis 5.1. BAU scenario The requirement of coal by power plants as per the model was 196.5 Mt in 1996/97 as compared with an actual level of 199.6 Mt (Coal Directory of India, 1997).
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Moreover, the model indicated that 2.4 Mt of SPM and 78.2 Mt of CO2 were emitted in 1996/97. The total system cost was Rs. 638.9 billion of which freight costs were Rs. 80.3 billion, coal costs were Rs. 104.3 billion, and plant costs were Rs. 447.4 billion while costs of ash disposal were Rs. 6.9 billion. The cost of coal and its movement comprise the operational costs of supplying coal to the power plants, while the other components relate to investment costs at the power plants and washeries. It is seen that the cost of coal movement constitutes nearly 44% of the total operational cost of supplying coal to the power plants, while the cost of coal is 56%. An analysis of the future years shows that the coal requirement increases from 196.5 Mt in 1996/97 to 234.7 Mt in 2001 and 291 Mt by 2006/07. The total system cost increases from Rs. 638.9 billion in 1996/97 to Rs. 818.3 billion in 2001/02 and Rs. 1023.2 billion in 2006/07. Moreover, emissions of SPM increase from 2.4 Mt in 1996/97 to 3.9 Mt in 2006/07 (an increase of 38%) while CO2 emissions increase from a level of 78.2 Mt in 1996/ 97 to 133.4 Mt in 2006/07 (an increase of 41%).
5.2. Free linkage scenario An analysis of the system costs under the free linkages scenario for 2001/02 indicates that although the quantity of coal transported is the same as that under the BAU, re-allocation of linkages can result in a reduction of as much as 27% of the freight charges for coal transportation incurred in the BAU scenario (Table 1). In 2001/02, coal movement decreases from 117.2 billion tons kilometer (Btkm) under the BAU to 81.5 Btkm under the free linkage case (a reduction of approximately 30% over the BAU). Moreover, in the BAU scenario, coal is transported up to a maximum distance of 2000 km while the model chooses to move coal only up to a maximum distance of 1700 km in the free linkage scenario (Fig. 2). The results of this scenario stress upon the need to revisit the historically set linkages to review the possibility of changes by diverting or managing routing of traffic on alternative routes.
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It must be noted however, that the model attempts to minimize total system costs and may thereby decrease freight between some nodes at the cost of increasing freight between other nodes. However, the process of linkage setting in reality would attempt to minimize costs of individual consumers and may even be based on individual consumers0 preferences that may not necessarily be a function of the cost of coal movement alone. Factors such as reliability and past associations are likely to play an important role in the decision-making with regard to linkage setting. The model can therefore at best provide indicative results in this regard and any changes in specific linkages must be examined more specifically keeping in mind other factors such as capacity constraints of the Indian Railways, priorities of movements along specific corridors, past relationships between suppliers and consumers, etc. 5.3. Scenarios for washery with single and multiple ash reduction targets As discussed earlier, the initial model runs with the option of introducing washeries using data on coal availability and quality as per published documents and literature indicated that the washery was not at all attractive. The data was therefore modified to account for grade slippages (based on discussions with consumers) before conducting the washery analysis. 5.3.1. Washery scenario with single target of ash reduction to 34%: Despite providing the model with a larger quantity of inferior grade coal, it was observed that although the washery became attractive, a total of only 5.5 Mt of F grade coal from Talcher, South Karanpura and Ib Valley coalfields was selected for washing in 2006/07 (Table 2). Since the model chooses only a small quantity of coal for washing, the reduction in system costs over the BAU was also marginal. The decrease in costs is attributable to marginal decreases in the costs of coal movement, reduced costs (O&M) at the power plants and some decreases with respect to ash handling and disposal costs at the power plants. However, the increase in cost of coal and the cost of constructing the washery
Table 1 Break-up of system costs (Million Rs.): 2001/02
Freight charges for coal transportation Pithead price of coal Power plant costs Cost of ash handling and disposal Total system costs
BAU scenario
Free linkages scenario
Change over BAU
87,624 136,641 589,922 8095 822,281
64,388 136,171 584,280 8078 792,917
23236(26.5%) 470(0.3%) 5641(0.9%) 16(0.1%) 29364(3.5%)
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Coa l m ove m e nt- BAU & Fre e linka ge (2001/02) 14 0
10 0%
80 %
Qu an tity (M t)
10 0
70 % 60 %
80
50 % 60
40 % 30 %
40
20 % 20
10 %
0
0
50 -2 01
24
22
01
-2
-2
10
30
0
0 90 01
-1
70 01
-1 18
01 16
01 14
20
0 50 -1
-1
30
10 01 12
01
-1
110
80
0
0
0 90
70
0 60
140
1-
50
0 30
0 1-
10 20
0
0% 0
0 0-
C u m u lative q u an tity m o ve d (%)
90 %
12 0
Dis tan ce o f tr an s p o r tatio n (k m ) Q uan tity of c oal tr ans porte d - B A U (Mt)
Q uan tity of c oal tr ans porte d - Fre e L inkag e ( Mt)
Cumulativ e quan tity -B A U (% )
Cumulativ e quan tity -Free Lin ka ge (% )
Fig. 2. Frequency distribution of coal movement (BAU vs free linkage—2001/02).
Table 3 Break-up of costs (Million Rs.): 2006/07
Table 2 Quantity of coal to be washed (by coalfield) Coalfield
Grade
Quantity (Mt)
Talcher South Karanpura Ib Valley
F F F
4.00 1.15 0.37
substantially offset the cost benefits (reductions) that accrue to the system (Table 3). It is also argued that there may not be substantial benefits in terms of decreased costs of coal movement, since the reduction in weight of extraneous matter may be offset to some extent by the addition of surface moisture during the process of washing. It is also observed that with the introduction of the washery, emissions of SPM and CO2 reduce by approximately 42,000 and 100,000 tons, respectively, as compared with the BAU levels in 2006/07. Further, a sensitivity analysis was carried out to examine the breakeven costs for the washery process in the above scenario. The coalfield-wise quantum of coal selected by the model for washing was examined for every 10% increase in beneficiation costs over the baseline (Fig. 3). It was observed that the coal at Talcher was the most sensitive to increases in washery costs and although the quantity of coal washed reduced even with a 30% increase in washery costs, all coal ceases to be taken up for washing with a 40% increase in costs over the baseline. Coal from the Ib Valley is insensitive to
Cost of coal transportation Cost of coal Cost of power plant Cost of washery Ash handling costs Total system costs
BAU scenario
Single target washery scenario
Change over BAU
112,249
111,986
263 (0.2%)
203,254 616,958
203,477 616,546
+223 (+0.1%) 412 (0.1%)
0 11,068
351 10,999
+351 (N.A.) 69 (0.6%)
943,529
943,360
169 (0.02%)
changes in washing costs till a 40% increase over the base costs beyond which the washery is not economically attractive. The coal from South Karanpura was observed to be highly insensitive to changes in washery costs and was not considered attractive for beneficiation unless washing costs more than doubled over those in the base case. Although the model results show that poor grade coal from South Karanpura, Ib Valley and Talcher coalfields are chosen for washing, it is clear that the economic benefits as such are not very substantial under a scenario of coal washing possibilities. It is therefore unlikely that washeries would be set up for beneficiation of noncoking coal supplied to thermal power plants in the absence of any stringent policy directives with regard to the environment or in terms of supply or movement of high-ash coal.
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327
S e nsitiv ity of coal be ne ficiation to incre ase in washe ry costs 6
Q uantity w as hed (M t)
5 4 Talc her (F grade) 3 2 Ib V alley (F grade) 1
S outh K aranpura (F grade)
0 1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
W as hing Cos t (inc reas e over bas eline)
Fig. 3. Sensitivity analysis of coal beneficiation with cost increases.
Table 4 Cost comparison—BAU and multiple target washery scenarios (Million Rs.): 2006/07
Cost of coal transportation Cost of coal Cost of power plant Cost of washery Ash handling costs Total system costs
BAU scenario
Multiple target washery scenario
Change over BAU
112,249
112,008
241 (0.2%)
203,254 616,958
203,652 616,279
+398 (+0.2%) 679 (0.1%)
11,068
0 363 10,985
+363 (N.A.) 83 (0.7%)
943,529
943,288
241 (0.02%)
5.3.2. Multiple washing targets The washery scenario with multiple targets of coal washing indicates marginal benefits in coal transportation costs as well as overall system costs as compared to the BAU in 2006.07 (Table 4). It is interesting to note that even with constant costs, the washery becomes increasingly unattractive, as we desire higher levels of ash reduction (Fig. 4). With a target of 25% ash coal, a minor quantity of coal is selected for washing, which is highly sensitive to increase in washery costs. Increases of even 20% beyond the existing washing costs render the washery process ineffective in the model for high levels of ash reduction. With a target of washing coal to 34% and 30% ash levels, a gradually increasing disincentive to wash coal is noticed as washery costs increase 60% above the existing costs, after which the model rejects washing of coal.
There is still much debate among experts about the yield of washeries for Indian thermal coal. It is felt that the yield of washeries could be much lower than that used in our model due to the specific nature of Indian coals. Indian coals have high levels of ‘‘near gravity material’’ that make coal washing unsuitable using current techniques. Improving the efficiency of the washery process is critical before beneficiation of thermal coals can be exploited to a greater extent in India. There is therefore a pressing need for developing and/or adapting appropriate technologies suitable for beneficiating Indian noncoking coal. 5.4. Dual price scenario The results of the dual price scenario are reflected in Fig. 5. It can be seen that as the transportation cost for washed coal decreases, it becomes more and more economical to wash coal from various coalfields. Although all the F grade coal from South Karanpura and Talcher coalfields are necessarily washed in the model even at the existing freight rates considered for washed coal, it is noticed that higher levels of coal are washed from other coalfields when freight rates are decreased. At the existing freight rates only 0.37 Mt of F grade coal from the Ib Valley coalfield was selected for washing, which increases to 3.26 Mt with a 10% discount in freight rate and further to 4 Mt when freight rate is decreased by more than 12.5% over the base rate. F grade coal from Korba coalfield also becomes economical to wash and transport when freight rates decrease by more than 5% over the base freights. Minor amounts of E grade coal from Jharia and F grade coal from Pench-Kanhan are selected for washing when freight rates for transporting washed coal are reduced by
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328
Sensitivity of coal beneficiation w ith increasing levels of ash reduction
Quantity to be washed (Mt)
6.0 5.0 4.0 34%
3.0
30%
2.0
25%
1.0 0.0 1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
Increase in washing cost over baseline Fig. 4. Sensitivity of coal beneficiation with increasing levels of ash reduction.
S ensitivity of coal w ashing un der differential freigh t policy 1 6 .0
P enc h-K anhan (F grade) Jharia (E grade)
Q uantity to be was hed (M t)
1 4 .0 1 2 .0
K orba (F grade) 1 0 .0 8 .0
Ib V alley (F grade) 6 .0 4 .0
Talc her (F grade)
S outh K aranpura (F grade)
2 .0
%
%
%
%
% .0 40
.5 37
.0 35
.5 32
.0
.5
%
%
30
27
% .5
% .0
% .5
% .0
.0 25
22
20
17
%
%
15
.5 12
5%
.0 10
7.
0% 5.
5% 2.
0.
0%
0 .0
Dis c ount rate for freight of was hed c oal (% )
Fig. 5. Sensitivity of coal beneficiation with decrease in freight rates for washed coal.
more than 20% against baseline rates. This clearly indicates that the quantity of coal to be washed is sensitive to freight rates. The total quantity of coal that is washed increases from 5.5 Mt with the existing freight rates for washed coal to 14.2 Mt when the rates are reduced by 40%. It is seen that even with a 10% reduction in freight rate, the quantity of coal selected for washing increases to about 12 Mt from the base level of about 5.5 Mt. This indicates that the introduction of monetary incentives for the movement of clean coal could provide an impetus to beneficiate thermal coals in India. Such a policy perspective is also recommended in terms of the benefits that accrue in terms of emission reductions from
washing. An examination of the levels of SPM emissions against the reductions in freight rates and the consequent levels of coal washed reflect the environmental benefits of coal washing (Fig. 6). 5.5. Technological improvement scenario A sensitivity run was conducted with progressively increasing constraints on the levels of SPM (0–10% of the base level emissions) under a scenario where coal washing technology could compete with alternative power generation technologies for the year 2006/07. The marginal costs of emission reduction under the three alternative options were examined (Fig. 7).
R. Mathur et al. / Energy Policy 31 (2003) 319–331
329
99.5%
SPM emission (compared to BAU)
99.4%
99.3%
99.2%
99.1%
99.0%
98.9%
98.8% 0%
5%
10%
15%
20%
25%
30%
35%
40%
Discount Rate for freight of washed coal (%)
Marginal cost of Emission Reduction ($/ton)
Fig. 6. Decrease of SPM emissions with reductions in freight rate of washed coal.
12000 10000 8000 6000 4000 2000 0 0%
2%
4%
6%
8%
10%
Reduction of SPM Emission (%) Sub critical & Coal Washery
IGCC & No Washery
Supercritical & No Washery
Fig. 7. Marginal cost of emission reduction.
It is seen that the marginal costs for SPM emission reductions by the washery process are higher than that by adopting the efficient power generation technologies. This indicates that even under a policy regime of environmental improvement, it is economically preferred to invest in alternative coal-based power generation technologies (super critical or IGCC) rather than investing in coal washing technology. The model results indicate that alternative technological options for power generation or management
measures such as the adoption of alternative linkages are economically more attractive than coal washing. However, unless power plants based on the alternative technologies fructify or linkages are rationally apportioned, it may be imperative for power plants located in some areas to use washed coal or import coal for blending in order to comply with environmental regulations. Further, in order to continue operation of existing power plants, washing may be necessary even at higher costs in the long term.
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6. Conclusion The highlights emanating from this study are briefly discussed below. Firstly, if the data on coal quality as published by the suppliers is valid, the model indicates adequate availability of good quality coal and the washery is not found to be attractive at all. Significant variation was observed in data regarding thermal coal quality as reported by the suppliers and consumers. This is a cause for concern since it can change the very finding of the study. Based on discussions carried out during the study, a situation of ‘‘passing the buck’’ was clearly observed between the Railway authorities, the coal suppliers and the electricity consumers. Although none of the parties accepted the existence of constraints in the availability or movement of coal, reality does indicate clearly the lack of availability of adequate quantity and quality of indigenous non-coking coal and the consequent increase in use of imported coal in the country. It is therefore more important to first recognize and address the fallacies and data gaps that exist in the Indian coal sector before a correct assessment can be made regarding the need for beneficiation technologies. The results of the initial model run also stress on the need to focus on identifying and supplying good quality coal reserves which can be achieved by adopting better mining techniques in order to produce coal of desirable quality and quantity in the future. With assumptions of grade slippage, the model chooses to introduce washery capacity for beneficiating a small quantity of inferior grade coal (F and G grades). It must be noted however, that the operational and environmental benefits are not very substantial, and washery costs nearly offset these benefits. The model thereby indicates little profit motive and investors are therefore unlikely to be lured into setting up washeries on their own. Moreover, it is argued that washery yields for Indian thermal coal could even be much lower than those considered in this study. In the short term, therefore, the introduction of additional washery capacity using current techniques are likely to be driven only by the enforcement of stringent policy directives with regard to the environment or in terms of restricting supply or movement of high-ash coal. In the long term, it is however, important to focus research and developmental (R&D) activities towards enhancing the efficiency of the washery technology for Indian thermal coal, especially in light of the recent competition from coal imports and given that the capacity of coal-based power generation plants is expected to play a dominant role in India. Investment should be directed towards technological development and adaptation for beneficiation techniques suited to Indian coal. In light of the existing environment, it is felt
that third parties should be encouraged to set up washeries if the consumers give a firm commitment for the off-take of washed coal at higher prices. The possibility of joint ventures and technology transfer should also be explored towards this end. Collaborations with experienced nations would be especially helpful in this regard—these may be both in terms of technology transfer, in terms of monetary support for R&D and/or in terms of support by way of infrastructure and hardware capabilities. In the same context, it must also be realized that coal washing is not the only option for beneficiating power grade coal. Other alternatives such as the options of blending of thermal grade coals with better quality of coal (imported/indigenous) must be examined. Power plants faced with environmental restrictions have already started to resort to this option over the last few years. The option of cleaner technologies and alternative generation technologies such as supercritical and IGCC technologies should be evaluated for the future. The study indicates that a reduction in freight rate for movement of washed coal significantly increases the attractiveness of the washery. The maximum sensitivity is observed at a freight reduction of about 10% as compared to the BAU levels. Accordingly, the introduction of a differential freight rate policy where charges for transporting washed coal are lower, could be instrumental in enhancing the adoption of thermal coal washing. On the contrary, a policy of fixing higher freight rates for the movement of washed coal (as was tried out initially in the country) would be expected to function against the objective of promoting washeries. Another point that emanates clearly from the study is the need for re-examining the optimality of the existing linkages for the movement of coal to power plants. The free linkage scenario indicates that costs of coal movement could be reduced by as much as 27% compared to the BAU costs in 2001/02 through a mere re-allocation of linkages for coal movement. The assignment of linkages in reality may be influenced by many extraneous factors and it may not be possible to reallocate all movement as indicated by the model. However, given the substantial benefits as indicated by the model, it would still be worthwhile to re-assess possibilities for alternative supply sources and routing of coal. In essence, this study indicates that one of the most basic requirements of the coal sector is to develop a system of accountability and transparency with regard to coal quality. In the short term, measures such as charging of differential freight rates for washed coal and re-allocation of linkages of coal movement should be adopted to realize overall gains to the economy. Coal suppliers should ensure a reliable supply of relatively better quality of coal to power plants located in sensitive areas and distant from the coalfields. This is especially important to protect the domestic coal industry against
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imports in the long run. Investments should be directed towards promoting further research for the development or adaptation of appropriate coal production and utilization techniques for Indian thermal coal.
References Classification of Non-coking Coals of India for the Ministry of Coal, Government of India. Central Fuel Research Institute. Council for Scientific & Industrial Research. Ministry of Science & Technology. Government of India, Dhanbad, Bihar, India, March 1998. Coal demand and linkages 1994/95 to 1996/97 and 2001/02—mid term appraisal of Eighth Five Year Plan. August 1994. Coal Directory of India, 1996–97. Calcutta: Ministry of Coal. Coal Controller’s Organization, 1997, 128pp.
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Fourth National Power Plan, 1997–2012. Central Electricity Authority, Government of India, 1997. Narasimhan, K.S., Bhoumik, S.K., Reddy, P.S.R., Prakash, S., Biswal, S.K., 1997. Washability characteristics of Indian coals. Journal of Mines, Metals and Fuels. 45, 233–237. Reuben, B.S., 1998. Advantages of coal beneficiation—AEC’S perspective. Coal Prep India ‘98: Proceedings of the International Conference on Coal Beneficiation, January 12–13, 1998, New Delhi, India. Sinha, A., 1998. Indian coal perspectives problems and solutions with focus on thermal coal. Coal Prep India ‘98: Proceedings of the International Conference on coal Beneficiation, January 12–13, 1998, New Delhi, India. Shahi, R.V., 1998. Need for environment friendly use of coal for power generation. Coal Prep India ‘98: Proceedings of the International Conference on Coal Beneficiation, January 12–13, 1998, New Delhi, India.