As summarized in Section 1, more than 90% of hydrogen is produced by fossil energy, and the chemical reaction equation is as follows: C + H 2 O → △ C O + H 2 Δ H = + 131 k J / m o l (1) C O + H 2 O → C O 2 + H 2 Δ H = − 41 k J / m o l (2)

Due to the scenario of coal districts in this article, the process of producing hydrogen from fossil energy is only coal-to-hydrogen. Assuming that the weight of coal mined at the time slot t is M _t (ton), and β _t (%) is the ratio of mined coal used as the raw material for coal gasification, the amount of hydrogen produced by coal in this time slot is f H 2 , t C o a l = α t C o a l ( β t M t ) (3) where α t C o a l represents the efficiency of coal gasification at t. This process is abbreviated as coal-to-hydrogen (C2H). Since the chemical reaction in (Eq. 1) requires heat absorption, the reaction equipment involved in the process is also one of the heat loads.

With technological breakthroughs (electric stacks, membrane electrodes, catalysts, etc.), policy promotion, and the construction of hydrogen refueling stations, hydrogen fuel vehicles have a bright future. The subsidy tilt for heavy trucks is more prominent in the new policy. In the latest policy in September 2020, the upper limit of bicycle subsidies for heavy trucks over 31t has increased by 36.5% (National Energy Administration, 2020).

The sales of hydrogen heavy trucks will be almost zero before 2020, but by 2030 it is estimated that sales will account for more than 50% of the overall hydrogen-fueled vehicles. With infrastructure, technological innovations and cost reductions pushing into a period of accelerated development, sales are expected to exceed 100,000 vehicles by 2030. World-renowned automobile companies (Toyota, Hyundai, Nikola, etc.) and consulting companies (Roland Berger, etc.) have fully discussed the feasibility of hydrogen heavy trucks. In China, various provinces are also stepping up their deployment in the hydrogen energy industry. For example, Guangdong Province will build about 300 hydrogen refueling stations, and Shanghai plans to plan close to 100 hydrogen refueling stations by 2023, and more than 30 will be completed and put into operation (Roland Berger, 2021).

Currently, one of the main factors restricting the development of hydrogen heavy trucks is the cost issue: the mainstream range is 60–80 ¥/kg, and some areas require a minimum of 40 ¥/kg, and the average subsidy is 10–12 ¥/kg. At present, the energy consumption cost of hydrogen heavy trucks per 100 km is at least 400 yuan, while the energy consumption cost of the same type of diesel vehicles is 180–200 ¥/km. In the future, with the increase in scale, the average cost of hydrogen to the station is expected to be reduced to 25 ¥/kg in 2030. If the hydrogen consumption per 100 km of heavy trucks is reduced to 7 kg, the competitiveness of hydrogen fuel cell heavy trucks in terms of energy consumption will gradually begin appear. Therefore, this article focuses on the application of hydrogen heavy trucks combined with IES in coal districts to achieve cost reduction through the coordination of multi-energy flows.

Table 1 shows the comparison between hydrogen heavy trucks and electric vehicles and diesel trucks in many aspects. It can be predicted that in the future when hydrogen energy production technology continues to increase and costs continue to fall, hydrogen heavy trucks will become more competitive.

In the actual production process, the amount of coal mined is directly proportional to the required transport capacity of heavy trucks. In this article, the load capacity and required quantity of each heavy truck are no longer considered separately. But from a global perspective, the relationship between the hydrogen consumption of hydrogen heavy trucks and the amount of coal mining is as follows: f H 2 , t T r u c k = α t T r u c k ( 1 − β t ) M t (4) where f H 2 , t T r u c k is the total hydrogen demand of heavy trucks at t and α t T r u c k is the transport hydrogen consumption coefficient of trucks. (1 − β _t )M _t represents the amount of coal that needs to be transported except for C2H.

The relationship between the wind power output P _wind (watts) and the average wind speed v (m/s) is as follows (Söder and Ackermann, 2012). P w i n d = 1 2 ρ A v 3 (5) where ρ indicates the air density (kg/m³). It can be seen from Eq. 5 that when the wind speed is between the cut-in wind speed and the rated wind speed, the ability of the wind turbine to capture wind energy P _wind is approximately proportional to the cube of the wind speed v ³.

In the current field of industrial production of hydrogen, the main technology is proton exchange membrane (PEM) electrolysers. Electric conversion to hydrogen is achieved by electrolyzing water. Emissions generated during the production of wind turbine blades are not considered in this paper. The equation of the electrolyzed water reaction is shown in Eq. 6. 2 H 2 O → electrify 2 H 2 ↑ + O 2 ↑ (6) Methanation is the conversion of COx to methane CH₄ through hydrogenation. The reaction equations are shown in Eq. 7 and Eq. 8. C O + 3 H 2 → cat . high temp C H 4 + H 2 O Δ H = − 206 k J / m o l (7) C O 2 + 4 H 2 → high press high temp C H 4 + 2 H 2 O Δ H = − 164 k J / m o l (8)

P2H and P2M technology have their own characteristics:

• P2H has higher reaction efficiency than P2M;

However, because the output of a wind turbine responds to changes in wind speed relatively quickly, even within 1 min, the output of the wind turbine may change many times with changes in wind speed. Therefore, in a specified period of time, the wind turbine output calculated by using the average wind speed is equal to the average actual wind turbine output power. The output characteristics of wind turbines can be known from Eq. 5, the wind power used for electrolysis is proportional to the cube of the average wind speed. Wind speed is trendy and random, and the accuracy of its value plays a decisive role in the evaluation of wind energy. The wind power output data from February 27, 2019 to February 27, 2020 are selected, and the operation results in the proposed IEGS are shown in Figure 4. The wind power output at the same time basically conforms to winter > annual > summer, in which winter is selected from December to February of the following year, and summer is selected from June to August. Combined with the weather data measured by the wind project, wind power output is influenced by natural factors such as temperature , wind speed and so on.

On the basis of ensuring the stable operation of wind power output, in order to show the characteristics of energy-saving, clean, and fast absorption of P2G equipment, the comparison results obtained are shown in Table 3. Due to the independent operation of the power system and the natural gas system, and the direct connection of wind power units to the grid, the electricity-gas system lacking a coupling and mutual aid relationship cannot absorb unstable and intermittent wind power in a timely manner. In particular, the cost of abandoning wind power is penalized, leading to higher operating costs for the IEGS without P2G equipment. If the ecological benefits brought by the consumption of carbon dioxide gas by P2G equipment are taken into consideration, the advantages of P2G equipment are more prominent.

Select 14:00 data to draw Figure 5A, which is based on the annual wind power output. It can be seen that among the initial energy sources, electricity and natural gas come from the power system and the natural gas system respectively, and renewable energy accounts for a considerable proportion. Through transitional energy conversion, the initial energy can be transformed into hydrogen (supplied to hydrogen heavy trucks), methane (can be directly injected into natural gas pipelines) and electricity (from power plants close to energy-consuming terminals).

In order to compare with the above-mentioned situation, simulate the scenario with a high proportion of renewable energy installed capacity (increasing the output of the wind turbine to 2 times), and draw Figure 5B. With the transformation and upgrading of the energy system, coal-fired power generation is gradually eliminated due to its heavy pollution and a greater proportion of coal is used for C2H. The continuous development of technologies such as electrolysis of water to produce hydrogen has reduced the energy consumption of the conversion of the entire coal district, and the power load in this scenario has also been reduced. In view of changes in coal transportation demand, the energy produced by wind turbines meets the demand for hydrogen heavy trucks, and more hydrogen is further converted into methane, which is injected into natural gas pipelines to meet the heat load demand.

To sum up, the proposed energy utilization mechanism has certain regulatory capabilities, and can be effectively adjusted to follow changes in transportation demand, renewable energy unit output, equipment energy consumption, etc., and provide a new path for coal district transformation.

Based on the hydrogen production process shown in Figure 2, it can be known that hydrogen production can pass through P2H and C2H. The typical daily hydrogen production and the corresponding conversion efficiency are plotted in Figure 6. In terms of the stacked bar chart, P2H and C2H technologies produce hydrogen, which is consumed by hydrogen heavy trucks to transport coal. The transportation volume of hydrogen heavy trucks is positively related to the required energy consumption. Because of its real-time change characteristics, the system needs to be optimized and adjusted. In detail, Figure 5A shows the energy conversion at 14:00, which is consistent with the corresponding value of the stacked bar chart. On the whole, the hydrogen production volume of the above two technologies is basically stable.

In terms of conversion efficiency, the conversion efficiency of P2H and C2H fluctuates relatively large due to environmental changes, price fluctuations, equipment characteristics and other factors, but they are all within a reasonable range. In contrast to the constraints of Eq. 9, the conversion efficiency of P2H is in the range of 57-73%. In the same way, the conversion efficiency of C2H is also within the normal working range, and there is no abnormal situation. Comparing the conversion efficiency of P2H and C2H, the former is basically higher, which is determined by complex reasons such as technical level.

Similarly, stacked bar chart of wind power converted to hydrogen and methane is drawn in Figure 7. The blue line in the figure represents the per-unit value of wind power output, which is derived from the annual situation in Figure 4. The figure shows in detail the ratio of energy generated by wind power into hydrogen and methane. The amount of hydrogen produced is related to the demand for hydrogen heavy trucks, and the remaining energy is further converted into methane. Obviously, the energy used for hydrogen production occupies a large proportion, about 5–10 times the energy used for methane production. In the future, as shown in Figure 5B, as the installed capacity of renewable energy increases, a greater proportion of wind power will be used for methane production.

Figure 8 shows the consumption of wind power by heavy trucks driven by different fuels. In the figure, the green dotted line represents the output of wind power. Using the mechanism proposed in the article, the hydrogen heavy truck (blue line) can basically absorb a large amount of wind power in time. Electric vehicles (red lines) can only absorb part of the renewable energy, which may involve complex conversion and storage of electrical energy with large delays. However, Diesel oil truck (yellow line) does not involve the conversion of renewable energy and cannot absorb any wind power by itself. It is worth noting that the gray shaded part between the green line and the blue line in the figure is the stable difference between the wind power output and the absorbed energy of the hydrogen heavy truck, which is exactly the energy consumption of P2M in Figure 7.

In order to alleviate the greater environmental pressure and implement carbon neutral initiatives, carbon emissions trading is considered in this section (Ricke et al., 2018; Zhang et al., 2020). According to the Status Report 2019 of International Carbon Action Partnership (ICAP), emissions trading systems of most countries in the world currently are in force, scheduled or under consideration. This article uses the carbon emissions trading of China and the European Union as a measurement standard, and calculates the operating costs of the three heavy trucks mentioned in Section 2.1.2. Assuming that emissions from renewable energy electrolysis at the production stage is taken out of study scope.

According to the results of carbon emissions trading in 2020, prices in China are about 30–40 ¥/tCO₂, but that in the EU has exceeded 30 €/tCO₂. Taking into account carbon emissions trading, the revised total cost is ( C t o t a l − C C O 2 ), where C C O 2 indicates the cost of CO₂ emissions and proportional to the amount of CO₂ emissions. According to Table 4, under China’s carbon emissions trading system, the operating cost of hydrogen heavy trucks compared to electric vehicles is reduced by 0.95%, which is 4.68% compared to diesel oil trucks. Under Europe’s stricter carbon emissions trading system, the percentages of cost reduction are 2.56 and 9.12%, respectively. Horizontally comparing the operating costs of hydrogen heavy trucks in China and the European Union, the operating costs in China are slightly higher by 1.31%. The result shows that diesel oil trucks or LNG trucks will be levied high carbon emission fees, which will continue to increase as carbon neutrality advances. The current price of hydrogen heavy trucks is basically similar to that of electric vehicles, because in the proposed scenario, the methanation process can absorb a part of CO₂. Therefore, hydrogen heavy trucks can not only achieve zero emissions on the basis of electric vehicles, but also absorb greenhouse gases, which are converted into profits here. With the continuous breakthrough of hydrogen energy technology and the inclination of government policies, the price of hydrogen energy will gradually drop and the scale of production will gradually expand. Further, hydrogen heavy trucks will have a broader prospect.

When the hot spare coefficient ρ is 0.05, the cumulative output ladder diagram of the unit is shown on the left side of Figure 9A, which is basically in line with the distribution characteristics of power consumption peaks and valleys in reality, that is, there is a peak power consumption around noon during the day. In order to characterize the output status of each unit, draw the ladder diagram on the right side of Figure 9A. Each curve is basically stable, and there are no several start and stop conditions within a short period of time, which plays a good role in the overall stability of the system.

Similarly, when the unit hot spare coefficient is 0.2, Figure 9B are available. The cumulative output ladder diagram of the unit is shown on the left side of Figure 9B. The ladder diagram on the right side of Figure 9B shows the output status of each unit and the curves are basically stable. However, some problems can be found in comparison with ρ=0.05. The right side of Figure 9B shows that the output of one unit has a short start and stop near 19:00, which is caused by the higher hot spare. Under the premise that a certain unit operating life and system stability are lost, the system’s ability to respond to emergencies can be greatly improved.

In this section, adjust the abandoning wind power punishment parameters appropriately, set to four values of 0.01, 0.05, 0.08, and 0.12 respectively. The operation results of IEGS are shown in Table 3. When the abandonment punishment parameter is too small, the operating cost is higher, second only to the operating cost under the condition of no P2G equipment, indicating that even if the punishment parameter is small, the amount of abandonment is large, which still leads to the system. The overall operating cost is high. With the increase of the abandonment punishment parameters, the operating cost of the system has been significantly reduced, which proves that considering the abandonment punishment cost in the optimization objective can make the system fully absorb wind power. However, if the punishment parameters for abandoning solar energy continue to increase, the operating cost of the system will increase. This is because a small amount of unconsumed wind power will be multiplied by a larger punishment coefficient, which will cause a certain punishment cost.

Further, as shown in the dotted line in Figure 10, it can be estimated that although there is a nonlinear relationship between the abandoning wind power punishment parameter and the operating cost, there should be a minimum value as shown in Figure 10. When the abandonment punishment parameter takes this value, the total operating cost of the system is the smallest, and the specific value needs to be further explored. Moreover, when the abandoning wind power punishment parameter is less than the optimal value, the curve drops faster, and the difference between the values of the abandoning wind power punishment parameter is not very large, but a large amount of abandonment energy multiplied by it will cause a larger punishment cost. Correspondingly, when the abandoning wind power punishment parameter is greater than the optimal value, even if the abandoning wind power punishment parameter becomes significantly larger, the cost curve rises slowly because the amount of abandonment remains at a small amount.