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ARTICLE
Rapid cost decrease of renewables and storage
accelerates the decarbonization of China’s power
system
Gang He 1,2✉, Jiang Lin 2,3 ✉, Froylan Sifuentes2,4, Xu Liu 2, Nikit Abhyankar2& Amol Phadke2✉
The costs for solar photovoltaics, wind, and battery storage have dropped markedly since
2010, however, many recent studies and reports around the world have not adequately
captured such dramatic decrease. Those costs are projected to decline further in the near
future, bringing new prospects for the widespread penetration of renewables and extensive
power-sector decarbonization that previous policy discussions did not fully consider. Here we
show if cost trends for renewables continue, 62% of China’s electricity could come from non-
fossil sources by 2030 at a cost that is 11% lower than achieved through a business-as-usual
approach. Further, China’s power sector could cut half of its 2015 carbon emissions at a cost
about 6% lower compared to business-as-usual conditions.
https://doi.org/10.1038/s41467-020-16184-x OPEN
1Department of Technology and Society, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY 11794, USA. 2International
Energy Analysis Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. 3Department of Agricultural and Resources Economics,
University of California, Berkeley, Berkeley, CA 94720, USA. 4Institute of Energy Studies, Environmental Sciences Department, Western Washington
University, Bellingham, WA 98225, USA. ✉email: gang.he@stonybrook.edu;j_lin@lbl.gov;aaphadke@lbl.gov
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China’s electricity system accounts for about half of the
country’s energy-related carbon dioxide (CO
2
) emissions,
which represent about 14% of total global energy-related
CO
2
emissions1. Decarbonizing China’s electrical system there-
fore is essential to the decarbonization of energy systems not only
in China but also globally. Further, given electricity’s increasing
role in China’s energy use, a low-carbon electrical system is key to
reducing CO
2
emissions from other economic sectors such as
transport, industry, and buildings.
Under the Paris Agreement, China committed to peak its CO
2
emissions and to supply 20% of its energy demand using non-
fossil sources by 2030. Such targets, however, are unlikely to limit
the worldwide temperature increase to 2 or 1.5 degrees above pre-
industrial levels2. Various studies have outlined strategies for
China to attain a high degree of non-emitting generation by
20503–6. Many recent studies and reports around the world have
not adequately captured the dramatic decrease in costs of
renewable energy and storage, however. For example, the World
Energy Outlook produced by the International Energy Agency
and the International Energy Outlook developed by the U.S.
Energy Information Administration have under-estimated the
development of renewables7–9.
Incorporating the new downward trend in costs of renewable
energy into models of the power sector is both relevant to mod-
eling efforts and required for developing appropriate policies. The
analysis described herein aims to incorporate recent trends in
renewable and storage costs so as to explore more ambitious
pathways to decarbonizing China’s power system by about 2030
and to offer insights on how those recent trends can reshape the
power system. The costs of solar photovoltaics (PV), wind, and
battery storage have decreased rapidly. The global weighted-
average levelized cost of electricity (LCOE) of utility-scale solar
PV, onshore wind, and battery storage has fallen by 77%, 35%, and
85% between 2010 and 2018, respectively10–13. Those cost trends
bring new possibilities for widespread penetration of renewable
energy sources and comprehensive power-sector decarbonization
that were not foreseen in previous policy discussions.
We focus on the following questions in this study: how would
China’s power system change given the rapid decrease in costs of
renewables and storage under more stringent CO
2
emissions
targets? What are the costs to achieve those changes in China’s
power system? How would those changes affect China’s regional
pattern of power development and transmission? By addressing
those questions, this paper is the first effort to reveal the impli-
cations of cost decrease on power systems and new perspectives
on clean power transition that are not visioned in the existing
literature.
We updated the SWITCH-China model14 and developed four
scenarios for 2030 to simulate and understand the effects of the
rapid decrease in renewable energy costs. The scenarios are: First,
business as usual scenario (BAU), which assumes the continua-
tion of current policies and moderate cost decreases in future
renewable costs. Second, low-cost renewables scenario (R), which
assumes the rapid decrease in costs for renewables and storage
will continue. Third, carbon constraints scenario (C50), which
has a carbon cap of 50% lower than the 2015 level in 2030 on top
of the R scenario. Fourth, deep carbon constraints scenario (C80),
which further constrain the carbon emissions from the power
sector to be 80% lower than the 2015 level by 2030.
Our modeling analysis shows if cost trends for renewables
continue, 62% of China’s electricity could come from non-fossil
sources by 2030 at a cost that is 11% lower than achieved through
a business-as-usual approach. Further, China’s power sector
could cut half of its 2015 carbon emissions at a cost about 6%
lower compared to business-as-usual conditions. An 80%
reduction in 2015 carbon emissions is technically feasible as early
as 2030, but requires about a 21% higher cost than the business-
as-usual approach, for a $21/tCO
2
cost of conserved carbon.
Results
Mix of generation capacities and power generation. As expec-
ted, rapid decreases in the costs of renewable energy sources lead
to the larger installation of wind and solar capacity. By 2030, the
low-cost renewables (R) scenario, compared with the BAU sce-
nario, would lead to an increase in wind capacity from 660 to 850
GW and in solar capacity from 350 to 1260 GW. The need for
power sector generators to incorporate flexibility in utilizing
resources would result in increasing storage capacity from 34 to
290 GW to support the integration of variable renewable
resources. The need for natural gas capacity would decrease from
300 to 170 GW, replaced by increasing renewable capacities
and storage capacities. Coal capacity would diminish from 750 to
700 GW (Fig. 1), about a 7% reduction.
Under the carbon constraints (C50) scenario, coal capacity
would decrease further to 520 GW by 2030, almost a 1/3
reduction compared with the BAU scenario. The deep carbon
constraints (C80) scenario would phase out coal further to about
200 GW, only 4% of total capacity. The decrease in coal use would
be offset primarily by renewables: 1920 GW of solar and 2000
GW of wind.
Under R scenario, coal-based generation would decrease from
4900 TWh in the BAU scenario to 3000 TWh by 2030, a 30%
reduction. Wind and solar production could provide 39% of
electricity need, with battery storage and natural gas supplement-
ing the increasing wind and solar supplies. The total share of non-
fossil generation could reach 62% in 2030. The C50 scenario
would cause coal generation to decline further to 2400 TWh (less
than half the amount generated under the BAU scenario), while
the share of non-fossil generation would increase to 77% in 2030.
The C80 scenario would reduce coal generation to about 960
TWh, or to about 10% of total power generation, while the share
of non-fossil generation would approach 90% in 2030 (Fig. 2).
Relying on variable wind and solar resources for electricity
could pose challenges to system operations. On days with
abundant wind and solar resources, upto 300 GW of storage
would be needed to balance the power system under the R
scenario. On days that provide minimal solar and wind power,
storage would be inadequate to make up for the shortage; natural
gas generation could fill the gap in order to satisfy peak load
requirements. Fig. 3shows that dispatch sources to meet
demands could be operationally manageable with the addition
of electricity from battery storage and natural gas, represented in
both the R scenario and the C50 scenario.
Power costs and carbon emissions. A low-cost renewables (R)
scenario could reduce carbon emissions significantly, from 3980
MtCO
2
under the BAU scenario (5% above the 2015 level) to
2970 MtCO
2
by 2030 (22% below the 2015 level), see Fig. 4a.
Given the remarkable and ongoing reductions in the cost of
renewable power, this 30% reduction in carbon emissions could
be achieved for a lower cost of power under the R scenario than
under the BAU scenario, see Fig. 4b. Power costs would decrease
from 73.52 $/MWh under the BAU scenario to 65.08 $/MWh
under the R scenario, an 11% reduction. Under the carbon
constraint (C50) scenario, carbon emissions in 2030 would be half
of those of 2015, on a trajectory to achieve an 80% reduction by
2050. The cost of power under the C50 scenario is calculated to be
69.47 $/MWh, only 7% higher than under the R scenario, but still
6% lower than under the BAU. In the deep carbon constraint
(C80) scenario, the cost of power would increase to 89.08
$/MWh, and 21% higher than under the BAU scenario. The cost
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of conserved CO
2
would be -$36/tCO
2
, -$9/tCO
2
, and $21/tCO
2
under the R scenario, C50 scenario, and C80 scenario, respec-
tively. China has already initiated a national cap-and-trade pro-
gram limiting the carbon emissions from the power sector with a
carbon price ranging from 20 RMB/tCO
2
(~$3/tCO
2
) to 100
RMB/tCO
2
(~$14.5/tCO
2
).
Changing Investment Mix. A low-cost renewables (R) scenario
would shift the cost structure of the power system from a fuel
intensive system to a more capital investment driven system, see
Fig. 5. The fuel cost of coal plants would decrease from about
$100 billion in the BAU scenario to about $65 billion in the R
scenario. New capital investment of solar, wind, and storage
capacity in the R scenario is only slightly higher than the BAU
scenario contribute to the lower cost of renewables and storage,
from $55 billion in the BAU scenario to about $65 billion in the R
scenario. The overall power system cost in the R scenario is $280
billion, 11% lower than that in the BAU scenario, $310 billion.
Total costs under C50 and C80 are $285 billion and $390 billion,
respectively in 2030.
10% 4%
-
1000
2000
3000
4000
5000
6000
B R C50 C80 B R C50 C80 B R C50 C80
2020 2025 2030
Installed capacity (GW)
Coal Gas Nuclear Hydro Wind Solar Storage
49% 49% 36% 28% 37% 33% 19% 10% 27% 20% 11% 4%
7% 7% 7% 7%
12% 8% 6% 7%
11% 5%
5%
7%
6% 6% 5% 5%
5% 5% 3% 3%
4% 3% 3% 2%
21% 21% 18% 16%
17% 15% 11% 9%
13% 10% 8% 6%
10% 10% 21% 30% 18% 19% 21%
47% 30%
19% 22%
36%
8% 8%
14%
14% 10% 19%
33%
21%
13% 35%
42%
35%
1% 2%
7%
3%
1%
9%
11%
10%
Fig. 1 National capacity mix for four scenarios in 2020, 2025, and 2030. The scale of the bar chart are the installed capacity by technologies, and the
data labels show the share of each technology in total capacity. Source data are provided as a Source Data file.
–1000
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10,000
11,000
B R C50 C80 B R C50 C80 B R C50 C80
2020 2025 2030
Power generation (TWh)
Storage Coal Gas Nuclear Hydro Wind Solar
2% 1% 3% 4% 4%
64% 64%
54% 48%
60% 54%
36%
24%
53%
38%
23%
9%
1%
2%
2%
1%
1%
4%
2%
0%
1%
2%
10% 10%
10%
10%
10%
10%
10%
10%
10%
9%
9%
9%
19% 19%
19%
19%
16%
16%
16%
16%
14%
14%
14%
13%
3% 3%
9% 13%
7%
8%
12% 28%
14%
11%
15%
28%
3% 3% 7% 8%
5% 10%
25%
19%
8%
28% 39% 39%
Fig. 2 National power-generating mix for four scenarios in 2020, 2025, and 2030. The scale of the bar chart are the generation by technologies, and the
data labels show the share of generation by each technology in total generation. Source data are provided as a Source Data file.
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Regional disparities and needs. Mapping the mix of resource
capacity and required new transmission under the R scenario
reveals regional disparities in the development of renewable energy
sources (Fig. 6). First, solar capacities are concentrated in the
northwest—in the provinces of Inner Mongolia, Qinghai and
Shaanxi. Each of those areas has more than 100 GW of solar
capacity. Wind capacities are more evenly distributed along the
northwest, northeast, and eastern coastal provinces. Xinjiang, Hei-
longjiang, Shaanxi, Guangxi, Jilin, and Shanxi provinces have the
greatest number of wind installations; each has more than 30 GW
2250
a
2000
1750
1500
1250
1000
750
500
250
0
–250
–500
12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24
12 24 12 24 12 24 12 24 12 24 12 24
Hour
Hour
12 24 12 24 12 24 12 24 12 24 12 24
12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24
12 24
Wind Solar Hydro Gas Coal NuclearStorage, discharge Storage, charge
12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24 12 24
Dispatched power, GW
Dispatched power, GW
2250
2000
1750
1500
1250
1000
750
500
250
0
–250
–500
2250
b
2000
1750
1500
1250
1000
750
500
250
0
–250
–500
2250
2000
1750
1500
1250
1000
750
500
250
0
–250
–500
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Fig. 3 Hourly dispatch sources in 2030 under the R and C50 scenarios. Two days, a normal day (•) and a peak day (••) are selected in each month to
represent the month. Black solid line is the system load. aHourly dispatch in the R scenario. bHourly dispatch in the C50 scenario. Source data are
provided as a Source Data file.
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of wind capacity. Bringing the power generated from renewable
sources to the areas of demand requires extensive transmission
infrastructure. The focus for new transmission capacities are the
three metropolitan areas of Jing-Jin-Ji, the Yangtze Delta, and the
Pearl River Delta. New transmission infrastructure is needed to
bring wind and solar energy from the northwest (Qinghai, Gansu,
Inner Mongolia, and Shaanxi) to the central and eastern China
grids; for example, from Inner Mongolia to Hebei, Beijing, and
Tianjin; from Yunnan to Guangxi and Guangdong; from Anhui
and Jiangsu to Zhejiang and Shanghai. The necessary transmission
capacity could be as great as 35 GW, which would double the
current maximum cross-provincial transmission capacity.
As shown in Fig. 7, regional disparities in demand, as well as
resource availability, from hydro, solar, and wind, lead to
different generation profiles for the BAU scenario. Across almost
all regions, for the BAU scenario, generation closely matches the
regional demand. The only exception is the Northwest grid
which, even under the BAU scenario, is expected to export
electricity to other regional grids. Under the R scenario, solar
and wind resources rich regions increase their electricity
generation dramatically, while regions with less solar and wind
availability see a decrease in their overall generation. Decreases
in generation across the Central, Eastern, and Southern grids,
come mostly from decreased electricity generation from coal in
these regions. This trend continues as the scenarios impose
increasingly stringent decarbonization goals across the national
grid, as shown in the third and fourth columns in each regional
graph.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2015 2020 2025 2030
Mt CO2
0
10
20
30
40
50
60
70
80
90
100
2015 2020 2025 2030
$/MWh
ab
B R C50 C80
Fig. 4 Carbon emissions and power costs to 2030 under four scenarios. a Carbon emissions and bPower costs are shown in the business as usual scenario
(B), the low-cost renewables scenario (R), the carbon constraints scenario (C50), and the deep carbon constraints scenario (C80), respectively. Power costs in
the R scenario and the C50 scenario are 11% and 6% lower than that of the BAU scenario in 2030, respectively. Source data are provided as a Source Data file.
0
50
100
150
200
250
300
350
400
B R C50 C80
Billions 2015 USD
New storage costs
New wind plant costs
New solar plant costs
New nuclear plant capital costs
New gas plant capital costs
Nuclear fuel costs
Gas fuel costs
Coal fuel costs
Existing plants o and m costs
Existing plants capital costs
New inter-provincial transmission costs
New local transmission and distribution costs
Existing inter-provincial transmission costs
Existing local transmission and distribution costs
Fig. 5 Distribution and costs of power sources under four scenarios in 2030. The costs are categorized into existing and new capacity and transmission.
Fossil fuel technologies and nuclear have fuel costs. Source data are provided as a Source Data file.
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Under the R scenario, the Northwest grid is a net exporter of
electricity to the Central, Northern, and Eastern grids. In
particular, in 2030, under this scenario, the Northwest grid is
expected to export 672, 287, and 90 TWh to the Central,
Northern, and Eastern grids, respectively. On the other extreme,
under the same scenario in 2030, the Eastern grid imports 287,
125, 111, 57, and 22 TWh from the Northwest, North, Central,
South and Northeast grids. As outlined in this research, and as
decarbonization priorities increase in the C50 and C80 scenarios,
the total electricity generation in the Eastern grid further decreases
and becoming increasingly import-dependent to meet its demand.
Future studies might consider studying the impact of a decrease in
costs for offshore wind, and demand response technologies on the
Eastern grid’s reliance on imports to meet its demand under more
stringent decarbonization goals by 2030 and beyond.
Under our current assumptions, we can observe that with the
assumed decrease in costs for solar, wind and storage technol-
ogies the Northwest region emerges as a national supplier of
carbon neutral electricity even as that choice requires increases in
transmission capacity across the Northwest and all other regions.
Although not shown by arrows in Fig. 7, one can infer that this
trend is further exacerbated by more stringent carbon reduction
goals across the national grid. In particular, we can see that under
the C80 scenario, the Northwest grid generation exceeds its own
demand by over 300%, while the Eastern grid produces only
about 50% of its total electricity demand.
Sensitivity analysis and uncertainties.Thepowersectorisa
dynamic, evolving system affected by costs, demands, and other
factors. We conducted sensitivity analyses on two key assumptions:
the capital costs of renewables (solar, wind, and storage), and future
electricity demand. Changes in both resource capacity and gen-
eration respond to changes in demand and costs. We consider two
sensitivity scenarios: D +20% assumes that demand increases lin-
early 20% until 2030; C +20% assumes that the capital costs of
solar, wind, and storage are 20% higher than under the R scenario.
Under the D +20% scenario, by 2030 the capacities of solar and
wind installations increase to 1890 GW and 1,040 GW, respectively,
whereas under the C +20% scenario, by 2030 the capacities of solar
and wind installations decrease to 980 GW and 650 GW, respec-
tively (Fig. 8a). The generation mix in the sensitivity scenarios
follows a very similar pattern as in the capacity mix (Fig. 8b).
The large-scale decarbonization of the power sector requires
that several processes take place simultaneously. First, both the
resource capacity and transmission infrastructure must be scaled
up quickly. Second, the investment needed for the infrastructure
transformation must be acquired and dedicated. Third, social and
economic equity must be addressed during the transition to lower
carbon power systems. Any or all of those processes could
encounter issues with the current physical framework and face
obstruction from current stakeholders.
There is also uncertainty to deploy large-scale of storage
capacity to integrate the renewables. Our results show in the R
Tibet
Xinjiang
Qinghai
Gansu
Sichuan
Yunnan
Jilin
Hunan
Hebei
Hubei
Guangxi
West Inner Mongolia
Shaanxi Henan
Anhui
Jiangxi
Guizhou Fujian
Liaoning
Heilongjiang
Shanxi Shandong
Guangdong
Jiangsu
Zhejiang
Chongqing
Ningxia
Taiwan
Hainan
Beijing
Tianjin
Shanghai
Hong_Kong
Coal
Gas
Solar
Storage
Nuclear
Hydro
Wind
<10 GW
10–15 GW
15–20 GW
20–25 GW
>25 GW
Total
Capacity
New
Transmission
East Inner Mongolia
Fig. 6 Provincial total capacity mix and new transmission lines required by 2030 under the R scenario. The pie charts shows the total power capacity
mix in each province, and the red lines show the new interprovincial transmission lines to bring electricity from resource centers to demand centers. Source
data are provided as a Source Data file.
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scenario system requires 307 GW of storage capacity to provide
about 250 TWh energy exchange (charge/discharge) and in the
C80 scenario about 525 GW of storage capacity to provide about
388 TWh of energy from storage in 2030. Storage is being used
about 2.2 and 2 hours per day to provide the 250 and 388 TWh of
storage in the R and C80 scenarios.
Pumped hydro capacity in China in 2015 was about 25 GW, and
has been expanding very quickly. It is estimated to have 100 GW,
0
1000
2000
3000
0
1000
2000
3000
0
1000
2000
3000
0
1000
2000
3000
0
1000
2000
3000
0
1000
2000
3000
0
1000
2000
3000
Coal
Gas
Solar
Storage
Nuclear
Hydro
Wind
Unit: TWh
20
100
200
400
800
Fig. 7 Regional generation, demand, and interregional transmission map for the R scenario in 2030. The different grids are shaded in different colors
based on the dominating energy source as the region decarbonizes. For example, the Northeastern grid is dominated by high wind energy penetration and
is therefore shaded green and the Central grid is dominated by hydro electricity generation and is therefore shaded blue. Each region shows a graph with
four bars representing the generation for the four different scenarios in order of increased carbon reduction (from left to right: BAU, R, C50, and C80,
respectively). The dotted line across all bars in each set of generation graphs represents the yearly demand in 2030 in each region, which stays constant
across the four scenarios. The magenta arrows point in the direction of the transmission flow between two regions. Source data are provided as a Source
Data file.
–
1000
2000
3000
4000
5000
R
D+20%
C+20%
R
D+20%
C+20%
R
D+20%
C+20%
2020 2025 2030
Installed capacity (GW)
(1000)
–
1000
2000
3000
4000
5000
6000
7000
8000
9000
10,000
11,000
12,000
R
D+20%
C+20%
R
D+20%
C+20%
R
D+20%
C+20%
2020 2025 2030
Power generation (TWh)
ab
WindSolarHydroGasCoal Nuclear Storage
Fig. 8 Sensitivity analyses of 2030 capacity and generation mixes under the D +20% and C +20% scenarios. a installed capacity mix. bpower
generation mix. D +20% assumes that demand increases linearly 20% until 2030; C +20% assumes that the capital costs of solar, wind, and storage are
20% higher than under the R scenario. Source data are provided as a Source Data file.
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at least 80 GW by 2025, and potentially up to 130 GW by 203015.
In this case, to reach 307 GW capacity of storage under the R
scenario in 2030, it would require battery storage to reach about
177 GW. With the increase of battery efficiency and performance,
the needed storage capacity would be smaller. However, it indeed is
very ambitious to deploy such a large scale of storage in a
comparatively short time, about 11.8GW annually during the
studying period. Supply chain and life cycle management,
economics of storage and policy support are essential to spur the
large-scale deployment in order to make such transition happen.
Discussion
The dramatic decrease in costs for renewable energy enables us to
model China’s power system and evaluate prospects for accel-
erating its decarbonization. Our modeling results show that if the
costs for solar, wind, and storage follow recent global trends, by
2030 China could derive 62% of needed electricity from non-
fossil sources. Total costs under the R scenario are 11% lower
than those under the BAU scenario. Under the carbon constraints
(C50) scenario, China could eliminate half of its 2015 carbon
emissions from the power sector by 2030 with 6% lower cost,
while delivering 77% of electricity from non-fossil sources. In the
deep carbon constraints (C80) scenario, an 80% emissions
reduction from the 2015 level is technically feasible by 2030 but
involves about a 21% higher power cost than under the BAU
scenario and a $21/tCO
2
cost of conserved carbon. China has
launched a national emissions-trading-system (ETS) with a price
range of $3-14.5/t CO
2
, and the carbon price is expected to rise to
an average of $16.5/t CO
2
, ranging $4-20/tCO
2
by 203016.
Although modeling results identify possible pathways to
accelerate the decarbonization of China’s power sector under the
four scenarios developed for this study, the speed and scale of
expanding the use of renewable energy could be enhanced or
impeded by government policies, stakeholder interests, and
capital market constraints, among other factors. Positive efforts
could include target setting and cost reduction, as exemplified in
the renewable portfolio standard in California and elsewhere.
Capacity auctions in China and India also create a pricing
mechanism to lower the cost of renewables, especially wind and
solar. Reforming the power market could create incentives to
reduce institutional barriers to trading power across regions and
to integrating renewable energy, thereby reducing the curtailment
of wind and solar energy observed in the Chinese power sector.
China’s power sector is in the midst of expansion and transi-
tion. The costs for energy from wind, solar, and storage are
affected by many factors such as policy drivers and technological
innovation. However, as indicated in the sensitivity analyses, the
structural transformation of China’s power sector is fairly con-
sistent as long as the cost of renewable technology follows the
global trend. This analysis indicates that fast decarbonization of
China’s power system is both technically feasible and econom-
ically beneficial to China’s development, as well as offering the
prospect of large emissions mitigation with a global impact.
Methods
SWITCH-China model. To most effectively model the impact of renewables on
China’s power system, we updated the SWITCH-China capacity expansion model
(Supplementary Table 1, Supplementary Note 1, 2). SWITCH, which is a loose
acronym for investment in solar, wind, hydro, and conventional technologies, is an
optimization model that has the objective function of minimizing the cost of
producing and delivering electricity based on projected demand through the
construction and retirement of various power generation, storage, and transmission
options available currently and at future target dates. The SWITCH-China model
provides high resolution in both the temporal and spatial dimensions, to simulate
the effect of the dramatically decreasing cost for incorporating renewable energy
into the power grid14. SWITCH-China runs on a provincial scale and utilizes
hourly data to simulate and optimize power system planning based on operational
constraints. SWITCH optimizes both the long-term investment and short-term
operation of the grid. The model incorporates a combination of current and
advanced grid assets. Optimization is subject to reliability, constraints on opera-
tions, and resource availability, as well as on current and potential climate policies
and environmental regulations17–21.
SWITCH-China’s modeling decisions regarding system expansion are based
on optimizing capital costs, operation and maintenance costs, and the variable
costs for installed power plant capacities and transmission lines (Supplementary
Note 2). Two primary options were available to help us decide which cost
projections, from 2015 to 2030, to use in SWITCH-China. LBNL has developed
projections of LCOE for utility-scale solar, wind, and storage to 2030. In
addition, NREL’s latest annual technology baseline (ATB) model projects
capacity costs for solar and wind technologies22. Although the LCOE is useful in
informing investment decisions for many situations, SWITCH-China uses
capital, operation and maintenance, and variable costs to develop investment
decisions. Thus SWITCH-China assumes that trajectories of capital costs for
solar, storage, and wind technologies for the R, C50 and C80 scenarios will
resemble NREL’s ATB projections to 2030. Trajectories of capital cost for the
baseline scenario utilize the original SWITCH-China cost assumptions
foradvancedtechnologiesto2030.Exceptforsolar,wind,andstorage,all
other costs follow the original SWITCH-China cost assumptions
(Supplementary Figs. 1 and 2; Supplementary Note 3). The CO
2
accounting
methods and electricity demand projection are detailed in Supplementary
Note 4 and 5.
Scenarios. We developed four scenarios in our analysis: business as usual scenario
(BAU), low-cost renewables scenario (R), carbon constraints scenario (C50), and
deep carbon constraints scenario (C80). Table 1summarizes the key assumptions
of the four scenarios.
Reporting summary. Further information on research design is available in
the Nature Research Reporting Summary linked to this article.
Data availability
The source data underlying Figs. 1to 8are provided as a Source Data file. All data used
for this analysis are available from cited publicly available sources or from the authors
upon reasonable request.
Code availability
Code used in AMPL and Python for this study are available from the authors upon
reasonable request.
Table 1 Model scenarios.
Business as usual (BAU) Low-cost
renewables (R)
Carbon constraints (C50) Deep carbon constraints (C80)
Base year 2015
Existing policies Continuation of current policies and no new coal plants after 2020 because of tight regulations on air pollution and institution of
carbon mitigation measures23
Future renewable
costs assumptions
Utilizing conventional models
for future renewable costs
Rapid decrease in costs for renewables and storage continues: dramatic decreases in wind,
solar, and storage costs as projected by Lawrence Berkeley National Laboratory (LBNL) and the
National Renewable Energy Laboratory (NREL)
Carbon constraints No No 50% reduction in power sector
CO
2
from 2015 level by 2030
80% reduction in power sector
CO
2
from 2015 level by 2030
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16184-x
8NATURE COMMUNICATIONS | (2020)11:2486 | https://doi.org/10.1038/s41467-020-16184-x | www.nature.com/naturecommunications
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Received: 6 November 2019; Accepted: 19 April 2020;
Published online: 19 May 2020
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Acknowledgements
The authors would like to thank Junfeng Hu and David Fridley for their comments. This
work was supported by the Energy Foundation China, the Hewlett and MJS Foundation
through the U.S. Department of Energy under Contract Number No. DE-AC02-
05CH11231.
Author contributions
G.H. and J.L coordinated the research. G.H., J.L., and A.P. contributed to the study
design. G.H. performed the modeling analysis, and led the writing of the paper. G.H., F.S,
X.L., and N.A. contributed to the data collection, figure drawing, and policy analysis. All
authors provided feedback and contributed to writing the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41467-
020-16184-x.
Correspondence and requests for materials should be addressed to G.H., J.L. or A.P.
Peer review information Nature Communications thanks the anonymous reviewer(s) for
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