The selected three clean energy technologies have different GHG mitigation potential at different locations. Solar PV and wind power systems are dependent on geographical conditions in particular. During the assessment of the application potential of the three clean power systems, the geographical conditions of these six locations are integrated into the analysis to calculate the actual power output of solar PV and wind power systems. The application potential of the three clean power systems are analyzed in three aspects: capacity factor of the clean energy power system, amount of GHG reduction on a unit economic cost scale, and the potential reduction range of each clean energy system in the six countries selected.
The geographical differences of solar energy among the six selected cities are characterized by the average amount of the total solar radiation incident on a horizontal surface on an annual base. The solar insolation data used in this analysis were the data collected by NASA for that geographical location during a 22-year time period (from July 1983 to June 2005) [
13]. The solar PV module efficiency used in the analysis is the average of the five efficiency values of the selected solar PV modules in
Table 1, namely 14.79%, as a representative of solar PV in that class. Since the solar PV power system produces DC power output, for supplying automotive manufacturing the solar PV DC power output must be converted to AC power prior to its actual use. In this analysis, the conversion efficiency from DC to AC power supply is taken as the typical value of 77% [
14].
3.1. Capacity Factor of Solar and Wind Power Systems
Although the energy density information can serve as the basic indicator of the application potential of clean energy power systems, further analyses are needed to understand more about the actual implementation of these clean energy power systems in different geographical locations. For solar and wind energy, we employ the capacity factor, a meaningful metric to assess their actual application potential in a specific geographic location, to assess the performance the clean energy systems at the selected locations. The capacity factor is defined as the ratio between the actual power output and the total rated power of the system available for the power generations. The expression of capacity factor is shown in Equation (4) below:
where:
CF: capacity factor of a power supply system
Poutput: actual power output of the supply system (kW)
Psystem: rated power of the supply system (kW)
The calculated capacity factors for solar PV and wind power systems at the six selected geographical locations are shown in
Figure 2. The actual power output for solar PV system is calculated by multiplying the local annual solar insolation, the module area, module numbers, module efficiency and the DC to AC conversion efficiency. The actual power output for wind turbines is calculated by applying the power curve of the wind turbine model. The power curve of a specific model of wind turbine is fitted by the power output values at different wind speeds supplied by the wind turbine manufacturer. The results demonstrate that for current solar PV power systems, the actual capacity factors are all below 20% at these six selected locations; while the capacity factor of wind can reach up to 47% at the six selected locations, depending on the local wind energy resources.
Figure 2.
Capacity factor of solar PV and wind power systems at the six selected locations.
Figure 2.
Capacity factor of solar PV and wind power systems at the six selected locations.
From
Figure 2, we can see that Cairo has the largest capacity factor for solar PV at 17.98%, followed by Mexico City (17.62%) and Sao Paulo (15.48%). The capacity factors for solar PV in Shanghai, Detroit and Bochum are 13.15%, 11.84% and 9.19%, respectively. From the comparison of the capacity factors at the six locations, it can be concluded that Cairo has the largest technical potential, followed by Mexico City, Sao Paulo, Shanghai, Detroit and Bochum.
For wind power systems, we find that Bochum has the largest wind capacity factor at 47.24%, followed by Shanghai (28.09%), Detroit (28.42%), Cairo (15.97%), Sao Paulo (6.48%) and Mexico City (3.39%). For the Equation (5) used in capacity factor calculations, we use the rated power as the denominator, which means for the same rated power, the larger the output power, the larger the capacity factor. For a wind turbine, the input power can be simply estimated by the following equation:
where:
Pi: input power of a wind turbine (W)
Dw: the wind power density (W/m2)
AS: the sweeping area of turbine blades (m2)
From Equation (5), it can be seen that the larger the wind power density, the larger the input power. Obviously, for two wind turbines with the same efficiency, the output power is positively correlated with the input power under the wind speed conditions before the rated power is reached. From this point of view, the geographical locations with a high wind power density can lead to a large power output of a wind turbine and accordingly produce a large capacity factor. In the previous energy density analysis as indicated in
Table 4, the results demonstrate that Bochum has the highest wind power density, followed by Shanghai, Detroit, Cairo, Sao Paulo and Mexico City. The capacity factor analysis results as shown in
Figure 2 are consistent with the energy density results shown in
Table 4.
3.2. Economic Analysis of GHG Mitigation
To promote the application of clean energy supply in industrial operations for GHG mitigation, the economic performance of the mitigation efforts must be understood and quantitatively assessed to assist decision-making in industrial sustainability management and practices. In this section, we provide quantitative assessment of the GHG mitigations through the three clean energy supply patterns at the six selected geographical locations, in terms of the amount of GHGs to be reduced on a base scale of $1,000 economic input (tons GHG reduction/$1,000).
Currently, the economic costs of solar PV, wind and fuel cells are very different, but the economic costs of clean power systems within the same category, such as the five selected solar PV modules, are approximately on the same level due to the international competition between their manufacturers. In this analysis, the clean energy power systems are selected as average of the representative power systems of each clean energy type available on the commercial market. For the economic costs of each type of clean power system, we use the average economic costs data statistically collected and recently released by U.S. Energy Information Administration (EIA) for estimating the economic performance of GHG mitigation efforts through clean energy supply [
16]. The economic cost data used on the solar PV, wind, and fuel cell power systems include overnight cost of each clean power system and the associated variable and fixed O&M costs [
16]. The economic costs and the LCA GHG emissions used on the three clean energy power systems are shown in
Table 5 below.
Table 5.
Costs and GHG emissions of clean energy power systems.
Table 5.
Costs and GHG emissions of clean energy power systems.
| Overnight Cost ($/kW) | Variable O&M ($/kWh) | Fixed O&M ($/kW) | LCA GHG Emissions (g/kWh) |
---|
Solar PV | 6171 | 0.00 | 11.94 | 72.4 1 |
Wind | 1966 | 0.00 | 30.98 | 10.84 2 |
Fuel Cell (NG) | 5478 | 0.049 | 5.78 | 683 3 |
Fuel Cell (H2) | 10,735 5 | 0.00 5 | 2147 5 | 83 4 |
As a result, the cost benefit of the GHG mitigation through clean energy supply is assessed by using the following expression:
where:
G: the amount of GHG reduction (ton/$1,000)
Elocal: emission factor of GHGs from local grid power supply (kg/kWh)
Ei: the life cycle GHG emissions of clean energy i (kg/kWh)
Ai: total installed capacity of clean energy power system i
Ti: operational life time of clean power system i (h)
CNi: overnight cost of clean power system i ($/kW)
Cvi: variable O&M cost of clean power system i ($/kWh)
CFi: fixed O&M cost of clean power system i ($/kWh)
The GHG emission factors,
Elocal, used in the economic analysis are the total CO
2eq values calculated based on the IPCC Global Warming Potential (GWP), as shown in
Table 6 below for the six selected cities.
Table 6.
GHG emission factors of local grid power supply.
Table 6.
GHG emission factors of local grid power supply.
Region | Emission Inventory (g/kWh) |
---|
CO2 | CH4 | N2O | CO2eq 3 |
---|
USA 1 | 676 | 0.01815 | 0.01053 | 680 |
Mexico 2 | 593 | 0.01676 | 0.00230 | 594 |
Brazil 2 | 93 | 0.00251 | 0.00106 | 93 |
China 2 | 839 | 0.01458 | 0.01841 | 845 |
Egypt 2 | 436 | 0.01365 | 0.00177 | 437 |
Germany 2 | 539 | 0.00637 | 0.00779 | 542 |
In the economic analysis, the amounts of GHG reductions are calculated separately for natural-gas fuel cells, and hydrogen fuel cells. The results (tons GHG reduction/$1,000 economic input) are shown in
Figure 3 below. The calculated results in
Figure 3 show the different mitigation effects of clean energy supply patterns based on the same amount of investment scenario at the six selected global locations.
Figure 3.
Economic analysis of GHG mitigation through clean energy supply.
Figure 3.
Economic analysis of GHG mitigation through clean energy supply.
The calculated results in
Figure 3 indicate that wind has the greatest application potential among the three types of clean energy power systems for GHG emission mitigation, in particular at those locations with high wind energy density and/or high GHG emissions from local grid power supply. Application of wind in Bochum and Shanghai can achieve a GHG reduction over 20 tons per $1,000 economic input, while the application of wind in such cities as Mexico City and Sao Paulo has almost negligible mitigation effects.
Although solar PV systems are considered very clean in the usage phase, the high cost of PV systems and the relatively high emissions from their manufacturing and material production stages make solar PV much less preferable than wind for GHG emission mitigation. From
Figure 3, the best mitigation effects from solar PV systems are only at the level of a quarter of the mitigation effects from wind applications, based on the same economic input scale.
The economic performance of fuel cell power systems is closely related to the type of fuel used. The natural gas based fuel cells, due to the CO
2 emission from the consumed natural gas, in most cases will further increase the total GHG emissions. The results in
Figure 3 indicate that the GHG mitigation through natural gas based fuel cell power system is only feasible in Shanghai, China, because of the high GHG emission factor of local power supply industry [
23]. In all other five locations, using natural gas based fuel cell power systems will increase the amount of GHG emissions.
The hydrogen fuel cell power system, based on the calculated results in
Figure 3, can achieve a mitigation effect on GHG emissions between solar and wind at the six selected locations. This is partially due to the high cost of the hydrogen based fuel cell power systems and the high energy density required for hydrogen production. It is expected that in the future the hydrogen powered fuel cells would have a greater application potential as the economic costs of the systems are lowered further and after better ways for hydrogen production and storage are identified.
As there are technical variations among the selected clean energy power systems, the cost benefit of using a specific clean energy technology might be different from each other. In order to characterize the sensitivity and uncertainty of using a specific power system among the selected models for each clean energy technology, the range of GHG reduction, in the unit of tons/$1,000, for the selected clean energy power systems are calculated for their applications at the six selected locations. The results are shown in
Figure 4 below, with the median value indicated for each mitigation range.
Figure 4.
Range of GHG mitigation potential through clean energy supply at the selected six locations.
Figure 4.
Range of GHG mitigation potential through clean energy supply at the selected six locations.
Figure 4 demonstrates that the selected wind power systems have the highest GHG mitigation potential among the three clean energy technologies, in particular for application in Detroit, Shanghai and Bochum. The highest GHG reduction from wind application in Shanghai can reach 30 tons per $1,000 economic input. Wind power supply in Bochum and Detroit can achieve up to 29 tons and 24 tons of reduction, respectively, per $1,000 economic input. As quantitatively indicated, the minimum amounts of GHG reduction from adoption of the selected wind turbines in Detroit, Shanghai and Bochum are still more than 13, 15, and 17 tons, respectively, per $1,000 economic input. When compared, the application of the selected solar PV models can only reduce an amount of GHG emissions less than 6 tons per $1,000 economic input. Fuel cell power systems are almost at the same level of GHG mitigation effect with solar PV systems. If using natural gas based fuel cell power systems, GHG mitigation is only feasible in Shanghai, China.
3.3. Range of GHG Mitigation Potential at Different Regions
The above analysis results are on applications of the selected representative clean power systems at specific geographical locations (cities). In order to understand the GHG mitigation potential of clean energy supply for broader areas, we have extended the analysis results to the country-wide geographical area, and assessed the range of GHG mitigation potential in the six countries selected for this study. The range of GHG mitigation potential are calculated for solar PV and wind power systems based on the selected average technical parameters shown in
Table 1 and
Table 2 above. The range of GHG mitigations from solar PV power supply is calculated by considering the best and worst power generation scenarios under the highest and lowest solar insolation conditions within the geographical boundary of the selected country. The range of GHG mitigation from wind power supply is calculated by considering the best and worst power generation scenarios under the highest wind energy density of that country and the minimum wind speed (4.47 m/s) required for wind turbine installation [
25]. The range of GHG mitigation from fuel cell power supply is calculated by considering the fuel differences of the power systems based on the technical parameters shown in
Table 3. The calculated range of GHG mitigation potential gives the maximum and minimum amount of GHGs which can be mitigated through each clean energy supply pattern in these six countries on the basis of the same economic input. The results are shown in
Figure 5 below, with the median value indicated for each mitigation range.
Figure 5.
Range of GHG mitigation potential through clean energy supply in the selected six countries.
Figure 5.
Range of GHG mitigation potential through clean energy supply in the selected six countries.
The calculated results in
Figure 5 demonstrate that the best GHG mitigation opportunity is in China. With $1,000 economic investment, the maximum amount of GHG reduction can be as high as 60 tons, while application of wind power systems in the United States and Germany can also obtain a maximum GHG reduction of between 40 and 50 tons. When compared with the wind supply pattern, application of solar and fuel cell power systems has much less potential for GHG mitigation in each country. The median values of GHG mitigation range from fuel cells and wind power supply are almost at the same level.
The maximum reduction of GHG emissions through clean energy supply depends on many factors. From the results of our analysis, the most important factors for an optimal GHG mitigation are on the selection of the clean energy technology and the geographical location for system installation. In this analysis, the technical differences of the selected power systems in each clean energy category are not fully assessed and benchmarked, but such differences are believed having very small influences on the decision-making in clean energy technology selections.