Research on the Structural Optimization of the Clean Energy Industry in the Context of Dual Carbon Strategy—A Case Study of Sichuan Province, China

Abstract

As a major province for hydroelectric power in China, Sichuan Province has witnessed a great amount of seasonal characteristics in its hydroelectric power, endowing the clean energy industry in Sichuan with the characteristics of unstable supply in different seasons, strong power transmission capacity, and low production capacity of other non-fossil energy sources (except hydroelectric power). In this study, the mathematical model method was used to construct a structural optimization model of the clean energy industry in Sichuan Province, and to enable a quantitative analysis of the rationalization of the clean energy industrial structure in Sichuan Province. The results are proved from the cost-effectiveness of low carbon emission that hydroelectric power > wind power > photovoltaic power > natural gas > coal > oil. This study shall find the theoretical structure of the clean energy industry in Sichuan Province in 2030 with a prediction of Sichuan Province’s total future energy output and a constraint of its industrial structure model of clean energy. This paper suggests that Sichuan Province should give priority to the development of non-fossil energy sources, increase the development and utilization of power transmission channels in wet seasons, and develop clean energy and high energy consumption industries and the construction of pumped storage power stations, so as to reduce and avoid the waste of energy resources. It is also suggested that Sichuan Province should focus on developing clean energy with the capability of peak shaving, such as hydrogen energy and natural gas, while developing smart grid and long-distance transmission technology to strengthen energy conservation and emissions-reduced power generation. On this basis, Sichuan will focus on the development and utilization of natural gas as an alternative to high-carbon energy, with a view to optimizing its industrial structure of clean energy and helping China achieve the dual-carbon goals.

1. Introduction

The Paris Agreement, which came into force in November 2016, proposes that the world is expected to be carbon neutral between 2051 and 2100. In September and December, 2020, Chinese General Secretary Xi Jinping announced China’s “carbon peaking and carbon neutrality” goals at the United Nations and the Climate Ambition Summit successively–that is, China strives to achieve peak carbon dioxide emissions in 2030 and achieve carbon neutrality by 2060. In September 2021, the Chinese government issued the “Opinions on Completely and Accurately Implementing the New Development Concept and Striving for Carbon Peak and Carbon Neutrality”, forming a top-level design and comprehensive deployment for completely, accurately and comprehensively implementing the new development concept, building a green low-carbon cycle and high-quality development economic system, and realizing the grand goal of carbon peak and carbon neutrality. With the global emphasis on green development and low-carbon circular development, as well as the proposal of China’s “carbon peaking and carbon neutrality” goals, the future energy industry will move forward in the direction of low-carbon transformation and high-quality development, in which the development of clean energy industry will obtain increasing acknowledgment and further promotion.

The rationalization of industrial structure is the rationalization of the economic and technological connection between industries, including the coordinated optimization of quantity proportion through resource reallocation. At present, scholars have conducted in-depth research on the optimization of industrial structure, such as establishing a multiple attribute decision-making objective model to evaluate the optimization of industrial structure [1]. In the policy environment concerning carbon neutrality, more research has considered the optimization of industrial structure under the constraints of water resources and energy consumption intensity [2,3,4,5,6]. With the deepening of the research on the optimization of industrial structure, scholars are no longer limited to the study of models. Instead, they have started to study the impact of industrial structure optimization on economic benefits and carbon emissions [7,8,9]. However, the above research mainly focuses on the optimization and adjustment between industries. In addition, some scholars also study the optimization and upgrading of certain industries. Zhou Y [10] established a model system for the optimal allocation of agricultural industrial structure, and applied the improved genetic algorithm to obtain the optimal algorithm of sustainable agricultural optimization sequencing. Qinru Li [11] carried out the research on the optimization and upgrading of agricultural industrial structure, and summarized the influencing factors. There are few studies on the optimization of the energy industry structure, mainly focusing on how to optimize the energy structure and the optimization mechanism of the energy structure [12,13,14]. Some studies pay attention to environmental objectives and establish a multi-objective optimization model of the energy structure based on life cycle assessment [15,16,17], which concerns several environmental impacts, rather than only focusing on carbon emissions [18,19]. Some scholars have also focused on the impact of low-carbon goals on the optimization and adjustment of the energy industrial structure [20,21]. As a concept, clean energy is introduced with environmentally-friendly development, which is also known as green energy. It is an energy form corresponding to traditional fossil energy. The industrial structure of clean energy refers to the proportion of hydro energy, wind energy, solar energy, and natural gas that can alleviate the structural proportion of energy at the production end that causes climate problems due to greenhouse gas emissions. However, the research on clean energy mainly focuses on the impact of clean energy on the economy [22], clean energy policies and measures [23], and clean energy technology [24]. Most of the relevant research is qualitative research, in which there is a lack of research on the optimization of the industrial structure of clean energy.

In the research of energy industry structural optimization, scholars have to date paid more attention to the macro top-level design and overall layout [25,26], and the optimization and adjustment between industries [27,28]. There is less quantitative research on the combination of “carbon peaking and carbon neutrality” goals and the structural optimization of regional energy industry, the supplementation of which thus provides innovative significance for this research. Sichuan’s energy resource endowment has the characteristics of affluent hydro energy and natural gas, insufficient coal and oil, and the complementary wind power and photovoltaic power, covering all the constituent elements of the clean energy industry development, and can support Sichuan in its construction of a world-class clean energy industry cluster. In addition, Sichuan also has the largest shale gas and hydropower resources in China, and the clean rate of the energy industry (the proportion of clean energy in the total energy output) is more than 80%. However, at present, the actual clean utilization rate of Sichuan energy is only 50%. One of the biggest reasons is that the energy industrial structure lacks further optimization. Therefore, it is of great theoretical and practical significance to study the optimization of clean energy industrial structure in Sichuan Province to find a clean energy industrial structure suitable for the development of the province, and help China achieve the “carbon peaking and carbon neutrality” goals strategic target and high-quality economic development.

Based on the establishment of the autoregressive model using Stata 17.0 to predict the total energy output of Sichuan Province in 2030, this paper uses the mathematical model method to construct the optimization model of the Sichuan clean energy industrial structure, and makes a quantitative analysis on the rationalization of the Sichuan clean energy industrial structure based on the exploitable amount of energy technology. In addition, according to the current situation and trend of energy power generation in Sichuan Province, this paper analyzes the seasonal characteristics of clean energy power generation in the province. Based on the results of this analysis, this paper puts forward countermeasures and suggestions for the further optimization of the clean energy industrial structure in Sichuan Province.

2. Model Establishment

2.1. Autoregressive Model

The autoregressive model allows the use the past value of a variable to predict the future value. For time data series, the model can essentially understand the complex internal structure of dynamic data, so as to achieve the best prediction in terms of minimum variance. The appropriate prediction method is first-order autoregression:

$$y_t={\beta }_0+{\beta }_1{y}_{t-1}+{\epsilon }_t(t=2,\cdots ,T)$$

(1)

In this model, yt refers to time series, β_i represents autoregressive weighting parameter, and ${\epsilon }_t$ is applied to show the error between the real value and the predicted value.

2.2. Structural Optimization Model of Energy Industry

The structural model of the energy industry is used to determine the optimal distribution ratio of various energies under the condition of total energy production. It is also used to obtain the production structure of oil, coal, natural gas, hydroelectric power, photovoltaic power, and wind power under the lowest carbon emissions and minimum energy cost. Therefore, the structural optimization model of the energy industry M [29] is constructed and its index construction and assumptions are shown in

Table 1. Index assumptions.

Indicators	Meaning (Counting Unit)	Indicators	Meaning (Counting Unit)
x1	Oil production (104 t)	C1	Oil carbon emissions (kg/104 t)
x2	Coal production (104 t)	C2	Coal carbon emissions (kg/104 t)
x3	Natural gas production (104 t)	C3	Natural gas carbon emissions (kg/104 t)
x4	Hydroelectric power production (108 kWh)	C4	Hydroelectric power carbon emissions (kg/108 kWh)
x5	Photovoltaic power production (108 kWh)	C5	photovoltaic power carbon emissions (kg/108 kWh)
x6	Wind power production (108 kWh)	C6	Wind power carbon emissions (kg/108 kWh)
fi	Coefficient of conversion of various energy sources to standard coal	D1	Oil thermal value (kcal/104 t)
A	CO2 emissions trading prices (yuan/t)	D2	Coal thermal value (kcal/104 t)
B1	Average price of oil (yuan/104 t)	D3	Natural gas thermal value ((kcal/104 t)
B2	Average price of coal (yuan/104 t)	D4	Hydroelectric power thermal value (kcal/108 kWh)
B3	Average price of natural gas (yuan/104 t)	D5	Photovoltaic power thermal value (kcal/108 kWh)
B4	Average price of hydroelectric power (yuan/108 kWh)	D6	Wind power thermal value (kcal/108 kWh)
B5	Average price of photovoltaic power (yuan/108 kWh)	E	2030 total thermal value of energy production in Sichuan
B6	Average price of wind power (yuan/108 kWh)	K	Nuclear energy, biomass energy (constant)

.

$$\mathrm{M}=\min f_i({\displaystyle {\sum }_{i=1}^6{B}_i{x}_i}+3.67A{\displaystyle {\sum }_{i=1}^6{C}_i{x}_i})=\min f_i\{{\displaystyle {\sum }_{i=1}^6(B_i+3.67A}{C}_i)x_i\}$$

(2)

where, 3.67 is the conversion coefficient of carbon weight to carbon dioxide weight (the carbon atomic weight is 12, and the carbon dioxide molecular weight is 44). The constraints are:

$$E={\displaystyle {\sum }_{i=1}^6{D}_i{x}_i}+K$$

(3)

3. Structural Model of the Energy Industry and Its Analysis

3.1. Research Area Background

The authors obtained the energy resource endowment of Sichuan Province through a study of the local government and the Sichuan Provincial Development and Reform Commission (http://fgw.sc.gov.cn/ (accessed on 1 August 2022)). Sichuan is rich in hydropower resources, with a theoretical reserve of 1.50 × 10⁸ kW, of which 1.48 × 10⁸ kW is technically exploitable; the Jinsha River, Yalong River and Dadu River are the largest hydroelectric power “rich areas” in China, with 79.2% of exploitable and concentrated hydropower resources in the province. In recent years, Sichuan province has witnessed the slowing development of its hydroelectric power industry, and its developed amount is gradually approaching the exploitable amount. The annual average growth of hydroelectric power installed capacity is 5.77%, and the annual average growth of power generation is 4.9%; by 2021, the power generation installed capacity in Sichuan had reached 114.3509 million kW, of which the hydroelectric power installed capacity is 88.8702 million kW, accounting for 77.7% of the power installed capacity in the province and 22.7% of the hydroelectric power installed capacity in China. Hydroelectric power generates 372.446 billion kWh, accounting for 26.1% of China, ranking first in China. (Show as Figure 1, the data in the figure only retains two decimal points, the same below.)

On the whole, Sichuan has no outstanding advantages in wind energy resources. However, wind energy resources are relatively concentrated in the northwest plateau of Sichuan, Ganzi Prefecture, Liangshan Prefecture and Panzhihua City in the southwest and northern mountainous area of Sichuan, and the wind power density can reach level 2 or above (wind energy per unit time perpendicular to the unit cross-sectional area of the air flow). Theoretically, the preliminary estimated development amount is about 48.5 million kW, but in fact, the development amount in an economic and technologically-feasible manner is about 20 million kW. Wind power construction in Sichuan started late, but it can be seen from Figure 2 that it has developed rapidly. By the end of 2015, the wind power installed capacity in Sichuan has reached 0.737 million kW, and by the end of 2021, it has reached 5.2729 million kW, with a power generation of 10.943 billion kWh. According to the objectives of “the “14th Five-Year Plan” for wind power development in Sichuan Province”, wind power installed capacity in the province will reach more than 10 million kW by 2025.

According to the total annual average radiation, sunshine hours and distribution in Sichuan Province, calculated by GIS system, the annual theoretical solar energy reserves in Sichuan are about 2.33 × 10²¹ joules, among which the three states and single city (Ganzi, Aba, Liangshan and Panzhihua) with the most abundant solar energy resources are about 1.67 × 10²¹ joules, accounting for 72% of the province. According to Figure 3, the photovoltaic power generation installed capacity in Sichuan was 1.959 million kW by the end of 2021, accounting for 0.6% of the national photovoltaic power generation installed capacity. At the same time, its power generation is 2.965 billion kWh, accounting for 0.66% in Sichuan. According to “the “14th Five-Year Plan” for the development of solar power generation in Sichuan Province”, the photovoltaic power generation installed capacity in the province will reach 12 million kW by 2025, with huge development space.

With a long history of natural gas exploitation, Sichuan is one of the three major gas-producing areas in China. According to Figure 4, the total natural gas resources in Sichuan Basin are 40 trillion cubic meters as of 2021, ranking first in China, including 18.21 trillion cubic meters of conventional gas and tight gas, and 21.63 trillion cubic meters of shale gas, also ranking first in China, with great development potential. However, the total proved reserves in Sichuan Basin are 6.4 trillion cubic meters, and the exploration rate is only 16%, due to the limitations of exploration technology. Therefore, exploration technology needs to be further improved. In 2021, Sichuan’s natural gas consumption will be 26.8 billion cubic meters, accounting for approximately 58.52% of Sichuan’s natural gas production in the same year and 7.2% of the country’s total natural gas consumption. The natural gas production data of Sichuan Province over the years are shown in Figure 4. The natural gas production of Sichuan Province in 2021 was 47.59 billion cubic meters; according to the development goals of “the “14th Five-Year Plan” for energy development in Sichuan Province, natural gas production is planned to reach 63 billion cubic meters by 2025, with an average annual growth rate of 8.4%. Natural gas is one of the important energy sources for China to achieve the double carbon goal. In addition, the construction of the Daqing southwest natural gas base is a national strategy. In the future, natural gas power generation will become one of the main energy sources of thermal power as peak value modulation energy.

3.2. Data Sources

In order to find the optimal structure of clean energy industry in Sichuan Province in 2030, it is necessary to predict the total energy production of the province in 2030 to constrain the structural model of the energy industry. In this study, only theoretical values are considered, and the impact of climate change is not considered. Figure 5 shows the total energy production data of Sichuan Province from 1990 to 2020 based on the China energy statistical yearbook and the Sichuan statistical yearbook.

In the structural optimization model of the energy industry, this paper determines that the current average price of China’s CO₂ trading market is 43.3 yuan/t according to the overall trading volume, gross merchandise volume and price trend of China’s carbon trading market in the eight months since its launch (2021.7–2022.3), and takes it as the CO₂ emission trading price of Sichuan Province, that is, A = 43.3 yuan/t. According to the price of the crude oil market trading center and the exchange rate of the US dollar, this paper calculates that the average price of crude oil in the stable market from 2019 to 2021 is 3949 yuan/t, which is taken as the average price of oil, that is, B₁ = 3949 × 10⁴ yuan/10⁴ t. According to the data of the international coal network, the average price of coal in 2021, which is closer to the future price trend, is 2172.2 yuan/t, which is taken as the average price of coal, that is, B₂ = 2172.2 × 10⁴ yuan/10⁴ t. According to the data of China’s natural gas trading market, the average price of compressed natural gas (CNG) and liquefied natural gas (LNG) in China in 2021 is selected, and the average value of both is calculated to be 3870 × 10⁴ yuan/10⁴ t, which is taken as the average price of natural gas, that is, B₃ = 3870 × 10⁴ yuan/10⁴ t. According to the data of Sichuan Electric Power Trading Center, the average value of the annual average price of conventional direct purchase and the average price of hydroelectric power consumption demonstration in 2021 is 1953 × 10⁴ yuan/10⁸ kWh, that is, B₄ = 1953 × 10⁴ yuan/10⁸ kWh. This paper selects the annual average price of conventional direct purchase of 2439 × 10⁴ yuan/10⁸ kWh in 2021 as the average price of photovoltaic and wind power, that is, B₅ = B₆ = 2439 × 10⁴ yuan/10⁸ kWh (https://coal.in-en.com/ (accessed on 1 August 2022)). In this paper, according to the carbon emission coefficient of each energy assumed by IPCC [30], the carbon emissions of oil, coal and natural gas are 583 kg/t, 756 kg/t and 448 kg/t respectively, that is, C₁ = 5830 t/10⁴ t, C₂ = 7560 t/10⁴ t, C₃ = 4480 t/10⁴ t. According to the carbon emission level of low-carbon energy in the whole life cycle proposed by Benjamin K. Sawakur (2008) [31], the carbon emissions of hydropower, solar and wind power generation in the whole life cycle are 13 g/kWh, 32 g/kWh and 10 g/kWh respectively, that is, C₄ = 1300 t/10⁸ kWh, C₅ = 3200 t/10⁸ kWh, C₆ = 1000 t/10⁸ kWh. According to the thermal value of Chinese standard coal, D₁ = 10 × 10¹⁰ kcal/10⁴ t, D₂ = 7 × 10¹⁰ kcal/10⁴ t, D₃ = 12 × 10¹⁰ kcal/10⁴ t; according to the power thermal value converter, the value of each power thermal value is 861 kcal/kWh, that is, D₄ = D₅ = D₆ = 861 kcal/kWh. According to the thermal value of each energy source, 1 kg oil equivalent = 1.4286 kg standard coal, that is, f₁ = 1/1.4286; f₂ = 1; 1 kg natural gas = 1.8732 kg standard coal, i.e., f₃ = 1/1.8732; 1 kWh = 0.12283 kg standard coal, i.e., f₄ = f₅ = f₆ = 1/12,283 (as shown in

Table 2. Model Data Fetching.

Indicators	Data	Indicators	Data
A	43.3 yuan/t	D1	10 × 1010 kcal/104 t
B1	3949 × 104 yuan/104 t	D2	7 × 1010 kcal/104 t
B2	2172 × 104 yuan/104 t	D3	12 × 1010 kcal/104 t
B3	3870 × 104 yuan/104 t	D4	8.61 × 1010 kcal/108 kWh
B4	1953 × 104 yuan/108 kWh	D5	8.61 × 1010 kcal/108 kWh
B5	2439 × 104 yuan/108 kWh	D6	8.61 × 1010 kcal/108 kWh
B6	2439 × 104 yuan/108 kWh	f1	1/1.4286
C1	5830 t/104 t	f2	1
C2	7560 t/104 t	f3	1/1.8732
C3	4480 t/104 t	f4	1/12,283
C4	1300 t/108 kWh	f5	1/12,283
C5	3200 t/108 kWh	f6	1/12,283
C6	1000 t/108 kWh

).

3.3. Empirical Analysis

3.3.1. Prediction of Total Energy Quantity

(1) Constructing the regression model

Figure 6 shows the growth trend of total energy output. As can be seen from the chart, when year = 2014, there is an extreme value. The study found that 2013 is the first year the implementation of the Party Central Committee made a decision on further reform and the first year of the coal market reforms, which leads to extreme data in that year. In order to reduce its impact on the regression results, this study deletes the extreme data. At the same time, it is found that the growth trend of the total energy output and the logarithmic growth trend of the total energy output are not stationary series, so the logarithm of the total energy output is made a first-order difference ($\Delta \ln E{n}_t=\ln E{n}_t-\ln E{n}_{t-1}$). Figure 7 shows the growth trend of logarithmic difference of total energy output, which shows that there is no obvious time trend and it can be roughly regarded as a stationary series. Because the logarithmic difference of En is approximately equal to the growth rate of En, that is:

$$\Delta \ln E{n}_t=\ln E{n}_t-\ln E{n}_{t-1}=\ln (\frac{E{n}_t}{E{n}_{t-1}})\approx \frac{E{n}_t}{E{n}_{t-1}}$$

(4)

In order to observe whether there is autocorrelation in the logarithmic difference of total energy output in Sichuan Province, autocorrelation plots are drawn by moving the average model (the number of steps is q) to investigate the autocorrelation coefficients of each order of the log difference of total energy production (Figure 8). The shaded part of the figure represents the confidence interval, indicating that the first-order autocorrelation coefficient is significantly not zero, and the other order autocorrelation coefficient is not significant. Because there is a first-order autocorrelation in the logarithm of total energy output, a first-order autoregressive model based on Ordinary Least Squares (OLS) estimation Δln$E{n}_t$is established on the basis of model (1).

$$\Delta \ln E{n}_t=k+\alpha \Delta \ln E{n}_{t-1}$$

(5)

Among them, the explained variable $\Delta \ln E{n}_t$ is the logarithmic difference of En, the explanatory variable $\Delta \ln E{n}_{t-1}$ is the lag term of En logarithmic difference, k is constant and $\alpha$ is the regression coefficient.

According to the collected data, $\Delta \ln E{n}_t$ is used to carry out first-order autoregression of $\Delta \ln E{n}_{t-1}$ (recorded as h). The regression results are shown in

Table 3. Autoregressive model.

Variables	(1) dlnEn
h	0.352 * (0.185)
Constant	0.0359 ** (0.0146)
Observations	28
R-squared	0.122

Standard errors in parentheses. (** p < 0.05, * p < 0.1, the same as below.)

. The regression coefficient of logarithmic difference lag term $\Delta \ln E{n}_{t-1}$ and constant term of total energy output in Sichuan Province are obtained. According to the regression results, the regression model (6) is obtained, and the logarithmic difference $\Delta \ln E{n}_t$ of total energy output in the t year is obtained. The expression of logarithmic $\ln E{n}_t$ of total energy output in t year is shown in Equation (7).

$$\Delta \ln E{n}_t=0.352\Delta \ln E{n}_{t-1}+0.0359$$

(6)

$$\ln E{n}_t=\ln E{n}_{t-1}+\Delta \ln E{n}_t$$

(7)

(2) Forecasting the total energy output in the future

According to model (6) and formula (7), the energy output of Sichuan Province in the future is obtained. The specific data are shown in

Table 4. Total future energy production in Sichuan Province (10,000 tons SCE).

Year	Total Energy Production (En)	lnEn	dlnEn
2021	21,552.19	9.978	0.0533
2022	22,763.58	10.033	0.0547
2023	24,054.60	10.088	0.0552
2024	25,423.12	10.143	0.0553
2025	26,871.09	10.199	0.0554
2026	28,402.13	10.254	0.0554
2027	30,020.63	10.310	0.0554
2028	31,731.43	10.365	0.0554
2029	33,539.77	10.420	0.0554
2030	35,451.16	10.476	0.0554

. The Energy Development Plan of Sichuan Province during the 14th Five-Year Plan shows that the comprehensive energy capacity in Sichuan will reach 2.57 × 10⁸ t standard coal by 2025, and the predicted target value is between the predicted value in 2024 and 2025, which is more accurate. According to the historical data, the predicted value accords with the development law of energy output, so the above prediction data is in line with the purpose of this paper and can be used.

3.3.2. Empirical Analysis of Energy Industry Structure

Since the model (2) is a linear function, the minimum value can be found at the boundary and brought into the Sichuan provincial data, we can get the theoretical optimal model:

$$M=\min \left(28.3\times {10}^4{x}_1+22.9\times {10}^4{x}_2+21\times {10}^4{x}_3+16x_4+20.3x_5+19.9x_6\right)$$

(8)

To reach the minimum value of M, according to the coefficient value, it is necessary to develop clean energy ${\mathrm{x}}_4$, ${\mathrm{x}}_5$and ${\mathrm{x}}_6$as much as possible. According to the above equation, when x₄ is the maximum and other energy sources are zero, M takes the minimum value.

According to the constraint $E={\displaystyle \sum }_{i=1}^6{D}_i{x}_i+K$, where E is the above predicted calorific value of 354.5116 million tons of standard coal for the total energy production of Sichuan Province in 2030, and the calorific value is converted into heat, the value is E = 248 × 10¹³ kcal; K is other clean energy such as nuclear energy, biomass energy, etc., which is regarded as a constant 0 for the sake of simplification because of a small data set. To bring in the data, we can get:

$$248\times {10}^{13}=10\times {10}^{10}{x}_1+7\times {10}^{10}{x}_2+12\times {10}^{10}{x}_3+8.61\times {10}^{10}{x}_4+8.61\times {10}^{10}{x}_5+8.61\times {10}^{10}{x}_6)$$

Simplification can be obtained:

$$248\times {10}^3=10x_1+7x_2+12x_3+8.61x_4+8.61x_5+8.61x_6)$$

(9)

When ${\mathrm{x}}_4$takes the maximum value and other unknowns take zero, M gets the minimum value, which can be obtained by bringing it into Equation (9), ${\mathrm{x}}_4$ = 28,803.7. That is, when ${\mathrm{x}}_1={ \mathrm{x}}_2={ \mathrm{x}}_3={ \mathrm{x}}_5={ \mathrm{x}}_6$ = 0, ${\mathrm{x}}_4$ = 28,803.7, M takes the minimum value.

According to the analysis of Equation (8), the optimal order of various energy industries development in Sichuan Province is as follows: hydropower > wind power > photovoltaic > natural gas > coal > oil.

3.3.3. Optimal Structure of Clean Energy Industry in Sichuan Province Based on Technologically-Exploitable Amount

(1) Hydropower

According to the above model data, M takes the minimum value when ${\mathrm{x}}_4$ = 2880.37 billion kWh, that is to say, when the hydropower capacity is 2.88037 trillion kWh, it meets the capacity demand of Sichuan Province in 2030, and at this time it is the energy industry structure with the lowest cost and the lowest price. However, due to the limitations of resources and exploitable capacity of hydropower capacity in Sichuan Province, the output of 2.88037 trillion kWh/year cannot be reached.

According to the data in Section 3.1, the theoretical reserves of hydropower resources in Sichuan Province are 150 million kW, and the technologically-exploitable amount is 148 million kW.

Table 5. Hydroelectric Power Generation Data in Sichuan Province by year.

Year	Installed Capacity (10,000 kW)	Electricity Generation (100 Million kWh)	Power Generation Time (Hour)
2015	6759.00	2640.00	3905.90
2016	7246.00	2721.80	3756.28
2017	7714.00	2909.90	3772.23
2018	7674.00	2982.20	3886.11
2019	7839.00	3075.50	3923.33
2020	8082.00	3514.00	4347.93
2021	8887.00	3724.46	4190.91

details the installed hydropower capacity, power generation, and generation time in Sichuan Province from 2015 to 2021.

According to the data of power generation time in Sichuan Province from 2015 to 2020, the average power generation time in Sichuan Province over this period is ${\mathrm{h}}_4$ = 3969 h. Based on the principle of maximizing development, according to the theoretical reserves of hydropower resources in Sichuan Province, the maximum hydropower capacity of Sichuan Province can reach 595.35 billion kWh. According to the current technologically-exploitable amount, the maximum hydropower capacity of Sichuan Province can reach 587.412 billion kWh.

With reference to the development goals of the Energy Development Plan of Sichuan Province during the 14th Five-Year Plan, it is planned that the installed capacity of hydropower in Sichuan Province will reach 105 million kW in 2025, and it is known that the installed capacity of hydropower in Sichuan Province will be 89 million kW in 2021. Therefore, the installed capacity of hydropower will increase by 16 million kW during the 14th Five-Year Plan period. If the theoretical reserve capacity of 150 million kW is to be reached in 2030, an additional 45 million kW will be needed between 2025 and 2030. To reach the current technologically-exploitable amount of 148 million kW by 2030, an additional 43 million kW will be needed between 2025 and 2030.

Firstly, according to the analysis of the actual situation, China’s hydropower technology leads the world, and the space and urgency of technological improvement is far less than other non-fossil energy power generation technologies such as wind power, photovoltaic power generation, nuclear power and so on. Secondly, judging by the growth rate, it is more realistic to calculate the increment of hydropower installation according to the amount of technology that can be developed. Thirdly, the hydropower reserves include resources that are not suitable for the development of hydropower stations, and the development costs are large and the benefits small. To sum up, according to the principle of maximum utilization of resources in this paper, the installed capacity of hydropower in 2030 is 148 million kW and the power generation time is 3969 h. At this time, the annual output of hydropower in Sichuan Province is 587.412 billion kWh, that is, ${\mathrm{x}}_4$ = 587.412 billion kWh.

(2) Wind power

According to Equation (8), priority is given to the development of hydropower, followed by wind power. When${ \mathrm{x}}_4$ = 5874.12, bring it into Equation (9), when ${ \mathrm{x}}_1={ \mathrm{x}}_2={ \mathrm{x}}_3={ \mathrm{x}}_5$ = 0, ${\mathrm{x}}_6$ = 22,926.83, at this time, M takes the minimum value.

According to the data in Section 3.1, the theoretically-exploitable amount of wind power in Sichuan Province is about 48.5 million kW, and the economically- and technologically-exploitable amount is about 20 million kW.

Table 6. Sichuan Province Wind Power Generation Data by year.

Year	Installed Capacity (10,000 kW)	Electricity Generation (100 Million kWh)	Power Generation Time (Hour)
2015	73.70	10.00	1356.85
2016	1250.00	21.00	1680.00
2017	210.50	49.50	2351.54
2018	253.00	55.00	2173.91
2019	324.70	73.20	2254.39
2020	426.00	86.20	2023.47
2021	527.29	109.43	2075.32

reports the installed capacity, power generation and generation time of wind power in Sichuan Province from 2015 to 2021. According to the data of generation time in Sichuan Province from 2015 to 2020, it can be seen that the generation time in 2015 and 2016 is obviously smaller, which is due to the amount of wind power installed and technical reasons. Based on the principle of maximizing development, according to the theoretically-exploitable amount of wind power in Sichuan Province of 48.5 million kW, the maximum wind power production capacity of Sichuan Province can reach 105.52 billion kWh. According to the current technologically-exploitable amount, the maximum wind power production capacity of Sichuan Province can reach 43.52 billion kWh. Thus, there is more room for the improvement of wind power technology.

According to the development goals of the Energy Development Plan of Sichuan Province during the 14th Five-Year Plan, wind power installation is planned to reach 10 million kW in 2025, and it is known that wind power installation in Sichuan Province will be 5.273 million kW in 2021. Therefore, wind power installation will increase by 4.727 million kW during the 14th Five-Year Plan period. If the theoretical reserve capacity of 48.5 million kW is to be reached in 2030, an additional 38.5 million kW will be needed from 2025 to 2030. To reach the current technologically-exploitable amount of 20 million kW by 2030, an additional 10 million kW will be needed between 2025 and 2030.

Firstly, according to the expected growth under the “14th Five-Year Plan”, the wind power installed increment is more realistic in accordance with the technically-exploitable volume. Secondly, huge progress has been made in current wind power technology, so it is easier to calculate it with the current technologically-exploitable volume. Therefore, according to the maximization of resource utilization principle, the wind power installation capacity in 2030 takes the value range of 20 million kW, and the power generation duration takes 2176 h. Meanwhile, the annual output of wind power in Sichuan Province is 43.52 billion kWh, i.e., x₆ = 43.52 billion kWh.

(3) Photovoltaic power generation

It can be seen from Equation (8) that photovoltaic power should be developed after giving priority to hydroelectric power and wind power development. When x₄ = 5874.12, and x₆ = 435.2, bring them into Equation (9). Meanwhile x₁ = x₂ = x₃ = 0, x₅ = 22,497, and M takes the minimum value.

It is known from the data in Section 3.1 that the exploitable amount of photovoltaic power generation technologically in Sichuan Province is 85 million kW. The installed photovoltaic power generation capacity, power generation capacity and power generation time in Sichuan Province from 2015–2021 are reported in

Table 7. PV power generation data in Sichuan Province by year.

Year	Installed Capacity (10,000 kW)	Electricity Generation (100 Million kWh)	Power Generation Time (Hour)
2015	36.00	1.51	419.44
2016	96.00	4.06	422.92
2017	134.80	16.40	1216.62
2018	181.00	14.68	811.05
2019	188.00	19.70	1047.87
2020	191.00	27.00	1413.61
2021	195.90	29.65	1513.53

. Observing the photovoltaic power generation time data from 2015 to 2020 in Sichuan Province, it can be seen that the amount of installed photovoltaic power generation and technology has led to significantly shorter power generation time. Therefore, the average time of photovoltaic power generation in this paper ignores the data of the most recent two years–that is, the average time of photovoltaic power generation in Sichuan Province is 1201 h. The effect of efficiency changes due to climate change is also excluded. Based on the principle of maximum development, the maximum photovoltaic power generation capacity in Sichuan Province can reach 102.085 billion kWh by following 85 million kW of exploitation volume theoretically.

Referring to the development goals of the “14th Five-Year Plan for Energy Development in Sichuan Province”, it is planned that the installed capacity of photovoltaic power generation will reach 12 million kW by 2025. Therefore, the installed capacity of photovoltaic power generation will increase by 10.041 million kW during the “14th Five-Year Plan”. To reach 85 million kW of technically-exploitable capacity by 2030, an additional 73 million kW would be needed from 2025 to 2030.

Depending on the principle of maximizing resource utilization, the installed capacity of photovoltaic power generation in 2030 is taken as 85 million kW, and the power generation time is taken as 1201 h. In this case, the annual output of photovoltaic power generation in Sichuan Province is 102.085 billion kWh, i.e., x₅ = 102.085 billion kWh.

(4) Natural gas

It can be seen from Equation (8) that natural gas should be developed after giving priority to hydroelectric power, wind power and photovoltaic power generation development. When x₄ = 5874.12, x₅ = 1020.85, and x₆ = 435.2, bring them into Equation (9). Meanwhile, x₁ = x₂ = 0, x₃ = 15,408.3, and M takes the minimum value.

It is known from the data in Section 3.1 that the total natural gas resources in Sichuan Basin are 40 trillion cubic meters, and the proved reserves are 6.4 trillion cubic meters. Because 1 cubic meter = 0.71 kg, the 6.4 trillion cubic meters of proved reserves are converted to 4544 million tons of natural gas by weight, which is fully capable of meeting the production capacity demand of x₃ = 154.083 million tons of natural gas.

Referring to the development target of the “14th Five-Year Plan for Energy Development in Sichuan Province”, the natural gas output is planned to reach 63 billion cubic meters by 2025, with an average annual growth of 8.4%. The natural gas output in Sichuan Province is known to be 47.59 billion cubic meters in 2021, so the natural gas production will increase by 15.41 billion cubic meters during the “14th Five-Year Plan”. To reach 154.083 million tons of natural gas (217 billion cubic meters) in 2030, an additional 154 billion cubic meters would be required between 2025 and 2030. If the growth is maintained at 8.4% during the “14th Five-Year Plan” period from 2025 to 2030, the natural gas increment will reach 94.3 billion cubic meters during the “15th Five-Year Plan” period.

It can be learned from

Table 8. Sichuan Province photovoltaic power generation installed future planning increment.

Type	Content	“The 14th Five-Year Plan” Period	“The 15th Five-Year Plan” Period
Natural Gas Increment	Based on maximum capacity requirements	154.10	1540.00
(100 million cubic meters)	At constant annual growth rate	154.10	313.00

that when the capacity is calculated at an average annual growth rate of 8.4%, the total output in 2030 amounts to 154 billion cubic meters, which cannot meet the demand of x₃ = 217 billion cubic meters. If the missing energy output is replaced by oil or coal when x₃ = 154 billion cubic meters (x₃ = 109.34 million tons of natural gas, the density of natural gas is 0.71Kg/m³ at 0 °C and 101.352Kpa at a standard atmospheric pressure, temperature of 0 °C and relative humidity of 0%), that is, x₃ = 10,934, x₄ = 5874.12, x₅ = 1020.85, x₆ = 435.2 are brought into Equation (9). If x₂ = 0, then x₁ = 5370; if x₁ = 0, then x₂ = 7671.4.

(5) Structural optimization of clean energy industry

To sum up, the structural optimization of clean energy industry in Sichuan Province is: x₁ = x₂ = 0, x₃ = 15,408.3, x₄ = 5874.12, x₅ = 1020.85, x₆ = 435.2, which reaches the optimum at this point (as in

Table 9. Optimal energy structure in Sichuan Province in 2030.

		Oil (10,000 t)	Coal (10,000 t)	Natural Gas (10,000 t)	Hydroelectricity (100 Million kWh)	Photovoltaic Power Generation (100 Million kWh)	Wind Power (100 Million kWh)
Energy Capacity	Model I	0	0	15,408.30	5874.12	1020.85	435.20
	Model II	0	7671.40	10,934.00	5874.12	1020.85	435.20
	Model III	5370.00	0	10,934.00	5874.12	1020.85	435.20

: Model I). The impact of natural gas extraction technology and the growth rate of exploitation volume may result in outputs that do not meet the model data. If the surplus energy output is replaced by coal (as shown in Table 9: Model II), the energy industrial structure of Sichuan Province is: x₁ = 0, x₂ = 7671.4, x₃ = 10,934, x₄ = 5874.12, x₅ = 1020.85, x₆ = 435.2. If the surplus energy output is replaced by oil (as shown in Table 9: Model III), the structure of the energy industry in Sichuan Province is: x₁ = 5370, x₂ = 0, x₃ = 10,934, x₄ = 5874.12, x₅ = 1020.85, x₆ = 435.2.

This paper studies the clean energy industry in Sichuan Province, including hydroelectric power, wind power, photovoltaic power generation, and natural gas, with the aim of ensuring the structural optimization of clean energy industry will help meet the demand for energy volume in Sichuan Province by 2030 (

Table 10. The optimal structure of clean energy industry in Sichuan Province in 2030 (Production capacity).

Content	Natural Gas (10,000 t)	Hydroelectricity (100 Million kWh)	Photovoltaic Power Generation (100 Million kWh)	Wind Power (100 Million kWh)
Energy production in 2030	15,408.30	5874.12	1020.85	435.20
(Proportion)	−212.00	−81.00	−14.00	−6.00
Energy production in 2021	475.90	3724.46	29.65	109.43
(Proportion)	−112.60	−124.00	−1.00	−3.70

). The structural optimization of clean energy industry in Sichuan Province in 2030 can be obtained by converting into installed capacity (as shown in

Table 11. Optimal structure of clean energy industry in Sichuan Province in 2030 (installed capacity).

Content	Natural Gas (100 Million Cubic Meters)	Hydroelectricity (10,000 kW)	Photovoltaic Power Generation (10,000 kW)	Wind Power (10,000 kW)
Installed capacity and production in 2030	2170	14,800	8500	2000

).

By analyzing the structural optimization of clean energy industry in Sichuan Province in 2030, it is found that the future energy development of Sichuan Province should pay attention to the construction and investment of wind power and photovoltaic power generation, and strive to improve the output of wind and photovoltaic hybrid power as the main drivers of clean energy in the future.

3.4. Seasonal Analysis of Clean Energy Industry

It is shown that the power generation trend data of all energy resources in Sichuan Province in 2020 in Figure 9 is in accordance with energy data from the IMedia Data Center (https://data.iimedia.cn/, accessed on 1 August 2022). It can be seen that hydroelectric power and wind power with strong seasonality are greatly affected by seasons. Hydroelectric power reaches its peak in summer and reaches its trough in winter when water resources are scarce, generating twice as much power at its peak as at its trough. Wind power, in contrast, peaks in winter, generating twice as much power at its peak as it its trough. Photovoltaic power generation is more stable over the entire year, with a trough in September and October each year. As the peak shaving power in Sichuan province, it is clear that thermal power is complementary to hydroelectric power.

The power generation trend of four energy resources is shown in Figure 9. It is found that Sichuan Province mainly relies on hydroelectric power for power generation, that wind power has little regulation capacity for hydroelectric power, and the main peak shaving energy is thermal power.

Whether there is a significant regulating capacity for power generation in Sichuan Province was explored basing on the calculation of the structural optimization of the clean energy industry in Sichuan Province. Firstly, the seasonal trend of total power generation is determined based on the available data; secondly, the data growth ratio derived from the energy model is used to derive the trend graph of each energy data in 2025, as shown in Figure 10. Similarly, the trend chart of each energy data in 2030 can be obtained on the basis of the optimal structure of the clean energy industry in Sichuan Province, as shown in Figure 11.

According to the above figure, the seasonality of clean power slows down significantly when clean energy is maximally exploited. Internally, the stability of the province’s power supply is greatly improved, and the regulating ability of wind power gradually emerges; externally, the power transmission capacity is greatly improved with stable supply and high utilization rate of the power transmission channel.

4. Conclusions and Discussion

The existing literature is more devoted to the structural optimization between industries [32,33]. Meanwhile, research on clean energy has focused on three areas: research on the impact of clean energy on the economy [34,35], research on clean energy policies and measures [36,37], and research on clean energy technology [38,39]. However, there are few studies on the structural optimization of clean energy industry structure. Based on this, this paper constructs a model on the structural optimization of clean energy industry structure in Sichuan Province under the conditions of lowest carbon emissions and minimum energy costs, mainly analyzing the following three aspects.

Firstly, an autoregressive test was conducted by using the data of total energy production in Sichuan Province in the past few years and an OLS model, calculating the total energy production in Sichuan Province from 2022 to 2030 as the constraint value for analyzing the model on structural optimization of the clean energy industry in future in Sichuan Province. At the same time, compared with the “14th Five-Year Plan for Energy Development in Sichuan Province”, the province’s energy capacity will reach 257 million tons of standard coal by 2025. It can be seen that the target value is between the predicted value of 2024 and 2025, which is relatively accurate. Moreover, based on the historical data, it is known that the predicted value is in line with the law of energy production development, so the data predicted by the prediction model conforms to the prediction purpose of this paper and can be adopted.

Secondly, under the constraint of total energy output, the optimization model of clean energy industrial structure in Sichuan Province with the lowest carbon emissions is constructed, and the order of energy development in Sichuan Province is analyzed as follows: hydropower, wind power, photovoltaic power generation, natural gas, coal, and oil. According to the optimization model of the future clean energy industry structure in Sichuan Province and the specific situation of its resource reserves and technology development, the optimal structure of the clean energy industry in Sichuan Province is studied under the constraint of the total energy output: hydropower (148 million kW), wind power (20 million kW), photovoltaic power (85 million kW), and natural gas (217 billion cubic meters). Therefore, Sichuan Province should vigorously develop hydroelectric power, wind power and photovoltaic power generation, improve the development of clean energy, and optimize the energy industry structure, in the hope of achieving the lowest carbon emissions and energy costs.

Finally, based on the seasonal analysis of power generation in Sichuan province across 12 months, it is found that hydroelectric power and wind power are greatly affected by seasons and have opposite effects. Wind power is a better energy for peak shaving, with the drought and power restrictions suffered by Sichuan Province in 2022 serving as good evidence. Photoelectric power generation is relatively stable throughout the year, and slightly higher in winter than in summer. Sichuan’s thermal power has outstanding ability to shave peaks, but the overall total power generation is unstable and seasonal, and its peak shaving ability is insufficient. According to the optimal structure of the model, seasonal analysis of power generation in Sichuan province is carried out. The results show that the future seasonality of clean energy industry in Sichuan province will obviously slow down, the stability of power supply in the whole province will be greatly improved, and the seasonal adjustment ability of photovoltaic power generation and wind power to hydroelectric power generation will be more and more obvious.

5. Policy Recommendations

5.1. Innovative Energy Technologies to Increase Clean Energy Production

Firstly, we need to enhance natural gas exploration technology and strengthen natural gas extraction and utilization. Sichuan ranks first in China in terms of total natural gas resources and has huge development potential. It is the main natural gas extraction area in China, with thousands of kilometers of existing gas extraction pipelines and transmission pipelines, which have formed a circular pipeline network running east-west and north-south, but the regional exploration rate is only 16% due to the limitations of exploration technology. Therefore, Sichuan Province should enhance natural gas exploration technology, accelerate the engineering progress of projects under construction, and improve the efficiency of natural gas resources exploration and development, as well as strengthen organization and coordination, and formulate preferential policies to encourage and promote natural gas utilization.

Secondly, we should develop pollution-free hydrogen production technology and promote the application of hydrogen energy. Hydrogen energy has been shown to be a bright prospect, and China’s National Development and Reform Commission and National Energy Administration have proposed making clean energy hydrogen production, and hydrogen energy and fuel cell technology innovation, the key tasks of China’s energy technology revolution innovation action. The abundant hydropower resources and natural gas resources in Sichuan Province, an important pillar for the energy transformation and high-quality development of the province, provide excellent conditions for the development of hydrogen energy. Therefore, in the future, Sichuan Province can develop pollution-free hydrogen production technology in two ways: first, adding the right amount of hydrogen gas to natural gas to reduce CO₂ emissions while improving the thermal efficiency of natural gas engines; and second, building hydrogen production plants in hydropower stations to directly use electricity from hydropower stations without crossing the grid and selling it to improve the consumption of abandoned hydropower.

5.2. Promote Clean Energy Alternatives and Optimize Energy Industry Structure

Sichuan should vigorously develop hydropower, wind power and photovoltaic power generation, and should also make greater efforts to promote clean energy substitution and optimize the energy industry structure. Ways to do this could include vehicle fuel substitution, production energy substitution and household fuel substitution. We can try to start with city buses, replace internal-combustion-powered cars with electric cars, and increase the contrast between the operating costs of internal-combustion-powered cars and electric cars, so as to encourage consumers to buy electric cars. Sichuan production energy in addition to iron and cement coal cannot be replaced by electricity; the rest can be electricity or gas instead of coal, which can not only reduce fuel costs, but also reduce the enterprise pile of coal and slag site occupation, reduce enterprise pollutant emissions, and improve the production environment. Now, there is the basic popularity of urban households with gas through the “inverted ladder tariff”—that is, the more electricity the lower the price of electricity, to encourage families to change electricity; support enterprises to develop intelligent sets of electric cookware, with automation and other new technologies to promote family gas to electricity.

5.3. Make up for Seasonal Shortcomings and Enhance Peaking Capacity

Firstly, we should strengthen thermal power flexibility transformation by building additional storage, energy storage and gas power transformation facilities to structurally improve the power grid’s peaking capacity; secondly, we should develop photovoltaic power generation, wind power and virtual power plant technology to bring into play the seasonal regulation of photovoltaic power generation and wind power on hydropower generation to enhance the stability of the province’s power supply; and finally, we should establish a market-based mechanism for cross-regional regulation. We should also strengthen regional cooperation to realize the allocation of peaking resources nationwide and enhance the emergency mutual reserve capacity.

6. Shortcomings and Prospects

This paper mainly aims to research the structural optimization of clean energy industry in Sichuan Province. This paper is a comprehensive study of clean energy industry, but some parts need to be further improved. First, the prediction model of Sichuan Province’s future energy output ignores the influence of other factors on the total energy output. Secondly, the structural optimization model of clean energy industry is constructed based on the current energy market price, carbon trading price and carbon emissions of current technology, while the paper does not take the volatility of market price and technology into consideration. In future research, other factors affecting total energy production should be added to optimize the total energy production forecasting model and enhance the robustness of the conclusions; at the same time, the influence of market price and technology volatility factors on the model should be considered in the future to improve the applicability of this optimization model.