The grid parity analysis of onshore wind power in China: A system cost perspective

Renewable Energy 148 (2020) 22e30

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The grid parity analysis of onshore wind power in China: A system cost perspective Hao Chen a, b, c, *, Xin-Ya Gao a, Jian-Yu Liu a, Qian Zhang a, Shiwei Yu a, b, **, Jia-Ning Kang c, Rui Yan d, Yi-Ming Wei c a

School of Economics and Management, China University of Geosciences, Wuhan, 430074, China Center for Energy Environmental Management and Decision-making, China University of Geosciences, Wuhan, 430074, China Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China d Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 July 2019 Received in revised form 5 November 2019 Accepted 30 November 2019 Available online 3 December 2019

The grid parity of wind generation has drawn increasing attention owing to the serious subsidy funding shortages in China, but scientific evidences for the grid parity feasibility are still not sufficient, because most studies have neglected the additional balancing cost and grid-connection cost caused by the wind generation. We hence develop an integrated methodology to analyze the grid parity of onshore wind generation from a system cost perspective, coupling a system generation cost model, a grid parity index model and a learning curve model. Key findings are summarized as follows: (1) The average system cost of wind generation declined from 0.84 yuan/kWh in 2006 to 0.57 yuan/kWh in 2017. Guangdong has the highest system cost, while Xinjiang costs the least. (2) The traditional LCOE approach underestimates the wind generation cost by about 15%, resulting in biased conclusions regarding the grid parity. (3) All the provincial grid parity indexes have values more than 1, indicating that the current grid parity of wind generation is impractical. (4) The national average grid parity time of wind generation are forecasted to be 2021, 2023 and 2026 when the on-grid coal generation prices are 0.50 yuan/kWh, 0.45 yuan/kWh and 0.40 yuan/kWh, respectively. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Wind Grid parity System cost LCOE China

1. Introduction Owing to the economies of scale and technological progress, renewable energy has been growing rapidly around the world. Global wind power capacity has reached 564 GW in 2019, a 33 times increase since 2000. There is also a 750 times increase in solar PV to 488 GW [1]. Financial supports from government subsidy policies are important forces for backing these achievements, such as Feed-in-Tariff (FIT), Renewable Portfolio Standards (RPS), and Tradable Green Certificates (TGC) [2e5]. However, many countries are facing heavy pressure of subsidy funding shortages or related

* Corresponding author. School of Economics and Management, China University of Geosciences, Wuhan, 430074, China. ** Corresponding author. School of Economics and Management, China University of Geosciences, Wuhan, 430074, China. E-mail addresses: [email protected] (H. Chen), [email protected] (X.-Y. Gao), [email protected] (J.-Y. Liu), [email protected] (Q. Zhang), [email protected] (S. Yu), [email protected] (J.-N. Kang), yr1900@163. com (R. Yan), [email protected] (Y.-M. Wei). https://doi.org/10.1016/j.renene.2019.11.161 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

fiscal problems, stimulating an urgent desire to reduce or cancel the subsidies for renewable energy [6,7]. The German government phased out FIT support for solar panels in 2017, the Spanish government decided to suspend all subsidies for renewable energy projects, the Chinese government reduced the subsidies for solar PV sharply by the 531 New Policy, and UK is also seeking to reduce government subsidies for renewables.1 The wind power development in China also faces subsidy challenges currently. With the subsidy policy of FIT, onshore wind energy in China has experienced rapid development during the past two decades (see Fig. 1), playing an important role in mitigating the climate change and addressing the air pollution problems [8e10]. The soaring wind generation has also resulted in a serious fiscal burden for the Chinese government. The subsidy funding shortages were 50 billion

1 This information is drawn from https://etn.global/news-and-events/spain-cutsrenewables-subsidies/.

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Voltage (UHV) transmission lines being built and put into operation in China, wind power can be transmitted to other regions with higher electricity prices, thus providing another new approach for them to achieve grid parity. To bridge these research gaps and provide decision-making support for the policy designs in the future, this study establishes an integrated methodology to analyze the grid parity of onshore wind generation from a system cost perspective, aiming at answering the following questions.

Fig. 1. Historical development of onshore wind energy in China (1994e2017).

yuan in 2016 and are estimated to reach 200 billion yuan in 2020.2 To address this subsidy dilemma, the Chinese government is planning to conduct the grid parity of wind generation from the supply side, indicating that wind generation will receive the same payment as the conventional generation sources, thus not receiving any subsidy from the government. Achieving the grid parity is an unavoidable development stage for the wind energy, and no healthy and competitive industry would rely on the subsidy abidingly [11]. However, the grid parity policy of wind energy should be progressed carefully, because improper and sudden abolishment of subsidy policies will result in company bankrupts, unemployment rates increase and social unreliability issues [12,13]. The feasibility of wind generation’s grid parity lies in the cost competitiveness of wind generation with other technologies [4,14,15]. Several studies have tracked the cost change patterns of wind generation in China, and stated that the technological learning effects have increased its cost competitiveness [16,17]. The Levelized Cost of Energy (LCOE) is the most popular tool employed to measure the wind generation cost, and it is calculated as the result of the total discounted generation cost divided by the discounted electricity generation over the whole lifetime [18,19]. However, it is flawed in the grid parity analysis of wind power, considering the following reasons. First, due to the data availability and small shares of wind generation, the cost components that most previous LCOE studies have taken into consideration are only within the power plants, neglecting the integration cost caused by the wind generation [20,21]. Compared with the fossil fuel generation, the wind generation is variable, uncertain and intermittent, thus causing balancing cost for the operation security of the power system [22,23]. Second, the wind farms are mostly located in remote areas, so additional transmission lines need to be built for their on-grid connections [24]. With the increasing amount of the wind generation, the integration cost and the grid connection cost become more significant and should not be neglected [25]. Underestimating or ignoring this cost leads to biased conclusions regarding the grid parity feasibility. At last, previous grid parity studies only analyzed the competition among different technologies within the same balancing area, and little attention has been devoted to exploring the impacts of interregional electricity transmission on the wind grid parity in China [26]. With the increasing number of Ultra High

2 See http://www.cred.org.cn/ and http://guangfu.bjx.com.cn/news/20171020/ 856554.shtml.

(1) What are the temporal change patterns and spatial distributions of wind generation cost in China? (2) Can the existing wind generators achieve grid parity? How will the interregional electricity trading affect the grid parity of wind generators? (3) What about the grid parity feasibility of newly-built wind generators in the future? What are the regional differences regarding their grid parity time? The remainder of this paper is organized as follows. Section 2 presents the methods. Section 3 shows the data. Section 4 describes the analysis and discussion of the results. Section 5 shows the conclusions. 2. Methods To conduct the grid parity analysis of onshore wind generation from a system cost perspective, this study develops an integrated methodology with three sub-models, namely a system generation cost model, a grid parity index model, and a grid parity time prediction model. The model framework is shown in Fig. 2. Using the LCOE framework and the techno-economic information of onshore wind power plants, the system generation cost model will first be established to quantify the wind generation cost. The outputs will show both the total amount and the structures of wind generation cost. Then, a grid parity index model will be constructed to investigate the grid parity feasibility of existing wind generators, by comparing the estimated system generation cost with the on-grid coal generation prices. At last, the grid parity time prediction model, which employs the learning curve techniques, will be established to predict the grid parity time of onshore wind generators. 2.1. The system generation cost model The most popular indicators for measuring the wind generation cost are the LCOE and the wind turbine cost [18,27,28]. The methodology of LCOE is easy and convenient to be used for the cost comparison among different technologies. The wind turbine cost, accounting for a major share of the total generation cost, is often employed to track the cost change patterns of wind energy investment [29,30]. However, these two aforementioned cost indicators only cover the wind generation cost within the power plants [31,32]. Neither of them can be used to conduct the grid parity analysis if the uniqueness of wind generation is considered [20,28]. On the one hand, wind generation is not dispatchable and needs auxiliary services from the power system, such as the ramping up/down services and additional spinning reserves [32]. On the other hand, the locations of wind farms are usually far from existing routes of the transmission lines, triggering additional grid costs to get connected [21]. To overcome the shortcomings of LCOE in comparing the wind generation cost with that of other dispatchable technologies, a concept of system LCOE has been proposed by Ref. [23]. Taking full consideration of profile cost, balancing cost and grid cost, the system LCOE is established by

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H. Chen et al. / Renewable Energy 148 (2020) 22e30

Fig. 2. Model framework.

adding the integration cost into the traditional LCOE. There is also an increasing trend in using the system LCOE to estimate the wind generation cost [25,33]. Therefore, this study establishes a system generation cost model to estimate the wind generation cost. The system cost of wind generation (LCOEs ) is calculated by equation (1), which adds the grid cost and the balancing cost to the traditional LCOE model.3

s

LCOE ¼

T X

, ½ðIt ,CA þ OMt Þ

t¼1

þ

T X

t¼1

, ½Lt

t¼1

þ

T X

1t 3 !t 3, ,0 T X 5 @ 1þr ½Et 1 þ rA 5

 GPIi ¼ LCOEsi BCi

1t 3 !t 3, ,0 T X ½Et @1 þ r A 5 1þr 5 t¼1

, ½ASt

1t 3 !t 3, ,0 T X 5 @ ½Et 1þr 1 þ rA 5

t¼1

RGPI ¼

N X

ui ,GPIi

(2)

(3)

i

t¼1

(1) The first row shows the traditional LCOE model, which is the result of the total discounted generation cost divided by the discounted electricity generation over the whole lifetime T. The second row exhibits the levelized grid cost of wind generation. The third row shows the balancing cost of wind generation. Here, It is the unit capital cost of the wind generator; CA is the installed capacity of the wind generator; OMt is the annual operations and maintenance (O&M) cost; Et is the annual electricity generation; r is the discount rate; Lt is the additional grid cost of the wind generator; ASt is the balancing cost caused by one kWh of wind generation. 2.2. The grid parity index model According to the current generation structure and the regulation

3

regime in China, the grid parity depends on the comparison results between wind generation cost and on-grid coal generation prices, because coal generation is the major competitor for the wind generation when achieving grid parity [7]. To assist the grid parity analysis, a grid parity index (GPIi ) is developed in equation (2). A wind generator can achieve grid parity if its grid parity index is less than 1, otherwise it cannot if its index is bigger than 1. Besides, we have also established a regional grid parity index (RGPI) to analyze the cost-effectiveness of wind generation at the regional level, see equation (3).

The profile cost is not included in our study because there is no electricity market in China now. Moreover, the electricity price is regulated by the National Development and Reform Commission (NDRC) and the profile of electricity price is very flat. However, the model can be modified to add profile cost in the future.

where BCi is the provincial on-grid coal generation prices; GPIi is the grid parity index; ui is the capacity share of wind generator i. There are several extensions of the basic grid parity index. On the one hand, it can be extended to consider the environmental cost impacts of coal generation, see the environmental grid parity index (GPIex i ) in equation (4). On the other hand, it can also be modified to consider the impacts of UHV transmission lines, see the UHV grid parity index (GPIuhv i ) in equation (5). s GPIex i ¼ LCOE i ðBCi þ CP , HRi , CEFi Þ

(4)

   GPIuhv ¼ LCOEsi þ TCi ðBCi þ CP , HRi , CEFi i

(5)

where CP is the social cost of carbon emissions; HRi is the heat rate of coal generator i; CEFi is the carbon emission coefficient of coal generator i; TCi is the UHV transmission cost of the wind generator i. 2.3. The grid parity time prediction model To analyze the grid parity of new onshore wind generators in the future, a learning curve model is first established to investigate the

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Table 1 Data sources and explanations. Indicators

Data source

Capital cost O&M cost Annual electricity generation Installed capacity Project lifetime Discount rate Grid cost Balancing cost Benchmark coal price Average heat rate of coal generators Avoided carbon emission intensity Transmission cost of UHV lines Consumer Purchase Index (CPI)

UNEP website and CCERE website UNEP website and CCERE website UNEP website and CCERE website UNEP website and CCERE website UNEP website and CCERE website Interim Measures for Economic Evaluation of Electric Power Engineering and Technical Renovation Projects China National Renewable Energy Center (CNREC) website He et al. [34] National Development and Reform Commission (NDRC) website China Electric Power Yearbook Chen et al. [35] National Development and Reform Commission (NDRC)a National Bureau of Statistics

a

http://www.ndrc.gov.cn/zcfb/zcfbtz/201805/t20180514_886105.html.

temporal cost change patterns of wind generation, see equation (6). b

LCOEst ¼ a,ðXt Þ

(6)

where Xt is the cumulative installed capacity in year t; a is the initial levelized cost of wind generation; b is the elasticity cost between cumulative installed capacity and levelized generation cost. The parameters (a and b) can be obtained by regressing the logarithm of equation (6), see equation (7). Using the estimated parameters ( b b), the learning rate (LR) of wind generation cost can be calculated from equation (8).

  log LCOEst ¼ logðaÞ þ b,logðXt Þ

(7)

b LR ¼ 1  2 b

(8)

Then, the grid parity time of wind generators can be forecasted based on the established learning curve model. The required cumulative installed capacity to reach the benchmark coal generation prices (BC) is shown in equation (9).

b Xt ¼ ðBC= b a Þ1= b

(9)

According to Yao et al. [26]; the capacity growth path of wind generators is assumed to follow an exponential growth path, see equation (10). h measures the growth rate of cumulative wind capacity.

Xt ¼ X0 ,eh,t

(10)

At last, the grid parity time (GPT) can be predicted by linking equation (9) and equation (10), see equation (11).

" b# ðBC= b a Þ1= b GPT ¼ ln h X0 1

(11)

3. Data This study investigates the grid parity analysis of onshore wind generation based on a large scale dataset of 2367 generators, which were newly came into operation from 2006 to 2017. Since the total number of wind generators represents 88% of the total installed capacity in 2017, they are well representative of the current grid parity status in China. The main data sources used in this study are

Table 2 Summary statistics of major parameters used in this study. Parameters Capital cost Installed capacity O&M cost Annual electricity generation Project lifetime Benchmark coal price Heat rate of coal generators Grid cost Balancing cost UHV transmission cost

It CA OMt Et T BCi HRi Lt ASt TCi

Unit

Mean

Min

Max

yuan/kW MW yuan/kW GWh year yuan/kWh gce/kWh million yuan/unit yuan/kWh yuan/kWh

8886 60.31 235 130 20 0.35 293 14.67 0.08 0.06

2837 4.50 112 12 20 0.25 206 3.63 0.06 0.04

11515 600.00 628 1567 20 0.44 321 29.90 0.12 0.08

shown in Table 1.4 The technical and financial data of wind generators are drawn from the websites of United Nations Environment Programme (UNEP) and Chinese Certified Emission Reduction Exchange (CCERE). As for the additional grid-connection cost of wind generators, we use the average grid-connection cost at the provincial level.5 For the additional balancing cost of wind generation, we use the estimation results from He et al. [34]; which quantified the average additional auxiliary service cost caused by the wind generation. The discount rate is selected as 8% in this study according to the government file named Interim Measures for Economic Evaluation of Electric Power Engineering and Technical Renovation Projects. A summary statistics of data used in this study is shown in Table 2, which exhibits the mean values, minimum values and maximum values of the input parameters. All prices and costs have been deflated to the 2017 constant price using the CPI. We can see that the grid-connection cost and balancing cost are significant and should not be ignored. Furthermore, these costs also differ a lot among different provinces, exhibiting the necessity to be taken into consideration in the grid parity analysis.

4 The data are drawn from the Project Design Documents (PDD) from UNEP and CCERE. These PDD files were firstly designed by the UNEP and began to be published in 2006, but were stopped publishing in 2013 due to the end of Kyoto protocol. Then, Chinese government used the same structure and statistical rules to continue publishing PDD in CCERE from 2013, so there is no deviation in the files from these two data sources. 5 Since it is difficult to obtain the additional grid-connection cost at the generator level, we use the average grid-connection cost of wind generators at the provincial level as a substitution.

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H. Chen et al. / Renewable Energy 148 (2020) 22e30

Fig. 3. The system LCOE of onshore wind generators in China (2006e2017).

4. Results and discussions 4.1. The temporal changes and spatial distributions of wind generation cost Before we conduct the grid parity analysis of wind generators in China, we first estimate their generation cost based on the system LCOE model. Fig. 3 shows the temporal change patterns of system generation cost from 2006 to 2017. We can see that the average system LCOE cost exhibits a downward trend, which declines from 0.84 yuan/kWh in 2006 to 0.57 yuan/kWh in 2017. The length of the confidence interval has also steadily narrowed, indicating that the cost uncertainties have reduced. Therefore, the wind development subsidized through FIT policy not only reduced the cost, but also lowered the investment risks. We have also investigated the spatial distributions of the wind generation cost. Fig. 4 ranks the capacity-weighted generation cost in 30 provinces, which is calculated based on the system cost of all the existing wind generators in 2017. We can see that most of the low-cost provinces are located in the resource-abundant areas, such as Xinjiang (0.63 yuan/kWh) and Ningxia (0.65 yuan/kWh), while the high-cost provinces are located in south of China, such as Guangdong (0.82 yuan/kWh) and Hainan (0.82 yuan/kWh). As for the cost structures, the grid cost share is 2% on average, while the share of balancing cost is 13% on average. Therefore, the traditional LCOE underestimates the wind generation cost by about 15%.

4.2. The grid parity analysis of existing wind generators Based on the estimated system generation cost, the provincial grid parity index is calculated to analyze the cost competitiveness between wind generation and coal generation (Fig. 5). We can see that all the grid parity indexes are higher than 1 (range from 1.71 to 2.63) in 30 provinces, indicating that it is difficult for the existing wind generators to achieve grid parity. Furthermore, the on-grid coal generation prices are found to affect the feasibility of grid parity greatly. For example, Ningxia and Xinjiang have the lowest on-grid coal generation prices, but hold the highest grid parity indexes. Hunan’s on-grid coal generation price ranks the first, but comes last in the grid parity index (Fig. 5). The grid parity index can be used in guiding the pilot area selections to implement wind grid parity policy, and the future looks bright to the provinces with

smaller values of grid parity indexes (Hunan and Zhejiang). The grid parity of wind generation does not only depend on the generation cost of different technologies, but also is affected by the government policies [36]. Compared with fossil fuel generation, wind generation is cleaner with less carbon emissions, so the carbon emission market will add the competitiveness of wind energy by internalizing the carbon emission cost [35,37,38]. Using the environmental grid parity index model developed in section 2, we estimate the total capacities that can achieve grid parity at different carbon emission prices (Fig. 6). We can see that the total capacities of wind generators show a significant nonlinear relationship with carbon emission prices. The capacity share increases slowly when the CO2 price is below 100 yuan/ton, but it will increase sharply from 6% to 97% when the carbon emission price increases from 150 yuan/ton to 450 yuan/ton. Almost all the wind generators can achieve grid parity when the carbon price surpasses 500 yuan/ton. However, although the national carbon emission market was established in 2017, there is still no real transaction up to now. Besides, the effectiveness of provincial pilot Emission Trading Systems (ETSs) is still questioned due to the lack of compulsory regulation forces [39]. The trade in pilot ETSs was not very active and the carbon prices were below 80 yuan/ton in 2018. Therefore, it is necessary to accelerate the progress of the market’ real operations to speed up the grid parity of wind generation. 4.3. The impacts of UHV lines on the grid parity of wind generation This section analyzes the grid parity of wind generation transmitted by different UHV transmission lines. Wind generation can be consumed not only in the province where it produces, but also in other provinces through the Ultra High Voltage (UHV) transmission lines. Moreover, one of the important motivations for the construction of these UHV lines is to address the serious wind curtailment problem according to the government file ‘Implementing Scheme to Solve the Curtailment Problem of Water, Wind and Solar’. In 2017, 190 TWh of electricity generated from renewables was transmitted by the UHV lines, accounting for 63% of the total electricity transmission.6 Applying the UHV grid parity index model to the nine UHV lines in 2017, we estimate the total installed capacity of wind generators that can achieve grid parity at different

6

http://zfxxgk.nea.gov.cn/auto87/201805/t20180522_3179.htm.

H. Chen et al. / Renewable Energy 148 (2020) 22e30

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Fig. 4. Average onshore wind generation cost in different provinces (2017).

a) Grid parity index

b) Benchmark coal generation prices (yuan/kWh)

Fig. 5. The grid parity index and benchmark coal generation prices in 2017.

carbon prices (Table 3). We can see that few generators can achieve grid parity when the carbon emission cost is 0 yuan/ton, this can also explain why there is a serious wind curtailment problem in North China [40]. However, the total capacity of grid parity wind generators will increase as the carbon emission price rises, which increases from 0.33 GW to 13.95 GW when the carbon price changes from 0 yuan/ton to 400 yuan/ton. The UHV line between Inner Mongolia and Tianjin contributes most to the grid parity of wind generators, while the UHV line between Shanxi and Hubei

contributes least to the grid parity of wind generators. Besides the absolute installed capacity of wind generators, we also show the shares of wind generation capacity that can achieve grid parity via the UHV lines. Taken the CO2 price of 200 yuan/ton as an example, the grid parity shares brought by the UHV lines are shown in Fig. 7. Provinces are highlighted in green to represent the origin places of UHV lines, which are also the wind resourceabundant areas in China. Arrows indicate the routes of the UHV transmission lines, whose ends indicate the transmission

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H. Chen et al. / Renewable Energy 148 (2020) 22e30

Fig. 6. The national grid parity capacity of wind generators at different carbon prices (2017).

Table 3 The grid parity capacity via the UHV lines at different carbon prices. UHV lines Origins

1 2 3 4 5 6 7 8 9 Average

Destinations Grid parity capacity (GW) at different CO2 prices (yuan/ton)

Inner Mongolia Jiangsu Inner Mongolia Shandong Inner Mongolia Tianjin Shanxi Hubei Shanxi Jiangsu Xinjiang Henan Ningxia Zhejiang Gansu Hunan Yunnan Guangdong

0

100

200

300

400

0.00 0.00 2.94 0.00 0.00 0.00 0.00 0.00 0.00 0.33

1.54 0.60 14.12 0.00 0.20 0.00 0.30 0.15 0.30 1.91

13.02 6.46 22.48 0.20 2.68 3.08 5.25 5.19 3.11 6.83

21.79 16.82 24.91 3.36 5.76 10.19 7.72 10.17 5.52 11.80

24.81 24.16 25.31 6.21 7.11 12.92 8.09 11.06 5.90 13.95

destinations. The numbers in the black bubbles represent the capacity shares of wind generators in the origins that can achieve grid parity via UHV lines. We can see that the UHV line between Inner Mongolia and Tianjin contributes to the largest share (89%) of grid parity capacity, followed by the UHV line between Ningxia and Zhejiang (65%). This can provide useful insights for the grid planning in the future, more transmission lines are suggested to be built between regions where UHV lines are more conducive to the grid parity of wind generation. 4.4. The grid parity analysis of wind generation in the future To analyze the grid parity of wind generators in the future, we have first calculated the learning rates of wind generation cost in different regions (Fig. 8). The spatial classification criterion of different regions is based on the FIT subsidy policy, region I has the lowest FIT level because it is the most abundant area of wind resources, while region IV obtains the highest FIT level due to its poorest resource conditions. We can see that regions with abundant wind energy resources generally have higher learning rates. Region I has the biggest learning rates (7.46%), while region III has the smallest learning rates (4.98%). Our estimation results are also similar to that in other studies, such as the 4% estimated in Qiu and Anadon [41] and the 4.4% estimated in Yao et al. [26]. However, the learning rates of China are much smaller than the learning rates of other countries (17% in Denmark, 18% in the EU and 32% in the US), because wind industry develops relatively late in China and more

Fig. 7. The grid parity shares of wind generation capacity via UHV lines in 2017.

technical progress has already been achieved in earlier international spillovers. Based on the estimated learning rates, we can predict the regional grid parity time, by considering different on-grid coal generation prices (Table 4). The average grid parity time of four regions is forecasted to be 2021 when the coal generation price is 0.50 yuan/kWh. It will increase to 2026 when the coal generation price decreases to 0.40 yuan/kWh. The grid parity time differs a lot among different regions even at the same coal generation price. Taken 0.45 yuan/kWh as an example, the maximum gap between the different grid parity time reaches up to 8 years (Region I and Region IV). Therefore, it is important to consider the regional differences when implementing the grid parity policy of wind generation. Regions or provinces with earlier grid parity time can be selected as pilots. In doing so, the wind energy industry can have sustainable development. 5. Conclusions The wind power industry is facing serious subsidy funding shortages in China, posing great challenges for its sustainable development. Although implementing the grid parity of wind

H. Chen et al. / Renewable Energy 148 (2020) 22e30

Fig. 8. Learning rates of wind generation cost at the regional level. Note: The dashed lines represent the learning rates estimated in previous studies, while the solid lines show the learning rates estimated in this study. The learning rates of different regions in China are marked as solid lines using different colors, which also correspond to the regional colors in the Chinese map.

Table 4 The achievement time of grid parity at different benchmark coal prices. Grid parity year

Benchmark coal prices (yuan/kWh) 0.40

0.45

0.50

Region I Region II Region III Region IV Average

2021 2026 2027 2031 2026

2019 2023 2024 2027 2023

2018 2020 2021 2024 2021

Note: The fractional part of the calculated grid year is omitted when it is smaller than 0.5, while others are rounded up.

generation is an apparent choice in the policy baskets, an immediate or unwise abolishment of the subsidy policy will result in serious negative influences on the wind industry. However, the current scientific evidences for the grid parity feasibility of wind generation are still not sufficient, and few studies have conducted the grid parity analysis from a system cost perspective. Therefore, this study has developed an integrated methodology to analyze the grid parity of onshore wind generation in China, during which we have obtained the following major conclusions: (1) The average system generation cost reduced by about 32% during the period from 2006 to 2017, but it is still very high when compared with the on-grid coal generation prices. All the provincial grid parity indexes are over 1, making the grid parity impractical. Besides, significant differences still exist among provinces regarding the generation cost. Guangdong has the highest system cost, while Xinjiang holds the lowest system cost. Therefore, it is recommended to be careful about abolishing the subsidy policy suddenly and rapidly, otherwise many wind generation companies will go bankrupt. It will be better to promote the grid parity policy in some pilot provinces first, such as provinces with lower grid parity indexes (Hunan and Zhejiang). In the meantime, it is also necessary to promote the R&D of wind energy technology, thus ensuring bigger technological progress of wind energy. (2) The traditional LCOE approach underestimates the wind generation cost by about 15%, resulting in biased conclusions

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regarding the grid parity. Therefore, it is important for the government to improve the methodology in the cost accounting and estimation, thus providing more scientific subsidy policy designs and investment signals. The integrated methodology proposed in this study has been demonstrated successful in the applications to wind energy, it can also be easily transferred to other renewable technologies, thus contributing to wiser technology policies. (3) Internalizing the carbon emission cost of coal generation is a promising approach to achieve the grid parity of wind generation. All the wind generators can achieve grid parity within the same province when the carbon price surpasses 500 yuan/ton. Wind generation can also obtain grid parity in other provinces via UHV lines, the UHV line between Inner Mongolia and Tianjin contributes most to the grid parity of wind generation, from both the capacity sizes and the capacity shares. Although benefits from the carbon emission trading market are clear, the market building process is still slow and there is no real transaction in the national ETS. Therefore, the government can take measures to speed up the construction of the national ETS, thus further increasing the cost competitiveness of the wind energy. (4) The average grid parity time in China are forecasted to be 2021, 2023 and 2026 when the on-grid coal generation prices are 0.50 yuan/kWh, 0.45 yuan/kWh and 0.40 yuan/kWh, respectively. Wind generators in resource-abundant areas can achieve grid parity earlier. Considering the significant heterogeneity in both the wind generation cost and the grid parity feasibility in different provinces, it is vital for the government to provide more timely and transparent signals for guiding the regional investment of new wind generators in the future, thus increasing the investment efficiencies of the power sector. Although this study has addressed several important issues concerning the grid parity of wind generation in China, some places can still be improved in future studies. For example, the change patterns of different cost components of wind generation can be analyzed when new data is available. Air pollution cost of coal generation can also be integrated into the grid parity analysis if more reliable data is obtained. These efforts will provide more insights for the development of wind energy in China. Author contributions section Hao Chen: Conceptualization, Methodology, Writing- Original draft preparation. Xin-Ya Gao:Data curation. Jian-Yu Liu:Visualization. Qian Zhang:Investigation. Shiwei Yu: Writing- Reviewing and Editing. Jia-Ning Kang: Software, Validation. Rui Yan: Software. Yi-Ming Wei: Supervision. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant number 71904180, 71822403,

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