To obtain the capacity factor (CF) of wind and solar power generation in each provincial region, we simulated through the renewables. ninja platform jointly developed by ETH Zurich and Imperial College of Technology (Pfenninger and Staffell, 2016), and obtained 8760 h of wind and solar power generation CF in each provincial region throughout the year, as the upper limit of wind turbine (WT) and photovoltaic (PV) power generation output. Specifically, for different provincial-level regions, we select five cities distributed in five directions to calculate the CF of wind energy and solar energy, respectively, and take the average value as the CF of wind energy and solar power generation in the provincial-level region. Similarly, the national average wind and solar CFs were averaged across 31 provincial-level regions. Due to data limitations, hourly provincial electric loads were obtained from provincial power grid companies or fitted based on typical provincial daily and annual load curves (National Energy Administration, 2020).

In addition, in order to make the article easy to understand and without losing rigor, we use abbreviated form for the names of 31 provincial-level regions in China. The specific correspondence is shown in Table 1 below.

In this model, the relevant techno-economic parameters of CWSEHs are unified in 2050, and the specific data are given in Table 2 below. Among them, the annual operation and maintenance (OM) cost of PV, WT, and ES are 0.01 of the unit investment cost of each piece of equipment, and the annual OM cost of ELY is 0.04 of the unit investment cost of it.

Figure 1 shows the schematic diagram of the CWSEHs structure. On the energy supply side, renewable energy power generation, such as wind and solar energy, and electric energy storage modules are considered dispatchable electric power to compensate for the imbalance between supply and demand. The network side considers wind and solar power generation through the existing power network for transmission and pays a certain transmission fee. On the load side, we consider that electric energy can be used to simultaneously meet the electrical load and the operation of power to hydrogen (P2H) equipment. The produced hydrogen energy can be used as fuel in transportation, power generation, and heating and can also be used as raw materials for oil refining and steel production.

Figure 2 shows the LCOH of wind-solar combined hydrogen production in 31 provincial-level regions in China. The darker the background color of each region, the higher the LCOH (See Supplementary Figure S1 in Supplementary Material for the composition of LCOH in each provincial region). Overall, there are significant regional differences in the LCOH of wind-solar combined hydrogen production in 31 provincial-level regions. It is estimated that in 2050, the highest provincial LCOH in China will be CQ and the lowest is Inner Mongolia, and the span between the two can reach 16 ¥/kg. The eight provinces with the highest LCOH are CQ, ZJ, HB, SC, XJ, GD, YN, and FJ, all of which are greater than 15 ¥/kg, and the main parts are located in the eastern coastal areas and central areas. While northern regions such as HL, LN, JL, and IM have lower LCOH, it can be found that the LCOH of different provincial regions has significant geographic differences, which are essentially related to the level of wind and solar resources and power generation in each region.

The above results show that the differences between LCOH and wind and solar resources are significantly correlated. In order to further study the impact of the two on LCOH, Figure 3 shows the relationship between LCOH and the proportion of PV and WT configuration capacity in each provincial region. The definition of PV and WT capacity ratio is shown in Eq. 18. It can be found that compared with WT, there is a higher correlation between the PV capacity ratio and LCOH. Moreover, regions with higher PV capacity ratios have higher LCOH. The results show that from the perspective of reducing LCOH to a certain extent, the configuration of WT is more cost-effective than the configuration of PV. Of course, this is also related to the wind and solar CF in each provincial area, and the configuration of WT in areas with low wind power CF will be of limited significance. C a p a c i t y   r a t i o ( i ) = C i C PV + C WT , i ∈ { P V , W T } (18)

In order to compare the impact of wind and solar characteristics on the system, we selected four representative provinces for typical analysis to ensure that they are under similar load fluctuations, as shown in the first four rows in Table 3.

Figure 4A shows that HB has the highest LCOH of 17.38 ¥/kg, SH has the lowest LCOH of only 8.48 ¥/kg, the difference is nearly 10 ¥/kg, and the LCOH of TJ and XZ are in the middle. Comparing the data in Table 3, we can see that HB has the lowest wind and solar CF, SH has the highest wind and wind CF, and TJ and XZ have higher wind and photovoltaic CF, respectively has a significant correlation with their LCOH. In addition, comparing TJ and XZ again shows that wind resources can reduce LCOH more than solar resources.

Figure 4B shows the configuration capacity of PV and WT in four regions. The configuration capacity of PV and WT has a positive relationship with the CF of PV and WT. The CF of TJ wind power is higher, so its WT configuration capacity is larger, XZ can configure more PV because of higher PV power generation CF. It is worth noting that the CF of wind and solar power generation of HB and SH are both higher or lower, and the system is more inclined to configure WT instead of PV at this time, which is consistent with the above analysis. The PV and WT transmission cost in Figure 4C is similar to the equipment configuration capacity in Figure 4B. Because the more WT or PV equipment capacity is equipped, the greater the power generation and the higher the transmission cost. Wind and solar differences significantly impact LCOH, WT, and PV capacity allocation and transmission costs. In areas with similar wind and solar resources, prioritizing WT equipment can further reduce LCOH, making it more competitive than PV equipment.

When studying the influence of regional electrical load fluctuations on system parameters, in order to avoid the influence of dimensions, we have normalized the electrical loads in different regions and selected two regions with similar wind and solar resources and significant differences in electrical load fluctuations as shown in the last two rows in Table 3.

The left panel in Figure 5A shows that the LCOH of XJ is higher than that of SA, but in Figure 5B, it is shown that the ES configuration capacity of SA is significantly higher than that of XJ, even more than two times that of XJ, which is due to the electrical load fluctuation of SA is bigger than XJ. Specifically, when the electrical load is low, the PV and WT power generation can meet the electrical load and the P2H system, and only a small amount or no ES is needed for compensation. While at a high electrical load, most or all of the PV and WT power generation is absorbed by the electrical load, and the P2H system needs to be compensated by ES. In addition, regardless of the fluctuation of the electrical load, the WT configuration capacity is higher than the PV configuration capacity (Figure 5B), and WT also occupies an absolute advantage in the transmission fee (Figure 5A), which highlighted that the configuration of WT has significant advantages and universality compared to PV again.

Figure 6 presents the LCOH sensitivity analysis of CWSEHs under different parameters. We calculated the LCOH at this time by making each parameter ±25% based on the benchmark scenario. It can be seen from Figure 6 that the average LCOH in China under the benchmark scenario is 9.82 ¥/kg, and the change in electrolyzer conversion efficiency has the most significant impact on LCOH, and a ±25% fluctuation will cause LCOH to change from 7.86 ¥/kg to 13.10 ¥/kg. The impact of the unit investment cost of WT, system lifetime, and discount rate on LCOH is gradually reduced but still has a significant impact. Changes in other parameters have a limited impact on LCOH, especially the unit investment cost of PV, and the transmission costs of PV can hardly make LCOH change, which is also related to the low PV configuration capacity. It is worth noting that as λ increases, the LCOH will gradually decrease, which means that when the hydrogen demand (electric power consumed by hydrogen production) increases, the LCOH will further decrease, which is consistent with the scale effect of system development. In addition, system lifetime, electrolyzer conversion efficiency, inflation rate, and λ remain negatively correlated with LCOH changes, which is also consistent with common sense.

Currently, the LCOH of hydrogen production using traditional fossil energy (such as coal and natural gas) is about 10 ¥/kg (National Alliance of Hydrogen and Fuel Cell, 2019), and according to our prediction in 2050, 27 of China’s 31 provincial-level regions have LCOH higher than 10 ¥/kg. It means that even by 2050, the competitiveness of renewable energy electrolysis hydrogen production is still limited compared with traditional fossil energy hydrogen production. Therefore, we considered six different subsidy policies, adopting a 50% investment cost subsidy for PV, WT, ELY, ES, and a 50% cost subsidy for PV and WT transmission costs. The subsidy effect is shown in Figure 7. The results show that, except for the two regions of CQ and XZ, subsidizing WT can reduce LCOH to the greatest extent, so that the LCOH in most regions is reduced to less than 10 ¥/kg, while CQ and XZ have the best subsidy effect on PV, which is mainly closely related to the landscape resources in different regions. Generally, the effect of subsidizing ES and PV is better than the other three subsidy policies except for the WT subsidy, which can significantly reduce LCOH. The ELY subsidy and the PV and WT transmission cost subsidy have the worst effect on reducing LCOH, which can only reduce about 0.5 ¥/kg.

Further, Figure 8 (the upper part) shows the annual cost of the six subsidy policies. Overall, the annual cost of various subsidy policies is proportional to the subsidy effect. The better the subsidy effect, the higher the annual subsidy cost. It is worth noting that CQ subsidizes PV with the best effect, but the annual cost of PV subsidy is less than that of WT subsidy, which is a very shocking situation because it shows that adopting PV subsidy in CQ can not only minimize LCOH, but also it can ensure that the annual subsidy cost is not the highest, which is a very cost-effective subsidy policy.

In response to the particular situation of CQ, we comprehensively considered the subsidy effect of various policies and the annual subsidy cost and proposed the concept of unit LCOH decrease cost, whose definition is shown in Eq. 19. In the equation, UM,i LCOH, Cost ^M,i , and ΔLCOH ^M,i are the unit LCOH decrease cost, annual subsidy cost, and LCOH decrease of the i ^th provincial region under the M ^th subsidy policy, respectively, Φ^COMP and Φ^PRO are the subsidy policy set and the provincial set, respectively. Figure 8 (the lower part) shows each provincial region’s unit LCOH decrease cost. It can be found that PV, WT, and ES subsidies have their advantages and disadvantages in different regions, but generally, the U _LCOH subsidized by PV, WT, and ES will be higher than that subsidized by ELY and transmission costs. In particular, although subsidizing ELY can obtain lower U _LCOH, it still has a limited effect on reducing LCOH because the predicted terminal hydrogen demand in 2050 is still small, and the required ELY equipment configuration capacity is limited. However, in the long run, there is excellent potential to subsidize ELY after the terminal hydrogen demand increases in the future. In addition, if the PV and WT transmission costs can be further reduced, this will benefit LCOH. U LCOH M , i = Cos t M , i Δ LCOH M , i , ∀ M ∈ Φ COMP , i ∈ Φ PRO (19)