Transport fuel supply and demand of the passenger car sector in

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Transport fuel supply and demand of the passenger car sector in China up to 2030: A modelling approach Ruijie He, Zheng Zhao, Pei Liu, and Zheng Li ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03649 • Publication Date (Web): 08 Mar 2018 Downloaded from http://pubs.acs.org on March 8, 2018

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ACS Sustainable Chemistry & Engineering

Transport fuel supply and demand of the passenger car sector in China up to 2030: A modelling approach Ruijie He,1 Zheng Zhao,1 Pei Liu ,*, 1Zheng Li1 1

Tsinghua BP Clean Energy Research and Education Centre, State Key Laboratory of Power Systems,

Department of Thermal engineering, Tsinghua University, Beijing, 100084, China *Corresponding author. E-mail: [email protected]

ABSTRACT China’s consumption of transport fuels, mainly gasoline and diesel, has been increasing in the last decade, but at rather different rates. It is expected that the increase rates will differ further due to slow down of the economy, mainly affecting diesel demand, and fast development of the private car sector thus fast increase in gasoline demand. On the supply side, a certain degree of uncertainty and flexibility also exists, mainly resulting from potential changes in oil import amount and quality, development of alternative liquid fuels, retrofitting refineries and building new ones, and others. In this paper, a virtual refinery model is established to analyze the productivity of gasoline and diesel in China up to 2030. This model is at a national level where all possible physical flows, oil products, and the primary and secondary processing routes are taken into consideration. On the demand side, we present a model to analyze gasoline demand from the private vehicle sector in various scenarios, covering different types of cars and concerning impacts of vehicle age distribution and penetration of alternative fuels. Results indicate that production ratio between diesel and gasoline in China can change in the range between 1.27 and 2.92. A gap of 20 million tonnes between demand and supply 1 ACS Paragon Plus Environment

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of gasoline may appear around 2019 and afterwards, which brings opportunities for alternative transport fuels and vehicles. Key words: ratio of diesel to gasoline; passenger car; demand of gasoline; scenario analysis

INTRODUCTION In China, most of the gasoline and diesel are obtained from the oil refinery industry. In recent years, annual gasoline and diesel production and consumption in China are roughly in balance [1]-[2]. This also indicates that the capacity and operational flexibility of the refining sector are rather critical to the future balance (or imbalance) between supply and demand. Many studies have shown that the demand for gasoline and diesel will increase greatly with rapid increase of vehicle population [3]-[4]. In this paper, we aim to tackle this problem via studying the production flexibility in refining sector and the feature of gasoline consumption in passenger car sector with the method of modeling analysis in order to figure out the matching case between supply and demand of gasoline and diesel in China. Many existing studies focus on energy consumption and pollution emissions of different developing routes and scenarios of energy saving and new energy vehicles. Ou et al. [5] established a model using passenger car ownership, average annual mileage, and fuel economy to calculate energy consumption and carbon emission. Based on this model, scenario analysis was adopted to discuss different development policies of new energy vehicles. Huo et al. [6] studied the effects of fuel economy, diesel fuel, electrification and fuel diversification on the energy consumption and carbon emissions of traffic 2 ACS Paragon Plus Environment

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departments in China. Liu et al. [7] deeply researched the reduction of energy consumption and carbon emission of traffic department in China with electric vehicle promotion. The calculating methods and basic ideas of these studies are worth learning. Nevertheless, they only considered the entire traffic department, lacking detailed modeling and calculation of gasoline consumption of passenger car sector. They only ended up with energy consumption in different developing scenarios, and didn’t allow for the impact of the energy consumption on production and consumption structure of domestic gasoline and diesel market. Under the restriction of the range of diesel to gasoline ratio in the refining department, the goals of gasoline consumption in China's passenger car sector also have not been discussed. Moreover, these previous studies only looked at fuel demand from the transport sector, and the focus of this work is analyzing supply and demand gap of gasoline and diesel so it is also of the essence to look at refining sector. The production of gasoline and diesel in the refining sector depends on two factors: one is the amount and the property of the crude, the other is the productivity of gasoline and diesel in refinery. The crude processed in the refining sector comprises two parts: domestics and imports. Crude oil from different places have different properties. The production of gasoline and diesel in the refining sector also depends on the structure of a refinery, its operational conditions, and crude oil properties. The production rate can change greatly under different circumstances. For instance, a petrochemical refinery produces more chemical light oil and naphtha, but less gasoline or diesel. A fuel refinery is quite on the opposite side, where gasoline and diesel are its major products [8]. At the national level, the production rate of gasoline and diesel also changes year by year,

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mainly following their demand. This indicates that the production range of the refining industry is not fixed. Instead, it exhibits an obvious wide range for operation. Therefore, quantifying this range is critical to calculate future supply capacity of gasoline and diesel. On the other hand, with the domestic economic slowing down and private passenger car sales increasing rapidly, the increasing demand of gasoline has become the main force to pull the growth of domestic oil product consumption. So it will be necessary for us to know the ownership and sales of passenger cars in China. In this paper, a model for different types of passenger cars is established and many factors which affect the ownership are analyzed separately. Vehicle age distribution in passenger car is also taken into consideration. With the development of technologies and support of policies, there are more and more passenger cars using alternative fuels, such as fuel ethanol, natural gas, electricity and so on. Based on life-cycle analysis, Hu et al. [9] assessed energy use and air pollutions with gasoline and its alternative fuels. They found that some alternative fuels such as methanol from natural gas should be chosen to reduce the external cost of net energy yield. Yan et al. [10] reviewed vehicle life cycle analysis in China and their research indicated it was a hot issue to study alternative fuels in China’s transport sector. Most of existing studies aim at determining the key factors which affect emissions by analyzing historical data [11]- [12], which is not very related to this research. He et al [13] used model calculation and designed five scenarios to forecast the energy consumption and the harmful emissions in China’s road transport sector up to 2030. The result shows that it is appropriate to promote both high-efficient pure electric and hybrid vehicles simultaneously and improve shares of both types by a wide margin for future policy implementation. The method they develop their model and


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scenarios can provide experience for our study, but they didn’t consider fuel ethanol in their study, which will affect gasoline consumption and also emissions. And the difference of development trend of alternative fuels in different area of China is also neglected. Therefore, in order to calculate accurate gasoline consumption of passenger car sector in China, the utilization of vehicle-use alternative fuels in the future is analyzed and forecasted quantitatively by considering all mentioned aspects above. This paper proceeds as follows. In section METHDOLOGY, a superstructure refinery model is established to study the flexibility of gasoline and diesel production and forecast the constraint on the supply of gasoline for use in the passenger car sector up to 2030. Then a model is designed to forecast passenger car sales and stocks, and alternative fuels are considered to calculate gasoline consumption in passenger car sector. The forecast results of models in section METHDOLOGY is presented in section FORECAST RESULTS. After that, the actual gasoline consumption rate and the average annual mileage of passenger car are analyzed, and an assumption as a reference scenario is hypothesized. The gasoline consumption in passenger car sector and other sectors in the future are calculated based on the assumption above. The consumption and supply in refining sector are compared and a major finding is that the gasoline supply will not meet the consumption since 2019. To meet this gap, measures in fuel economy and average annual mileage are considered and analyzed. Conclusions will be presented in section CONCLUSIONS.



METHODOLOGY Virtual refinery model Model structure and formulation The refining process in China can be divided into three parts: primary processing, secondary processing and extended processing. Extended processing is a deepening processing procedure, mainly to produce chemicals. As this article mainly aims at the productivity of gasoline and diesel, and extended processing has no direct impacts on production of these two kinds of product, only primary and secondary processing are considered in this study.

Figure 1. The structure of virtual refinery model at the national level in China


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Taking all the primary and secondary processing technologies into consideration, we establish a virtual refinery model at the national level, in which the capacity of each unit is the summation of corresponding unit capacity in the whole country. The connection between units includes all feasible connections based on actual refining process. Figure 1 shows the structure and the details of the model [14]. A superstructure model is constructed to cover all possible physical flows, oil products, and processing routes. And the model needs to satisfy the constraints of the basic laws of physics. Table 1 lists the parameters and variables that appear in the virtual refinery model, as well as the related physical meaning. Table 1 Nomenclature of variables and parameters of the virtual refinery model Parameters and variables symbol

Physical meaning

i,k

Equipments of the primary and secondary processing

j

All the oil in the process, including the initial crude oil, the intermediate oil and the final product

INPUTi

Mass of raw material which flows into equipment i

CAPi

Maximum capacity of equipment i

OUTPUTi , j

Mass of product j in equipment i

RATIOi , j

Mass yield of product j in equipment i

PROj

Final output mass of product j

FEA_CONk , i , j

0/1 parameter to check whether the product j produced by equipment i flows into equipment k

FEA_PROi , j

0/1 parameter to check whether the product j produced by equipment i is part of the final product j

For any processing equipment i, the mass of the product j is equal to the product of the raw material mass which flows into equipment i and the mass yield of quality j: OUTPUTi , j =INPUTi ×RATIOi , j


(1)


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In order to meet the mass conservation, the product mass yield should meet the following conditions: RATIOi , j =100%

(2)

j

The total mass of the raw material flowing into the equipment i shall not be greater than the capacity of this equipment: INPUTi ≤CAPi

(3)

The total mass of the raw material flowing into the equipment k is equal to the sum of all products that are allowed to flow into the equipment k in the model: INPUTk = OUTPUTi , j ×FEA_CONk , i , j

(4)

i,j

In the model, FEA_CONk , i , j is introduced to express the connection between each equipment, and the value of this parameter corresponds to the physical structure in the model. The mass of the final product j is equal to the sum of all products that are allowed to be included in the final product j during the process of processing: PROj = OUTPUTi , j ×FEA_PROi , j

(5)

i

The process of crude oil processing and optimization calculation based on this model are shown in figure 2.


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Figure 2 Framework of the virtual refinery model and optimization Model settings for primary processing In primary processing, atmospheric and vacuum distillation is considered. Raw material of this unit is crude oil and four main products are naphtha, diesel and kerosene (D&K), vacuum gas oil (VGO) and vacuum residuum (VR). domestic crude and import crude are considered separately. The property of the domestic crude is relatively stable. The fraction of distillation for domestic crude can be obtained from the crude oil property data in domestic oil fields. For import crude, the source is relatively stable. We select typical data to represent the crude oil property in main import regions and use source of import crude in 2014 as the criterion to get the fraction rate of import crude oil. Fraction rates are listed in Supporting information. After getting the fraction of domestic and import crude, if the share of domestic or imports is obtained, the fraction of mixing crude is calculated. In recent years, domestic produced crude oil production is relatively stable, around a level of 200 million tonnes per year [15]. Imported crude oil amount increases with China's oil demand every year. In 2013, imported oil accounted for 57 percent of China’s total oil supply [1]. From the view 9 ACS Paragon Plus Environment


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point of energy security, oil import dependency should not be too high, and there should be an upper limit for oil import. In this work, this upper limit for oil import is set to be 70 percent [4], i.e., at maximum 70 percent of China’s oil can come from oil import. Then the proportion of domestic and import crude in the future can be calculated, which shows in Table 2. Table 2. Proportion of domestic and import crude in China 2010 46% 54%

Domestic Import

2011 45% 55%

2012 43% 57%

2013 42% 58%

2015 41% 59%

2020 36% 64%

2025 34% 66%

2030 30% 70%

Based on the above data, the fraction of mixing crude from 2015 to 2030 can be calculated. As Table 3 shows, with oil import dependency higher, the share of naphtha and D&K rises, this is beneficial to produce more gasoline and diesel Table 3. Fraction rates of mixing crude oil Naphtha

D&K

VGO

VR

2015

11.7%

26.9%

26.6%

32.4%

2020

12.1%

27.6%

26.6%

31.3%

2025

12.3%

27.9%

26.7%

30.8%

12.6% 28.5% Model settings for secondary processing

26.7%

29.9%

2030

Secondary processing aims to further process the fraction of primary processing, so as to increase the yield of naphtha, improve the quality and increase the variety of oil products. This processing includes several types, such as catalytic cracking, delay coking, hydro cracking, catalytic reforming and hydro treating. The productivities of different processing types are limited in a feasible range and inputted to the model as initial parameters, which are shown in Supporting information. 10 ACS Paragon Plus Environment

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Model settings for capacity of different process For primary processing capacity, it will reach 940 million tonnes in 2030 [15]. For secondary processing capacity, extrapolation is done based on average annual growth rate (AAGR) of secondary processing capacity from 2010 to 2014 [15]. Figure S6 in Supporting Information shows the calculated forecast of capacity for different process from 2015 to 2030. Model settings for other products According to the data [1] in the statistical yearbook and existing research [16][24], the mass yield of various oil products from 2010 to 2014 is listed in table 4. The purpose of this section is to discuss the productivity adjustment of gasoline and diesel fuel, so the gasoline and diesel yield is not limited. For other oil products, based on the minimum and maximum values of all kinds of oil products in the past five years, the yield range of this kind of oil is set up and input to the model as initial parameter. Table 4 Yield range setting of the main oil products in China based on 2010~2014 yield 2010

2011

2012

2013

2014

Yield range setting

Gasoline

18.0%

18.2%

19.2%

20.5%

21.9%

/

Diesel

37.1%

37.2%

36.5%

36.1%

35.1%

/

Kerosene

4.0%

4.2%

4.6%

5.2%

6.0%

4.0%~6.0%

Chemical oil

6.2%

6.2%

6.5%

6.7%

6.4%

6.1%-6.8%

Heavy oil

13.1%

11.5%

12.1%

12.7%

12.0%

11.4%-13.2%

Petroleum coke

3.3%

3.9%

4.2%

4.9%

4.8%

3.3%-5.0%

LPG

4.9%

4.9%

4.8%

5.2%

5.4%

4.8%-5.5%



Modeling for ownership and sales of passenger car In this section, a model for studying ownership and sales of passenger car is presented. This model and result are the basis of studying fuel consumption of passenger car sector. Passenger cars are divided into three categories: private passenger car, official passenger car and rental passenger car, due to different ownerships and annual mileages [5][25][26].

Figure 3. Framework of the passenger car model and optimization Modeling for passenger car ownership Private passenger car ownership In order to reflect the influence of car price and purchase desire in the model, two indices are introduced, namely Price Index (PI) and Purchase Desire Index (PDI). PI is the ratio of car price each year to the benchmark price, which is 2003 level. As the price of passenger cars is decreasing year by year, the PI value decreases gradually with the


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increase of the year. PDI also takes 2003 as the base year, and the value increases as the year increases. Figure 4 shows the index with the change of year.

PDI value PI value

1.2 1.1 1.0

Value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.9 0.8 0.7 0.6 0.5 2002

2004

2006

2008

2010

2012

year

Figure 4. Price index and purchasing desire index of private passenger car in China based on 2003 Considering that car price declining is more sensitive to medium income group, we use normal distribution function D(x) to describe this effect:  

−  1 D ( x) = e 2πσ

( x −u )2  2σ 2 

(6)

, µ = 2.5, σ = 3.5, x = income

Taking effect of car price and purchasing desire into consideration, we use PI, PDI and D(x) to modify annual disposable income [27]:



Modified _ income( x, t ) =

x × PDI (t ) 1 − (1 − PI (t )) × D( x)

(7)

x = income, t = year

250

Private car ownership / 1000 persons

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

200

150

100

50

0

0

1

2

3

4

5

6

7

8

9

10

11

12

Modified income (10 thousand yuan (2010RMB))

Figure 5. Relationship between private car ownership per thousand people and modified income, between 2003 and 2012

Figure 5 shows the relationship between private passenger car ownership per thousand people and modified income (MI). It can be seen that good correlation of the data is observed. Private passenger car ownership per thousand people increases as MI grows. Because of limited amount of data, only conditions are considered that the modified income is less than 100 thousand yuan. In actual cases, when MI increases to a certain degree, the growth of private passenger car ownership per thousand people decreases and tend to a fixed value. According to existing researches [26] [28], we can use Gompertz function to fit this variation:


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S ( y ) = ae

( −be ) − cy

(8)

Fitting based on the data in figure 5, three parameters can be obtained:

a = 550, b = 5.5, c = 0.22 . Then we get the function between private passenger car ownership per thousand people and MI.

S ( MI ) = 550e

( −5.5e

−0.22 MI

)

(9)

To calculate private passenger car ownership per thousand people, we need to get the distribution of annual disposable income. The distribution of annual disposable income in China obeys the law of lognormal distribution [26] [28]:

F ( x) =

1 2π σ x

e

 ( ln ( x ) − u )2  −    2σ 2  

(10)

σ and µ depend on per capita disposable income α and Gini index G:

 1 2 u+ σ  2 

α = e

 σ  , G = 2F  | 0,1 − 1  2 

(11)

Based on the analysis above and the data from National Bureau of Statistics, the model for private passenger car ownership is described as follows:

Onwership ( t ) Private =

TP ( t ) ∞ F ( x ) S ( MI ( x ) ) dx 1000 ∫x =0

(12)

x = income ( yuan) , TP = population, t = year Figure S7 in Supporting Information shows the comparison between results of calculation from the model and actual data from 2004 to 2013. We can see that the two sets of data are basically identical, which indicates the model is reliable. 15 ACS Paragon Plus Environment


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Official passenger car ownership Official passenger cars are owned by government and public institutions, which are mainly used for official business. As shown in figure S8 in Supporting Information, it can be seen that there is good linear correlation between ownership of official passenger car and GDP. So the model for official car ownership can be established by linearly fitting as follows: Onwership ( t )Official = 18.553GDP ( t ) + 85.264, t = year

(13)

Rental passenger car ownership Ownership of rental passenger car is controlled based on related policies. Figure S9 in Supporting Information shows the relationship between ownership of rental passenger car and urbanization rate. Overall, ownership of rental passenger car and urbanization rate is positively correlated. Linear relationship is assumed in the research, and the model for rental passenger car ownership can be established by linearly fitting as follows:

Onwership ( t ) Rental = 111.28UR ( t ) + 44.133, t = year,UR = urbanization rate (14) Modeling summary In the model for passenger car ownership, the input parameters are: population, per capita disposable income, Gini index, GDP and urbanization rate, which are obtained from the National Bureau of Statistics of China [29].. Provided forecast of these parameters, ownership of passenger car in the future can be calculated with the model established above.


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Modeling for vehicle age distribution The survival rule of passenger car should be studied as technologies of fuel economy improve with time. Based on existing researches [30], Weibull distribution can be used to describe the survival rule of passenger car:

SR ( x ) = e

x −  T 

k

(15)

It indicates the car proportion of survival after serving x years. k and T are parameters depended on the type of cars. Table 5 shows the parameters of different passenger cars: Table 5. Parameters for survival function of passenger car in China Parameters T k Private 14.46 4.79 Official 13.11 5.33 Rental

6

∞

Figure 6 shows the survival curve of passenger car in China, we can see that private passenger car has the longest survival time, followed by official passenger cars. Rental passenger car scraps sharply when serving fixed years, since China implements a force scrap rule for rental passenger car. We assume that after 6 years serving, rental passenger car scraps directly [30].



Private passenger vehicle Official passenger vehicle Rental passenger vehicle

100%

Vehicle survival rate

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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80%

60%

40%

20%

0% 0

5

10

15

20

25

Using year

Figure 6. Passenger car survival rule in China According to the sales and the survival rule in each year, we can calculate the vehicle age distribution for passenger car in every year as follows:

Ownership ( i ) = ∑ Sale ( j ) × SR ( i − j ), i, j = year, j ≤ i j

(16)

Alternative fuel analysis In order to calculate accurate gasoline consumption of passenger car sector in China, the utilization of vehicle-use alternative fuels in the future should be analyzed and forecasted quantitatively. [31]-[32] According to the type of fuels, vehicle-use alternative fuels are divided into 3 classes: biomass fuel (mainly includes ethanol and methanol), natural gas and electric power (EV, PHEV and fuel cell electric vehicle (FCV)). In this paper, only the development of ethanol, natural gas, EV and PHEV in China is considered since others are negligible and have no clear policy for commercial promotion. Then we get a 18 ACS Paragon Plus Environment

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forecasting for each of them. Cars using natural gas and EV/PHEV requires the new technology of engine and related power structure. The production, sale and promotion of these kinds of passenger car will affect the ownership and sales of the traditional gasoline passenger cars. Since fuel ethanol is mixed in gasoline in China, it doesn’t affect the type of passenger car, and it will be considered when calculating fuel consumption of passenger car. The alternative fuel analysis is combined with the model in section “Modeling for ownership and sales of passenger car” and results can be obtained, which are the basis for further calculation in fuel consumption. Analysis of fuel ethanol According to different raw materials, there are 3 types of fuel ethanol, which should be analyzed separately. The ethanol produced from grain plants, non-grain plants and agricultural/forestry residues are considered as the 1, 1.5 and 2 generation (1G, 1.5G and 2G) ethanol respectively. [33] The government put forward that production capacity of grain plants ethanol will not increase in the future, indicating no increase in the production of 1G ethanol. [34] It’s assumed that in the next 15 years the capacity of 1G ethanol will maintain at the present level of 190 tonnes per year. The 1.5G ethanol begins to demonstrate since 2007 and has a capacity of about 40 tonnes per year at present in China. To preserve "food security", China has set a minimum area for the country's farmland which limits the potential capacity of the 1.5G ethanol [35]. The raw material for the 2G ethanol is cellulose which is mainly comes from agricultural and forestry residues. The mass yield of ethanol out of cellulose is assumed to be 23% to get the forecast of potential production for the 2G ethanol in China. 19 ACS Paragon Plus Environment


Based on the analysis above, we can get the forecast of fuel ethanol production in China between 2016 and 2030, just as shown in figure 7.

450 400

Cellulose ethanol Non-grain plants ethanol Grain plants ethanol

350

(10 thousand tonnes)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

300 250 200 150 100 50 0 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

year

Figure 7. Forecast of fuel ethanol production in China between 2016 and 2030 Analysis of natural gas vehicle Based on existing researches, natural gas resource in China has large potential to be surveyed in the future, and with great amount of imports, the supply of natural gas can easily satisfy the demand [38]. Vehicle-use natural gas utilization has two forms: compressed natural gas (CNG) vehicle and liquefied natural gas (LNG) vehicle. Nearly all natural gas passenger cars are CNG ones in China. LNG vehicles are mainly trunks and coaches which are not considered in this paper. CNG taxies began to replace gasoline ones in 2008 and the ownership of CNG taxies increase fast since then. In 2014, about 53% of taxies in China are CNG ones. We 20 ACS Paragon Plus Environment

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assume that the share of CNG taxies will reach 90% in 2020 and maintains until gasoline taxies reach 0. According to policies of EV and PHEV, we consider that EV taxies will emerge in 2016 and will increase by 1% per year. Thus we get the shares of taxies ownership in China between 2015 and 2030 which is shown in figure 8.

100%

Shares of taxies ownership

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


80%

60%

40%

20%

CNG EV Gasoline

0% 2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

year

Figure 8. Shares of taxies ownership in China between 2015 and 2030 Analysis of EV and PHEV There are encouraging policies and financial supports for EV and PHEV in China since 2010. The whole country has been divided into 3 groups in EV and PHEV promotion based on economic and environment situations in different regions. [39]-[40] The 1st group covers atmospheric pollution control key areas including Beijing, Shanghai, Tianjin, Hebei, Shanxi, Jiangsu, Zhejiang, Shandong, Guangdong and Hainan. The 2nd group covers Midland provinces including Anhui, Jiangxi, Henan, Hubei, Hunan and Fujian. Other provinces are in the 3rd group.



Table 6 shows the percentage of new passenger car sales in each year that EV and PHEV must account for between 2016 and 2020 in the 3 groups of China according to related policies. We assume that the actual sales of new passenger car between 2016 and 2020 will match the requirements. Data between 2020 and 2030 will be obtained by extrapolation. The forecast of EV and PHEV shares of new passenger car sales in the 3 groups is shown in figure 9. Table 6. Requirements on new passenger car sales in China between 2016 and 2020[36] Echelon 10 thousand 2016 2017 2018 2019 2020 1st Sales 3 3.5 4.3 5.5 7 Percentage 3% 4% 5% 8% 10% 2nd Sales 1.8 2.2 2.8 3.8 5 Percentage 2% 3% 4% 5% 6% 3rd Sales 1 1.2 1.5 2 3 Percentage 0.50% 1% 1.50% 2% 3%

EV and PHEV Shares of new passenger car sales

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1st (10) 2ed (6) 3rd (16)

50%

40%

30%

20%

10%

0%

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

year

Figure 9. EV and PHEV Shares of new passenger car sales in the 3 groups between 2016 and 2030 (the number of cities in brackets)


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As Figure 10 shows, based on historical data, we can get shares of private and business car sales in the 3 groups between 2006 and 2015, while the forecast data between 2016 and 2030 can be obtained by extrapolation. It is clear that the 2nd and 3rd groups will contribute more in the growth of passenger and business car ownership in China in future.

100%

Shares of private and business car sales

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


90% 80% 70% 60% 50% 40% 30% 20%

1st (10) 2ed (6) 3rd (16)

10% 0% 2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 10. Shares of private and business car sales in the 3 groups (the number of cities in brackets) Based on the data above and the model in section “Modeling for ownership and sales of passenger car”, EV and PHEV sales between 2016 and 2030 can be calculated. According to the EV and PHEV sales in recent 5 years, EV accounts for more percentage than PHEV and is more recommended by the government. PHEV is more acceptable at present for the absence of charging pipes [40].



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We think that shares of EV will increase in the future with more supports of the government and more charging pipes built. So it is assumed that there will be 1% increase of EV shares per year from 66% in 2015 [41].

Gasoline consumption of passenger car sector Analysis of actual gasoline consumption rate of passenger car The average nominal gasoline consumption per hundred kilometers of passenger car decreases gradually since 2003, from 9.11 L/100 km before 2003 to 7.31 L/100 km in 2013. The data after 2013 refers to the policy that in 2020, the average nominal gasoline consumption per hundred kilometers should fall down to 5.0 L/100 km [42]. Data after 2020 can be forecasted based on different scenario settings. As a reference scenario, it is assumed that the data maintains at 5.0 L / 100 km from 2020 to 2030 [42]-[43]. In recent years, with the rapid increase of the number of passenger cars, traffic congestion appears frequently, which improves the average actual fuel consumption of passenger cars. Hence the actual gasoline consumption is generally higher than nominal gasoline consumption. Table 7 shows gasoline consumption ratio of actual to nominal for passenger car in different years [44]. Table 7. Average gasoline consumption ratio of actual to nominal for passenger car Year 2008 Actual/Nominal 1.12

2009 1.15

2010 1.17

2011 1.2

2012 1.24

2013 1.24

2014 1.27

It indicates that ratio of actual to nominal gasoline consumption increases every year. Considering cases in developed countries, the actual gasoline consumption will not increase infinitely, but turn to a relatively stable value. 24 ACS Paragon Plus Environment

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In this paper, we assume the nominal gasoline consumption per hundred kilometers is the lower limit of the actual consumption. 10% increment is hypothesized for the bad driving habits. We assume that the maximum ratio of actual to nominal gasoline consumption is 1.4. The relationship between the ratio of actual to nominal fuel consumption and ownership of passenger cars per thousand people is also established, which is shown in figure 11. According to the changing law, Gompertz is used to fitting the data, where x is the ownership of passenger car per thousand people:

Ratio ( x ) = 1.4e

Ratio of actual to nominal

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


( −0.321e

−0.0138 x

)

(17)

1.4

1.3

1.2

1.1

1.0 0

50

100

150

200

250

300

350

Passenger car ownership/1000 people

Figure 11. Relationship between average passenger car gasoline consumption ratio of actual to nominal and passenger car ownership per thousand people



Based on the formula below and the vehicle age distribution, we calculate the actual gasoline consumption per hundred kilometers in year i of passenger car produced in year j. actual _gasoline_consumption ( i, j ) = Ratio ( i ) × nominal_gasoline_consumption ( j ) , i, j = year, j ≤ i

11

Private & Business car Taxi

10

L/100km

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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9 8 7 6 2000

2005

2010

2015

2020

2025

2030

year

Figure 12. Average actual gasoline consumption rate of passenger car, between 2004 and 2030 Figure 12 shows the average actual gasoline consumption per hundred kilometers of the two types of passenger car from 2004 to 2013. The ratio maintains almost constant between 2004 and 2015, and drops from 2016. The decrease of taxi is faster than that of private and business passenger car because the vehicle age of taxi is much shorter. Analysis of average annual mileage of passenger car The average mileage per year is absence of nationwide statistics. The taxi average annual mileage data from 2002 to 2009 in research [33] is selected in our model. We assume the average annual mileage of taxi maintains at 100 thousand kilometers since


(18)

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2010. For private and business passenger cars, calculations can be conducted based on the data we get in previous sections. The result is shown in figure 13.

3.0

10 thousand km

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2.5 2.0 1.5 1.0 0.5 0.0 2004

2006

2008

2010

2012

2014

2016

year Figure 13. Average annual mileage of private and business passenger car, between 2004 and 2015 It indicates that the average annual mileage of private and business passenger car declines by year, from 29,000 km in 2004 to 10,000 in 2011 and maintains since then. We assume that it will maintain at the level in 2015, which is around 9,500 km in the next 15 years.

FORECAST RESULTS Forecast of fuel supply Based on the analysis above (section “Virtual refinery model”), the minimum and maximum production of gasoline and diesel and the flexibility between the two products can be obtained. Figure 14 shows optimization results of the productivity of crude oil processing products under the two optimal objectives. According to calculation results,



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production ratio between diesel and gasoline in China (hereafter called D/G ratio) can change in the range between 1.27 and 2.92. The yields of gasoline and diesel change from 25.5% and 32.4% respectively to 15.5% and 45.3% respectively when D/G ratio increases from minimum to maximum.

100% 90% 80% 70%

Liquefied petroleum gas Petroleum coke Heavy oil Chemical oil Kerosene Diesel Gasoline

60% 50% 40% 30% 20% 10% 0% D/G ratio min

D/G ratio max

Figure 14. Productivity of crude oil processing products under two optimal objectives Based on the adjusting range of D/G ratio we get, the gasoline supply limit of passenger vehicle sector which will meet the D/G ratio can be calculated:

Gasoline supply limit of passenger car sector = ! − " ! %ℎ$ ($%% ⁄" $%

(19)

Combined with the forecast data and calculation formula, upper limit of gasoline supply in China’s passenger car sector can be forecasted, as shown in table 8. Table 8. Upper limit of gasoline supply in passenger car sector to 2030


Page 29 of 44 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Maximum gasoline supply

Gasoline demand in other sectors (Forecast)[7,36]

Upper limit of gasoline supply in passenger car sector

17500

13780

1700

12080

2016

17763

13986

1670

12316

2017

18029

14196

1650

12546

2018

18299

14409

1630

12779

2019

18574

14625

1610

13015

2020

18852

14844

1590

13254

2021

19079

15023

1570

13453

2022

19308

15203

1550

13653

2023

19539

15385

1530

13855

2024

19774

15570

1510

14060

2025

20011

15757

1490

14267

2026

20211

15914

1470

14444

2027

20413

16073

1450

14623

2028

20617

16234

1430

14804

2029

20824

16397

1410

14987

2030

21032

16561

1390

15171

Diesel demand (Forecast) 2015

Forecast of ownership and sales of passenger car According to existing researches and extrapolation, we get the input data of the model. Based on our model and the input data, we can calculate passenger car ownership between 2016 and 2030. The result is shown in Figure 15.



50000

40000

10 thousand

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Taxi Business Private

30000

20000

10000

0 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

year

Figure 15. Passenger car ownership forecast in China between 2015 and 2030 A great increase in passenger car ownership in China can be seen in the next 15 years. Most of the increase is contributed by the fast growth in private passenger car. According to the analysis of the vehicle age distribution in this chapter, we can get passenger car sales in each year in the future. The results of calculation are shown in figure S10 to figure S12 in Supporting Information, and part of the results are shown in table 9. Table 9 Passenger car sale forecast in China between 2015 and 2030 (10 thousand) Year

2015

2020

2025

2030

Replacement

152.65

1310.43

2111.81

2720.04

New demand

2216.65

1238.76

1423.16

1407.94

Replacement

45.23

95.32

114.84

137.95

New demand

69.13

70.16

70.16

70.16

private passenger car

business passenger car


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taxi

16.85

17.00

23.41

20.43

Forecast for passenger car sales and ownership in different fuels To simplify the alternative fuel analysis, we merge the two types of passenger car into one category, as private and business passenger cars are difficult to forecast separately. The results in this part are divided into two categories, one is private and business sector shown in figure 16 and the other is taxi shown in figure 17.

5000

Ownership Sales PHEV EV CNG Gasoline

45000 40000 35000

4500 4000 3500

30000

3000

25000

2500

20000

2000

15000

1500

10000

1000

5000

500

Sales (10 thousand)

50000

Ownership (10 thousand)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0

0 2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 16. Ownership and sales of private and business passenger car in China based on fuels



Ownership Sales EV CNG Gasoline

25

200

20

150

15

100

10

50

5

Sales (10 thousand)

250

Ownership (10 thousand)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0

0 2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 17. Ownership and sales of taxi in China based on fuels

It can be seen that in private and business car sector, gasoline car still accounts for most percentage of the total in sales and ownership. In taxi sector, gasoline car decrease by year and will disappear in 2025. CNG car will be in dominant position in sales and ownership of taxi, while EV accounts for the rest percentage of the total in the future. The results in this part are the basis for further calculation in fuel consumption.

Forecast for gasoline consumption of passenger car sector Based on the assumption in previous parts, we can calculate the gasoline consumption of passenger car sector. Figure 18 shows the result of calculation. We can


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see that though the speed of growth slows down by year, the amount of increase is still very high.

18000 16000

10 thousand tonnes

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


14000 12000 10000 8000 6000 4000 2000 0 2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 18. Gasoline consumption in passenger car sector in China between 2016 and 2030

ANALYSIS AND DISCUSSION Comparison of gasoline supply and consumption in China Gasoline is mainly consumed in passenger car sector in China, while other sectors also accounts for small percent of the total, which cannot be neglected. According to existing researches [45], the data of gasoline consumption in other sectors can be obtained. Combined with gasoline consumption in passenger car sector (section



“Analysis of gasoline consumption of passenger car sector”), we can get the total consumption of gasoline in China in the future, which is shown in figure 19.

20000

Upper limit of gasoline supply Passenger car sector Other sectors

18000 16000

10 thousand tonnes

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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14000 12000 10000 8000 6000 4000 2000 0 2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 19. Gasoline consumption in China between 2016 and 2030 Based on the analysis in section “Forecast of fuel supply”, the maximal supply of gasoline from refining sector in China is shown as a line in figure 19. It can be concluded that the gasoline supply from refining sector cannot meet the gasoline consumption since 2019 and the gap between gasoline supply and consumption can reach to 20 million tonnes. Effective measures should be taken to reduce gasoline consumption in passenger car sector.


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Improving targets of reducing gasoline consumption Ownership of gasoline passenger car, average annual mileage and actual gasoline consumption rate are the three key factors to affect gasoline consumption in passenger car sector. Here only average annual mileage and actual gasoline consumption rate are considered in this section as impacts of other factors are negligible compared with them. For each of the two factors, a typical representative scenario is studied respectively. Based on studying the annual improvement rate previously via analyzing historical data, a fuel economy improvement scenario is designed to analyse its impact on the demand side. For private and business passenger car, the nominal gasoline consumption rate of new cars will fall to 4.0L/100km in 2025 and 3.0L/100km in 2030. The average actual fuel economy of private and business passenger car is shown in figure 20. We can see that the actual data does not decline too much as the nominal data of new cars due to ownership of “old cars”.

5.0L/100km between 2020 and 2030 improvement scenario

10.0 9.5 9.0

L/100km

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 20. Average actual fuel economy of private and business passenger car



In this fuel economy scenario, the result of gasoline consumption is shown in Figure 21. It can be seen that with the improvement of fuel economy between 2020 and 2030, there is a great decline of gasoline consumption between 2025 and 2030. Total gasoline consumption begins to decrease from 2025 in the fuel economy scenario. But due to the delayed effect, the gasoline consumption between 2020 and 2025 doesn’t change much compared to the reference scenario. The supply still can’t meet the consumption from 2019.

Upper limit of gasoline supply Passenger car sector Other sectors

18000 16000 14000

Passenger car sector

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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12000 10000 8000 6000 4000 2000 0 2016

2018

2020

2022

2024

2026

2028

2030

year

Figure 21. Gasoline consumption in China in fuel economy improvement scenario The average annual mileage changes little between 2011 and 2015 due to our results of calculation. Here we assume that the average annual mileage would fall from 9,500 kilometers to 9,000 km between 2019 and 2024, and maintain at 9,000 km since then. This is the case of reducing mileage scenario. 36 ACS Paragon Plus Environment

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In this scenario, the ownership and sales of gasoline passenger car are calculated to satisfy the upper limit of the passenger car gasoline supply, and the results are compared with the reference scenario. The gasoline consumption in this condition is shown in Figure 22 and the comparison of ownership and sales is shown in Figure 23.

reference scenario reducing mileage scenario upper limit of gasoline supply in passenger car sector

Gasoline consumption

18000 17000 16000 15000 14000 13000 12000 11000 10000 9000 8000 2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

year

Figure 22 Gasoline consumption in constrained condition

3500

40000

3000

35000

Ownership of passenger car

Sales of passenger car

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2500 2000 1500 1000

30000 25000 20000 15000 10000

reference scenario reducing mileage scenario

500

reference scenario reducing mileage scenario

5000 0

0 2014

2016

2018

2020

2022

2024

2026

2028

2030

2014

2032

2016

2018

2020

2022

2024

2026

2028

2030

2032

year

year

Figure 23 Ownership and sales of gasoline passenger car under the upper limit of the gasoline supply in reducing mileage scenario



It can be seen in figure 23 that in reducing mileage scenario, sales of gasoline passenger cars before 2020 remained at around 20 million if the upper limit of the gasoline supply has to be satisfied, and after 2020 the growth rate of the sales is consistent with the reference. Even after 2025 sales growing faster passenger car gasoline consumption will not over constraint value. In the two scenarios, the number of gasoline passenger cars have a gap of about 40 million in 2030, and after 2025 the gasoline passenger car sales growth rate will increase in reducing mileage scenario, the difference of gasoline passenger car ownership between two scenarios will gradually reduce. According to the above analysis, in order to satisfy the constraint value, 2015 to 2020 is the key period to control the growth of gasoline passenger car sales, keeping the sales at a level of around 20 million. It can be finished by promoting passenger cars in the new energy or increasing the number of cities which restrict the gasoline passenger car purchase. After 2020 the restriction of gasoline passenger cars sales can be relaxed. Then with the increase of passenger car ownership, the number of cities which implement tail number limit rule can be gradually increased to reduce the passenger average annual mileage. When the passenger average annual mileage decreases to 9,000 km in 2024, it will be not necessary to impose further restrictions on the mileage. After 2025 due to the actual average fuel consumption level of gasoline passenger cars has been further reduced, even if gasoline passenger car sales growth improved, the upper limit of the gasoline supply still can be satisfied. Therefore, after 2025, sales of gasoline passenger cars can be encouraged to make up for the demand for gasoline vehicles from 2015 to 2020.


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CONCLUSIONS In this paper, a virtual refinery model is established to analyze the productivity of gasoline and diesel. Based on the calculation of the model, minimum and maximum production of gasoline and diesel and the flexibility between them are obtained. Combined with the forecast data in literatures and calculation formula, upper limit of gasoline supply in China’s passenger car sector can be forecasted. We also study the ownership and sales of passenger car in China. A model for different types of passenger cars is built and many factors which affect the ownership are analyzed separately. Vehicle age distribution in passenger car is also taken into consideration. Sales in each year are developed based on the ownership and vehicle age analysis. The forecast of sales and ownership of passenger car between 2016 and 2030 is obtained as the basis of further study. In order to calculate accurate gasoline consumption of passenger car sector in China, alternative fuels and technologies are studied. Fuel ethanol, natural gas, EV and PHEV are analyzed in detail. Combined with the results in section “Modeling for ownership and sales of passenger car”, the forecast for sales and ownership of passenger car in different fuels is obtained and the conclusion is found that in private and business car sector, gasoline car still accounts for most percentage of the total in sales and ownership. In taxi sector, gasoline car decreases by year and will disappear in 2025. CNG car will be in dominant position in sales and ownership of taxi, EV accounts for the rest percentage of the total in the future. We analyze the actual gasoline consumption rate and the average annual mileage of passenger car. Then an assumption is hypothesized as a



reference scenario. The gasoline consumption in passenger car sector and other sectors in the future are calculated based on the assumption above. In the last part, we compare the consumption and supply in refining sector. The result indicates that the gasoline supply in refining sector cannot meet the gasoline consumption since 2019. Effective measures should be taken to reduce gasoline consumption in passenger car sector. The basis requirement is that the average annual mileage should fall to 9000 km in 2024 and maintain in the next years, and control the growth of gasoline passenger car sales before 2020. After 2020 the restriction of gasoline passenger cars sales can be relaxed. In this way, the gasoline supply from the refining sector in China can meet the gasoline consumption in the future.

ASSOCIATED CONTENT

Supporting Information Consistency of information on properties of domestic and imported crude oil, productivities of different processing types during secondary processing, sensitivity analysis of virtual refinery model and other related figures This material is available free of charge via the Internet at http://pubs.acs.org.

ACKNOWLEDGEMENT The authors gratefully acknowledge the support by The National Key Research and Development of China (2016YFE0102500), Shanxi Key Research and Development Program (201603D312001), National Natural Science Foundation of China (71690245), and the Phase Ⅲ Collaboration between BP and Tsinghua University.


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Abstract Graphic



Two models are designed to forecast the supply and demand of gasoline in China's transport sector in the future with comparison and analysis.


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Transport fuel supply and demand of the passenger car sector in

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