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Geographical distribution of biomass and potential sites of rubber wood fired power plants in Southern Thailand
Available online at www.sciencedirect.com
Biomass and Bioenergy 26 (2004) 47 – 59
Geographical distribution of biomass and potential sites of rubber wood !red power plants in Southern Thailand P. Krukanonta , S. Prasertsanb;∗ a Energy
Economics Laboratory, Department of Socio-Environmental Energy Science, Graduate School of Energy Science, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan b Department of Mechanical Engineering, Prince of Songkla University, Hat-Yai, Songkla 90112, Thailand Received 24 September 2002; received in revised form 14 March 2003; accepted 2 April 2003
Abstract Biomass residues from rubber trees in rubber producing countries have immense potential for power production. This paper presents the case of the south peninsular of Thailand, where the rubber industry is intense. Mathematical models were developed to determine the maximum a1ordable fuel cost and optimum capacity of the power plant for a given location of known area-based fuel availability density. GIS data of rubber growing was used to locate the appropriate sites and sizes of the power plants. Along 700 km of the highway network in the region, it was found that 8 power plants are !nancially feasible. The total capacity is 186:5 MWe . The fuel procurement area is in the range of less than 35 km. ? 2003 Elsevier Ltd. All rights reserved. Keywords: Biomass; Power generation; Optimization; GIS
1. Introduction As an agriculture-based country, biomass energy potential in Thailand is estimated at 65 PJ a year [1]. Biomass energy contributes about 25 –30% of primary energy need in Thailand. Its major role still exists in household use and in small and local industries like brick !ring and lime making, however. One of the most important biomass is rubber wood. Thailand produces about one-third of the world’s natural rubber. Out of 2:72 million ha of the peninsular area in the South, 1:67 million ha are rubber plantations, which
∗
Corresponding author. Fax: +66-74-212893. E-mail addresses: [email protected] (P. Krukanont), [email protected] (S. Prasertsan).
makes Southern Thailand the single largest rubber plantation region in the world. As the economic life of the trees is 25 –30 years, about 3– 4% of the rubber growing area is cut down for replanting annually. Over its lifetime, the rubber trees store solar energy in the form of biomass, which weighs more than 180 t=ha. The rubber wood becomes raw material for sawmills and wood product factories, e.g., furniture, kitchenware and wooden toys. Thailand is supporting private power producers to install totally 300 MWe electricity facilities from renewable sources, and rubber wood residue is the potential candidate. Assessing the risks connected to fuel supply is vital for the successful operation of large-scale bio-energy project. Junginger et al. [2] determine and minimize the fuel supply risk by focussing on the variation of residue quantity, limited accessibility, utilization by other competitors and
0961-9534/04/$ - see front matter ? 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0961-9534(03)00060-6
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P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Nomenclature Cls Cts Cws (Cws )o (Cws )o Eo D fe i Is IRR km LHV m MCwet n N pd
speci!c wage per capita of labour cost (USD person−1 yr −1 ) unit cost of transportation (US Cent t −1 km−1 ) unit cost of wood wastes (at the site of source) (US Cent t −1 ) optimal unit cost of wood wastes (US Cent t −1 ) optimal unit cost of wood wastes calculated by availability (US Cent t −1 ) optimal electricity output (MWe ) radius of targeted locations (km) electricity export factor (% or decimal) discount rate (%) speci!c cogeneration investment (USD MW−1 e ) internal rate of return (%) maintenance coeJcient (% or decimal) lower heating value of fuel (MJ kg−1 ) number of months (month) moisture content (wet basis) (% or decimal) economic cogeneration lifetime (yr) number of workers (person) price of thermal energy (US Cent kWh−1 )
logistic risks. Unlike the supply risks being faced by the short-rotation crops, the supply of rubber wood residues is quite secure in the long term. The rubber wood residues comprise small branches left in the plantation (54% of total biomass) and sawmill wastes (32%). On an annual basis, rubber wood residues in the forms of sawdust and wood o1-cuts in the sawmills, small branches left in the farm and wood residue in the factories are estimated at 4125 × 103 , 6917 × 103 and 833 × 103 t, respectively [3]. They are “real” wastes where there is no competitive user. In a previous study, Prasertsan and Krukanont [3] suggested the fuel purchasing strategy based on the fuel moisture content and the area-based availability density. They developed mathematical modes to be used as a tool for project developers to strategically negotiate with the fuel suppliers on the biomass price. It was
pec pee (QB )o (QBo )D (QBo ) QD R Ro (Ro )D (Ro ) t B CO
price of electricity capacity (US Cent MW−1 month−1 ) price of electricity energy (US Cent kWh−1 ) optimal boiler thermal load (MWth ) optimal boiler load calculated by targeted location (MWth ) optimal boiler load calculated by availability (MWth ) process heat demand (MWth ) radius of biomass plantation (km) optimal radius of biomass plantation (km) optimal radius calculated by targeted location (km) optimal radius calculated by availability (km) annual cogeneration operating time (h) boiler eJciency (% or decimal) cogeneration eJciency (% or decimal) annual speci!c wood waste availability (t km−2 yr −1 )
found that the most inMuential parameters a1ecting the a1ordable fuel costs are speci!c investment cost, electricity export factor, fuel moisture content and selling price of electrical energy. It has been concluded that biomass power plants should be small and placed in strategic locations supported by suJcient fuel availability density. Equipped with the simulation program for fuel purchasing strategy, one can locate the appropriate site and size of the power plant, if the area-based fuel availability density is known. Some earlier studies reported the development of computer programs and/or GIS data to identify the proper locations of power generation based on the geographical availability of biomass fuel, and other energy-related parameters [4–7]. Voivontas et al. [5] developed a decision support system (DSS) to identify the sites of
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
economically exploited biomass potential based on the electricity production cost. It was reported that the main parameters that a1ect the locations and number of bioenergy conversion facilities are plant capacity and spatial distribution of the available biomass potential. Following the !rst part of this study [3], this paper presents the GIS data of rubber growing area in the South of Thailand. The simulation program was developed to locate suitable sites for the rubber wood residue-!red power plants. 2. Materials and method
49
Table 1 List of selected provinces and comparison of growing rubber areas with paper-printed and digital-based calculations Province
Rubber areas Rubber areas Di1erences as registered calculated (%) (km2 ) digitally (km2 )
Suratthani Nakornsrithammarat Trang Patthalung Satoon Songkla Yala Pattani Narathiwas
2660 2250 1695 822 450 2640 1512 434 1424
2501 2240 1769 867 443 2687 1304 457 1335
−6 0 +4 +5 −2 +2 −14 +5 −6
2.1. Mapping of rubber wood area The recent (1998) survey of rubber tree growing areas in Thailand was carried out by the Rubber Research Institute of Thailand (RRIT) [8]. Images from the Landsat 5-TM satellite were used to analyse the rubber growing areas. The interpretation of the images’ information was con!rmed by a !eld survey. The RRIT’s information is available in paper-printed format with a scale of 1:100,000. The rubber tree mapping not only provides planting area quantitatively, but also gives the age distribution and area-based density. The rubber plantations were classi!ed into 2 subsets: young rubber trees (less than 5 years old) and mature trees (over 5 years old), which, for the whole country, totally cover areas of 259; 269 ha and 1; 654; 595 ha, respectively [9]. The plantations were classi!ed into 18 subgroups according to the age and percentage of the (area-based) rubber tree density. For example, group 9 represents the young rubber trees 21– 40% (of area), mature rubber trees 61–80% and non-rubber trees 0 –18%. The paper maps were digitized and a graphic software was developed to provide a “decision support system” (DSS) that can predict the area-based availability density of the rubber wood residues and determine the proper locations of power plants for a set of given conditions. Details of program development are given in [3,10]. Altogether 93 maps were digitized, merged and stored as one !le. The program is able to calculate the true rubber growing area (km2 ) and estimate the amount of rubber wood residue (t) in any targeted location. To verify the accuracy of
digitization, the calculated areas are then compared with the oJcial data from the Rubber Research Institute of Thailand [8]. It was found that the program calculated the total rubber planting area in each province quite accurately. Table 1 shows that the error, as compared to the areas registered by the Rubber Research Institute, was in the order of ±6%, except for the case of Yala, where the digitized map predicts the area less by 14%. The low prediction in Yala provides a safety margin for investment, as it estimates the fuel supply conservatively. The digitized map of rubber growing areas is, therefore, considered a reliable source of information about the availability of the rubber wood fuel. It is then applied as fuel reserve mapping for the possible locations of rubber wood !red power plants in Thailand. 2.2. Cost of biomass fuel If it is assumed that the fuel procurement area of the power plant is a circle. Detailed analysis in the previous study [3] showed that the cost of wood residue (Cws ) can be written as a function of the radius (R) as in Eq. (1). The radius R implies the size of the power plant. Thus, the fuel cost varies with the size of the power plant. Too large and too small power plants would expect to a1ord a lower fuel cost (at farm gate) to compensate for the transportation cost and the lack of economy of scale, respectively. The optimum radii of power plants (Ro ) are described in Eqs. (2) and
50
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
(3) for a cogeneration plant and a fully condensing plant, respectively. By substituting Ro into Eq. (1), the optimum cost of wood waste (Cws )o is obtainable. It should be made clear at this stage that (Cws )o is the maximum a1ordable fuel cost obtained at the expense of a !xed capacity of the power plant (!xed by constant Ro ), while Cws is the maximum a1ordable cost for a power plant of any capacity. In other words, (Cws )o is the maximum value of all Cws . It is the upper limit and must not be exceeded while negotiating fuel purchasing. Otherwise it is impossible to develop a power plant project (of any size) in that area if the required !nancial return (IRR) is !xed. Subsequently, the thermal boiler load and the electricity power are described by Eqs. (4) and (5), respectively. Cws = R−2 + R + ; where 1 =
m tQD pd − tQD fe pee + fe pec t
Is [km fa + 1] − tfa =−
(1)
2Cts ; 3
−
N
(Cls N )u
= (LHV )B CO
;
u=1
mfe pec Is [km fa +1] fe pee + − t tfa
(1 + i)n − 1 ; i(1 + i)n 1=3 2 ; Ro = N 1=3 3 (Cls N )u ; Ro = Cts
;
fa =
(2) (3)
u=1
QB =
R2 (LHV )B ; t
(4)
R2 (LHV )B CO (5) − QD : t As presented in Eqs. (2)–(5), the size of a power plant mainly depends on the area-based availability density of biomass fuel ( ) and the radius of the area for the fuel procurement. The higher the density of fuel availability, the larger the feasible capacity of the power plant. But large fuel procurement area in a low E=
availability density region may not be the feasible due to the excessive transportation cost of fuel. In the previous study, the purchasing strategy of the fuel in various conditions of fuel availability density, power plant sizes and !nancial returns was given [3]. 2.3. Location of power plants In order to apply the decision support system model in real practice, the digitized map is used to pinpoint suitable locations for power plants. It is assumed that the young and mature rubber trees are uniformly distributed in terms of area and age in the power plant expected area. The biomass procurement area is assigned to be a circle having the power plant at its centre. The centre of the circle moves along the main highway, where high-voltage transmission line is available for grid connection. It moves in steps of 50 km. As a narrow peninsular, the road network in the southern part of Thailand is linked to highway number 41 (Fig. 1). The total distance of more than 700 km from Suratthani province in the northernmost part to Narathiwas province in the southernmost part is the study area for power plant installations. In order to cover potential sites in other provinces, highways number 404 and 409 are incorporated into Trang and Yala provinces, respectively. The developed program calculates the area-based fuel availability density and suggests the optimum size of the power plant and the maximum a1ordable fuel cost for any interesting location. The boundaries of fuel procurement areas of the two adjacent sites are kept at least 10 km apart. 2.4. Clari8cation of important parameters In order to avoid confusion, some parameters need clari!cation. Since the growing density of the rubber trees is not uniformly distributed over the region, the area-based availability density is inevitably location dependent. The area-based availability density (t km−2 yr −1 ) is calculated from the area-based biomass production (residues at sawmills and small branches in plantation). The calculation takes into account the fact that the trees have economic life of 25 –30 years. Hence, the annual cut-down area is at least 3% of the planting area. The optimum radii in Eqs. (2) and (3) are based on the fuel availability density in a speci!ed circular area. In the calculation process,
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Fig. 1. Selected provinces in the South of Thailand for the potential rubber wood !red power plants.
51
52
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59 Assume radius Di
Calculate fuel availability, ()
Di+1 = Di + ((Ro)- Di)
Calculate optimal radius, (Ro) Eq (2)
(Ro) < Di
NO
plant is suitable for an area covering a radius of 35 km. However, a power plant of any size deviating from the optimum one can be set up, but at the expense of a lower maximum a1ordable fuel cost; hence, the bargaining power. In this case, the project development starts !rstly by selecting the location and any (arbitrary) radius distance (D). By assigning the radius distance is optimum radius (Ro )D , the optimum size of the power plant is calculated as (QBo )D . In the case that (QBo )D is larger than (QBo ) , it is considered that (QBo )D is an “oversized” power plant.
Over Sized (Insufficient fuel)
YES Proper Sized (Sufficient fuel) Calculate size of power plant (QB)o , (E)o Eqs (4) and (5)
Calculate maximum affordable fuel cost, (Cws)o Eq (1)
Fig. 2. Calculation Mow diagram of power plant design with proper size approach.
it is !rstly assumed that the power plant is located at the centre of a circle having radius D. The fuel availability density is then calculated within this area. By including the transportation cost, the optimum fuel supply radius (Ro ) , the maximum a1ordable fuel cost (Cws )o and the optimum boiler capacity (QBo ) are obtainable for the required IRR. If (Ro ) is less than or equal to D, (QBo ) is called the “proper size” power plant, which means that the fuel is guaranteed (both cost and quantity). The calculation of the “proper size” power plant is basically an iteration process as depicted in Fig. 2. To cope with the unforeseeable error (e.g., non-uniform distribution of rubber trees and road network), (Ro ) should be less than D by 5 km. The case study is applied for the Yala power project as detailed in the previous part of this study [3]. Some technical parameters of the power plant are tabulated in Table 2. The fully condensing power plant is used for the study case. The calculation progressed from D = 20; 30 and 35 km. The corresponding (Ro ) are 27.9, 28.7 and 29:5 km, while the corresponding fuel availability densities are 178, 164, and 151 t km−2 yr −1 . Thus, the power
3. Results and discussion 3.1. Geographical distribution of fuel cost Incorporating with the digitally mapped rubber growing areas, the availabilities of rubber wood residues are calculated within circular areas of 35 km radius. The calculation for the power plant moved at a step of 50 km along the highways. Fig. 3 shows the wood waste availability densities along the highways in Southern Thailand. Apparently, Songkla and Trang provinces are the biomass-rich regions, where the peak availability densities are 247 and 242 t km−2 yr −1 , respectively. The low potential areas are at the start and the ends of the highways and in Pattalung province. Suratthani is the topmost province in the peninsular that starts to have rubber trees in substantial density. The ends of highways in Yala and Narathiwas are low in biomass potential as they approach the mountain range of the Thai-Malaysian border where natural rain forest is reserved. Pattalung is a rice growing area. Fig. 4 is the (Ro ) based on the fuel availability density estimated within the area of 35 km radius along the highways. It is obvious that (Ro ) inversely varies with the fuel availability density. All locations, except at the start of the highway 41, (Ro ) is less than 35 km, which implies the feasibility of power plant projects in terms of fuel supply. Based on the technical data of power plant in Table 2, the distribution of power plant sites along the highways is presented in Fig. 5. The capacities of the power plants vary according to the fuel availability density (as previously appeared in Fig. 3). It is noticeable that the fuel availability density has minimal e1ect on the size of the fully condensing power
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Trang 242 217
247
Highway 404
200
222
192
Narathiwas 180
174
167 164 153
150
151
149
141
130
Highway 409 Songkla
50
118
101 Patthalung
71
114
Nakornsrithammarat
100 Suratthani
Wood Waste Availability (t km-2 y-1)
250
53
Yala
0 0
100
200
300
400
500
600
700
800
Highway Distant (km) Fig. 3. Distributions of wood waste availability in the South of Thailand. 40.0 Cogeneration plants Fully condensing plants
Optimal radius Ro, ψ(km)
37.6
Set vaule D = 35km
35.0
34.6 33.5 32.1
31.8
30.8
30.0
28.3
29.4
29.1
28.5
29.3 27.6
27.0 25.9 25.0
29.9 28.8 27.9
25.7
25.0
24.8
Yala
Trang 20.0 0
100 Suratthani
200
300
400
500
600
700
800
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 4. Optimal radius of power plant in the South of Thailand (D = 35 km, IRR = 15%).
plant in comparison to a cogeneration power plant. The advancement of the extraction steam turbine and the higher overall eJciency of the cogeneration
power plant are responsible for the high sensitivity of its capacity with respect to the fuel availability. It was found that, in the range of availability density
54
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Table 2 Technical parameters for a rubber wood !red power plants Parameters
Unit
Fully condensing plant Running time Process Steam Demand, (fully condensing plant) Process Steam Demand, (cogeneration plant) Overall eJciency, (fully condensing plant) Overall eJciency, (cogeneration plant) Boiler eJciency Electrical export factor
t QD QD CO B fe
8000 0 27.2 20 60 75 90
h yr −1 MWth MWth % % % %
Biomass Nominal higher heating value Moisture content (wet basis) Unit of transportation cost
HHV MCwet Cts
17.9 35 6.977
MJ kg−1 % US Cent t −1 km−1
Economic parameters Speci!c investment, (fully condensing plant) Speci!c investment, (cogeneration plant) Internal rate of return Maintenance coeJcient Economic life time Price of electricity capacity Price of electricity energy Price of thermal energy (steam)
Is Is IRR km n pec pee pd
0.8 1 15 3 20 702.33 1.977 0.6061
Million-USD MW−1 e Million-USD MW−1 e % % yr US Cent kW−1 month−1 US Cent kWh−1 US Cent kWh−1
200 –250 t km−2 yr −1 , the sensitivity of cogeneration is 0:109 MWe (t km−2 yr −1 )−1 , while the corresponding !gure for the fully condensing power plant is 0:037 MWe (t km−2 yr −1 )−1 . The fuel cost at the farm gate varies with the fuel availability density as presented in Fig. 6. The higher the fuel availability density, the higher the fuel cost that can be a1orded. With higher income from the sale of electricity and steam, the cogeneration power plant can accept the fuel cost at about 2.5 times higher than that of the fully condensing power plant. Fig. 7 shows the capacity and the IRR of the oversized cogeneration power plants (based on (Ro )D = 35 km). The electricity generating capacity of oversized cogeneration power plant varies drastically with the fuel availability density and the capacity is relatively higher than that of the “proper size” one (see also Fig. 5). The corresponding maximum a1ordable fuel costs for the oversized cogeneration plants at different locations are plotted in Fig. 8. Although the oversized cogeneration power plants have a higher
maximum a1ordable fuel cost, the expected IRR also decreases. It also implies that too large cogeneration power plants are not !nancialy competitive. However, it should be kept in mind that Figs. 7 and 8 are not general conclusions (quantitatively and qualitatively) because the optimum radius of the cogeneration power plant depends closely with !nancial return (fa ) and process heat demand (QD ) (see Eq. (2)). It suggests that the biomass cogeneration should be decentralized by having small plants separated by appropriate distances. The renewable energy scheme in Thailand provides !nancial support for the small power producers (SPPs). A certain subsidy is given to the power producers to o1set the high production cost of biomass electricity. The optimum capacity of the power plant in this study is determined from the !nancial feasibility point of view. However, for the sake of sustainability, if the need for biomass-derived electricity increases, the oversized power plant is not avoidable. Financial subsidy is needed then to meet the required IRR
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59 50.0
55
Cogeneration Plant Fully Condensing Plant
Electricity Output, (MWe)
40.0
30.0
20.0
10.0 Trang
Yala
0.0 0
100 Suratthani
200
300
400
500
600
700
800
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Optimal Unit Cost of Wood Waste (Cws)o, (US Cent t-1)
Fig. 5. Electricity output in di1erent regions (D = 35 km, IRR = 15%).
700
Cogeneration Plant Fully Condensing Plant
650 600 550 500 450 400 350 300 250 200 150 100
Yala
Trang
50 0 0
100 Suratthani
200
300
400
500
600
700
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 6. Optimal unit cost of wood waste in di1erent regions (D = 35 km, IRR = 15%).
800
56
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59 150
18 Electricity Output
140
IRR 16
130
14
110 100
12
90 80
10
70
8
IRR (%)
Electricity Output, (MWe)
120
60 50
6
40 4
30 20
2
10
Yala
Trang
0 0
100 Suratthani
200
300
400
500
600
0 800
700
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 7. Electricity output and IRR for cogeneration power plants in various provinces calculated by (Ro )D = 35 km.
Optimal Unit Cost of Wood Waste (Cws)o, (US Cent t-1)
900 IRR = 15%, (Ro) 800 Varied IRR, (Ro)D = 35 km 700 600 500 400 300 200 100
Trang
Yala
0 0
100 Suratthani
200
300
400
500
600
700
800
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 8. Optimal unit cost of wood waste for cogeneration power plants with di1erent IRRs calculated by IRR = 15% and (Ro )D = 35 km.
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
57
Fig. 9. Potential locations of rubber wood !red power plants in the South of Thailand.
(for feasible investment). It was found that the percentage of subsidy varies linearly with the percentage of oversizing. For example, at the 100% oversize, the percentage of subsidy is 10.3% of the selling price to the grid (which is 1:977 US Cent kWh−1 at present). 3.2. Potential locations of rubber wood 8red power plants Based on the concept of the !nance-led power project development (i.e., set the required IRR), the
fuel availability density and the mathematical models developed in the previous study [3] together with the digitized map can provide the potential locations for rubber wood !red power plants. Fig. 9 displays the rubber tree growing areas in the South of Thailand along with the circle of fuel procurement areas of the potential sites. The radii of the power plants vary with fuel availability densities. It is speci!ed as a potential site only when the optimal radius of fuel procurement circle (Ro ) is less than the radius that is used in the determination of the fuel availability density (see also
58
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Table 3 Potential locations in Southern Thailand for rubber wood !red power plants (IRR = 15%) Power plant number
Province
Power plant locations (latitude/longitude)
Power plant radius, Ro (km)
Wood waste availability, (t km−2 yr −1 )
Power plant capacity, Eo (MWe )
Optimal unit cost of wood wastes (Cws )o (US Cent t −1 )
1 2 3 4 5 6 7 8
Suratthani Suratthani Nakornsrithammarat Trang Songkla Songkla Yala Narathiwas
17–18◦ /14 –15◦ 51–52◦ /35 –36◦ 95 –96◦ /74 –75◦ 31–32◦ /69 –70◦ 93–94◦ /40 –41◦ 45 –46◦ /83–84◦ 28–29◦ /43–44◦ 82–83◦ /100 –101◦
35 30 30 25 30 25 30 30
109 234 229 237 159 309 164 186
19.1 24.7 24.5 24.8 21.7 27.0 21.9 22.8
195.72 247.25 245.97 248.01 222.83 262.98 224.90 233.12
Fig. 2). Eight locations are assessed as potential sites. Table 3 lists the details of these sites for the fully condensing power plant case. The cogeneration case was not given since the heat-to-power ratio must be speci!ed !rst. Songkla has the highest potential with a total capacity of 48:7 MWe from the two power plants. Suratthani has the second highest potential with a total capacity of 43:8 MWe , and it is also from the two power plants. Totally, the rubber wood residue in Southern Thailand has a potential for total capacity of 186:5 MWe .
Acknowledgements
4. Conclusions
References
Although the rubber wood residues seem to be plentiful in the rubber growing area, it still needs careful study for power generation projects. The area-based fuel availability density is a crucial factor in decision making of the site and size of the power plant. Cogeneration project is more complicated as the heat-to-power ratio plays an important role in the !nancial return of the project. The biomass power project should start by understanding the implication of the fuel procurement, especially the maximum affordable fuel cost, which depends on many factors such as the moisture content, the capacity of the power plant, the required IRR, etc. Since rubber trees are a long-rotation crop, the certainty of long-term supply of wood residues makes it very attractive to power plant developers. However, this study concludes that GIS information and appropriate simulation models are vital for decision making.
The authors express their gratitude to the Energy Technology and Clean Technology Center, The National Science and Technology Development Agency (Thailand) for the !nancial support of this study. Mrs. Avita Thipsak from S.T. Fortum Engineering Co., Ltd. (Thailand) is also gratefully acknowledged, who took the time for a dedicated work on digitized mappings.
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[9] Dansagoonpon S, Sinthurahat S. Agro-ecological zoning for rubber in the south. Rubber Research Institute, [vol. 1 & 2]. Department of Agricultural, Ministry of Agriculture and Co-operatives, Thailand, 2000. [10] Prasertsan S, Krukanont P. Study of area-based potential of rubber wood for energy production in Southern Thailand. Final report submitted to the clean technology and energy technology center. The National Science and Technology Development Agency, Bangkok, August 2002.
Biomass and Bioenergy 26 (2004) 47 – 59
Geographical distribution of biomass and potential sites of rubber wood !red power plants in Southern Thailand P. Krukanonta , S. Prasertsanb;∗ a Energy
Economics Laboratory, Department of Socio-Environmental Energy Science, Graduate School of Energy Science, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan b Department of Mechanical Engineering, Prince of Songkla University, Hat-Yai, Songkla 90112, Thailand Received 24 September 2002; received in revised form 14 March 2003; accepted 2 April 2003
Abstract Biomass residues from rubber trees in rubber producing countries have immense potential for power production. This paper presents the case of the south peninsular of Thailand, where the rubber industry is intense. Mathematical models were developed to determine the maximum a1ordable fuel cost and optimum capacity of the power plant for a given location of known area-based fuel availability density. GIS data of rubber growing was used to locate the appropriate sites and sizes of the power plants. Along 700 km of the highway network in the region, it was found that 8 power plants are !nancially feasible. The total capacity is 186:5 MWe . The fuel procurement area is in the range of less than 35 km. ? 2003 Elsevier Ltd. All rights reserved. Keywords: Biomass; Power generation; Optimization; GIS
1. Introduction As an agriculture-based country, biomass energy potential in Thailand is estimated at 65 PJ a year [1]. Biomass energy contributes about 25 –30% of primary energy need in Thailand. Its major role still exists in household use and in small and local industries like brick !ring and lime making, however. One of the most important biomass is rubber wood. Thailand produces about one-third of the world’s natural rubber. Out of 2:72 million ha of the peninsular area in the South, 1:67 million ha are rubber plantations, which
∗
Corresponding author. Fax: +66-74-212893. E-mail addresses: [email protected] (P. Krukanont), [email protected] (S. Prasertsan).
makes Southern Thailand the single largest rubber plantation region in the world. As the economic life of the trees is 25 –30 years, about 3– 4% of the rubber growing area is cut down for replanting annually. Over its lifetime, the rubber trees store solar energy in the form of biomass, which weighs more than 180 t=ha. The rubber wood becomes raw material for sawmills and wood product factories, e.g., furniture, kitchenware and wooden toys. Thailand is supporting private power producers to install totally 300 MWe electricity facilities from renewable sources, and rubber wood residue is the potential candidate. Assessing the risks connected to fuel supply is vital for the successful operation of large-scale bio-energy project. Junginger et al. [2] determine and minimize the fuel supply risk by focussing on the variation of residue quantity, limited accessibility, utilization by other competitors and
0961-9534/04/$ - see front matter ? 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0961-9534(03)00060-6
48
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Nomenclature Cls Cts Cws (Cws )o (Cws )o Eo D fe i Is IRR km LHV m MCwet n N pd
speci!c wage per capita of labour cost (USD person−1 yr −1 ) unit cost of transportation (US Cent t −1 km−1 ) unit cost of wood wastes (at the site of source) (US Cent t −1 ) optimal unit cost of wood wastes (US Cent t −1 ) optimal unit cost of wood wastes calculated by availability (US Cent t −1 ) optimal electricity output (MWe ) radius of targeted locations (km) electricity export factor (% or decimal) discount rate (%) speci!c cogeneration investment (USD MW−1 e ) internal rate of return (%) maintenance coeJcient (% or decimal) lower heating value of fuel (MJ kg−1 ) number of months (month) moisture content (wet basis) (% or decimal) economic cogeneration lifetime (yr) number of workers (person) price of thermal energy (US Cent kWh−1 )
logistic risks. Unlike the supply risks being faced by the short-rotation crops, the supply of rubber wood residues is quite secure in the long term. The rubber wood residues comprise small branches left in the plantation (54% of total biomass) and sawmill wastes (32%). On an annual basis, rubber wood residues in the forms of sawdust and wood o1-cuts in the sawmills, small branches left in the farm and wood residue in the factories are estimated at 4125 × 103 , 6917 × 103 and 833 × 103 t, respectively [3]. They are “real” wastes where there is no competitive user. In a previous study, Prasertsan and Krukanont [3] suggested the fuel purchasing strategy based on the fuel moisture content and the area-based availability density. They developed mathematical modes to be used as a tool for project developers to strategically negotiate with the fuel suppliers on the biomass price. It was
pec pee (QB )o (QBo )D (QBo ) QD R Ro (Ro )D (Ro ) t B CO
price of electricity capacity (US Cent MW−1 month−1 ) price of electricity energy (US Cent kWh−1 ) optimal boiler thermal load (MWth ) optimal boiler load calculated by targeted location (MWth ) optimal boiler load calculated by availability (MWth ) process heat demand (MWth ) radius of biomass plantation (km) optimal radius of biomass plantation (km) optimal radius calculated by targeted location (km) optimal radius calculated by availability (km) annual cogeneration operating time (h) boiler eJciency (% or decimal) cogeneration eJciency (% or decimal) annual speci!c wood waste availability (t km−2 yr −1 )
found that the most inMuential parameters a1ecting the a1ordable fuel costs are speci!c investment cost, electricity export factor, fuel moisture content and selling price of electrical energy. It has been concluded that biomass power plants should be small and placed in strategic locations supported by suJcient fuel availability density. Equipped with the simulation program for fuel purchasing strategy, one can locate the appropriate site and size of the power plant, if the area-based fuel availability density is known. Some earlier studies reported the development of computer programs and/or GIS data to identify the proper locations of power generation based on the geographical availability of biomass fuel, and other energy-related parameters [4–7]. Voivontas et al. [5] developed a decision support system (DSS) to identify the sites of
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
economically exploited biomass potential based on the electricity production cost. It was reported that the main parameters that a1ect the locations and number of bioenergy conversion facilities are plant capacity and spatial distribution of the available biomass potential. Following the !rst part of this study [3], this paper presents the GIS data of rubber growing area in the South of Thailand. The simulation program was developed to locate suitable sites for the rubber wood residue-!red power plants. 2. Materials and method
49
Table 1 List of selected provinces and comparison of growing rubber areas with paper-printed and digital-based calculations Province
Rubber areas Rubber areas Di1erences as registered calculated (%) (km2 ) digitally (km2 )
Suratthani Nakornsrithammarat Trang Patthalung Satoon Songkla Yala Pattani Narathiwas
2660 2250 1695 822 450 2640 1512 434 1424
2501 2240 1769 867 443 2687 1304 457 1335
−6 0 +4 +5 −2 +2 −14 +5 −6
2.1. Mapping of rubber wood area The recent (1998) survey of rubber tree growing areas in Thailand was carried out by the Rubber Research Institute of Thailand (RRIT) [8]. Images from the Landsat 5-TM satellite were used to analyse the rubber growing areas. The interpretation of the images’ information was con!rmed by a !eld survey. The RRIT’s information is available in paper-printed format with a scale of 1:100,000. The rubber tree mapping not only provides planting area quantitatively, but also gives the age distribution and area-based density. The rubber plantations were classi!ed into 2 subsets: young rubber trees (less than 5 years old) and mature trees (over 5 years old), which, for the whole country, totally cover areas of 259; 269 ha and 1; 654; 595 ha, respectively [9]. The plantations were classi!ed into 18 subgroups according to the age and percentage of the (area-based) rubber tree density. For example, group 9 represents the young rubber trees 21– 40% (of area), mature rubber trees 61–80% and non-rubber trees 0 –18%. The paper maps were digitized and a graphic software was developed to provide a “decision support system” (DSS) that can predict the area-based availability density of the rubber wood residues and determine the proper locations of power plants for a set of given conditions. Details of program development are given in [3,10]. Altogether 93 maps were digitized, merged and stored as one !le. The program is able to calculate the true rubber growing area (km2 ) and estimate the amount of rubber wood residue (t) in any targeted location. To verify the accuracy of
digitization, the calculated areas are then compared with the oJcial data from the Rubber Research Institute of Thailand [8]. It was found that the program calculated the total rubber planting area in each province quite accurately. Table 1 shows that the error, as compared to the areas registered by the Rubber Research Institute, was in the order of ±6%, except for the case of Yala, where the digitized map predicts the area less by 14%. The low prediction in Yala provides a safety margin for investment, as it estimates the fuel supply conservatively. The digitized map of rubber growing areas is, therefore, considered a reliable source of information about the availability of the rubber wood fuel. It is then applied as fuel reserve mapping for the possible locations of rubber wood !red power plants in Thailand. 2.2. Cost of biomass fuel If it is assumed that the fuel procurement area of the power plant is a circle. Detailed analysis in the previous study [3] showed that the cost of wood residue (Cws ) can be written as a function of the radius (R) as in Eq. (1). The radius R implies the size of the power plant. Thus, the fuel cost varies with the size of the power plant. Too large and too small power plants would expect to a1ord a lower fuel cost (at farm gate) to compensate for the transportation cost and the lack of economy of scale, respectively. The optimum radii of power plants (Ro ) are described in Eqs. (2) and
50
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
(3) for a cogeneration plant and a fully condensing plant, respectively. By substituting Ro into Eq. (1), the optimum cost of wood waste (Cws )o is obtainable. It should be made clear at this stage that (Cws )o is the maximum a1ordable fuel cost obtained at the expense of a !xed capacity of the power plant (!xed by constant Ro ), while Cws is the maximum a1ordable cost for a power plant of any capacity. In other words, (Cws )o is the maximum value of all Cws . It is the upper limit and must not be exceeded while negotiating fuel purchasing. Otherwise it is impossible to develop a power plant project (of any size) in that area if the required !nancial return (IRR) is !xed. Subsequently, the thermal boiler load and the electricity power are described by Eqs. (4) and (5), respectively. Cws = R−2 + R + ; where 1 =
m tQD pd − tQD fe pee + fe pec t
Is [km fa + 1] − tfa =−
(1)
2Cts ; 3
−
N
(Cls N )u
= (LHV )B CO
;
u=1
mfe pec Is [km fa +1] fe pee + − t tfa
(1 + i)n − 1 ; i(1 + i)n 1=3 2 ; Ro = N 1=3 3 (Cls N )u ; Ro = Cts
;
fa =
(2) (3)
u=1
QB =
R2 (LHV )B ; t
(4)
R2 (LHV )B CO (5) − QD : t As presented in Eqs. (2)–(5), the size of a power plant mainly depends on the area-based availability density of biomass fuel ( ) and the radius of the area for the fuel procurement. The higher the density of fuel availability, the larger the feasible capacity of the power plant. But large fuel procurement area in a low E=
availability density region may not be the feasible due to the excessive transportation cost of fuel. In the previous study, the purchasing strategy of the fuel in various conditions of fuel availability density, power plant sizes and !nancial returns was given [3]. 2.3. Location of power plants In order to apply the decision support system model in real practice, the digitized map is used to pinpoint suitable locations for power plants. It is assumed that the young and mature rubber trees are uniformly distributed in terms of area and age in the power plant expected area. The biomass procurement area is assigned to be a circle having the power plant at its centre. The centre of the circle moves along the main highway, where high-voltage transmission line is available for grid connection. It moves in steps of 50 km. As a narrow peninsular, the road network in the southern part of Thailand is linked to highway number 41 (Fig. 1). The total distance of more than 700 km from Suratthani province in the northernmost part to Narathiwas province in the southernmost part is the study area for power plant installations. In order to cover potential sites in other provinces, highways number 404 and 409 are incorporated into Trang and Yala provinces, respectively. The developed program calculates the area-based fuel availability density and suggests the optimum size of the power plant and the maximum a1ordable fuel cost for any interesting location. The boundaries of fuel procurement areas of the two adjacent sites are kept at least 10 km apart. 2.4. Clari8cation of important parameters In order to avoid confusion, some parameters need clari!cation. Since the growing density of the rubber trees is not uniformly distributed over the region, the area-based availability density is inevitably location dependent. The area-based availability density (t km−2 yr −1 ) is calculated from the area-based biomass production (residues at sawmills and small branches in plantation). The calculation takes into account the fact that the trees have economic life of 25 –30 years. Hence, the annual cut-down area is at least 3% of the planting area. The optimum radii in Eqs. (2) and (3) are based on the fuel availability density in a speci!ed circular area. In the calculation process,
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Fig. 1. Selected provinces in the South of Thailand for the potential rubber wood !red power plants.
51
52
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59 Assume radius Di
Calculate fuel availability, ()
Di+1 = Di + ((Ro)- Di)
Calculate optimal radius, (Ro) Eq (2)
(Ro) < Di
NO
plant is suitable for an area covering a radius of 35 km. However, a power plant of any size deviating from the optimum one can be set up, but at the expense of a lower maximum a1ordable fuel cost; hence, the bargaining power. In this case, the project development starts !rstly by selecting the location and any (arbitrary) radius distance (D). By assigning the radius distance is optimum radius (Ro )D , the optimum size of the power plant is calculated as (QBo )D . In the case that (QBo )D is larger than (QBo ) , it is considered that (QBo )D is an “oversized” power plant.
Over Sized (Insufficient fuel)
YES Proper Sized (Sufficient fuel) Calculate size of power plant (QB)o , (E)o Eqs (4) and (5)
Calculate maximum affordable fuel cost, (Cws)o Eq (1)
Fig. 2. Calculation Mow diagram of power plant design with proper size approach.
it is !rstly assumed that the power plant is located at the centre of a circle having radius D. The fuel availability density is then calculated within this area. By including the transportation cost, the optimum fuel supply radius (Ro ) , the maximum a1ordable fuel cost (Cws )o and the optimum boiler capacity (QBo ) are obtainable for the required IRR. If (Ro ) is less than or equal to D, (QBo ) is called the “proper size” power plant, which means that the fuel is guaranteed (both cost and quantity). The calculation of the “proper size” power plant is basically an iteration process as depicted in Fig. 2. To cope with the unforeseeable error (e.g., non-uniform distribution of rubber trees and road network), (Ro ) should be less than D by 5 km. The case study is applied for the Yala power project as detailed in the previous part of this study [3]. Some technical parameters of the power plant are tabulated in Table 2. The fully condensing power plant is used for the study case. The calculation progressed from D = 20; 30 and 35 km. The corresponding (Ro ) are 27.9, 28.7 and 29:5 km, while the corresponding fuel availability densities are 178, 164, and 151 t km−2 yr −1 . Thus, the power
3. Results and discussion 3.1. Geographical distribution of fuel cost Incorporating with the digitally mapped rubber growing areas, the availabilities of rubber wood residues are calculated within circular areas of 35 km radius. The calculation for the power plant moved at a step of 50 km along the highways. Fig. 3 shows the wood waste availability densities along the highways in Southern Thailand. Apparently, Songkla and Trang provinces are the biomass-rich regions, where the peak availability densities are 247 and 242 t km−2 yr −1 , respectively. The low potential areas are at the start and the ends of the highways and in Pattalung province. Suratthani is the topmost province in the peninsular that starts to have rubber trees in substantial density. The ends of highways in Yala and Narathiwas are low in biomass potential as they approach the mountain range of the Thai-Malaysian border where natural rain forest is reserved. Pattalung is a rice growing area. Fig. 4 is the (Ro ) based on the fuel availability density estimated within the area of 35 km radius along the highways. It is obvious that (Ro ) inversely varies with the fuel availability density. All locations, except at the start of the highway 41, (Ro ) is less than 35 km, which implies the feasibility of power plant projects in terms of fuel supply. Based on the technical data of power plant in Table 2, the distribution of power plant sites along the highways is presented in Fig. 5. The capacities of the power plants vary according to the fuel availability density (as previously appeared in Fig. 3). It is noticeable that the fuel availability density has minimal e1ect on the size of the fully condensing power
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Trang 242 217
247
Highway 404
200
222
192
Narathiwas 180
174
167 164 153
150
151
149
141
130
Highway 409 Songkla
50
118
101 Patthalung
71
114
Nakornsrithammarat
100 Suratthani
Wood Waste Availability (t km-2 y-1)
250
53
Yala
0 0
100
200
300
400
500
600
700
800
Highway Distant (km) Fig. 3. Distributions of wood waste availability in the South of Thailand. 40.0 Cogeneration plants Fully condensing plants
Optimal radius Ro, ψ(km)
37.6
Set vaule D = 35km
35.0
34.6 33.5 32.1
31.8
30.8
30.0
28.3
29.4
29.1
28.5
29.3 27.6
27.0 25.9 25.0
29.9 28.8 27.9
25.7
25.0
24.8
Yala
Trang 20.0 0
100 Suratthani
200
300
400
500
600
700
800
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 4. Optimal radius of power plant in the South of Thailand (D = 35 km, IRR = 15%).
plant in comparison to a cogeneration power plant. The advancement of the extraction steam turbine and the higher overall eJciency of the cogeneration
power plant are responsible for the high sensitivity of its capacity with respect to the fuel availability. It was found that, in the range of availability density
54
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Table 2 Technical parameters for a rubber wood !red power plants Parameters
Unit
Fully condensing plant Running time Process Steam Demand, (fully condensing plant) Process Steam Demand, (cogeneration plant) Overall eJciency, (fully condensing plant) Overall eJciency, (cogeneration plant) Boiler eJciency Electrical export factor
t QD QD CO B fe
8000 0 27.2 20 60 75 90
h yr −1 MWth MWth % % % %
Biomass Nominal higher heating value Moisture content (wet basis) Unit of transportation cost
HHV MCwet Cts
17.9 35 6.977
MJ kg−1 % US Cent t −1 km−1
Economic parameters Speci!c investment, (fully condensing plant) Speci!c investment, (cogeneration plant) Internal rate of return Maintenance coeJcient Economic life time Price of electricity capacity Price of electricity energy Price of thermal energy (steam)
Is Is IRR km n pec pee pd
0.8 1 15 3 20 702.33 1.977 0.6061
Million-USD MW−1 e Million-USD MW−1 e % % yr US Cent kW−1 month−1 US Cent kWh−1 US Cent kWh−1
200 –250 t km−2 yr −1 , the sensitivity of cogeneration is 0:109 MWe (t km−2 yr −1 )−1 , while the corresponding !gure for the fully condensing power plant is 0:037 MWe (t km−2 yr −1 )−1 . The fuel cost at the farm gate varies with the fuel availability density as presented in Fig. 6. The higher the fuel availability density, the higher the fuel cost that can be a1orded. With higher income from the sale of electricity and steam, the cogeneration power plant can accept the fuel cost at about 2.5 times higher than that of the fully condensing power plant. Fig. 7 shows the capacity and the IRR of the oversized cogeneration power plants (based on (Ro )D = 35 km). The electricity generating capacity of oversized cogeneration power plant varies drastically with the fuel availability density and the capacity is relatively higher than that of the “proper size” one (see also Fig. 5). The corresponding maximum a1ordable fuel costs for the oversized cogeneration plants at different locations are plotted in Fig. 8. Although the oversized cogeneration power plants have a higher
maximum a1ordable fuel cost, the expected IRR also decreases. It also implies that too large cogeneration power plants are not !nancialy competitive. However, it should be kept in mind that Figs. 7 and 8 are not general conclusions (quantitatively and qualitatively) because the optimum radius of the cogeneration power plant depends closely with !nancial return (fa ) and process heat demand (QD ) (see Eq. (2)). It suggests that the biomass cogeneration should be decentralized by having small plants separated by appropriate distances. The renewable energy scheme in Thailand provides !nancial support for the small power producers (SPPs). A certain subsidy is given to the power producers to o1set the high production cost of biomass electricity. The optimum capacity of the power plant in this study is determined from the !nancial feasibility point of view. However, for the sake of sustainability, if the need for biomass-derived electricity increases, the oversized power plant is not avoidable. Financial subsidy is needed then to meet the required IRR
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59 50.0
55
Cogeneration Plant Fully Condensing Plant
Electricity Output, (MWe)
40.0
30.0
20.0
10.0 Trang
Yala
0.0 0
100 Suratthani
200
300
400
500
600
700
800
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Optimal Unit Cost of Wood Waste (Cws)o, (US Cent t-1)
Fig. 5. Electricity output in di1erent regions (D = 35 km, IRR = 15%).
700
Cogeneration Plant Fully Condensing Plant
650 600 550 500 450 400 350 300 250 200 150 100
Yala
Trang
50 0 0
100 Suratthani
200
300
400
500
600
700
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 6. Optimal unit cost of wood waste in di1erent regions (D = 35 km, IRR = 15%).
800
56
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59 150
18 Electricity Output
140
IRR 16
130
14
110 100
12
90 80
10
70
8
IRR (%)
Electricity Output, (MWe)
120
60 50
6
40 4
30 20
2
10
Yala
Trang
0 0
100 Suratthani
200
300
400
500
600
0 800
700
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 7. Electricity output and IRR for cogeneration power plants in various provinces calculated by (Ro )D = 35 km.
Optimal Unit Cost of Wood Waste (Cws)o, (US Cent t-1)
900 IRR = 15%, (Ro) 800 Varied IRR, (Ro)D = 35 km 700 600 500 400 300 200 100
Trang
Yala
0 0
100 Suratthani
200
300
400
500
600
700
800
Nakornsrithammarat Patthalung Songkla Narathiwas Highway Distant, (km)
Fig. 8. Optimal unit cost of wood waste for cogeneration power plants with di1erent IRRs calculated by IRR = 15% and (Ro )D = 35 km.
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
57
Fig. 9. Potential locations of rubber wood !red power plants in the South of Thailand.
(for feasible investment). It was found that the percentage of subsidy varies linearly with the percentage of oversizing. For example, at the 100% oversize, the percentage of subsidy is 10.3% of the selling price to the grid (which is 1:977 US Cent kWh−1 at present). 3.2. Potential locations of rubber wood 8red power plants Based on the concept of the !nance-led power project development (i.e., set the required IRR), the
fuel availability density and the mathematical models developed in the previous study [3] together with the digitized map can provide the potential locations for rubber wood !red power plants. Fig. 9 displays the rubber tree growing areas in the South of Thailand along with the circle of fuel procurement areas of the potential sites. The radii of the power plants vary with fuel availability densities. It is speci!ed as a potential site only when the optimal radius of fuel procurement circle (Ro ) is less than the radius that is used in the determination of the fuel availability density (see also
58
P. Krukanont, S. Prasertsan / Biomass and Bioenergy 26 (2004) 47 – 59
Table 3 Potential locations in Southern Thailand for rubber wood !red power plants (IRR = 15%) Power plant number
Province
Power plant locations (latitude/longitude)
Power plant radius, Ro (km)
Wood waste availability, (t km−2 yr −1 )
Power plant capacity, Eo (MWe )
Optimal unit cost of wood wastes (Cws )o (US Cent t −1 )
1 2 3 4 5 6 7 8
Suratthani Suratthani Nakornsrithammarat Trang Songkla Songkla Yala Narathiwas
17–18◦ /14 –15◦ 51–52◦ /35 –36◦ 95 –96◦ /74 –75◦ 31–32◦ /69 –70◦ 93–94◦ /40 –41◦ 45 –46◦ /83–84◦ 28–29◦ /43–44◦ 82–83◦ /100 –101◦
35 30 30 25 30 25 30 30
109 234 229 237 159 309 164 186
19.1 24.7 24.5 24.8 21.7 27.0 21.9 22.8
195.72 247.25 245.97 248.01 222.83 262.98 224.90 233.12
Fig. 2). Eight locations are assessed as potential sites. Table 3 lists the details of these sites for the fully condensing power plant case. The cogeneration case was not given since the heat-to-power ratio must be speci!ed !rst. Songkla has the highest potential with a total capacity of 48:7 MWe from the two power plants. Suratthani has the second highest potential with a total capacity of 43:8 MWe , and it is also from the two power plants. Totally, the rubber wood residue in Southern Thailand has a potential for total capacity of 186:5 MWe .
Acknowledgements
4. Conclusions
References
Although the rubber wood residues seem to be plentiful in the rubber growing area, it still needs careful study for power generation projects. The area-based fuel availability density is a crucial factor in decision making of the site and size of the power plant. Cogeneration project is more complicated as the heat-to-power ratio plays an important role in the !nancial return of the project. The biomass power project should start by understanding the implication of the fuel procurement, especially the maximum affordable fuel cost, which depends on many factors such as the moisture content, the capacity of the power plant, the required IRR, etc. Since rubber trees are a long-rotation crop, the certainty of long-term supply of wood residues makes it very attractive to power plant developers. However, this study concludes that GIS information and appropriate simulation models are vital for decision making.
The authors express their gratitude to the Energy Technology and Clean Technology Center, The National Science and Technology Development Agency (Thailand) for the !nancial support of this study. Mrs. Avita Thipsak from S.T. Fortum Engineering Co., Ltd. (Thailand) is also gratefully acknowledged, who took the time for a dedicated work on digitized mappings.
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