1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi: 10.1016/j.egypro.2017.03.760
Energy Procedia 105 ( 2017 ) 3326 – 3331
ScienceDirect
The 8
thInternational Conference on Applied Energy – ICAE2016
Impacts of emission reduction target and external costs on
provincial natural gas distribution in China
Xueqin Yang
a, Hailong Li
b*, Fredrik Wallin
b, Zhen Wang
aa
Academy of Chinese Energy Strategy, China University of Petroleum(Beijing), Beijing 102249, P. R. China
bSchool of Business, Society & Engineering, Malardalen University, Vasteras SE-72123, Sweden
Abstract
Natural gas is playing a more and more important role in emission reduction, and it is regarded as inevitable choice for the future energy consumption. In this study, a mathematical model was developed to identify an optimal solution for natural gas distribution in China. In line with previous research studies, the economic cost is the most important criterion that was considered. Additionally, the external cost of emissions was included as a second criterion. In order to satisfy the energy conservation and emission reduction target, the paper contributes with an optimization approach of the provincial distribution of natural gas with the aim to minimize the comprehensive costs. The problem was solved using Lingo software. An important contribution of the paper is that external costs was considered in the optimization of natural gas distribution from a provincial level perspective.
© 2016 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of ICAE
Keywords: natural gas; distribution optimization; external costs; emission reduction
Nomenclature
D demand IC investment cost
i province uti maximum utilization capacity spl supply n conversion factor
e emission factor E emissions
re CO
2 emission reduction target rE energy consumption reduction target
Ec external cost ecf external cost factor
* Corresponding author. Tel.: +46 0736620784. E-mail address: hailong.li@mdh.se.
© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
The pollution and climate change have become even more serious in the world. China’s energy consumption and pollutant emissions have dramatically increased as well. Geographic variations in carbon emissions have resulted in different degrees of environmental pollution across the country. For instance, China´s capital Beijing is suffering of seriously air pollutions, and many related health and life issues have been pointed out, which left the government faced with unprecedented challenge. In its 12th Five-Year Plan
[1], a compulsory target has been proposed, with a target for 17 percent reduction in carbon intensity from 2010 levels.
As a substitute for coal and oil, natural gas is considered as viable choice to reduce the above mentioned problem. Natural gas was only accounted for 4.9% of China’s energy consumption in 2012. The Chinese government anticipates to increase its natural gas share to around 10% by 2020 [2]. In order to develop the natural gas market and to promote natural gas use, the government proposed policies on the utilization of natural gas in 2012. These policies were formulated for the purpose of encouraging, guiding and regulating the field for utilizing natural gas. In this paper, we aim to optimize natural gas distribution from a provincial level perspective, which could achieve high valuable allocation of natural gas resources to contribute to emission pollution.
2. Methodology
The problem was solved through mathematical programming, with the aim of optimizing the national gas distribution system. Based on the decision variables, parameters and constraints, an objective function was developed and the optimal solution could be obtained.
2.1 Structure
In this study, the optimization model is decomposed to energy consumption system, economy system and carbon emission system, the detailed model introduction is given as follows.
Fig.1. Framework of optimization model 2.1.1 Economic system
From the perspective of economic system, total costs of energy consumption after optimization should be minimum. For each kind of energy source, economic cost could be expressed as:
݂ଵൌ ݅ܿ ൈ ݍ
here, stands for the amount of coal equivalents for each kind of energy source. The investment cost is used to represent the unit cost of energy consumption, so minimization of the energy consumption costs could be expressed as below:
F1(Q)ൌ ܯ݅݊ σୀଵσୀଵܫܥൈ ܳ
= ܯ݅݊ σୀଵሺܥܱܣܮ݊ܫܥ ܱܫܮ݊ܫܥ ܩܣܵ݊௦ܫܥ௦ሻ
(2)
2.1.2 Pollutant emission system
The pollutant emission cost is expressed as external costs. External costs of energy are related to environmental and social impacts, which could be categorized as below: health, agriculture, buildings, eco-system and climate change [3]. All these impacts are usually not reflected in the energy price, but if including external cost, it could contribute to least-cost solutions from a social-cost perspective.
External costs of energy have been widely discussed in literature. Qingyu Zhang [4] summarized the external cost factors of air pollutants in China. ExternE is the acronym for “External Costs of Energy” and a synonym for a series of projects starting from early 90s till 2005. All the results of the ExternE have been made available on the ExternE project webpage [3]. They adjusted the ExternE results to reflect the aggregation level of China, and climate change external cost was adopted the result of CO2 control costs.
The main pollutant emissions considered in this paper are CO2, SO2, NOx and PM10.
External costs vary from province to province because of differences in the cost of reducing emissions. According to the results of China, we could estimate the external cost factors for different provinces, population density and disposable income per capita were selected as adjustment factors. This method is adopted from Rong Zhang [5], when he estimated the external costs of Chinese freight transport. The initial estimate formula of external costs could be expressed as: external cost = unit cost × degree of harm × intensity × volume [6]. So, the external cost of coal could be expressed as:
ܧభ ൌ ܥܱܣܮ݊ሺ݁ଶ݂݁ܿைଶ ݁ௌைଶ݂݁ܿௌை௫ ݁ேை௫݂݁ܿேை௫ ݁ெଵ݂݁ܿெଵሻ
(3) Total external costs should be the sum of each kind of energy source (j):
F2(Q)=Min σଷୀܧܿ (4)
2.1.3 Energy consumption system
While optimizing distribution, the amount should satisfy some basic requirements as well: (1) total energy consumption should meet the minimum demand of energy:
ܥܱܣܮ݊ ܱܫܮ݊ ܩܣܵ݊௦ ܦ
(5) (2) regional natural gas distribution should be no less than the minimum amount (equals demand of the last year) and could not exceed the maximum utilization capacity [7]:
ܩܣܵ ܦ௦ (6) ܩܣܵ ܩܣܵ௨௧ (7)
(3) oil consumption in each region should be no more than its maximum supply:
ܱܫܮ ܱܫܮ௫ (8) (4) total consumption of oil and gas should be no more than national supply level:
σୀଵܩܣܵ ܩܣܵ௦ (9)
σୀଵܱܫܮ ܱܫܮ௦ (10) (5) the solution should satisfy the emission reduction and energy conservation target for each province:
ைೌೌమାைூమାீௌೌೞೌೞమ ீమబభయ ாమబభబమ ீమబభబכ ሺͳ െ ݎ ሻ (11) ைೌାைூାீௌೌೞ ீమబభయ ா ீమబభబכ ሺͳ െ ݎ ாሻ (12)
2.2 Multi-objective optimization model
When establishing the optimization model some assumptions are necessary:
(1) Because of the data availability, year 2013 is our target year instead of 2015. So the emission reduction target of 12th Five-Year Plan is adjusted to in 2013, which is assumed to be half of the CO2 emission reduction target in 2015.
(2) In the model, the petroleum quantity is fixed to be equal to the quantity in 2013, in this way, we could pay more attention on the change of the coal and gas.
(3) CO2 emission is assumed to only come from fossil fuel, in this paper, it includes coal, oil and natural
gas.
Therefore, the final objective would be minimization of the above costs.
Min F(Q)=F1(Q)+ F2(Q) (13) ܥܱܣܮ݊ ܱܫܮ݊ ܩܣܵ݊௦ ܦ ܩܣܵ ܦ௦ ܩܣܵ ܩܣܵ௨௧ ܱܫܮ ܱܫܮ௫ σୀଵܩܣܵ ܩܣܵ௦ σୀଵܱܫܮ ܱܫܮ௦ ைೌೌమାைூమାீௌೌೞೌೞమ ீమబభయ ாమబభబమ ீమబభబכ ሺͳ െ ݎ ሻ ைೌାைூାீௌೌೞ ீమబభయ ா ீమబభబכ ሺͳ െ ݎ ாሻ
3. Results and Discussions
3.1 Scenario comparison
Natural gas and coal consumption will be changed after optimization, which will lead to better results in terms of external costs and CO2 emissions. Compared with non-optimized scenario, external costs in
majority of regions decrease to some extent, and total external costs in China could decrease by 10%. The same as external costs, CO2 emissions will decrease in most of regions as well and it could decrease by 7%
in total. Table 2 shows it in detail. Taking Beijing as an example. External costs would decrease 14% and CO2 emission would decrease 2%. The reduction rate is not the highest one because of its large base and
special urban function.
If we use the current calculation model, external cost would account for 87% of the total cost, which has again proved that external cost is so significant to be considered in the cost system. In this way, the distribution model could be more reasonable and available.
Table 1. Comparison of external costs and CO2 emissions
Province
Non-optimized scenario Optimized scenario External costs (ten million RMB) CO2 emission (104 tons) External costs (ten million RMB) CO2 emission (104 tons) Beijing 9,61 10003 8,24 9781 Tianjin 10,97 15067 9,23 14498
Hebei 8,74 64570 6,55 56738 Shanxi 5,80 71764 4,05 59007 Inner Mongolia 0,63 69190 0,45 58841 Liaoning 6,40 47224 5,42 45557 Jilin 1,21 22911 1,07 23502 Heilongjiang 0,92 30844 0,76 29543 Shanghai 71,38 21842 52,93 18423 Jiangsu 26,75 62900 19,02 52606 Zhejiang 12,07 35730 9,55 32655 Anhui 4,92 33273 3,83 30203 Fujian 3,03 21308 2,45 19672 Jiangxi 1,49 16309 1,21 15319 Shandong 21,80 83146 19,59 86675 Henan 9,72 54122 8,34 54002 Hubei 3,38 29935 3,41 34536 Hunan 2,90 25792 2,69 27514 Guangdong 14,47 49376 12,07 46347 Guangxi 1,05 16712 0,86 15778 Hainan 0,31 3920 0,18 2953 Chongqing 1,83 14319 1,90 16944 Sichuan 1,60 31705 1,47 33084 Guizhou 1,40 27623 1,22 28218 Yunnan 0,74 21267 0,73 24283 Shannxi 2,18 37083 1,54 30650 Gansu 0,21 15218 0,16 13419 Qinghai 0,01 5364 0,01 3655 Ningxia 0,56 17130 0,36 13059 Xinjiang 0,13 32795 0,08 22177 Total 226,20 988443 179,35 919641
3.2 Regional natural gas distribution
Under optimized scenario, oil and gas consumptions are different from the actual consumption in 2013. Consumption changes could be shown clearly in figure 2 and figure 3.
As the above table shows, coal consumption will decrease in majority of provinces compared with the actual consumption in 2013, which confirms with the trend of emission reduction. It could also indicate that external cost is relatively lower in these provinces, so they could sacrifice a little bit to achieve countries’ reduction targets.
On the contrary, natural gas consumption of some provinces increase significantly. Especially in Beijing, Shanghai, Jiangsu and Hainan, it shows that natural gas has higher value in these provinces, because the cost to reduce emissions is relatively higher in these provinces. So, the distribution of natural gas would be inclined to these provinces
4. Conclusion and implications
We establish the natural gas distribution model in this paper, which is aimed to minimize the energy consumption cost and external cost. The objective is set under the constraints that CO2 emission reduction
target should be satisfied. Through the model, the following results could be obtained:
(1) Natural gas consumption in some provinces increase significantly after optimization, it indicates that natural gas has higher value in these regions. On the contrary, for those regions where coal consumption increase indicates that they have to sacrifice to achieve national level goal, due to their relatively low costs. (2) The optimized scenario could lead that external costs and CO2 emissions decrease in majority of
regions. Total external costs in China decrease by 10%, CO2 emissions will decrease by 7% in total.
(3) Based on the criteria of comprehensive costs and emission reduction target, some provinces could be picked out as key regions, and they should be paid more attention to take the lead to carry out emission decreasing measures in the future.
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