The electricity generated by PV system is calculated by using the following equation [25] :

where E_PV is hourly electricity generation from the PV system (kWh), H is amount of solar radiation (kWh/m²), K is power conversion factor (−), P is system’s peak capacity of PV (kW_p), 1 is solar peak radiation, i.e., 1 kW/m². The hourly solar radiation data of the reference [24] are used for calculating the hourly electric power of PV system.

In this study, the high performance PV P250a Plus produced by Panasonic whose module conversion efficiency and maximum power per module are 19.5% and 250 W [26] , respectively is adopted for PV system. The size of each PV module is 1580 mm × 812 mm × 35 mm. To calculate K, the performance value of state-of-the-art commercial devise is used. K is calculated by using the following equation [1] :

where K_p is power conversion efficiency of power conditioner (−), K_m is correction factor decided by module temperature (−), K_i is power generation loss by interconnecting and dirty of module surface (−). In this study, K_p and K_i are set at 0.945 and 0.95, respectively. K_p is assumed by referring to the performance of commercial power conditioning device VBPC259B3 manufactured by Panasonic [27] . K_m is calculated by the following equation [1] :

where T_m is PV module temperature (degree Celsius), T_s is temperature under standard test condition (=25 degree Celsius) (degree Celsius), C is temperature correction factor which is 0.35 [28] (%/degree Celsius). The temperature characteristics of PV module which is adopted for this study are referred. T_m is calculated by using the following equation [29] :

where T_a is ambient air temperature (degree Celsius), U_m is wind velocity over module of PV (m/s). In this equation, the convection heat transfer by wind around the PV module is considered.

The meteorological data, such as solar radiation, the ambient air temperature, and wind velocity of the four Asian cities were from the data base of METPV- ASIA from 1999 to 2005 [24] .

Table 1 and Figure 2 show monthly and annual mean temperature [30] , and monthly precipitation [31] for the four Asian cities, respectively. These data are used for estimation of electricity generated by PV system and discussion later.

The Type-S electrolyzer manufactured by IHT [32] [33] whose H₂ production rate, power consumption and electrolysis efficiency are 760 Nm³/h, 4.45 kHW/Nm³ and 79.5%, is used in this study. The amount of H₂ could be produced by the power generated from PV system is calculated by the following equation [1] :

where V_H2 is amount of H₂ produced (Nm³), P_e is power consumption (kWh/Nm³), is electrolysis efficiency (−). In this study, it is assumed that the electrolyzer can be operated following the power generation characteristics of PV system every time and the produced H₂ can be stored as well as used instantaneously.

It is assumed that the H₂ produced by electolyzer would be used to generate power through a polymer electrolyte fuel cell (PEFC) system. H₂ is converted into electricity by FC following the below equation [1] :

where is power generation efficiency of latest PEFC stationary system based on lower heating value (=0.39) [34] , Q is lower heating value of H₂ (=242) (kJ/mol). It is assumed that the energy loss for operating pump to preserve and provide gases is ignored.

It is necessary that the H₂ produced by water electrolysis using the power generated by PV system is converted into a transportable media before shipped to Japan, due to low energy density of H₂ in gas form. In this study, liquefaction and conversion of H₂ into organic hydride are considered as the transportable media of H₂.

In the case of liquefied H₂ transportation, the volume of H₂ is changed by 1/800 by means of liquefaction [35] . Since the energy efficiency of liquefaction of H₂ is 73.6% [36] , the energy consumption of liquefaction is estimated by the energy efficiency and the amount of H₂ to be liquefied. The volume of tank truck is 23 m³ [37] and the volume of tanker is 2500 m³ [38] , which are leading-edge types. The fuel for tank truck is light oil and the fuel efficiency is 2.34 km/L [39] . The fuel for tanker is heavy oil and the fuel efficiency is 0.87 km/L [40] . The speed of the tanker is 36.1 km/h [41] and the boil-off rate is 0.4%/day [38] , which is used for counting H₂ loss in marine transportation. The lower heating value of light oil and heavy oil are 35.5 MJ/L [42] and 46.4 MJ/L [43] , respectively. The CO₂ emission coefficient of light oil and heavy oil are 2.62 kg-CO₂/L [44] and 3.41 kg-CO₂/L [43] , respectively. For land transportation, it is assumed to transport from the each city to the nearest sea port, then to Tokyo port in Japan through marine transportation by the tanker. The energy consumption of transportation as well as the amount of CO₂ emission in the transportation process is estimated using the distance between each city and the nearest sea port as well as the sea port and Tokyo port in Japan.

In the case of organic hydride transportation, H₂ can be transported by the volume of 1/500 compared to the gases H₂ at atmospheric pressure [35] . This study considers the reaction of C₇H₈ and H₂ into C₇H₁₄ as the conversion process of organic hydride. Since the enthalpy loss of C₇H₁₄ for emitting H₂, i.e., the necessary energy for emitting H₂ which is an endothermic reaction is 67.5 kJ/mol-H₂ [45] ; the energy consumption of conversion process of organic hydride is estimated by the enthalpy loss and the amount of H₂. In this study, the energy consumption of absorbing H₂ is neglected since absorbing H₂ by C₇H₈ is an exothermic reaction. Since the organic hydride can be transported by general tank truck and tanker for petroleum oil [46] , this study assumes the volume of tank truck and tanker are 20 m³ [47] for land transportation and 20000 m³ [48] for marine transportation, respectively. The fuel for tank truck is light oil and the fuel efficiency is 2.34 km/L [39] . The fuel for tanker is heavy oil and the fuel efficiency is 0.87 km/L [40] .

At first, the optimum tilt angle and orientation angle to install solar panel for each city is investigated using the hourly meteorological data base [24] for the four Asian cities. The monthly and annual solar radiation are estimated summing the hourly data in order to decide the annual optimum tilt angle and orientation angle for power generation of PV system. After that, the power generation characteristics of PV system under the optimum tilt angle and orientation angle condition are investigated.

As an example, Figure 3 shows the relationship between tilt angle and amount of solar radiation for different orientation angles in the case of Kuala Lumpurin January. As to orientation angle, 0 degree means south and the angle increases by 30 degree in a clockwise fashion. Table 2 lists the annual amount of solar radiation for different tilt angles and orientation angles in the case of Kuala Lumpur. From this table, it is seen that the highest amount of solar radiation is obtained at tilt angle of 20 degree and orientation angle of 270 degree (i.e. east). Table 3 lists the annual amount of solar radiation for different tilt angles and orientation angles in the case of Kolkata. From this table, it is seen that the highest amount of solar radiation is obtained at tilt angle of 30 degree and orientation

angle of 0 degree (i.e. south). Table 4 lists the annual amount of solar radiation for different tilt angles and orientation angles in the case of Beijing. From this table, it is seen that the highest amount of solar radiation is obtained at tilt angle of 50 degree and orientation angle of 0 degree (i.e. south). Table 5 lists the annual amount of solar radiation for different tilt angles and orientation angles in the case of Ulan Bator. From this table, it is seen that the highest amount of solar radiation is obtained at tilt angle of 60 degree and orientation angle of 0 degree (i.e. south).

According to Tables 2-5, the optimum tilt angel increases with increasing latitude. In addition, the optimum orientation angle is 0 degree except Kuala Lumpur. Kuala Lumpur is located near the equator and the solar altitude is the highest on the spring equinox day and the autumnal equinox day, which are different from the other cities. Therefore, the optimum orientation angle for Kuala Lumpur would be different compared to the other cities. The power of PV system is estimated under the condition installing solar panel at this optimum tilt angle and orientation angle.

Figure 4 shows the monthly electricity generated by the PV system in the four

cities. In this estimation, it was assumed that the PV system consists of 10,000 solar panels (=2.5 MW; one panel = 0.25 kW). The annual electricity generated by the PV system for Kuala Lumpur, Kolkata, Beijing and Ulan Bator are 3.56 GWh, 3.74 GWh, 3.92 GWh and 4.25 GWh, respectively.

According to Figure 4, it is seen that with the same solar PV panels, the PV system in Ulan Bator could be generated the largest amount of annual power output due to its lower annual mean ambient temperature and precipitation. On the other hand, in the case of Kuala Lumpur, the annual mean temperature and annual precipitation are the highest among the four cities, thus generating the smallest annual power output. In addition, it is revealed from Figure 4 that the monthly electricity generated by the PV system is the highest in March irrespective of city.

Table 6 lists the monthly amount of produced H₂ by water electrolysis using the electricity generated by PV system consisting of 10,000 PV panels or modules having total peak capacity of 2.5 MW, assumed to be installed in four cities. The annual amount of H₂ produced by water elerctrolys is using the electricity generated by the PV system installed in Kuala Lumpur, Kolkata, Beijing and Ulan Bator are 28.4 Mmol, 29.8 Mmol, 31.3 Mmol and 33.9 Mmol, respectively.

Table 6 also lists the decreased volume of liquefied H₂ by boil-off rate. It increases with increasing marine transportation distance and total amount of liquefied H₂.

According to Table 6, the largest amount of produced H₂could be delivered to Tokyo is obtained in the case of Ulan Bator, while the smallest amount is obtained in the case of Kuala Lumpur. These results follow the characteristics of electricity generated by the PV system shown in Figure 3.

Table 7 lists the annual amount of produced H₂, the volume of liquefied H₂,

the number of tank truck transporting for all liquefied H₂, the amount of CO₂ emission for all tank truck and the energy consumption for all tank truck for four Asian cities. In addition, Table 7 also lists the number of tanker transporting for all liquefied H₂, the amount of CO₂ emission for a tanker, the energy consumption for a tanker, the energy consumption in all transportation process, the amount of CO₂ emission in all transportation process, the energy consumption for liquefaction, the electricity generated by FC in Tokyo (after transportation), the total energy consumption and the ratio of total energy consumption to calorific value of H₂ delivered in Tokyo for four Asian cities. In this estimation, the 2.5 MW PV system was assumed to be installed in the source cities.

According to Table 7, it is revealed that the largest amount of CO₂ emission and energy consumption for all tank truck are obtained in the case of Ulan Bator, while the smallest amount of CO₂ emission and energy consumption for all tank truck are obtained in the case of Kuala Lumpur. Since the distance from Ulan Bator to the nearest seaport is very long according to Table 8 [49] and more number of tank truck is needed compared to the other cities, the amount of CO₂ emission and energy consumption for all tank truck are larger in the case of Ulan Bator. On the other hand, the distance from Kuala Lumpur to the nearest seaport is the shortest and the number of tank truck is also the fewest among four cities, resulting that the amount of CO₂ emission and energy consumption for all tank truck are smaller in the case of Kuala Lumpur.

As to marine transportation, it is revealed that the largest amount of CO₂ emission and energy consumption for a tanker are obtained in the case of Kolkata, while the smallest amount of CO₂ emission and energy consumption for a tanker are obtained in the case of Beijing and Kuala Lumpur. In this study, the amount of CO₂ emission and energy consumption for a tanker depend on marine transportation distance only. Since the marine transportation distance in the case of Kolkata is the longest and those in the case of Beijing and Kuala Lumpur are the shortest among four cities, the above mentioned characteristics on marine transportation are lead.

As to all transportation process, it is revealed that the largest amount of CO₂ emission and energy consumption are obtained in the case of Ulan Bator since the impact of land transportation is significant. On the other hand, it is seen that the smallest amount of CO₂ emission and energy consumption are obtained in the case of Beijing. Though the impact of land transportation is not small in the case of Beijing, it is believed that this result is obtained by smaller impact of the marine transportation compared to the other cities.

It is known that the electricity generated by the FC system is about 20% of that by the PV system under the assumption of this study. Since the amount of energy is decreased by energy conversion, it is important to transport the large amount of H₂ for fulfilling the energy demand of remote country.

Table 7 reveals that the total energy consumption during the transportation and the ratio of total energy consumption to calorific value of H₂delivered are the largest in the case of Ulan Bator, while they are the smallest in the case of Beijing. In the case of Ulan Bator, the ratio is even greater than 1 which means it would cost more energy to transport H₂ than energy (contained in H₂) delivered. Table 7 implies Beijing is the best energy effective case.

Table 9 lists the annual amount of produced H₂, the volume of organic hydride, the number of tank truck transporting for all organic hydride, the amount of CO₂ emission for all tank truck and the energy consumption for all tank truck for four Asian cities. In addition, Table 9 also lists the number of tanker transporting for all organic hydride, the amount of CO₂ emission for a tanker, the energy consumption for a tanker, the energy consumption in all transportation process, the amount of CO₂ emission in all transportation process, the energy consumption for liquefaction, the electricity generated by FC in Tokyo (after transportation), the total energy consumption and the ratio of total energy consumption to calorific value of H₂ after transportation for four Asian cities. In this estimation, the monthly amount of produced H₂ by water electrolysis using the

electricity generated by the PV system consisting of 10,000 PV panels or modules having total peak capacity of 2.5 MW as shown in Table 6 is used.

According to Table 9, it is revealed that the largest amount of CO₂ emission and energy consumption for all tank truck are obtained in the case of Ulan Bator, while the smallest amount of CO₂ emission and energy consumption for all tank truck are obtained in the case of Kuala Lumpur, which is the same tendency as H₂ liquefaction case. Since the distance from Ulan Bator to the nearest sea port is very long according to Table 8 [49] and more number of tank truck is needed compared to the other cities, the amount of CO₂ emission and energy consumption for all tank truck are larger in the case of Ulan Bator. On the other hand, the distance from Kuala Lumpur to the nearest sea port is the shortest and the number of tank truck is also the fewest among four cities, resulting that the amount of CO₂ emission and energy consumption for all tank truck are smaller in the case of Kuala Lumpur.

As to marine transportation, it is revealed that the largest amount of CO₂ emission and energy consumption for a tanker are obtained in the case of Kolkata, while the smallest amount of CO₂ emission and energy consumption for a tanker are obtained in the case of Beijing and Kuala Lumpur, which can be explained by the same discussion as H₂ liquefaction case. In this study, the amount of CO₂ emission and energy consumption for a tanker depend on marine transportation distance only. Since the marine transportation distance in the case of Kolkata is the longest and that in the case of Beijing and Kuala Lumpur is the shortest among four cities, the above mentioned characteristics on marine transportation are obtained.

As to all transportation process, it is revealed that the largest amount of CO₂ emission and energy consumption are obtained in the case of Ulan Bator, while it is seen that the smallest amount of CO₂ emission and energy consumption are obtained in the case of Beijing, which can be explained by the same discussion as H₂ liquefaction case.

After delivered in Tokyo by organic hydride, the electricity generated by FC system is about 21% of that by the PV system under the assumption of this study.

Table 9 reveals that the total energy consumption and the ratio of total energy consumption to calorific value of H₂ delivered are the largest in the case of Ulan Bator, while those are the smallest in the case of Beijing, which is the same tendency as H₂ liquefaction case. Since the annual amount of produced H₂ in the case of Beijing is the largest compared to the other cities except Ulan Bator and the energy consumption in all transportation process is the smallest among four cities, Beijing is the best energy effective case.

Comparing the ratio of total energy consumption to calorific value of H₂ delivered between H₂ liquefaction case and organic hydride case, the ratio in the case of H₂ liquefaction is smaller than that in the case of organic hydride. When converting gases H₂ into liquefied H₂ or organic hydride, the volume ratios of H₂ liquefaction and conversion into organic hydride are 1/800 and 1/500, respectively. Therefore, the converted volume after H₂ liquefaction is smaller than that after conversion into organic hydride. In addition, the volume of tank truck for liquefied H₂ is larger than that for organic hydride, resulting that the number of tank truck for land transportation of liquefied H₂ is smaller. Consequently, the ratio of total energy consumption to calorific value of H₂ after transportation in the case of H₂ liquefaction is smaller than that in the case of organic hydride due to impact of land transportation.

From the investigation of this study, it is revealed that the proposed energy supply chain is the optimum in the case of Beijing. On the other hand, the introduction of proposed energy supply chain is not effective in the case of Ulan Bator due to very long land transportation distance.