The competitiveness of provincial electric power supply in China: Based on a bottom-up perspective

Electrical Power and Energy Systems 116 (2020) 105557

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The competitiveness of provincial electric power supply in China: Based on a bottom-up perspective

T

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Haitao Lei, Xilong Yao , Jin Zhang College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Provincial electric power supply Competitiveness Carbon emissions TIMES model

There is a serious problem of overcapacity in the electric power industry which is harmful to its sustainable development. To seek a rational industry planning and reduce the harm from overcapacity, we investigate the competitiveness of provincial electric power supply in overall and different types of electric power industry by constructing a TIMES model that considers the differences of provincial technology and resource endowment in the BAU (the Business as Usual) and low carbon (LC1, LC2) scenarios. The results show that Xinjiang and Jiangxi have strong competitiveness, while Beijing, Tianjin and Jilin have weak competitiveness in terms of overall electric power industry in BAU scenario. Thermal power supply will decrease by 8.65% and 13.73% in 2030 in LC1 and LC2 scenarios respectively. Anhui and Shanxi have strong competitiveness, while Beijing and Tianjin have weak competitiveness in term of thermal power supply. In 2030, renewable energy power supply will increase by 1.95% and 3.09% in LC1 and LC2 scenarios respectively. Inner Mongolia and Hubei have strong competitiveness, while Tianjin and Jilin have weak competitiveness in terms of renewable energy power supply.

1. Introduction China today has the world's largest electric power supply system, accounting for 25.4% of the world's electricity generation in 2017 [1]. However, a lot of problems have arisen during the rapid development of electric power industry recently. On the one hand, there is a serious problem of overcapacity in the electric power industry due to unreasonable investment without the consideration of provincial heterogeneity [2]. On the other hand, China's electric power industry is facing the pressure of achieving low carbon target, as its CO2 emissions account for about 11.1% of the world's total emissions [3]. In order to seek an optimal solution of energy, economy and environment, it is therefore necessary to study the competitiveness of provincial electric power supply in overall and different types of electric power industry under carbon emission constraints, which can provide a reference for rational planning of regional distribution of electric power supply. Existing studies mainly focus on evaluating the competitiveness of a single power generation technology like ocean thermal energy conversion technology or offshore wind power generation [4–7], and identify its advantages over other technologies. Despite their role in providing references for technology upgrading in electric power supply system, these studies, however, cannot give an optimal allocation of different electric power technologies when realizing the best

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performance of electric power supply system. Another strand of literature evaluates the competitiveness of different types of electric power supply to propose a development order of different electric power technologies for optimizing energy systems [8–10]. These studies use simple and intuitive methods such as AHP and ANP in the decision-making process, which can be easily understood and applied by decision makers and investors. Nevertheless, there are some obvious disadvantages in these methods. For one thing, it is easy to cause different evaluation results due to different opinions and supplementary information when comparative scales are developed by industry experts and academic institutions [10]. For another, it is also difficult to describe in detail the paths of electric power technologies in different regions because of their complexity and diversity. This paper aims to investigate the competitiveness of provincial electric power supply in overall and different types of electric power industry by constructing a TIMES (The Integrated MARKAL-EFOM System) model that considers total national demand for electricity, provincial differences in resource endowments and different scenarios, under the goal of minimizing the total cost of the national power supply system. We contribute to existing literature in three aspects. First, the constraints of provincial resource endowment and electric power technology in the TIMES model are set up to take into account their provincial heterogeneity. This can provide a more detailed technical

Corresponding author. E-mail address: [email protected] (X. Yao).

https://doi.org/10.1016/j.ijepes.2019.105557 Received 10 July 2019; Received in revised form 9 September 2019; Accepted 17 September 2019 0142-0615/ © 2019 Elsevier Ltd. All rights reserved.

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disadvantages in the above methods. Because it is easy to cause differences in expert evaluation due to different opinions and supplementary information. Meanwhile, it is also difficult to describe the paths of electric power technologies in different regions because of their complexity and diversity. On the other hand, some methods are commonly used to study the related issues of the competitiveness of power supply. Some analyze the driving factors of the competitiveness of power supply through econometric and system dynamic models [7,18–20]. These methods can be used to assess strategic projects in a short given time. Yet, some details are likely to be ignored, which results in inaccuracy [20]. Others attempt to conduct a comprehensive assessment of power supply competitiveness from macroeconomic perspective by top-down models [21–23]. This method can explain the mechanisms and impacts of the policy based on economic laws as well as describe the interaction mechanism of the coordination of energy and economic system [24]. However, it is difficult to balance the separation of different electric power technologies and regional differences because of the heterogeneities in provincial resource endowment and electric power technology. Fortunately, the bottom-up energy technology model can describe in detail the complex energy sectors in different regions from technical perspective to analyze the reasons why policies can affect technologies [5]. The TIMES model, as a representative of the bottom-up models, is widely used in the planning and analysis of energy systems at home and abroad. It was first used by Chen et al. [11,25–32] to study China's energy problems, which has brought a wide range of practical impact. The TIMES model is often used to discuss low carbon problems in the electric power sector. As a supplement to existing literature, this paper studies the competitiveness of provincial electric power supply in overall and different types of electric power industry by building a TIMES model that considers provincial differences in resource endowments and technologies. The electric power resources are optimized from three aspects of energy, economy and environment, which can provide a new perspective for the integration of China's electric power resources. The details are shown in Tables 1 and 2.

path for future development of electric power industry. Second, the Business as Usual (BAU) and low carbon scenarios are set to compare the optimum levels of provincial electric power supply to provide a reference for the policy formulation of electric power sector. More specifically, the BAU scenario is set based on Huang et al. [11], while the low carbon scenario are not confined to the simulation of existing national policies. Two representative low carbon scenarios are selected for analysis by observing the changes of model results (carbon emissions are reduced by 10% and 15% respectively compared with the BAU scenario). Third, the economic, technical and environmental performances of power generation technology are taken as three indicators of competitiveness evaluation. Existing literature focuses on the single economic, technical or environmental performance of the competitiveness of electric power technology, which is likely to overlook the impact of the comprehensive impacts of them. TIMES model, as a “3E” collaborative model, can take into account the economic, technical and environmental performances of various technologies, and optimize power supply allocation with the goal of minimizing the total cost of power energy system, so as to meet the national electricity demand. We use the optimized provincial electric power supply under different power technology to analyze the competitiveness of provincial electric power industry under the constraint of these three indicators. The rest of the paper is structured as follows: Section 2 describes literature review; Section 3 covers methodology that describes the framework of China's TIMES power sector model and illustrates scenario setting; Section 4 provides the results and discussions; Section 5 draws main conclusions and provides some policy implications. 2. Literature review Currently, a number of literature focuses on studying the competitiveness of electric power industry from the economic and technical level. For example, Styles and Jones [12] studied the current and future economic competitiveness of energy crop for power generation in Ireland. Naqvi et al. [13] discussed the economic feasibility of using mixed biomass compost to generate electricity from waste gasification under different scenarios by considering four factors: electricity demand and utilization level, costs of variable biomass mix, a combined business model of state and home handicraft industry and government investment. Garðarsdóttir et al. [14] studied of the impact of increased flexibility of coal-fired generating units on the cost-optimal electric power system. Nicholson et al. [15] investigated how carbon pricing can change the relative competitiveness of low carbon based power generation technologies. deLlano-Paz et al. [16] explored the environmental and social effects of renewable energy under low carbon structure in Europe in 2030, and further assessed the environmental benefits of electric power technology. However, the above literature focuses on the single electric power technology, and a comprehensive evaluation of power technology competitiveness is likely to be neglected. Thus, some scholars attempt to comprehensively evaluate the competitiveness of single power generation technology in global (national) power supply system from the perspectives of economy, technical performance and environment [4–7]. However, the optimal allocation of different types of electric power technologies in power supply system is rarely involved. Only a few evaluate the competitiveness of different types of electric power technologies by giving their development order. For instance, Zangeneh et al. [10] evaluated the economic, technical and environmental attributes of distributed power generation technologies in Iran, and obtained a priority order of power generation schemes by AHP method. But there has a limitation of the AHP method, which is likely to ignore the feedback and interdependence between standards [17]. To make up this limitation, the ANP method has been employed by Kabak and Dağdeviren [9] evaluated different renewable energy sources in Turkey. Moreover, Katal and Fazelpour [8] adopted VIKOR method to evaluate different types of electricity in Iran. However, there are some

3. Methodology TIMES model, as a bottom-up 3E (energy, economy and environment) collaboration model, can reflect the comprehensive impacts of economic, technical and environmental performance on the competitiveness of electric power technology. Existing literature focuses on the power technology competitiveness from the perspective of economy, technology and environment, respectively. For example, some studies analyze the economic competitiveness through the comparison of investment, fixed and variable costs [12,13]. Some study technological competitiveness by comparing some technical parameters [33]. Others study the environmental competitiveness through comparing the carbon emission reduction technologies [18]. These studies are likely to overlook the comprehensive impact of economic, technical and environmental performance on the competitiveness of power technology. The TIMES model synthesizes the advantages of MARKAL model that is good at optimization algorithm and EFOM model that focuses on energy flow optimization [34]. It can run through the whole process from primary energy production to terminal power demand by incorporating economic, technical and environmental parameters of various technologies (shown in Table 3). This allows optimizing power supply allocation with the aim of minimizing the total cost of power energy system under the constraints of regional resources, power demand and carbon emissions [31]. In this paper, the TIMES model of China's electric power sector considering the differences of resource endowment and power technology level between different regions is constructed. Moreover, the optimized power supply under different power technology is taken as a comprehensive index under the influence of economic, technological and environmental performances, so as to 2

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Table 1 Other authors contribution to this theme (1). Author

single power generation technology

Fthenakis et al. [4] Takeshita [5] Timilsina et al. [6] Nagababu et al. [7] Katal and Fazelpour [8] Kabak and Dağdeviren [9] Zangeneh et al. [10] Styles and Jones [12] Naqvi et al. [13] Garðarsdóttir et al. [14] Nicholson et al. [15] deLlano-Paz et al. [16] Wang and Li [18] Wen and Yan [19] Skribans and Balodis [20] Li et al. [21] Meng et al. [22] Zhang et al. [23] This paper

✓ ✓ ✓ ✓

Optimized configuration of multiple power generation technologies

✓ ✓ ✓ ✓ ✓ ✓

Economic performance

Technical performance

Environmental performance

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓

✓ ✓

✓

✓ ✓ ✓ ✓ ✓

✓

✓

analyze the competitiveness of provincial power industry. Note that the competitiveness of electric power supply of different provinces derived in this paper refers to the potential when the optimal balance of energy, economy and environment can be achieved based on the national electricity market for power supply competition. Thus, the competitive potential of an electric power technology may be fundamentally different from the actual situation. This indicates that corresponding measures must be implemented in order to overcome the pending obstacles in a timely manner. However, some provinces may not be able to achieve the cumulative potential due to the total cost of the energy system or the constraints of the overall power system. We introduce the construction of TIMES model of China's electric power sector in Sections 3.1 and 3.2 based on the report of Mishra et al. [35].

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓

Table 3 Economic, technical, and environmental parameters of the TIMES model input. Economic parameters

Technical parameters

Environment

Discount rate Technological discount rate

Existing Stock/Capacity Commodity Input(s)/Output(s) Efficiency or Consumption Fuel share for multi-fuel technologies Technical life or Retirement profile Maximum availability Starting year for new technologies Construction time Market share / Fuel share

Emission coefficients Environmental targets

Investment cost Fixed and Variable O&M costs Commodity price (energy, material and emissions) Economical life

3.1. The TIMES model of China's electric power sector 3.1.1. Hypothesis

and logistics (including their price). This equilibrium covers the stages from primary energy production to terminal power demand. The supply-demand equilibrium mode has the economic logic of

(1) TIMES model can calculate partial equilibrium through energy market, that is, the model can simultaneously calculate energy flow Table 2 Other authors contribution to this theme (2). Author

Fthenakis et al. [4] Takeshita [5] Timilsina et al. [6] Nagababu et al. [7] Katal and Fazelpour [8] Kabak and Dağdeviren [9] Zangeneh et al. [10] Styles and Jones [12] Naqvi et al. [13] Garðarsdóttir et al. [14] Nicholson et al. [15] deLlano-Paz et al. [16] Wang and Li [18] Wen and Yan [19] Skribans and Balodis [20] Li et al. [21] Meng et al. [22] Zhang et al. [23] This paper

Regional differences of resource endowment

Regional differences in technology levels

✓ ✓ ✓

✓ ✓ ✓

SD, econometric model, AHP, ANP et al.

top-down models

bottom-up models

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓

✓ ✓ ✓ ✓ ✓ ✓

✓ ✓

✓ ✓ ✓ ✓

✓

✓

3

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3.1.2. Model As shown in Fig. 1, the TIMES model of China's electric power sector is divided into three modules, namely, power energy system module, power demand module and carbon emission module. The objective of the model is to minimize the total cost of the power system (as shown in Formula (1):

maximizing the surplus (i.e., minimizing the total net cost of the system), and it has the following attributes: (a) Technology output is a linear function of its input. The equation of TIMES model is linear, but the product function of TIMES model is highly nonlinear, thus the non-linear function can be expressed in the form of linear function arranged in sequence. In the TIMES model, the transverse distance of the countersupply function represents that goods are produced strictly in a linear form by a certain technology. As production increases, the mix of one or more resources is exhausted, so the system must enable a different and more expensive technology or technology combination to produce additional commodity units, although the unit consumption is higher. Therefore, the increment of staircase function produced by each change of product mix is higher than that of time length. The width of any particular length depends on the potential of the technology or the availability of resources in the alternative technology portfolio. This further verifies the comprehensiveness of index selection in this paper. (b) We attempt to achieve total economic surplus by maximizing the whole term. (c) Energy market is completely competitive. The model is set closer to the real market by setting emission constraints, subsidies and other settings. (d) The market price of each commodity is equal to the marginal price of the whole system. Note that marginal value pricing does not simply mean profit zero of the supplier, but means the profit should coincide with the supplier's surplus. (e) Each stakeholder maximizes its own interests or utility. (2) The model is based on a period of five years, with 2015 as the base year and 2015–2030 as the planning period. (3) The cost of provincial power allocation is neglected for research needs.

y = years

NPV = Min ∑ Xi

∑

(1 + d y )2015 − y × ANNCOST (y ) (1)

y = 2015

ANNCOST (y ) = Inv cos t (y ) + Fix cos t (y ) + Var cos t (y ) − Salvage (y ) (2) where NPV denotes the net present value of total cost of electric energy system (target function of TIMES model of China’s electric power sector); Xi denotes the energy flow from primary energy products to terminal power demand that to be solved, that is the optimized power supply solution; y denotes the year; d denotes discount rate; ANNCOST (y) denotes the total cost in year y ; Inv cos t , Fix cos t and Var cos t denote the technology investment cost, operation and maintenance cost and variable cost related to power energy respectively; Salvage denotes residual value of relevant technologies for electric energy in elimination. The power energy system module is the most important part of the TIMES model, in which the difference of provincial resource constraints are considered. More specifically, the national power sector will be decomposed into the provincial level to describe the power generation capacity of thermal power, hydropower, nuclear power, wind power and solar power in 31 provinces, municipalities and autonomous regions of China (except Hong Kong, Macao and Taiwan). The constraint equations are as follows: Provincial resource endowment constraints (i.e. primary energy supply is not greater than resource inflow) (3)

Xi = 1 ⩽ SUP

Balance of energy carriers in each technological link (i.e. energy conversion in each link should be equal to consumption in the next link Power demand module

GDP

Urbanization rate

Economic structure

Industrial electricity efficiency

Power energy system module

Carbon emission module

Carbon emission path

Emission constraints

The total cost of the power system

Energy extraction technology Resource constraints

Primary energy supply

Non-fossil energy use

Energy processing and conversion Processing and conversion technology

Thermal power

Beijing, Tianjin, Shanxi, Jilin, etc

Hydropower

Nuclear power

Wind power

Solar power generation Fig. 1. TIMES model system of China's electric power sector. 4

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representative low carbon scenarios: one is 10% less than BAU scenario (LC1), the other is 15% less than BAU scenario (LC2). Total carbon emission constraints (i.e., the sum of carbon emissions from all sectors should not exceed a certain total emission limit)

Table 4 Regional distribution. Region

Provincial-level region

Eastern

Liaoning (LN), Beijing (BJ), Tianjin (TJ), Hebei (HEB), Shandong (SD), Jiangsu (JS), Shanghai (SH), Zhejiang (ZJ), Fujian (FJ), Guangdong (GD), Hainan (HAN) Heilongjiang (HLJ), Jilin (JL), Shanxi (SAX), Henan (HEN), Hubei (HUB), Anhui (AH), Hunan (HUN), Jiangxi (JX) Inner Mongolia (IM),Xinjiang (XJ), Qinghai (QH), Gansu(GS), Ningxia (NX), Shaanxi (SX), Sichuan (SC), Chongqing (CQ), Yunnan (YN), Guizhou (GZ), Guangxi (GX), Tibet (TB)

Central Western

ENVi, y = b Xi, y = b ⩽ ENV (y = b)

3.3. Data The data used are mainly from field research, industry report, government planning report (the 13th Five-Year Plan for Power Development, and Renewable Energy Development, etc.) and related literature [38–42]. The following data sources are also used: China Environmental Statistics Yearbook, China Power Yearbook, China Energy Statistics Yearbook and data from Professional Knowledge Service System for Energy. Table 4 shows the regional distribution that is divided based on Wen and Yan [19]. Tibet is divided into the western region.

at least)

Fi Xi − Xi + 1 ⩾ 0

(4)

Capacity, production and operation limitation of power technology (i.e. energy production should not exceed capacity or production and operation limitation of power technology)

Fi Xi ⩽ CAPi

(5)

where Xi = 1 is the primary energy supply; SUP is the provincial energy resources vector; Fi is the energy conversion efficiency matrix from primary energy products to terminal power demand; CAPi is a process capacity vector of relevant power technology. In power demand module, basing on the findings of Liu [36], we build a co-integration model to forecast the national electricity demand by considering the growth rate of GDP, the change of urbanization rate, economic structure and industrial electricity efficiency. In this paper, the national electricity demand is taken as a constraint in the TIMES model (as shown in Formula (6):

Fi = e Xi = e, y ⩾ DEM (y )

(7)

where ENVi, y = b represents carbon emissions from all stages in year b ; b represents the year 2020, 2025 and 2030; ENV (y = b) denotes the total control level of carbon emissions in the year b .

4. Results and discussion 4.1. The competitiveness of provincial electric power supply of overall power industry Tables 5–7 show the competitiveness of provincial electric power supply of overall power industry under BAU, LC1 and LC2 scenarios, respectively. In BAU scenario, the top five provinces in terms of the competitiveness of electric power supply in 2020 will be Jiangxi, Xinjiang, Shandong, Jiangsu, and Anhui, with Jiangxi, Hebei, Anhui, Xinjiang, and Shandong in 2025, and Jiangxi, Hubei, Inner Mongolia, Anhui, and Guangdong in 2030. The competitiveness of electric power supply of Beijing, Tianjin and Jilin will be at a disadvantage from 2020 to 2030. In LC1 scenario, the top four provinces in terms of the competitiveness of electric power supply in 2020 will be Jiangxi, Xinjiang, Anhui, and Shandong. Hubei will surpass Jiangsu and rank fifth due to its abundant hydropower resources. The top five provinces in terms of the competitiveness of electric power supply in 2025 will be Jiangxi, Anhui, Hubei, Hebei, and Jiangsu. Hubei will continue to take an advantage in the competitiveness of electric power supply by virtue of its abundant hydropower resources. Although Jiangsu will be not as good as Hubei, it will be better than Shandong. The top five provinces in terms of the competitiveness of electric power supply in 2030 will be Jiangxi, Inner Mongolia, Anhui, Hubei, and Guangdong. The competitiveness of electric power supply of Beijing, Tianjin and Jilin will be at a disadvantage from 2020 to 2025. In 2030, Tianjin, Jilin and Ningxia will have the weakest competitiveness in electric power supply. This is because the thermal power supply in Ningxia will be limited under the constraint of carbon emissions. Regarding to LC2 scenario, the top five provinces in terms of the competitiveness of electric power supply will be Jiangxi, Shandong, Xinjiang, Jiangsu, and Anhui. The development of renewable energy power in these provinces will be further enhanced with the strengthening of carbon emission constraints. The top five provinces in terms of the competitiveness of electric power supply will be Jiangxi, Hebei, Jiangsu, Anhui, and Hubei. Jiangsu and Hubei will surpass Xinjiang and Shandong by virtue of their abundant hydropower resources. In 2030, the top five provinces in terms of the competitiveness of electric power supply will be Inner Mongolia, Jiangxi, Anhui, Hubei, and Henan. The competitiveness of electric power supply of Beijing, Tianjin and Jilin will be at a disadvantage from 2020 to 2025. In 2030, the competitiveness of electric power supply of Tianjin, Jilin and Ningxia will be at a disadvantage.

(6)

where Fi = e represents the energy conversion efficiency matrix of enduse energy system; Xi = e, y denotes the energy flow of end-use energy system; DEM (y ) represents the terminal power demand vector. Section 3.2.2 presents the detailed description of scenario setting of carbon emission module. 3.2. Scenario setting 3.2.1. BAU scenario We set the BAU scenario mainly based on the 13th Five-Year Plan for renewable energy development, power development, and wind power development [11], with 2015 as the base year. Both power generation index and economic index of renewable energy are set. For example, total renewable energy generation will account for at least 27% of total electricity generation by 2020, where wind power will account for at least 6% of the total annual power generation. The price of wind power projects can compete with that of local coal-fired power plants by 2020, and the price of photovoltaic project can be equivalent to that of power grid. 3.2.2. Low carbon scenario To study the impact of carbon emission reduction in the power sector on the competitiveness of different types of electric power supply in the national electricity market, we employ the peak scenario method that is developed by Ma and Chen [37]. A series of scenarios were run based on different percentage of carbon emission reduction, and two representative low carbon scenarios were selected. More specifically, a series of models were run under the constraints of reducing carbon emissions by a certain percentage in 2020 and 2030 (the percentage of carbon emission reduction is accumulated from low to high). We found that the optimization results of the model are not significant when the reduction of carbon emissions is less than 10%, and electric power supply cannot meet electric power demand when the reduction of carbon emissions is more than 15%. Therefore, we selected two 5

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Table 5 The competitiveness of provincial electric power supply of overall power industry from 2020 to 2030 under BAU Scenario. Rank

BAU2020

Proportion of power supply

BAU2025

Proportion of power supply

BAU2030

Proportion of power supply

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

JX XJ SD JS AH HUB IM GD HEB HEN SAX QH SX GX GZ SC FJ NX HLJ SH HUN GS CQ TB LN HAN YN ZJ JL TJ BJ

14.46% 11.59% 8.46% 8.28% 7.20% 7.01% 6.80% 5.09% 3.97% 3.06% 2.91% 2.62% 2.57% 2.50% 2.06% 2.00% 1.95% 1.94% 1.35% 1.02% 0.89% 0.89% 0.57% 0.39% 0.32% 0.10% 0.00% 0.00% 0.00% 0.00% 0.00%

JX HEB AH XJ SD JS HUB IM GD SH HUN HEN SAX SC QH SX GX ZJ YN GZ FJ NX HLJ GS CQ TB LN HAN JL TJ BJ

14.29% 8.21% 8.16% 7.46% 7.14% 6.98% 5.91% 5.67% 4.30% 2.95% 2.84% 2.58% 2.46% 2.36% 2.21% 2.16% 2.11% 2.09% 2.08% 1.74% 1.65% 1.63% 1.14% 0.75% 0.48% 0.33% 0.27% 0.08% 0.00% 0.00% 0.00%

JX HUB IM AH GD ZJ HEN SAX XJ SX HEB CQ JS LN SD SH HUN SC QH GX YN GZ FJ HLJ TB GS HAN NX JL TJ BJ

10.52% 8.14% 6.68% 6.59% 6.10% 5.51% 5.46% 5.34% 5.24% 4.61% 4.48% 3.76% 3.66% 3.57% 3.35% 2.38% 2.29% 1.90% 1.78% 1.70% 1.68% 1.40% 1.33% 0.92% 0.86% 0.61% 0.14% 0.00% 0.00% 0.00% 0.00%

Table 6 The competitiveness of provincial electric power supply of overall power industry from 2020 to 2030 under LC1 Scenario.

Table 7 The competitiveness of provincial electric power supply of overall power industry from 2020 to 2030 under LC2 Scenario.

Rank

LC12020

Proportion of power supply

LC12025

Proportion of power supply

LC12030

Proportion of power supply

Rank

LC22020

Proportion of power supply

LC22025

Proportion of power supply

LC22030

Proportion of power supply

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

JX XJ AH SD HUB JS IM GD HEB HEN SAX SH QH GX SC SX GZ FJ YN NX HLJ ZJ HUN GS CQ TB LN HAN BJ TJ JL

14.90% 8.98% 7.42% 7.27% 7.22% 7.19% 5.81% 5.25% 3.41% 3.16% 3.00% 2.75% 2.70% 2.58% 2.57% 2.39% 2.12% 2.01% 1.69% 1.66% 1.39% 1.29% 0.92% 0.92% 0.58% 0.40% 0.33% 0.10% 0.00% 0.00% 0.00%

JX AH HUB HEB JS XJ SD IM GD HUN HEN CQ SAX SH QH SC GX SX GZ FJ ZJ YN NX HLJ GS TB LN HAN BJ TJ JL

14.29% 8.16% 8.00% 7.99% 7.73% 7.35% 5.95% 4.87% 4.30% 2.84% 2.58% 2.57% 2.46% 2.25% 2.21% 2.15% 2.11% 1.95% 1.74% 1.65% 1.56% 1.38% 1.36% 1.14% 0.75% 0.33% 0.27% 0.08% 0.00% 0.00% 0.00%

JX IM AH HUB GD HEN SAX SX GZ HEB LN XJ SD BJ HUN JS CQ ZJ SH QH SC GX FJ YN HLJ TB GS HAN TJ JL NX

10.72% 10.10% 6.72% 6.58% 6.22% 5.56% 5.44% 5.04% 4.86% 4.28% 3.64% 3.43% 3.41% 3.13% 2.34% 2.21% 2.11% 2.03% 1.85% 1.82% 1.77% 1.74% 1.35% 1.14% 0.94% 0.88% 0.62% 0.07% 0.00% 0.00% 0.00%

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

JX SD XJ JS AH HUB GD IM HEB HEN SH QH SAX SC HUN GX SX FJ GZ ZJ YN NX HLJ GS CQ TB LN HAN BJ TJ JL

15.41% 9.13% 8.44% 7.19% 7.12% 7.06% 4.81% 4.36% 3.41% 2.79% 2.75% 2.68% 2.65% 2.54% 2.51% 2.49% 2.20% 2.01% 1.96% 1.70% 1.69% 1.66% 1.27% 0.81% 0.51% 0.40% 0.33% 0.10% 0.00% 0.00% 0.00%

JX HEB JS AH HUB XJ GD SAX SX IM SD CQ LN HEN SH QH SC HUN GX FJ GZ ZJ YN NX HLJ GS TB HAN BJ TJ JL

13.76% 7.99% 7.96% 7.91% 7.87% 6.91% 5.01% 4.24% 3.89% 3.69% 3.52% 2.51% 2.35% 2.28% 2.25% 2.19% 2.08% 2.05% 2.04% 1.65% 1.61% 1.39% 1.38% 1.36% 1.04% 0.66% 0.33% 0.08% 0.00% 0.00% 0.00%

IM JX AH HUB HEN QH HLJ GD GZ HEB SAX BJ XJ SD SX CQ JS LN ZJ SH SC HUN GX FJ YN TB GS HAN TJ JL NX

10.91% 10.46% 6.52% 6.48% 5.31% 5.24% 5.20% 5.04% 4.75% 4.28% 3.49% 3.44% 3.43% 3.41% 3.20% 2.07% 2.02% 1.93% 1.89% 1.85% 1.71% 1.69% 1.68% 1.35% 1.14% 0.88% 0.54% 0.07% 0.00% 0.00% 0.00%

Note: keep two decimal places.

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gradually increase from 2020 to 2025, with the growth rate being 0.12% and 0.11%, respectively. Conversely, the thermal power supply in these two scenarios will decrease by 53% and 52% from 2025 to 2030, respectively. In LC2 scenario, although the national electricity demand will increase from 2020 to 2030, the thermal power supply will continue to decline, with its supply in 2025 being 9.89% lower than that in 2020, and its supply in 2030 being 47% lower than 2025. Overall, the proportion of thermal power supply to total power supply will decline from 2020 to 2030 in all scenarios. In BAU scenario, the top five provinces in terms of the competitiveness of thermal power supply in 2020 will be Shandong, Jiangsu, Inner Mongolia, Hebei and Xinjiang, which will be the same in 2025. In 2030, the top five provinces in terms of the competitiveness of thermal power supply will be Jiangsu, Guangdong, Henan, Shanxi, and Anhui. The competitiveness of thermal power supply of Beijing, Tianjin and Shanghai will be at a disadvantage from 2020 to 2030 due to the limitation of resource endowment. The details are shown in Fig. 2. With regard to LC1 scenario, the top five provinces in terms of the competitiveness of thermal power supply will be Shandong, Jiangsu, Inner Mongolia, Xinjiang and Guangdong from 2020 to 2025. In 2030, the top five provinces in terms of the competitiveness of thermal power supply will be Guangdong, Henan, Shanxi, Anhui, and Inner Mongolia. Inner Mongolia, relying on its resource advantages, will surpass Jiangsu and take the leading position. Like the case in BAU scenario, the competitiveness of thermal power supply of Beijing, Tianjin, and Shanghai will be still at a disadvantage from 2020 to 2030. The details are shown in Fig. 3. In LC2 scenario, the top five provinces in terms of the competitiveness of thermal power supply in 2020 will be Shandong, Jiangsu, Inner Mongolia, Hebei and Xinjiang. In 2025, the top five provinces will be Jiangsu, Shandong, Inner Mongolia, Hebei and Xinjiang. Although Jiangsu and Shandong have advantages in thermal power supply, Jiangsu will surpass Shandong in 2025 compared to 2020. The top five provinces in terms of the competitiveness of thermal power supply in 2030 will be Guangdong, Inner Mongolia, Henan, Shanxi, and Anhui. The competitiveness of thermal power supply of Beijing, Tianjin and Shanghai will be at a disadvantage during 2020–2030. The details are shown in Fig. 4.

15.32%

44.54%

14.19%

11.69%

7.06%

7.2%

(a) SD JS IM HEB XJ OTHER

7.65% 7.08%

5.84% 3.59% 3.53%

72.3%

(b) JS GD HEN SAX AH OTHER

4.3. The competitiveness of provincial renewable energy and nuclear energy power supply

8.72% 6.91%

The national solar power and wind power supply will show an increasing trend over year in different scenarios, and the wind power is far more competitive than solar power. The wind power supply will increase by 12.29, 8.01 and 6.01 times in BAU, LC1 and LC2 scenarios, respectively. There will be no significant change in hydropower and nuclear power supply in these three scenarios from 2020 to 2030. In terms of the proportion of different types of electric power to total power supply, the proportion of hydropower, nuclear power and solar power will be declining, while the proportion of wind power will be rising. In BAU scenario, the provinces with the strongest competitiveness of wind power supply will vary significantly during 2020–2030, indicating a rapid momentum in wind power development. The competitiveness of wind power supply in Guangxi, Fujian and Sichuan will be obviously superior to that in other provinces in 2020. The top five provinces in terms of the competitiveness of wind power supply in 2025 will be Hebei, Shanghai, Jiangxi, Zhejiang and Anhui. In 2030, the top five provinces in terms of the competitiveness of wind power supply will be Inner Mongolia, Hebei, Chongqing, Henan and Shaanxi. Provinces such as Beijing, Tianjin and Jilin will be at a disadvantage in terms of wind power supply from 2020 to 2030. The details are shown in Fig. 5. Regarding to LC1 scenario, the top five provinces in terms of the competitiveness of wind power supply will be Guangxi, Sichuan, Shanghai, Yunnan and Fujian. In 2025, the top five provinces in terms

5.89%

5.61%

4.77% 68.1%

(c) Fig. 2. Proportion of China's provincial thermal power supply in thermal power supply market under BAU scenario. (a) year 2020; (b) year 2025; (c) year 2030.

4.2. The competitiveness of provincial thermal power supply The national thermal power supply varies significantly in different scenarios. In BAU and LC1 scenarios, thermal power supply will

7

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SD JS IM HEB XJ OTHER

14.04%

13%

15.13%

41.29%

12.91%

47.31%

10.98%

12.28% 7.51% 7.15% (a)

7.96%

SD JS IM XJ GD OTHER

JS SD IM HEB XJ OTHER

14.03%

(a)

10.45%

15.28%

12.99%

9.77%

47.37%

49.98%

9.62% 10.97%

7.75%

7.5% 7.14%

(b)

GD HEN SAX AH IM OTHER

(b)

GD IM HEN SAX AH OTHER

15.13%

7.61%

14.13%

38.95%

41.29%

13.63%

12.91%

12.28%

7.96%

(c)

12.06%

10.45%

9.76%

(c)

11.47%

Fig. 3. Proportion of China's provincial thermal power supply in thermal power supply market under LC1 scenario. (a) year 2020; (b) year 2025; (c) year 2030.

Fig. 4. Proportion of China's provincial thermal power supply in thermal power supply market under LC2 scenario. (a) year 2020; (b) year 2025; (c) year 2030.

of the competitiveness of wind power supply will be Hebei, Chongqing, Jiangxi, Anhui and Hubei. Compared to BAU scenario, Hebei, Jiangxi and Anhui will still maintain an advantage in the competitiveness of wind power supply, while Shanghai and Zhejiang will be surpassed by Chongqing and Hubei. In 2030, the top five provinces in terms of the competitiveness of wind power supply will be Inner Mongolia, Hebei,

Henan, Shaanxi and Guizhou. Compared to BAU scenario, Guizhou's wind power competitiveness will surpass that of Chongqing, while Inner Mongolia, Hebei, Henan and Shaanxi will still maintain an advantages in the competitiveness of wind power supply. The competitiveness of wind power supply in Beijing, Tianjin, Jilin will be at a

8

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GX SC SH YN FJ OTHER

13.2%

14%

35%

12.7%

13.2%

44.42%

10.23% 13.1%

9.8%

12%

9.65%

(a)

HEB SH JX ZJ AH OTHER

(a)

HEB CQ JX AH HUB OTHER

22.65%

12.7%

20.24%

38.42% 47.26%

8.13%

9.73% 8.13%

9.73% 8.12%

9.73%

(b) IM HEB CQ HEN SX OTHER

8.12%

9.73%

(b)

IM HEB HEN SX GZ OTHER

11.74%

14.89%

8.21% 7.49%

6.19% 6.01%

6.18%

59.59%

61.5%

6.01%

6.18% 6%

(c) (c)

Fig. 6. Proportion of China's provincial wind power supply in wind power supply market under LC1 scenario. (a) year 2020; (b) year 2025; (c) year 2030.

Fig. 5. Proportion of China's provincial wind power supply in wind power supply market under BAU scenario. (a) year 2020; (b) year 2025; (c) year 2030.

of the competitiveness of wind power supply will be Hebei, Chongqing, Shaanxi, Hubei and Anhui. Compared to BAU scenario, Hebei and Anhui will still maintain an advantage in the competitiveness of wind power supply, while Chongqing, Shaanxi and Hubei will exceed Shanghai, Jiangxi and Zhejiang in the competitiveness of wind power supply. The top five provinces in terms of the competitiveness of wind

disadvantage. The competitiveness of Beijing's wind power supply will obviously enhance. The details are shown in Fig. 6. In LC2 scenario, the top five provinces in terms of the competitiveness of wind power supply in 2020 will be Guangxi, Sichuan, Shanghai, Zhejiang and Hunan. In 2025, the top five provinces in terms 9

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change in the provincial characteristics of the competitiveness of solar power, hydropower and nuclear power supply. In all scenarios, the competitiveness of solar power supply in Heilongjiang, Shanghai, Inner Mongolia, Sichuan, Tibet and Qinghai will obviously superior to that in other provinces from 2020 to 2030; the competitiveness of hydropower supply in Jiangsu, Anhui, Jiangxi, Hubei, Sichuan, Guizhou, Shaanxi, Qinghai and Tibet will obviously superior to that in other provinces from 2020 to 2030; Hubei will have the strongest competitiveness in hydropower supply; nuclear power is distributed in Liaoning, Zhejiang, Fujian, Guangdong, Guangxi and Hainan; Guangdong will have the strongest competitiveness in nuclear power supply from 2020 to 2025; in 2030, Zhejiang will surpass Guangdong and become the most competitive province in nuclear power supply.

13.84%

30.85%

13.84%

13.81%

5. Conclusions

13.83% HEB CQ SX HUB AH OTHER

This paper studies the competitiveness of provincial electric power supply in overall and different types of electric power industry by constructing a TIMES model that considers the differences of provincial technology and resource endowment, under the constraints of carbon emissions in the electric power sector. The main conclusions are as follows:

13.83%

(a)

16.22%

(1) For national electric power supply: Jiangxi, Anhui and Hubei have competitive advantages, while Beijing, Tianjin and Jilin have competitive disadvantages over 2020–2030 when the national power sector's carbon emissions fall by 10%; when the national power sector's carbon emissions fall by 15%, Jiangxi and Anhui have competitive advantages, while Tianjin and Jilin have competitive disadvantages over 2020–2030; here, the competitiveness of electric power supply in Hubei will significantly improve from 2025 to 2030 due to abundant hydropower resources, and the competitiveness of electric power supply in Beijing will obviously increase in 2030 due to the improvement of its competitiveness in renewable energy. (2) For thermal power supply: Inner Mongolia and Guangdong have competitive advantages, while Beijing, Tianjin Shanghai and Liaoning have competitive disadvantages over 2020–2030 when the national power sector's carbon emissions fall by 10%; the competitiveness of thermal power supply in Shandong and Jiangsu will decrease in 2030, while the competitive advantages of thermal power supply in Henan, Shanxi and Anhui will be in the leading position in 2030; when the national power sector's carbon emissions decline by 15%, Inner Mongolia has competitive advantages, while Beijing, Tianjin Shanghai and Liaoning have competitive disadvantages over 2020–2030; the competitiveness of thermal power supply in Shandong, Jiangsu and Hebei will decline, while the competitive advantages of thermal power supply in Henan, Shanxi, Anhui and Guangdong will be in the leading position in 2030. (3) For renewable energy and nuclear power supply: the competitive advantages of wind power supply in Inner Mongolia, Qinghai, Hebei and Heilongjiang will increase with the strengthening of carbon emission constraints. The carbon emission constraints have little impact on changes in provincial characteristics of the competitiveness of solar power, hydropower and nuclear power supply. The competitiveness of solar power supply in Heilongjiang, Shanghai, Inner Mongolia, Sichuan, Tibet and Qinghai is obviously superior to that in other provinces. The competitiveness of hydropower supply in Jiangsu, Anhui, Jiangxi, Hubei, Sichuan, Guizhou, Shaanxi, Qinghai and Tibet is obviously superior to that in other provinces in LC1 and LC2 scenarios, and Hubei has the strongest competitiveness in hydropower supply. The competitiveness of nuclear power supply in Liaoning, Zhejiang, Fujian, Guangdong, Guangxi and Hainan is obviously superior to that in other provinces in LC1 and LC2 scenarios. Guangdong has the strongest competitiveness in nuclear power supply from 2020 to 2025, and Zhejiang

6.52%

6.51% 57.74% 6.51%

6.5%

(b) IM HLJ HEB QH HEN OTHER

14.65%

7.49%

7.37% 58.65% 5.92% 5.92%

(c) Fig. 7. Proportion of China's provincial wind power supply in wind power supply market under LC2 scenario. (a) year 2020; (b) year 2025; (c) year 2030.

power supply in 2030 will be Inner Mongolia, Heilongjiang, Hebei, Qinghai and Henan. Carbon emission constraints will lead to a significant increase in wind power supply across the country. The wind power supply in Chongqing and Shaanxi will be restricted due to the limitation of wind power installation and resource endowment. Qinghai and Heilongjiang have an advantage over Chongqing and Shaanxi in the competitiveness of wind power supply. The details are shown in Fig. 7. Compared to thermal power and wind power, there will have little 10

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will surpass Guangdong and became the most competitive province in 2030.

Learning Institutions of Shanxi and Program for the Innovative Talents of Higher Education Institutions of Shanxi (“PTIT”).

Some important policy implications may arise from our findings.

References

(1) The regional advantages should be combined with expansion potential to realize the optimal allocation of electric power resources. When the national power sector's carbon emissions decline by 10%, the competitive advantages of power supply in Jiangxi, Anhui and Hubei should be fully utilized; the development of electric power in Xinjiang and Inner Mongolia should be actively guided; meanwhile, the development and investment of electric power industry in Beijing, Tianjin and Jilin should be controlled. When the carbon emissions of the national power sector decrease by 15%, the competitive advantages of power supply in Jiangxi and Anhui should be fully utilized; the development of electric power in Hubei and Inner Mongolia should be actively guided; meanwhile, the development and investment of electric power industry in Tianjin and Jilin should be controlled. (2) Thermal power industry: when the national power sector's carbon emissions decline by 10%, the competitive advantages of thermal power supply in Guangdong and Inner Mongolia should be fully utilized; the development of thermal power in Shanxi, Anhui and Henan should be given priority; the decarbonization process of thermal power in Jiangsu and Shandong should be actively guided; and the expansion of thermal power in Beijing, Tianjin, Shanghai and Liaoning should be strictly controlled; when the national power sector's carbon emissions decline by 15%, the competitive advantages of thermal power supply in Inner Mongolia should be fully utilized; the development of thermal power in Shanxi, Anhui, Henan and Guangdong should be given priority; the decarbonization process of thermal power in Jiangsu, Shandong and Hebei should be actively guided; and the expansion of thermal power in Beijing, Tianjin, Shanghai and Liaoning should be strictly controlled. (3) Renewable energy and nuclear power: suitable measures should be taken according to local conditions, that is, solar power and wind power should be actively developed in Inner Mongolia, Qinghai and Heilongjiang; the development of hydropower resources in Hubei, Tibet and Sichuan should be emphasized; the centralized development of nuclear power in Zhejiang and Guangdong should be paid enough attention to. It is necessary to adapt to local conditions and make precise decisions.

[1] BP. BP Statistical Review of World Energy June 2018. London, UK: BP P.L.C.; 2018. [2] Lin J, Kahrl F, Liu X. A regional analysis of excess capacity in China’s power systems. Resour Conserv Recycl 2018;129:93–101. [3] Alva CAH, Li X. Power sector reform in China – an international perspective. Paris, France: IEA; 2018. [4] Fthenakis V, Mason JE, Zweibel K. The technical, geographical, and economic feasibility for solar energy to supply the energy needs of the US. Energy Policy 2009;37:387–99. [5] Takeshita T. A preliminary assessment of the competitiveness of ocean thermal energy conversion technologies. Adv. Mater. Res. 2013;827:195–202. [6] Timilsina GR, Cornelis van Kooten G, Narbel PA. Global wind power development: economics and policies. Energy Policy 2013;61:642–52. [7] Nagababu G, Kachhwaha SS, Savsani V. Estimation of technical and economic potential of offshore wind along the coast of India. Energy 2017;138:79–91. [8] Katal F, Fazelpour F. Multi-criteria evaluation and priority analysis of different types of existing power plants in Iran: an optimized energy planning system. Renew. Energy 2018;120:163–77. [9] Kabak M, Dağdeviren M. Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Convers Manage 2014;79:25–33. [10] Zangeneh A, Jadid S, Rahimi-Kian A. A hierarchical decision making model for the prioritization of distributed generation technologies: a case study for Iran. Energy Pol. 2009;37:5752–63. [11] Huang W, Ma D, Chen WY. Connecting water and energy: assessing the impacts of carbon and water constraints on China’s power sector. Appl Energy 2017;185:1497–505. [12] Styles D, Jones MB. Current and future financial competitiveness of electricity and heat from energy crops: a case study from Ireland. Energy Policy 2007;35:4355–67. [13] Naqvi M, Yan J, Dahlquist E, Naqvi SR. Off-grid electricity generation using mixed biomass compost: a scenario-based study with sensitivity analysis. Appl Energy 2017;201:363–70. [14] Garðarsdóttir SÓ, Göransson L, Normann F, Johnsson F. Improving the flexibility of coal-fired power generators: impact on the composition of a cost-optimal electricity system. Appl Energy 2018;209:277–89. [15] Nicholson M, Biegler T, Brook BW. How carbon pricing changes the relative competitiveness of low-carbon baseload generating technologies. Energy 2011;36:305–13. [16] deLlano-Paz F, Calvo-Silvosa A, Iglesias Antelo S, Soares I. The European lowcarbon mix for 2030: the role of renewable energy sources in an environmentally and socially efficient approach. Renew Sustain Energy Rev 2015;48:49–61. [17] Sun M. Comparison of Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). Manage World 2014;10:67–8. [18] Wang Y, Li J. Spatial spillover effect of non-fossil fuel power generation on carbon dioxide emissions across China's provinces. Renew Energy 2019;136:317–30. [19] Wen L, Yan F. Regional differences and influencing factors in the CO2 emissions of China’s power industry based on the panel data models considering power-consuming efficiency factor. Environ Dev Sustain 2017;20:1987–2007. [20] Skribans V, Balodis M. Development of the Latvian energy sector competitiveness system dynamic model. In: 9th International Scientific Conference on Business and Management. Vilnius Gediminas Tech Univ, Fac Business Management, Vilnius, Lithuania 2016. [21] Li W, Zhang YW, Lu C. The impact on electric power industry under the implementation of national carbon trading market in China: a dynamic CGE analysis. J Cleaner Prod 2018;200:511–23. [22] Meng S, Siriwardana M, McNeill J, Nelson T. The impact of an ETS on the Australian energy sector: an integrated CGE and electricity modelling approach. Energy Econ 2018;69:213–24. [23] Zhang LR, Li YK, Jia ZJ. Impact of carbon allowance allocation on power industry in China’s carbon trading market: computable general equilibrium based analysis. Appl Energy 2018;229:814–27. [24] Qi TY, Zhang XL, He JK. Review of research on global energy-economic computable general equilibrium model. Chinese J Populat Resour Environ 2016;26:42–8. [25] Liu J, Chen WY, Liu DS. Scenario analysis of China's future energy demand based on TIMES model system. Energy Procedia 2011;5:1803–8. [26] Chen WY, Yin X, Zhang HJ. Towards low carbon development in China: a comparison of national and global models. Clim Change 2013;136:95–108. [27] Yin X, Chen WY. Trends and development of steel demand in China: a bottom–up analysis. Resour Policy 2013;38:407–15. [28] Chen WY, Yin X, Ma D. A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissions. Appl Energy 2014;136:1174–83. [29] Yin X, Chen WY, Eom J, Clarke LE, Kim SH, Patel PL, et al. China's transportation energy consumption and CO2 emissions from a global perspective. Energy Policy 2015;82:233–48. [30] Ma D, Chen WY, Yin X, Wang LN. Quantifying the co-benefits of decarbonisation in China’s steel sector: an integrated assessment approach. Appl Energy 2016;162:1225–37. [31] Shi JC, Chen WY, Yin X. Modelling building’s decarbonization with application of China TIMES model. Appl Energy 2016;162:1303–12. [32] Zhang HJ, Chen WY, Huang WL. TIMES modelling of transport sector in China and USA: comparisons from a decarbonization perspective. Appl Energy

Our research can provide insights for power sector managers to plan the regional distribution of electric power supply. It can not only achieve the comprehensive development of energy, economy and environment in the national electric power sector, but also maximize the potential of different types of regional electric power development. With the increasing proportion of renewable energy generation and the maturity of energy storage technology, future research should focus on the mixed energy system of wind, light, water, nuclear, thermal and reserve for interactive development. This can provide a multi-functional complementary and technological path for the sustainable development of power sector. Declaration of Competing Interest The author declare that there is no conflict of interest. Acknowledgments This work was supported by the National Natural Science Foundation of China (grant numbers 41401655), Program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi and Program for the Philosophy and Social Sciences Research of Higher 11

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H. Lei, et al.

2017;57:1070–5. [38] Guo Z, Ma LW, Liu P, Jones I, Li Z. A multi-regional modelling and optimization approach to China's power generation and transmission planning. Energy 2016;116:1348–59. [39] Guo Z, Cheng R, Xu ZF, Liu P, Wang Z, Li Z, et al. A multi-region load dispatch model for the long-term optimum planning of China’s electricity sector. Appl Energy 2017;185:556–72. [40] Song D, He Y. Study on cost of wind power generation based on double-factor learning curve. Northeast Electr Pow Technol 2017;38:1–3. [41] Zhang X, Wu Q, Li J, Zhou N. Analysis of the supply safety and influence factors of Chinese uranium resources. Resour Admin Legal Syst 2017;30:18–22. [42] Zhao C. Development status and outlook for nuclear power in China. Nucl Pow Eng 2018;39:1–3.

2016;162:1505–14. [33] Akisawa A, Miyazaki T, Kashiwagi T. Theoretical analysis of the optimal configuration of co-generation systems and competitiveness of heating/cooling technologies. Energy 2010;35:4071–8. [34] Loulou R, Goldstein G, Kanudia A, Lettila A, Remme U. Documentation for the TIMES Model. Queanbeyan, Australia: Noble Soft System Pty Ltd; 2009. [35] Mishra U, Wu JZ, Chiu ASF. Effects of carbon-emission and setup cost reduction in a sustainable electrical energy supply chain inventory system. Energies 2019;12:1226. [36] Liu XY. China's electricity demand forecast and sustainable development of the power industry. Xiamen University; 2009. [37] Ma D, Chen WY. China's carbon emissions peak path-based on China TIMES model—based on China TIMES model. J Tsinghua Univ (Science and Technology)

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