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Least cost electricity generation options based on environmental impact abatement
Environmental Science & Policy 6 (2003) 533–541
Least cost electricity generation options based on environmental impact abatement Jerasorn Santisirisomboon a,b , Bundit Limmeechokchai a,∗ , Suparchart Chungpaibulpatana a a
Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22 Thammasat Rangsit Post Office, Pathumthani 12121, Thailand b Faculty of engineering, Ramkhamhaeng University, Huamak, Bangkapi, Bangkok 10240, Thailand
Abstract The power sector in Thailand is the largest contributor to CO2 emissions. There is high potential to mitigate CO2 emission via alternative power generating plants. Alternative plants considered in this study include nuclear plants, integrated gasification combined cycle plants, biomass-based plants and supercritical thermal power plants. The biomass-based plants considered here are fueled with four types of biomass; paddy husk, municipal solid waste (MSW), fuel wood and corncob. The methodology for the optimal expansion plan of the power generating system over the planning horizon is based on the least-cost approach. The results from the least-cost planning analyses show that the nuclear alternative has the highest potential to mitigate not only CO2 but also other airborne emissions. Moreover, the nuclear option is the most effective abatement strategy for CO2 reduction due to its negative incremental cost of CO2 reduction. © 2003 Elsevier Ltd. All rights reserved. Keywords: Least cost electricity generation; Nuclear power; Biomass; Supercritical thermal power; Integrated gasification combined cycle; CO2 mitigation
1. Introduction The power sector has an important implication for all countries economic development. It plays a critical role in energy consumption and environmental pollution emissions in Thailand. During the period 1990–1997, electricity demand increased at an average annual growth rate of 11.6% and the demand in 1997 was 82,431 GWh (DEDP, 1997). However, this high growth trend was considered unlikely to continue because of the economic recession in 1997. The electricity demands in 1998 and 1999 were 2.4 and 1.2% lower than the demand in 1997 (DEDP, 1999). The power industry, which has been built and operated under a “supply–follows–demand” philosophy, has always been able to fulfill its obligation by providing adequate and secure supplies of electricity at the lowest practicable cost. The objective of the power generation plan is to seek for the most economical generation expansion scheme to achieve a certain reliability level to meet the forecasted demand increase in a certain period of time. However, this has led to an onerous operating strategy requiring high plant margins and extensive environmental impacts on the range from the local to the global scale as it is a major energy consum∗
Corresponding author. Tel.: +662-986-9009x2206; fax: +662-986-9009x2501. E-mail address: [email protected] (B. Limmeechokchai). 1462-9011/$ – see front matter © 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsci.2003.08.004
ing sector and a large contributor of environmental pollution emissions (especially CO2 ). Based on the recent report of the Department of Energy Development and Promotion (DEDP), the power sector is the highest CO2 emission sector (DEDP, 1999). Thereby it has the highest potential to mitigate CO2 emission significantly. There are many mechanisms for reducing CO2 emissions from the power sector and one of the most practical mechanisms is the switching from high- to less- or non-carbon intensive sources of generation (Hadley and Short, 2001). Four alternative power plants (including nuclear, integrated gasification combined cycle plants (IGCC), biomass and supercritical plants) are considered for the mitigation of CO2 emission in this study. The main objective of this study is to evaluate the most economical power generation expansion plan that not only meets the requirement of electricity demand, but also mitigates CO2 emission. The impacts of such alternatives on the primary energy supply are also assessed.
2. Final electricity and peak load demand projection The economic sector in Thailand comprises 5 sectors: agricultural, commercial, industrial, residential and transport. The electricity demand projection in these sectors was done by using the end-use model. The projected
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Agriculture Transport Residential Commercial Industrial
250,000
200,000
GWh
150,000
100,000
50,000
2000
2005
2010
2015
2020
2025
Fig. 1. Final electricity demand projection by economic sector in selected years.
electricity demand in Thailand is projected to increase from 83,571 GWh in 2000 to 206,743 GWh in 2025 with an annual growth rate of 3.7% (Fig. 1). The difference between electricity generation and demand is measured by the values of transmission and distribution (T&D) losses, the own use of electricity in the power plants and the use for pumping in the pump storage hydro-power plants. Hence, the annual peak load demand is estimated by the following equation. PLDt =
[ELDEMt (1+OwmUse+TDLosst ) + PSt ] × 1000 SysLFt × 8760 (1)
where PLDt is the peak load demand in year t (MW), ELDEMt is the electricity demand in year t (GWh), OwnUset is the fraction of own use of electricity in year t (%), TDLosst is the fraction of transmission and distribution loss (%), PSt is the electricity use for pump storage plants in year t (GWh), and SysLFt is the system load factor. The electricity for own use and the T&D losses are assumed to be 3.5 and 7.0%,respectively (DEDP, 1999). The electricity used for pump storage in the existing power-plant system is 638 GWh (DEDP, 1999). The system load factor in year t (SysLFt ) is estimated from the area under the annual-normalized load duration
curve (LDC): 1 LDCt dtn SysLFt =
(2)
0
where LDCt is the polynomial function of degree 5, in which the normalized time acts as an independent variable and the normalized load as a dependent variable, in year t, and tn is the normalized time. The annual load duration curve was calculated from the system load shapes in 1998 with a load factor of 73%. The percentages of T&D losses, own use and electricity used for the pump storage of the hydro power plant are applied to the whole planning horizon. The corresponding electricity generation and peak load demand are presented in Table 1.
3. Least cost power generation expansion plan 3.1. Introduction: methods and the five case studies The methodology for the optimal expansion plan for the power generating system over the planning horizon is based on the least cost concept. The optimum power generation expansion plan is evaluated in terms of minimum discounted total costs. In this study, the least-cost model used to estimate
Table 1 Electricity peak load demand in selected years Year
Electricity demand (GWh)
Own use (GWh)
Transmission loss (GWh)
Distribution loss (GWh)
Pump storage (GWh)
Purchase (GWh)
Total generation (GWh)
Peak load demand (MW)
2000 2005 2010 2015 2020 2025
83571 105814 126652 149931 176225 206743
3295 4165 4980 5891 6919 8112
2825 3570 4269 5049 5930 6953
3766 4760 5692 6732 7907 9271
638 638 638 638 638 638
7599 7599 7599 7599 7599 7599
85917 110769 134052 160062 189441 223539
13405 17283 20915 24974 29558 34878
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the optimal expansion plan is the Wien Automatic System Planning Package version IV (WASP-IV). It is designed for the economically optimal generation expansion plan for an electric utility system within users’ specified constraints. The model utilizes probabilistic estimations of system production cost, unserved energy cost, system reliability, and dynamic programming methods of optimization for comparing the costs of alternative system expansion plans (IAEA, 1998). The technological and economic benefits of every generation expansion scheme in WASP-IV are weighted by using present value of cost. The objective function of WASP-IV can be expressed as Bj =
T
¯ j,t ] ¯ j,t + M ¯ j,t + O [I¯j,t − S¯ j,t + F¯ j,t + L
(3)
t=1
where Bj is the objective function attached to the expansion plan j, I the capital investment costs, S the salvage value of investment costs, F the fuel costs, L the fuel inventory costs, M the non-fuel operating and maintenance costs, O the cost of the energy not served, t the time in year 1, 2, . . . , T , and T is the planning period. The over bar indicates discounted values to a reference year or the base year at a given discount rate i. The optimal plan is defined by minimizing Bj among all j. The output of the WASP-IV model is the optimum system expansion plan within the constraints given by users. The model also calculates the optimum mixed generation of each power
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plant to meet the electricity demand under the same set of constraints. The five case studies of power generation expansion plans considered in this study are the base case, the nuclear case, the integrated gasification combined cycle (IGCC) case, the biomass case and the supercritical case. The minimum and maximum reserve margins in all cases are 15 and 25% respectively. Apart from the reserve margin constraints, constraints on fuel supply are also considered. The natural gas supplied through pipelines from the neighboring countries is limited to 2809 million standard cubic feet per day (mmscfd), which is adequate for the generation of approximately 11,650 MW of combined-cycle power plant or about 19 units of 600 MW capacities (EGAT, 1999). The import of liquefied natural gas (LNG) for the combined-cycle power plant will be considered after the natural gas supplied through pipelines reaches saturation. The development of indigenous lignite and hydro power plants is not taken into account due to the public resistance. The potential coal-fired power plant in this study is fueled with imported low-sulfur content coal. 3.2. The base case Five types of technology for electricity generation are introduced as potential plants in the base case. These power generation technologies are all currently commercially available. The operating characteristics of these potential plants are presented in Table 2.
Table 2 Operating characteristics of testing power plants Technology
Typical size (MW)
Life time (year)
Operating characteristics of potential power plants Thermal Coal-fireda 1000 25 Oil-fireda 1000 25
Capital cost (US$/kW)
Fixed O&M (US$/kW-month)
Variable O&M (US$/MWh)
Fuel cost (US$/106 kcal)
Heat rate (kcal/kWh)
837 736
1.2120 0.8875
0.8899 0.6515
7.50 10.07
2322 2341
Combined cycle Natural gasa LNGa
600 600
20 20
557 557
0.8378 0.8378
0.8197 0.8197
12.69 16.70
1860 1860
Gas turbine Diesela
200
15
395
0.6337
0.4652
18.68
2743
1020 1154 1329 1510 1612 1407 1510
3.084 1.265 1.265 5.000 5.167 4.875 5.000
0.0000 0.7251 0.7251 7.0000 4.0000 10.0000 7.0000
1.19 7.50 7.50 1.15 0.00 4.27 2.61
2322 1755 1810 3105 3554 2766 3105
Operating characteristics of testing power plants Thermal 592 30 Nuclearb IGCCc 380 25 400 25 Supercriticalc 100 30 Paddy huskd MSWe 25.5 30 100 30 Fuel woodf 100 30 Corncobd
Note: LNG stands for liquefied natural gas. IGCC stands for integrated gasification combined cycle. MSW stands for municipal solid waste. a EGAT (2000). b International Energy Agency (IEA). c Paffenbarger and Bertel (1998). d United States Department of Energy (U.S. DOE). e California Energy Commission (CEC). f Electric Power Research Institute (EPRI).
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3.3. The nuclear case
eration from biomass is supplied as generation constraints to avoid the shortage of biomass.
The nuclear power plant has fuel resource potential for the reduction of environmental emissions from electricity generation. The nuclear plant introduced in this study is a 592-MW pressurized water reactor type. This plant will be introduced to the WASP-IV model from 2015 onward. The operating characteristics of this plant are presented in Table 2. 3.4. The IGCC case Coal gasification is a process that converts solid coal into synthetic gases comprising mainly carbon monoxide and hydrogen. The Integrated Gasification Combined Cycle (IGCC) converts coal into gaseous fuel and then burns it in a combined cycle gas turbine. The IGCC plant introduced in this study is based on the technology availability in USA. The net capacity of this plant is 380 MW with a net efficiency of 49% (Paffenbarger and Bertel, 1998). Since this type of power plant is expected to be commercially available after 2005, it is introduced to the WASP-IV model from 2005 onward. 3.5. The biomass case Biomass is considered as a renewable energy source that has high potential in air pollution abatement and is cost effective (Boudri et al., 2002). Four types of biomass power plant are considered in this study. Since the biomass supply is limited, the potential of power generation from biomass needs to be estimated to ensure the power system reliability. The available biomass for the electricity generation is the excess supply of biomass from the demands in the industrial and residential sectors (Santisirisomboon et al., 2001). The types of biomass for the electricity production are projected on the basis of the availability and heat rate of each type of biomass plant. The availability factor or plant load factors of biomass-fired plants are assumed to be 80%. The maximum potential of biomass for the electricity generation (Table 3) is applied as a capacity expansion constraint in the model. In addition, the estimated potential of electricity gen-
3.6. The supercritical power plant case The efficiency of a thermal power plant can be increased by using the supercritical steam cycle. Overall plant efficiency can be increased from 37% with the subcritical steam cycle to 48% with the supercritical steam cycle. In this study, the supercritical coal-fired thermal power plant is introduced as a potential plant in the WASP-IV model from 2005 onward. 4. Results 4.1. Expansion capacity The power generation expansion capacity in the base case (see Table 4) is expected to increase from 21,252 MW in 2000 to 46,305 MW in 2025. In 2025, it is estimated that a 22,000 MW (about 48% of all power generation) coal-fired power plant will be installed. The share of coal-based plant is expected to decrease by introducing a nuclear power plant in 2025. The nuclear plant is able to replace 14,000 MW of the coal-fired power plant. An introduction of IGCC plant in this year is able to reduce total expansion capacity by 1240 MW. At the end of the planning horizon, the biomass-based plants should be able to reduce the dependency of coal-fired and diesel-fired plants by 5000 and 600 MW, respectively. Although the fuel wood has a high potential to generate electricity, it still cannot compete with other types of power plants due to its high generation cost. In 2025, the total generation expansion capacity in the supercritical case will be reduced by 1200 MW due to the introduction of high-efficient supercritical power plant. 4.2. Electricity generation The principal electricity generation during the first half of the planning horizon in the base case comes from natural gas. About 50% of the power generation in 2000 came from
Table 3 Potential of biomass for electricity generation in Thailand Year
2000 2005 2010 2015 2020 2025
Paddy husk
Corncob
Fuel wood
MSW
Capacity (MW)
Generation (GWh)
Capacity (MW)
Generation (GWh)
Capacity (MW)
Generation (GWh)
Capacity (MW)
Generation (GWh)
400 400 400 300 300 300
2803 2803 2803 2102 2102 2102
1800 2300 3100 3900 4600 5000
12613 16116 21722 27328 32232 35035
0 500 500 1000 1600 1600
0 3504 3504 7007 11211 11211
128 179 204 281 332 383
949 1329 1519 2088 2468 2848
Note: MSW stands for municipal solid wastes.
J. Santisirisomboon et al. / Environmental Science & Policy 6 (2003) 533–541 Table 4 Power generation expansion capacity (unit: MW) Power plant type
2000
The base case Hydro Thermal Gas turbine Combined cycle Purchase Total
2020
2025
Plant type The base case Hydro Thermal Gas turbine Combined cycle Purchase
2878 11075 3954 13828 2673
2878 17400 5754 11400 2673
2878 22000 7354 11400 2673
21252
24832
29174
34408
40105
46305
Total 2878 6443 1686 7572 2673
2878 7623 2286 9372 2673
2878 8925 2486 12212 2673
2878 12443 3754 12628 2673
2878 19280 4954 10200 2673
2878 22208 7154 11400 2673
21252
24832
29174
34376
39985
46313
2878 6443 1686 7572 2673
2878 8143 2286 8772 2673
2878 12385 2286 8012 2673
2878 11295 3554 12628 2673
2878 16620 5154 11400 2673
2878 20360 7754 11400 2673
21252
24752
28234
33028
38725
45065
2878 6443 1686 7572 2673
2878 7051 2286 9972 2673
2878 9329 2286 11612 2673
2878 11679 2954 13828 2673
2878 17681 4954 11400 2673
2878 22206 6754 11400 2673
21252
24859
28778
34012
39586
45911
2878 6443 1686 7572 2673
2878 8223 2286 8772 2673
2878 12325 2286 8012 2673
2878 11875 3154 12628 2673
2878 17200 4754 11400 2673
2878 20400 7754 11400 2673
21252
24832
28174
33208
38905
45105
The supercritical case Hydro Thermal Gas turbine Combined cycle Purchase Total
2015
2878 8925 2486 12212 2673
The biomass case Hydro Thermal Gas turbine Combined cycle Purchase Total
2010
2878 7623 2286 9372 2673
The IGCC case Hydro Thermal Gas turbine Combined cycle Purchase Total
Table 5 Electricity generation by type of power plant (unit: GWh)
2878 6444 1686 7572 2673
The nuclear case Hydro Thermal Gas turbine Combined cycle Purchase Total
2005
537
gas-fired plants. In the future, however, the power generation from gas-fired plants is expected to decline due to the supply constraint of natural gas, whereas the use of coal is expected to increase. However, this situation will not happen if nuclear and biomass options are introduced. In the nuclear case, a significant amount of coal used for electricity generation is replaced by nuclear since the generation cost of nuclear power plant is lower than other possible plants, especially for that of coal-fired plants. Biomass-based plants are evaluated to estimate the electricity generation capacity. In 2025, the electricity generation from biomass plants is estimated to be 38,359 GWh that will account for about 17% of the total generation. The introduction of IGCC and supercritical thermal plants does not affect the change in fuel mixture for electricity generation. However, the apportionment of electricity generation from each fuel type is slightly changed when com-
2000 4221 33165 2097 46434
Total
Total
4221 73199 3075 79566
2020
2025
4221 116981 4952 63287
4221 150637 6298 62383
7599
7599
7599
7599
7599
118368
141651
167661
197040
231138
4221 44818 2324 59406
4221 54412 2110 73310
4221 81569 2903 71367
4221 126758 4007 54455
4221 150326 6377 62616
7599
7599
7599
7599
7599
93516
118368
141651
167661
197040
231138
4221 33165 2097 46434
4221 49073 2208 55267
4221 80207 2033 47591
4221 80184 3143 72513
4221 116983 4802 63434
4221 146810 8086 64422
7599
7599
7599
7599
7599
7599
93516
118368
141651
167661
197040
231138
4221 41111 2223 63215
4221 58203 2077 69551
4221 77211 2087 76544
4221 119227 4061 61932
4221 152728 5307 61283
7599
7599
7599
7599
7599
118368
141651
167661
197040
231138
4221 49442 2090 55017
4221 79974 2112 47745
4221 82983 2393 70464
4221 119619 3850 61751
4221 147047 7968 64303
7599
7599
7599
7599
7599
118368
141651
167661
197040
231138
93516
The supercritical case Hydro 4221 Thermal 33165 Gas turbine 2097 Combined 46434 cycle Purchase 7599 Total
4221 54412 2110 73310
2015
7599
The biomass case Hydro 4221 Thermal 33165 Gas turbine 2097 Combined 46434 cycle Purchase 7599 Total
4221 44818 2324 59406
2010
93516
The nuclear case Hydro 4221 Thermal 33165 Gas turbine 2097 Combined 46434 cycle Purchase 7599
The IGCC case Hydro Thermal Gas turbine Combined cycle Purchase
2005
93516
pared to the base case. The electricity generation from the WASP-IV model is shown in Table 5. 4.3. Fuel requirement In the base case, the natural gas is the main fuel used in the power sector until the middle of the planning horizon. About 52% of fuel requirement for the power generation in 2000 came from natural gas. However, it is estimated that the largest fuel consumption for power generation in the future comes from coal. The introduction of nuclear and biomass-based plants would result in the reduction of coal requirement. The
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Table 6 Fuel requirements for electricity generation (unit: ktonnes) Fuel type
2000
2005
2010
2015
2020
2025
The base case Fuel Oil Lignite Imported coal Diesel Natural gas
4557 4790 0 228 10329
3594 5234 2791 305 12600
2737 4897 5620 253 14391
1436 3481 11533 521 14434
1421 2321 21580 1029 11055
0 0 31513 1609 10600
19904
24524
27899
31405
37406
43721
4557 4790 0 0 228 10329
3594 5234 2791 0 305 12600
2737 4897 5620 0 253 14391
1411 3472 9863 3561 477 13074
1330 2287 10841 13307 783 9560
0 0 10919 21206 1628 10641
19904
24524
27899
31831
38108
44394
4557 4790 0 228 10329
3459 5172 3846 277 11888
2329 4417 11769 255 9999
1330 3411 13226 567 13194
1396 2303 21710 995 11069
0 0 30823 2079 10964
19904
24641
28767
31729
37474
43866
The biomass case Fuel Oil 4557 Lignite 4790 Imported coal 0 Paddy husk 0 Corncob 0 MSW 0 Diesel 228 Natural gas 10329
3540 5243 1398 764 0 371 281 13235
2562 4882 1343 764 7749 552 255 13729
1320 3462 6940 764 8267 594 259 13897
1374 2316 15496 764 9882 816 790 10824
0 0 23925 764 12293 890 1349 10402
19904
24832
31837
35503
42262
49624
The supercritical case Fuel Oil 4557 Lignite 4790 Imported coal 0 Diesel 228 Natural gas 10329
3408 5163 3969 248 11833
2352 4445 11681 276 10030
1267 3411 13856 365 12834
1348 2300 22296 737 10787
0 0 30873 2049 10942
24621
28783
31732
37468
43863
Total The nuclear case Fuel Oil Lignite Imported coal Nuclear Diesel Natural gas Total The IGCC case Fuel Oil Lignite Imported coal Diesel Natural gas Total
Total
Total
19904
nuclear case is able to reduce the largest coal requirement, followed by the biomass case, the IGCC case and the supercritical case. However, the total fuel requirements of all cases are higher than the base case, especially for the biomass case due to the lowest efficiency of biomass-based plant among all candidates. Fuel requirements corresponding to the expansion plan are shown in Table 6. 4.4. Air pollution emissions Based on the total fuel requirements for electricity generation, the corresponding air pollution emissions from such conversions are estimated and presented in Table 7. The dramatic decrease of CO2 emissions in the nuclear case results
from an increase of the number of nuclear plants. In the nuclear case, the emission reduction is not only limited to the CO2 but also other airborne emissions. In 2025, the substitution of some coal-fired plants with nuclear plants will be able to reduce about 50% of SO2 and NOx . Although the CO2 emissions from the biomass case are lower than the base case, other types of air pollutant emissions such as CO and TSP are higher due to lower efficiency and incomplete combustion of biomass-fired power plants. The IGCC case is able to reduce a significant level of CO2 emission due to the recognized technology for the reduction of CO2 emission. However, a significant amount of SO2 is increased due to the increased use of coal for electricity generation. In the supercritical case, the CO2 emission reduction is slight compared to the base case as it is a coal-fired plant with higher efficiency compared to the conventional coal-fired power plant. 4.5. Other energy transformation processes The introduction of alternative power plants does not only impact the power generation expansion plan but also the capacity expansion of oil refinery and natural gas processing plant. The installed capacity of oil refineries and natural gas processing plants are presented in Table 8. The corresponding environmental emissions from oil refineries and natural gas processing plants are shown in Table 9. 4.6. Primary energy supply The primary energy supply in the nuclear and the biomass case is higher than the base case due to lower efficiency of both plants compared to the coal-fired plant (Rosen, 2000). Nevertheless, both cases reduce the dependency on fossil fuel for electricity generation. Both the IGCC and the supercritical case are able to reduce the primary energy supply, however, the electricity generation is still dependent on fossil-fuel plants. The corresponding primary energy supplies of all cases are presented in Fig. 2. 4.7. CO2 emission reduction costs The comparison of the cost for the reduction of CO2 emission in all cases is presented in Table 10. The CO2 reduction costs of the nuclear, IGCC and biomass cases are −11.13, −2.85 and −1.52 US$/tonne, respectively. The accumulated incremental cost of the nuclear case is much lower than the base case due to its low generation cost. When the investment cost to avoid 1 tonne of carbon emission is negative, the alternative can be said to be an economic option regardless of any emission reduction (Sims et al., 2003). Hence, the nuclear case is the most effective abatement strategy for the CO2 reduction followed by the IGCC and biomass cases.
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Table 7 Air pollution emissions from the power sector in selected years (unit: 1000 tonnes) Year
CO2
The base case 2000 2005 2010 2015 2020 2025
67034.98 83701.21 94928.83 108375.29 137442.24 162002.66
17.16 33.99 50.26 80.08 127.54 177.58
The nuclear case 2000 2005 2010 2015 2020 2025
67034.98 83701.21 94928.83 97841.54 87676.07 75882.45
The IGCC case 2000 2005 2010 2015 2020 2025
NOx
N2 O
4.26 4.69 4.95 4.76 4.26 4.06
200.00 256.03 295.98 345.80 442.52 530.49
0.46 0.57 0.63 0.72 0.98 1.15
17.16 33.99 50.26 69.59 70.24 72.40
4.26 4.69 4.95 4.33 3.47 3.47
200.00 256.03 295.98 311.14 279.54 247.37
67034.98 82410.21 92988.42 97035.70 122786.26 144112.66
17.16 28.87 35.70 42.44 83.46 123.39
4.26 5.06 6.27 7.17 6.96 7.40
The biomass case 2000 67034.98 2005 79162.84 2010 74807.98 2015 86573.56 2020 110485.31 2025 128944.47
17.16 83.05 641.41 706.86 863.76 1080.00 17.16 38.78 75.13 89.47 130.40 175.37
The supercritical 2000 2005 2010 2015 2020 2025
case 67034.98 85641.42 106294.35 112907.00 138548.42 161491.59
CO
CH4
NMVOCs
SO2
TSP
4.20 5.18 5.89 6.63 7.90 9.23
382.00 346.86 255.74 156.83 163.41 78.24
187.23 194.53 77.48 14.41 10.35 1.35
0.46 0.57 0.63 0.65 0.60 0.46
4.20 5.18 5.89 5.97 5.24 4.90
382.00 346.86 255.74 151.44 132.96 32.51
187.23 194.53 77.48 14.22 9.89 1.11
200.00 247.83 272.55 287.98 373.82 446.71
0.46 0.52 0.46 0.41 0.63 0.74
4.20 5.20 6.08 6.70 7.92 9.27
382.00 389.52 449.61 391.85 400.11 355.03
187.23 190.17 68.66 14.81 11.08 2.35
4.26 6.22 16.09 16.58 18.47 21.40
200.00 246.87 265.75 312.77 399.96 477.31
0.46 0.71 2.00 2.17 2.71 3.25
4.20 7.40 23.96 25.80 30.72 37.00
382.00 343.97 264.07 168.03 180.71 102.74
187.23 200.76 95.02 33.39 33.13 29.15
4.26 4.45 3.88 4.32 4.11 4.24
200.00 263.01 334.54 361.33 446.98 527.88
0.46 0.59 0.78 0.77 1.00 1.15
4.20 5.20 6.08 6.70 7.91 9.26
382.00 389.04 449.41 375.03 383.62 356.30
187.23 192.03 80.71 27.13 23.43 17.79
Note: NMVOCs stands for non-methane volatile organic compounds. TSP stands for total suspended particulate matter.
Table 8 Installed capacity of energy transformation processes Year
The nuclear case
The IGCC case
The biomass case
The supercritical case
863 947 1191 1445 1718 1718
863 947 1196 1455 1741 1741
863 947 1180 1445 1704 1704
863 948 1186 1442 1739 1739
Natural gas processing plant (mmscf per day) 2000 2131 2131 2005 2131 2131 2010 2131 2131 2015 2131 2131 2020 2131 2131 2025 2131 2131
2113 2113 2113 2113 2113 2113
2136 2136 2136 2136 2136 2136
2105 2105 2105 2105 2105 2105
Oil refinery 2000 2005 2010 2015 2020 2025
The base case (103
barrel per day) 863 947 1193 1457 1717 1717
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Table 9 Environmental emissions from oil refinery and natural gas processing plant in selected years (unit: 103 tonnes) Year
CO2
CO
CH4
NOx
N2 O
NMVOCs
TSP
The base case 2000 2005 2010 2015 2020 2025
10650.31 12191.34 14228.87 16848.19 19535.27 22945.40
3.82 4.37 5.09 6.03 7.00 8.22
0.19 0.22 0.25 0.30 0.35 0.41
28.62 32.76 38.23 45.28 52.49 61.66
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.51 1.75 2.06
0.40 0.47 0.54 0.65 0.75 0.88
The nuclear case 2000 2005 2010 2015 2020 2025
10650.31 12191.34 14228.87 16804.19 19371.27 22958.80
3.82 4.37 5.09 6.02 6.94 8.23
0.19 0.22 0.25 0.30 0.35 0.41
28.62 32.76 38.23 45.16 52.05 61.70
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.51 1.74 2.06
0.40 0.47 0.54 0.64 0.74 0.88
The IGCC case 2000 2005 2010 2015 2020 2025
10650.31 12155.66 14163.04 16856.89 19511.25 23257.95
3.82 4.36 5.07 6.04 6.99 8.33
0.19 0.22 0.25 0.30 0.35 0.42
28.62 32.67 38.06 45.30 52.43 62.50
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.51 1.75 2.08
0.40 0.47 0.54 0.65 0.75 0.89
The biomass case 2000 2005 2010 2015 2020 2025
10650.31 12179.76 14215.28 16653.45 19377.44 22772.69
3.82 4.36 5.09 5.96 6.95 8.16
0.19 0.22 0.25 0.30 0.35 0.41
28.62 32.73 38.20 44.76 52.07 61.19
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.50 1.74 2.04
0.40 0.47 0.54 0.64 0.74 0.87
The supercritical case 2000 10650.31 2005 12129.50 2010 14181.34 2015 16706.76 2020 19341.80 2025 23237.54
3.82 4.34 5.08 5.99 6.93 8.33
0.19 0.22 0.25 0.30 0.35 0.42
28.62 32.60 38.10 44.89 51.98 62.45
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.49 1.73 2.08
0.40 0.47 0.54 0.63 0.74 0.89
180 Base case Nuclear case IGCC case Biomass case Supercritical case
Primary energy supply ('000 ktoe)
160 140 120 100 80 60 40 20 0 2000
2005
2010
2015
2020
Fig. 2. Primary energy supply of all cases in selected years.
2025
J. Santisirisomboon et al. / Environmental Science & Policy 6 (2003) 533–541 Table 10 Avoided cost of CO2 reduction in the power sector Case study
Accumulated incremental cost (US$ 106 )
The The The The
−6388.91 −577.49 −714.01 158.22
nuclear case IGCC case biomass case supercritical case
Accumulated CO2 emissions reduction (106 tonnes) 574.26 202.71 468.31 −76.02
CO2 avoided cost (US$/tonne)
−11.13 −2.85 −1.52 –
In this study, the introduction of supercritical thermal power plant in the supercritical case is not significant in terms of CO2 reduction as it results in negative accumulated CO2 reduction but positive accumulated incremental cost.
5. Conclusions At present, electricity in Thailand is mainly generated from gas-fired power plants. It appears in the base case that this situation will continue until the middle of the present planning horizon. After that, additional capacity of coal-fired power plants is projected. The electricity generation from lignite-fired power plants is decreasing due to the serious environmental impact of SO2 . The electricity generation from fuel oil is also decreasing due to the high generation cost compared to other base-load plants. None of the additional capacity of LNG combined-cycle is incorporated in the power generation expansion planning in the planning horizon. The LNG plant is not able to compete with other prospective plants due to its high fuel cost. In the base case, natural gas and coal are the main fuel supply for the power sector in the planning horizon. However, a significant number of coal-fired plants will be replaced by nuclear plants in the nuclear case due to its low generation cost. The substitution of biomass power plants for coal-fired plants is also appearing in the biomass case for the same reason. Due to a large demand of coal and natural gas for electricity generation, the infrastructure to accommodate the growing amount of imported fuel should be prepared. The infrastructure includes pipeline systems for importing natural gas and coal port handling systems for the coal-fire power plants. The power sector contributes substantially to environmental emissions, especially CO2 . Hence, environmental abatement strategies are significant in this sector. Moreover, the implementation of abatement options in this sector are easier than in other demand sectors due to the fact that the power sector is not as individual as other demand sectors. Various mitigation options in the power sector are examined here, to investigate potential CO2 reduction strategies. It was found that the nuclear case had the highest potential of conserving fossil fuel as well as mitigating environmental emissions followed by the biomass case and the IGCC
541
case. The supercritical case is found to be not good option for the mitigation of CO2 emissions due to its high accumulated CO2 emissions and incremental costs. In terms of avoided cost per tonne of CO2 , the nuclear case is the most attractive option followed by the IGCC case and the biomass case. References Boudri, J.C., Hordijk, L., Kroeze, C., Amann, M., Cofala, J., Bertok, I., Panwar, T.S., Gupta, S., Singh, D., Kumar, A., Vipradas, M.C., Dadhich, P., Srivastava, L., 2002. The potential contribute of renewable energy in air pollution abatement in China and India. Energy Policy 30, 409–424. Department of Energy Development and Promotion (DEDP), 1997, Thailand Energy Situation 1997, Ministry of Science Technology and Environment, Thailand. Department of Energy Development and Promotion (DEDP), 1999, Thailand Energy Situation 1999, Ministry of Science Technology and Environment, Thailand. Electricity Generating Authority of Thailand (EGAT), 1999. EGAT Power Development Plan PDP 1999, Thailand. Electricity Generating Authority of Thailand (EGAT), 2000. EGAT Power Development Plan PDP 2000, Thailand. Hadley, S.W., Short, W., 2001. Electricity sector analysis in the clean energy future study. Energy Policy 29, 1285–1298. International Atomic Energy Agency (IAEA), 1998. WASP-IV, A Computer Code for Power Generating System Expansion Planning Version WAS-IV: User’s Manual, Austria. Paffenbarger, J. A., Bertel, E., 1998. Results from the OECD Report on International Projections of Electricity Generating Costs. Proceeding of IJPGC 98. Rosen, M.A., 2000. Energy- and exergy-based comparison of coal-fired and nuclear steam power plants. Energy Int. J. 1 (3), 180–192. Santisirisomboon, J., Limmeechokchai, B., Chungpaibulpatana, S., 2001. Impacts of biomass power generation and CO2 taxation on electricity generation expansion planning and environmental emissions. Energy Policy 29 (19), 975–985. Sims, R.E.H., Rogner, H.-H., Gregory, K., 2003. Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation. Energy Policy 31, 1315– 1326.
Bundit Limmeechokchai is an associate professor in energy technology program at SIIT, Thammasat University. He earned his M.Eng. and D.Eng. (Energy Economics and Planning) from Asian Institute of Technology (AIT), Thailand. His research interests are energy conservation and management, demand-side management and integrated resource planning, energy–environment system modeling, Greenhouse gas (GHG) emissions and GHG mitigation options in the energy sector. Jerasorn Santisirisomboon earned his Ph.D. (Mechanical Engineering) from SIIT, Thammasat University in 2001. In 1994 he began his research carrier with Thailand Environment Institute after earning his M.Sc. in energy technology from King Mongkut’s University of Technology Thonburi. His area of specialization includes modeling of energy and environment, energy conservation, GHG emissions from energy sector, renewable energy, optimization of energy utilization. Suparchart Chungpaibulpatana is an associate professor in energy technology program at SIIT. He earned his M.Eng. and D.Eng. (Energy Technology) from AIT. His research interests are thermal engineering, solar energy, energy conservation and management.
Least cost electricity generation options based on environmental impact abatement Jerasorn Santisirisomboon a,b , Bundit Limmeechokchai a,∗ , Suparchart Chungpaibulpatana a a
Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22 Thammasat Rangsit Post Office, Pathumthani 12121, Thailand b Faculty of engineering, Ramkhamhaeng University, Huamak, Bangkapi, Bangkok 10240, Thailand
Abstract The power sector in Thailand is the largest contributor to CO2 emissions. There is high potential to mitigate CO2 emission via alternative power generating plants. Alternative plants considered in this study include nuclear plants, integrated gasification combined cycle plants, biomass-based plants and supercritical thermal power plants. The biomass-based plants considered here are fueled with four types of biomass; paddy husk, municipal solid waste (MSW), fuel wood and corncob. The methodology for the optimal expansion plan of the power generating system over the planning horizon is based on the least-cost approach. The results from the least-cost planning analyses show that the nuclear alternative has the highest potential to mitigate not only CO2 but also other airborne emissions. Moreover, the nuclear option is the most effective abatement strategy for CO2 reduction due to its negative incremental cost of CO2 reduction. © 2003 Elsevier Ltd. All rights reserved. Keywords: Least cost electricity generation; Nuclear power; Biomass; Supercritical thermal power; Integrated gasification combined cycle; CO2 mitigation
1. Introduction The power sector has an important implication for all countries economic development. It plays a critical role in energy consumption and environmental pollution emissions in Thailand. During the period 1990–1997, electricity demand increased at an average annual growth rate of 11.6% and the demand in 1997 was 82,431 GWh (DEDP, 1997). However, this high growth trend was considered unlikely to continue because of the economic recession in 1997. The electricity demands in 1998 and 1999 were 2.4 and 1.2% lower than the demand in 1997 (DEDP, 1999). The power industry, which has been built and operated under a “supply–follows–demand” philosophy, has always been able to fulfill its obligation by providing adequate and secure supplies of electricity at the lowest practicable cost. The objective of the power generation plan is to seek for the most economical generation expansion scheme to achieve a certain reliability level to meet the forecasted demand increase in a certain period of time. However, this has led to an onerous operating strategy requiring high plant margins and extensive environmental impacts on the range from the local to the global scale as it is a major energy consum∗
Corresponding author. Tel.: +662-986-9009x2206; fax: +662-986-9009x2501. E-mail address: [email protected] (B. Limmeechokchai). 1462-9011/$ – see front matter © 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsci.2003.08.004
ing sector and a large contributor of environmental pollution emissions (especially CO2 ). Based on the recent report of the Department of Energy Development and Promotion (DEDP), the power sector is the highest CO2 emission sector (DEDP, 1999). Thereby it has the highest potential to mitigate CO2 emission significantly. There are many mechanisms for reducing CO2 emissions from the power sector and one of the most practical mechanisms is the switching from high- to less- or non-carbon intensive sources of generation (Hadley and Short, 2001). Four alternative power plants (including nuclear, integrated gasification combined cycle plants (IGCC), biomass and supercritical plants) are considered for the mitigation of CO2 emission in this study. The main objective of this study is to evaluate the most economical power generation expansion plan that not only meets the requirement of electricity demand, but also mitigates CO2 emission. The impacts of such alternatives on the primary energy supply are also assessed.
2. Final electricity and peak load demand projection The economic sector in Thailand comprises 5 sectors: agricultural, commercial, industrial, residential and transport. The electricity demand projection in these sectors was done by using the end-use model. The projected
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Agriculture Transport Residential Commercial Industrial
250,000
200,000
GWh
150,000
100,000
50,000
2000
2005
2010
2015
2020
2025
Fig. 1. Final electricity demand projection by economic sector in selected years.
electricity demand in Thailand is projected to increase from 83,571 GWh in 2000 to 206,743 GWh in 2025 with an annual growth rate of 3.7% (Fig. 1). The difference between electricity generation and demand is measured by the values of transmission and distribution (T&D) losses, the own use of electricity in the power plants and the use for pumping in the pump storage hydro-power plants. Hence, the annual peak load demand is estimated by the following equation. PLDt =
[ELDEMt (1+OwmUse+TDLosst ) + PSt ] × 1000 SysLFt × 8760 (1)
where PLDt is the peak load demand in year t (MW), ELDEMt is the electricity demand in year t (GWh), OwnUset is the fraction of own use of electricity in year t (%), TDLosst is the fraction of transmission and distribution loss (%), PSt is the electricity use for pump storage plants in year t (GWh), and SysLFt is the system load factor. The electricity for own use and the T&D losses are assumed to be 3.5 and 7.0%,respectively (DEDP, 1999). The electricity used for pump storage in the existing power-plant system is 638 GWh (DEDP, 1999). The system load factor in year t (SysLFt ) is estimated from the area under the annual-normalized load duration
curve (LDC): 1 LDCt dtn SysLFt =
(2)
0
where LDCt is the polynomial function of degree 5, in which the normalized time acts as an independent variable and the normalized load as a dependent variable, in year t, and tn is the normalized time. The annual load duration curve was calculated from the system load shapes in 1998 with a load factor of 73%. The percentages of T&D losses, own use and electricity used for the pump storage of the hydro power plant are applied to the whole planning horizon. The corresponding electricity generation and peak load demand are presented in Table 1.
3. Least cost power generation expansion plan 3.1. Introduction: methods and the five case studies The methodology for the optimal expansion plan for the power generating system over the planning horizon is based on the least cost concept. The optimum power generation expansion plan is evaluated in terms of minimum discounted total costs. In this study, the least-cost model used to estimate
Table 1 Electricity peak load demand in selected years Year
Electricity demand (GWh)
Own use (GWh)
Transmission loss (GWh)
Distribution loss (GWh)
Pump storage (GWh)
Purchase (GWh)
Total generation (GWh)
Peak load demand (MW)
2000 2005 2010 2015 2020 2025
83571 105814 126652 149931 176225 206743
3295 4165 4980 5891 6919 8112
2825 3570 4269 5049 5930 6953
3766 4760 5692 6732 7907 9271
638 638 638 638 638 638
7599 7599 7599 7599 7599 7599
85917 110769 134052 160062 189441 223539
13405 17283 20915 24974 29558 34878
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the optimal expansion plan is the Wien Automatic System Planning Package version IV (WASP-IV). It is designed for the economically optimal generation expansion plan for an electric utility system within users’ specified constraints. The model utilizes probabilistic estimations of system production cost, unserved energy cost, system reliability, and dynamic programming methods of optimization for comparing the costs of alternative system expansion plans (IAEA, 1998). The technological and economic benefits of every generation expansion scheme in WASP-IV are weighted by using present value of cost. The objective function of WASP-IV can be expressed as Bj =
T
¯ j,t ] ¯ j,t + M ¯ j,t + O [I¯j,t − S¯ j,t + F¯ j,t + L
(3)
t=1
where Bj is the objective function attached to the expansion plan j, I the capital investment costs, S the salvage value of investment costs, F the fuel costs, L the fuel inventory costs, M the non-fuel operating and maintenance costs, O the cost of the energy not served, t the time in year 1, 2, . . . , T , and T is the planning period. The over bar indicates discounted values to a reference year or the base year at a given discount rate i. The optimal plan is defined by minimizing Bj among all j. The output of the WASP-IV model is the optimum system expansion plan within the constraints given by users. The model also calculates the optimum mixed generation of each power
535
plant to meet the electricity demand under the same set of constraints. The five case studies of power generation expansion plans considered in this study are the base case, the nuclear case, the integrated gasification combined cycle (IGCC) case, the biomass case and the supercritical case. The minimum and maximum reserve margins in all cases are 15 and 25% respectively. Apart from the reserve margin constraints, constraints on fuel supply are also considered. The natural gas supplied through pipelines from the neighboring countries is limited to 2809 million standard cubic feet per day (mmscfd), which is adequate for the generation of approximately 11,650 MW of combined-cycle power plant or about 19 units of 600 MW capacities (EGAT, 1999). The import of liquefied natural gas (LNG) for the combined-cycle power plant will be considered after the natural gas supplied through pipelines reaches saturation. The development of indigenous lignite and hydro power plants is not taken into account due to the public resistance. The potential coal-fired power plant in this study is fueled with imported low-sulfur content coal. 3.2. The base case Five types of technology for electricity generation are introduced as potential plants in the base case. These power generation technologies are all currently commercially available. The operating characteristics of these potential plants are presented in Table 2.
Table 2 Operating characteristics of testing power plants Technology
Typical size (MW)
Life time (year)
Operating characteristics of potential power plants Thermal Coal-fireda 1000 25 Oil-fireda 1000 25
Capital cost (US$/kW)
Fixed O&M (US$/kW-month)
Variable O&M (US$/MWh)
Fuel cost (US$/106 kcal)
Heat rate (kcal/kWh)
837 736
1.2120 0.8875
0.8899 0.6515
7.50 10.07
2322 2341
Combined cycle Natural gasa LNGa
600 600
20 20
557 557
0.8378 0.8378
0.8197 0.8197
12.69 16.70
1860 1860
Gas turbine Diesela
200
15
395
0.6337
0.4652
18.68
2743
1020 1154 1329 1510 1612 1407 1510
3.084 1.265 1.265 5.000 5.167 4.875 5.000
0.0000 0.7251 0.7251 7.0000 4.0000 10.0000 7.0000
1.19 7.50 7.50 1.15 0.00 4.27 2.61
2322 1755 1810 3105 3554 2766 3105
Operating characteristics of testing power plants Thermal 592 30 Nuclearb IGCCc 380 25 400 25 Supercriticalc 100 30 Paddy huskd MSWe 25.5 30 100 30 Fuel woodf 100 30 Corncobd
Note: LNG stands for liquefied natural gas. IGCC stands for integrated gasification combined cycle. MSW stands for municipal solid waste. a EGAT (2000). b International Energy Agency (IEA). c Paffenbarger and Bertel (1998). d United States Department of Energy (U.S. DOE). e California Energy Commission (CEC). f Electric Power Research Institute (EPRI).
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3.3. The nuclear case
eration from biomass is supplied as generation constraints to avoid the shortage of biomass.
The nuclear power plant has fuel resource potential for the reduction of environmental emissions from electricity generation. The nuclear plant introduced in this study is a 592-MW pressurized water reactor type. This plant will be introduced to the WASP-IV model from 2015 onward. The operating characteristics of this plant are presented in Table 2. 3.4. The IGCC case Coal gasification is a process that converts solid coal into synthetic gases comprising mainly carbon monoxide and hydrogen. The Integrated Gasification Combined Cycle (IGCC) converts coal into gaseous fuel and then burns it in a combined cycle gas turbine. The IGCC plant introduced in this study is based on the technology availability in USA. The net capacity of this plant is 380 MW with a net efficiency of 49% (Paffenbarger and Bertel, 1998). Since this type of power plant is expected to be commercially available after 2005, it is introduced to the WASP-IV model from 2005 onward. 3.5. The biomass case Biomass is considered as a renewable energy source that has high potential in air pollution abatement and is cost effective (Boudri et al., 2002). Four types of biomass power plant are considered in this study. Since the biomass supply is limited, the potential of power generation from biomass needs to be estimated to ensure the power system reliability. The available biomass for the electricity generation is the excess supply of biomass from the demands in the industrial and residential sectors (Santisirisomboon et al., 2001). The types of biomass for the electricity production are projected on the basis of the availability and heat rate of each type of biomass plant. The availability factor or plant load factors of biomass-fired plants are assumed to be 80%. The maximum potential of biomass for the electricity generation (Table 3) is applied as a capacity expansion constraint in the model. In addition, the estimated potential of electricity gen-
3.6. The supercritical power plant case The efficiency of a thermal power plant can be increased by using the supercritical steam cycle. Overall plant efficiency can be increased from 37% with the subcritical steam cycle to 48% with the supercritical steam cycle. In this study, the supercritical coal-fired thermal power plant is introduced as a potential plant in the WASP-IV model from 2005 onward. 4. Results 4.1. Expansion capacity The power generation expansion capacity in the base case (see Table 4) is expected to increase from 21,252 MW in 2000 to 46,305 MW in 2025. In 2025, it is estimated that a 22,000 MW (about 48% of all power generation) coal-fired power plant will be installed. The share of coal-based plant is expected to decrease by introducing a nuclear power plant in 2025. The nuclear plant is able to replace 14,000 MW of the coal-fired power plant. An introduction of IGCC plant in this year is able to reduce total expansion capacity by 1240 MW. At the end of the planning horizon, the biomass-based plants should be able to reduce the dependency of coal-fired and diesel-fired plants by 5000 and 600 MW, respectively. Although the fuel wood has a high potential to generate electricity, it still cannot compete with other types of power plants due to its high generation cost. In 2025, the total generation expansion capacity in the supercritical case will be reduced by 1200 MW due to the introduction of high-efficient supercritical power plant. 4.2. Electricity generation The principal electricity generation during the first half of the planning horizon in the base case comes from natural gas. About 50% of the power generation in 2000 came from
Table 3 Potential of biomass for electricity generation in Thailand Year
2000 2005 2010 2015 2020 2025
Paddy husk
Corncob
Fuel wood
MSW
Capacity (MW)
Generation (GWh)
Capacity (MW)
Generation (GWh)
Capacity (MW)
Generation (GWh)
Capacity (MW)
Generation (GWh)
400 400 400 300 300 300
2803 2803 2803 2102 2102 2102
1800 2300 3100 3900 4600 5000
12613 16116 21722 27328 32232 35035
0 500 500 1000 1600 1600
0 3504 3504 7007 11211 11211
128 179 204 281 332 383
949 1329 1519 2088 2468 2848
Note: MSW stands for municipal solid wastes.
J. Santisirisomboon et al. / Environmental Science & Policy 6 (2003) 533–541 Table 4 Power generation expansion capacity (unit: MW) Power plant type
2000
The base case Hydro Thermal Gas turbine Combined cycle Purchase Total
2020
2025
Plant type The base case Hydro Thermal Gas turbine Combined cycle Purchase
2878 11075 3954 13828 2673
2878 17400 5754 11400 2673
2878 22000 7354 11400 2673
21252
24832
29174
34408
40105
46305
Total 2878 6443 1686 7572 2673
2878 7623 2286 9372 2673
2878 8925 2486 12212 2673
2878 12443 3754 12628 2673
2878 19280 4954 10200 2673
2878 22208 7154 11400 2673
21252
24832
29174
34376
39985
46313
2878 6443 1686 7572 2673
2878 8143 2286 8772 2673
2878 12385 2286 8012 2673
2878 11295 3554 12628 2673
2878 16620 5154 11400 2673
2878 20360 7754 11400 2673
21252
24752
28234
33028
38725
45065
2878 6443 1686 7572 2673
2878 7051 2286 9972 2673
2878 9329 2286 11612 2673
2878 11679 2954 13828 2673
2878 17681 4954 11400 2673
2878 22206 6754 11400 2673
21252
24859
28778
34012
39586
45911
2878 6443 1686 7572 2673
2878 8223 2286 8772 2673
2878 12325 2286 8012 2673
2878 11875 3154 12628 2673
2878 17200 4754 11400 2673
2878 20400 7754 11400 2673
21252
24832
28174
33208
38905
45105
The supercritical case Hydro Thermal Gas turbine Combined cycle Purchase Total
2015
2878 8925 2486 12212 2673
The biomass case Hydro Thermal Gas turbine Combined cycle Purchase Total
2010
2878 7623 2286 9372 2673
The IGCC case Hydro Thermal Gas turbine Combined cycle Purchase Total
Table 5 Electricity generation by type of power plant (unit: GWh)
2878 6444 1686 7572 2673
The nuclear case Hydro Thermal Gas turbine Combined cycle Purchase Total
2005
537
gas-fired plants. In the future, however, the power generation from gas-fired plants is expected to decline due to the supply constraint of natural gas, whereas the use of coal is expected to increase. However, this situation will not happen if nuclear and biomass options are introduced. In the nuclear case, a significant amount of coal used for electricity generation is replaced by nuclear since the generation cost of nuclear power plant is lower than other possible plants, especially for that of coal-fired plants. Biomass-based plants are evaluated to estimate the electricity generation capacity. In 2025, the electricity generation from biomass plants is estimated to be 38,359 GWh that will account for about 17% of the total generation. The introduction of IGCC and supercritical thermal plants does not affect the change in fuel mixture for electricity generation. However, the apportionment of electricity generation from each fuel type is slightly changed when com-
2000 4221 33165 2097 46434
Total
Total
4221 73199 3075 79566
2020
2025
4221 116981 4952 63287
4221 150637 6298 62383
7599
7599
7599
7599
7599
118368
141651
167661
197040
231138
4221 44818 2324 59406
4221 54412 2110 73310
4221 81569 2903 71367
4221 126758 4007 54455
4221 150326 6377 62616
7599
7599
7599
7599
7599
93516
118368
141651
167661
197040
231138
4221 33165 2097 46434
4221 49073 2208 55267
4221 80207 2033 47591
4221 80184 3143 72513
4221 116983 4802 63434
4221 146810 8086 64422
7599
7599
7599
7599
7599
7599
93516
118368
141651
167661
197040
231138
4221 41111 2223 63215
4221 58203 2077 69551
4221 77211 2087 76544
4221 119227 4061 61932
4221 152728 5307 61283
7599
7599
7599
7599
7599
118368
141651
167661
197040
231138
4221 49442 2090 55017
4221 79974 2112 47745
4221 82983 2393 70464
4221 119619 3850 61751
4221 147047 7968 64303
7599
7599
7599
7599
7599
118368
141651
167661
197040
231138
93516
The supercritical case Hydro 4221 Thermal 33165 Gas turbine 2097 Combined 46434 cycle Purchase 7599 Total
4221 54412 2110 73310
2015
7599
The biomass case Hydro 4221 Thermal 33165 Gas turbine 2097 Combined 46434 cycle Purchase 7599 Total
4221 44818 2324 59406
2010
93516
The nuclear case Hydro 4221 Thermal 33165 Gas turbine 2097 Combined 46434 cycle Purchase 7599
The IGCC case Hydro Thermal Gas turbine Combined cycle Purchase
2005
93516
pared to the base case. The electricity generation from the WASP-IV model is shown in Table 5. 4.3. Fuel requirement In the base case, the natural gas is the main fuel used in the power sector until the middle of the planning horizon. About 52% of fuel requirement for the power generation in 2000 came from natural gas. However, it is estimated that the largest fuel consumption for power generation in the future comes from coal. The introduction of nuclear and biomass-based plants would result in the reduction of coal requirement. The
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Table 6 Fuel requirements for electricity generation (unit: ktonnes) Fuel type
2000
2005
2010
2015
2020
2025
The base case Fuel Oil Lignite Imported coal Diesel Natural gas
4557 4790 0 228 10329
3594 5234 2791 305 12600
2737 4897 5620 253 14391
1436 3481 11533 521 14434
1421 2321 21580 1029 11055
0 0 31513 1609 10600
19904
24524
27899
31405
37406
43721
4557 4790 0 0 228 10329
3594 5234 2791 0 305 12600
2737 4897 5620 0 253 14391
1411 3472 9863 3561 477 13074
1330 2287 10841 13307 783 9560
0 0 10919 21206 1628 10641
19904
24524
27899
31831
38108
44394
4557 4790 0 228 10329
3459 5172 3846 277 11888
2329 4417 11769 255 9999
1330 3411 13226 567 13194
1396 2303 21710 995 11069
0 0 30823 2079 10964
19904
24641
28767
31729
37474
43866
The biomass case Fuel Oil 4557 Lignite 4790 Imported coal 0 Paddy husk 0 Corncob 0 MSW 0 Diesel 228 Natural gas 10329
3540 5243 1398 764 0 371 281 13235
2562 4882 1343 764 7749 552 255 13729
1320 3462 6940 764 8267 594 259 13897
1374 2316 15496 764 9882 816 790 10824
0 0 23925 764 12293 890 1349 10402
19904
24832
31837
35503
42262
49624
The supercritical case Fuel Oil 4557 Lignite 4790 Imported coal 0 Diesel 228 Natural gas 10329
3408 5163 3969 248 11833
2352 4445 11681 276 10030
1267 3411 13856 365 12834
1348 2300 22296 737 10787
0 0 30873 2049 10942
24621
28783
31732
37468
43863
Total The nuclear case Fuel Oil Lignite Imported coal Nuclear Diesel Natural gas Total The IGCC case Fuel Oil Lignite Imported coal Diesel Natural gas Total
Total
Total
19904
nuclear case is able to reduce the largest coal requirement, followed by the biomass case, the IGCC case and the supercritical case. However, the total fuel requirements of all cases are higher than the base case, especially for the biomass case due to the lowest efficiency of biomass-based plant among all candidates. Fuel requirements corresponding to the expansion plan are shown in Table 6. 4.4. Air pollution emissions Based on the total fuel requirements for electricity generation, the corresponding air pollution emissions from such conversions are estimated and presented in Table 7. The dramatic decrease of CO2 emissions in the nuclear case results
from an increase of the number of nuclear plants. In the nuclear case, the emission reduction is not only limited to the CO2 but also other airborne emissions. In 2025, the substitution of some coal-fired plants with nuclear plants will be able to reduce about 50% of SO2 and NOx . Although the CO2 emissions from the biomass case are lower than the base case, other types of air pollutant emissions such as CO and TSP are higher due to lower efficiency and incomplete combustion of biomass-fired power plants. The IGCC case is able to reduce a significant level of CO2 emission due to the recognized technology for the reduction of CO2 emission. However, a significant amount of SO2 is increased due to the increased use of coal for electricity generation. In the supercritical case, the CO2 emission reduction is slight compared to the base case as it is a coal-fired plant with higher efficiency compared to the conventional coal-fired power plant. 4.5. Other energy transformation processes The introduction of alternative power plants does not only impact the power generation expansion plan but also the capacity expansion of oil refinery and natural gas processing plant. The installed capacity of oil refineries and natural gas processing plants are presented in Table 8. The corresponding environmental emissions from oil refineries and natural gas processing plants are shown in Table 9. 4.6. Primary energy supply The primary energy supply in the nuclear and the biomass case is higher than the base case due to lower efficiency of both plants compared to the coal-fired plant (Rosen, 2000). Nevertheless, both cases reduce the dependency on fossil fuel for electricity generation. Both the IGCC and the supercritical case are able to reduce the primary energy supply, however, the electricity generation is still dependent on fossil-fuel plants. The corresponding primary energy supplies of all cases are presented in Fig. 2. 4.7. CO2 emission reduction costs The comparison of the cost for the reduction of CO2 emission in all cases is presented in Table 10. The CO2 reduction costs of the nuclear, IGCC and biomass cases are −11.13, −2.85 and −1.52 US$/tonne, respectively. The accumulated incremental cost of the nuclear case is much lower than the base case due to its low generation cost. When the investment cost to avoid 1 tonne of carbon emission is negative, the alternative can be said to be an economic option regardless of any emission reduction (Sims et al., 2003). Hence, the nuclear case is the most effective abatement strategy for the CO2 reduction followed by the IGCC and biomass cases.
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Table 7 Air pollution emissions from the power sector in selected years (unit: 1000 tonnes) Year
CO2
The base case 2000 2005 2010 2015 2020 2025
67034.98 83701.21 94928.83 108375.29 137442.24 162002.66
17.16 33.99 50.26 80.08 127.54 177.58
The nuclear case 2000 2005 2010 2015 2020 2025
67034.98 83701.21 94928.83 97841.54 87676.07 75882.45
The IGCC case 2000 2005 2010 2015 2020 2025
NOx
N2 O
4.26 4.69 4.95 4.76 4.26 4.06
200.00 256.03 295.98 345.80 442.52 530.49
0.46 0.57 0.63 0.72 0.98 1.15
17.16 33.99 50.26 69.59 70.24 72.40
4.26 4.69 4.95 4.33 3.47 3.47
200.00 256.03 295.98 311.14 279.54 247.37
67034.98 82410.21 92988.42 97035.70 122786.26 144112.66
17.16 28.87 35.70 42.44 83.46 123.39
4.26 5.06 6.27 7.17 6.96 7.40
The biomass case 2000 67034.98 2005 79162.84 2010 74807.98 2015 86573.56 2020 110485.31 2025 128944.47
17.16 83.05 641.41 706.86 863.76 1080.00 17.16 38.78 75.13 89.47 130.40 175.37
The supercritical 2000 2005 2010 2015 2020 2025
case 67034.98 85641.42 106294.35 112907.00 138548.42 161491.59
CO
CH4
NMVOCs
SO2
TSP
4.20 5.18 5.89 6.63 7.90 9.23
382.00 346.86 255.74 156.83 163.41 78.24
187.23 194.53 77.48 14.41 10.35 1.35
0.46 0.57 0.63 0.65 0.60 0.46
4.20 5.18 5.89 5.97 5.24 4.90
382.00 346.86 255.74 151.44 132.96 32.51
187.23 194.53 77.48 14.22 9.89 1.11
200.00 247.83 272.55 287.98 373.82 446.71
0.46 0.52 0.46 0.41 0.63 0.74
4.20 5.20 6.08 6.70 7.92 9.27
382.00 389.52 449.61 391.85 400.11 355.03
187.23 190.17 68.66 14.81 11.08 2.35
4.26 6.22 16.09 16.58 18.47 21.40
200.00 246.87 265.75 312.77 399.96 477.31
0.46 0.71 2.00 2.17 2.71 3.25
4.20 7.40 23.96 25.80 30.72 37.00
382.00 343.97 264.07 168.03 180.71 102.74
187.23 200.76 95.02 33.39 33.13 29.15
4.26 4.45 3.88 4.32 4.11 4.24
200.00 263.01 334.54 361.33 446.98 527.88
0.46 0.59 0.78 0.77 1.00 1.15
4.20 5.20 6.08 6.70 7.91 9.26
382.00 389.04 449.41 375.03 383.62 356.30
187.23 192.03 80.71 27.13 23.43 17.79
Note: NMVOCs stands for non-methane volatile organic compounds. TSP stands for total suspended particulate matter.
Table 8 Installed capacity of energy transformation processes Year
The nuclear case
The IGCC case
The biomass case
The supercritical case
863 947 1191 1445 1718 1718
863 947 1196 1455 1741 1741
863 947 1180 1445 1704 1704
863 948 1186 1442 1739 1739
Natural gas processing plant (mmscf per day) 2000 2131 2131 2005 2131 2131 2010 2131 2131 2015 2131 2131 2020 2131 2131 2025 2131 2131
2113 2113 2113 2113 2113 2113
2136 2136 2136 2136 2136 2136
2105 2105 2105 2105 2105 2105
Oil refinery 2000 2005 2010 2015 2020 2025
The base case (103
barrel per day) 863 947 1193 1457 1717 1717
540
J. Santisirisomboon et al. / Environmental Science & Policy 6 (2003) 533–541
Table 9 Environmental emissions from oil refinery and natural gas processing plant in selected years (unit: 103 tonnes) Year
CO2
CO
CH4
NOx
N2 O
NMVOCs
TSP
The base case 2000 2005 2010 2015 2020 2025
10650.31 12191.34 14228.87 16848.19 19535.27 22945.40
3.82 4.37 5.09 6.03 7.00 8.22
0.19 0.22 0.25 0.30 0.35 0.41
28.62 32.76 38.23 45.28 52.49 61.66
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.51 1.75 2.06
0.40 0.47 0.54 0.65 0.75 0.88
The nuclear case 2000 2005 2010 2015 2020 2025
10650.31 12191.34 14228.87 16804.19 19371.27 22958.80
3.82 4.37 5.09 6.02 6.94 8.23
0.19 0.22 0.25 0.30 0.35 0.41
28.62 32.76 38.23 45.16 52.05 61.70
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.51 1.74 2.06
0.40 0.47 0.54 0.64 0.74 0.88
The IGCC case 2000 2005 2010 2015 2020 2025
10650.31 12155.66 14163.04 16856.89 19511.25 23257.95
3.82 4.36 5.07 6.04 6.99 8.33
0.19 0.22 0.25 0.30 0.35 0.42
28.62 32.67 38.06 45.30 52.43 62.50
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.51 1.75 2.08
0.40 0.47 0.54 0.65 0.75 0.89
The biomass case 2000 2005 2010 2015 2020 2025
10650.31 12179.76 14215.28 16653.45 19377.44 22772.69
3.82 4.36 5.09 5.96 6.95 8.16
0.19 0.22 0.25 0.30 0.35 0.41
28.62 32.73 38.20 44.76 52.07 61.19
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.50 1.74 2.04
0.40 0.47 0.54 0.64 0.74 0.87
The supercritical case 2000 10650.31 2005 12129.50 2010 14181.34 2015 16706.76 2020 19341.80 2025 23237.54
3.82 4.34 5.08 5.99 6.93 8.33
0.19 0.22 0.25 0.30 0.35 0.42
28.62 32.60 38.10 44.89 51.98 62.45
0.02 0.02 0.02 0.03 0.03 0.04
0.95 1.09 1.27 1.49 1.73 2.08
0.40 0.47 0.54 0.63 0.74 0.89
180 Base case Nuclear case IGCC case Biomass case Supercritical case
Primary energy supply ('000 ktoe)
160 140 120 100 80 60 40 20 0 2000
2005
2010
2015
2020
Fig. 2. Primary energy supply of all cases in selected years.
2025
J. Santisirisomboon et al. / Environmental Science & Policy 6 (2003) 533–541 Table 10 Avoided cost of CO2 reduction in the power sector Case study
Accumulated incremental cost (US$ 106 )
The The The The
−6388.91 −577.49 −714.01 158.22
nuclear case IGCC case biomass case supercritical case
Accumulated CO2 emissions reduction (106 tonnes) 574.26 202.71 468.31 −76.02
CO2 avoided cost (US$/tonne)
−11.13 −2.85 −1.52 –
In this study, the introduction of supercritical thermal power plant in the supercritical case is not significant in terms of CO2 reduction as it results in negative accumulated CO2 reduction but positive accumulated incremental cost.
5. Conclusions At present, electricity in Thailand is mainly generated from gas-fired power plants. It appears in the base case that this situation will continue until the middle of the present planning horizon. After that, additional capacity of coal-fired power plants is projected. The electricity generation from lignite-fired power plants is decreasing due to the serious environmental impact of SO2 . The electricity generation from fuel oil is also decreasing due to the high generation cost compared to other base-load plants. None of the additional capacity of LNG combined-cycle is incorporated in the power generation expansion planning in the planning horizon. The LNG plant is not able to compete with other prospective plants due to its high fuel cost. In the base case, natural gas and coal are the main fuel supply for the power sector in the planning horizon. However, a significant number of coal-fired plants will be replaced by nuclear plants in the nuclear case due to its low generation cost. The substitution of biomass power plants for coal-fired plants is also appearing in the biomass case for the same reason. Due to a large demand of coal and natural gas for electricity generation, the infrastructure to accommodate the growing amount of imported fuel should be prepared. The infrastructure includes pipeline systems for importing natural gas and coal port handling systems for the coal-fire power plants. The power sector contributes substantially to environmental emissions, especially CO2 . Hence, environmental abatement strategies are significant in this sector. Moreover, the implementation of abatement options in this sector are easier than in other demand sectors due to the fact that the power sector is not as individual as other demand sectors. Various mitigation options in the power sector are examined here, to investigate potential CO2 reduction strategies. It was found that the nuclear case had the highest potential of conserving fossil fuel as well as mitigating environmental emissions followed by the biomass case and the IGCC
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case. The supercritical case is found to be not good option for the mitigation of CO2 emissions due to its high accumulated CO2 emissions and incremental costs. In terms of avoided cost per tonne of CO2 , the nuclear case is the most attractive option followed by the IGCC case and the biomass case. References Boudri, J.C., Hordijk, L., Kroeze, C., Amann, M., Cofala, J., Bertok, I., Panwar, T.S., Gupta, S., Singh, D., Kumar, A., Vipradas, M.C., Dadhich, P., Srivastava, L., 2002. The potential contribute of renewable energy in air pollution abatement in China and India. Energy Policy 30, 409–424. Department of Energy Development and Promotion (DEDP), 1997, Thailand Energy Situation 1997, Ministry of Science Technology and Environment, Thailand. Department of Energy Development and Promotion (DEDP), 1999, Thailand Energy Situation 1999, Ministry of Science Technology and Environment, Thailand. Electricity Generating Authority of Thailand (EGAT), 1999. EGAT Power Development Plan PDP 1999, Thailand. Electricity Generating Authority of Thailand (EGAT), 2000. EGAT Power Development Plan PDP 2000, Thailand. Hadley, S.W., Short, W., 2001. Electricity sector analysis in the clean energy future study. Energy Policy 29, 1285–1298. International Atomic Energy Agency (IAEA), 1998. WASP-IV, A Computer Code for Power Generating System Expansion Planning Version WAS-IV: User’s Manual, Austria. Paffenbarger, J. A., Bertel, E., 1998. Results from the OECD Report on International Projections of Electricity Generating Costs. Proceeding of IJPGC 98. Rosen, M.A., 2000. Energy- and exergy-based comparison of coal-fired and nuclear steam power plants. Energy Int. J. 1 (3), 180–192. Santisirisomboon, J., Limmeechokchai, B., Chungpaibulpatana, S., 2001. Impacts of biomass power generation and CO2 taxation on electricity generation expansion planning and environmental emissions. Energy Policy 29 (19), 975–985. Sims, R.E.H., Rogner, H.-H., Gregory, K., 2003. Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation. Energy Policy 31, 1315– 1326.
Bundit Limmeechokchai is an associate professor in energy technology program at SIIT, Thammasat University. He earned his M.Eng. and D.Eng. (Energy Economics and Planning) from Asian Institute of Technology (AIT), Thailand. His research interests are energy conservation and management, demand-side management and integrated resource planning, energy–environment system modeling, Greenhouse gas (GHG) emissions and GHG mitigation options in the energy sector. Jerasorn Santisirisomboon earned his Ph.D. (Mechanical Engineering) from SIIT, Thammasat University in 2001. In 1994 he began his research carrier with Thailand Environment Institute after earning his M.Sc. in energy technology from King Mongkut’s University of Technology Thonburi. His area of specialization includes modeling of energy and environment, energy conservation, GHG emissions from energy sector, renewable energy, optimization of energy utilization. Suparchart Chungpaibulpatana is an associate professor in energy technology program at SIIT. He earned his M.Eng. and D.Eng. (Energy Technology) from AIT. His research interests are thermal engineering, solar energy, energy conservation and management.