Climate change impacts on potential solar energy production: A study case in Fukushima, Japan

Journal Pre-proof Climate change impacts on potential solar energy production: A study case in Fukushima, Japan Kazutaka Oka, Wataru Mizutani, Shuichi Ashina PII:

S0960-1481(20)30148-8

DOI:

https://doi.org/10.1016/j.renene.2020.01.126

Reference:

RENE 12989

To appear in:

Renewable Energy

Received Date: 27 June 2019 Revised Date:

13 December 2019

Accepted Date: 26 January 2020

Please cite this article as: Oka K, Mizutani W, Ashina S, Climate change impacts on potential solar energy production: A study case in Fukushima, Japan, Renewable Energy (2020), doi: https:// doi.org/10.1016/j.renene.2020.01.126. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Kazutaka Oka: Conceptualization, Methodology, Software, Validation, Writing - Original Draft. Wataru Mizutani: Software, Visualization, Data Curation. Shuichi Ashina: Conceptualization, Writing- Reviewing and Editing, Funding acquisition.

Climate change impacts on potential solar energy production: a study case in Fukushima, Japan Kazutaka OKA *†, Wataru MIZUTANI ** and Shuichi ASHINA* * National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan ** Mizuho Information and Study Institute Inc., 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan † Corresponding author ([email protected])

1

Abstract

2

To construct a long-term energy plan for renewable energies such as photovoltaics

3

(PV), the influence of meteorological conditions on energy yield must be considered.

4

It is necessary to understand how climate change impacts energy generation. For that

5

purpose, a method to estimate PV energy generation considering climate change

6

scenarios was developed. The developed method can estimate hourly generation

7

amounts with spatial resolutions of 1-km mesh. It was applied to Fukushima

8

Prefecture, located in the northeast region of Japan. To establish future scenarios

9

three Representative Concentration Pathways (RCP) and seven Global Climate

10

Models (GCM) were analyzed, and uncertainties caused by differences in future

11

scenarios were investigated. The results show that annual generation of PV energy

12

was estimated to increase on average by 1.7% in 2030, 3.9% in 2050, and 4.9% in

13

2070 due to climate change. Energy plans and adaptation actions are expected to be

14

performed so the country is prepared for future impacts.

15 16

Key words: Climate change, impact assessment, climate scenario, renewable energy,

17

photovoltaic energy

18

1

1

1. Introduction

2

Several researches clearly show that renewable energy sources, such as solar and

3

wind, will be essential for the transition to a low carbon (or decarbonized) society

4

both in Japan and in the world. For example, the Intergovernmental Panel on Climate

5

Change Fifth Assessment Report (IPCC AR5) revealed that the stringent mitigation

6

scenario leading to 430–530 ppm CO 2 eq in 2100 requires the introduction of

7

renewable energy at a rate of 194–282 EJ/yr (IPCC, 2014b [1]). The IPCC Special

8

Report on Global Warming of 1.5 °C concluded, with high confidence, that renewable

9

energy should supply 70%–85% of electricity in 2050 (IPCC, 2018 [2]). In Japan, the

10

Plan for Global Warming Countermeasures (term: 2016–2030) endorsed by the

11

Cabinet in 2016 (Japan, 2016 [3]), and the Japan’s Intended Nationally Determined

12

Contribution (INDC), submitted to United Nations Framework Convention on

13

Climate Change (UNFCCC) in 2015 (Japan, 2015a [4]), suggest the maximum

14

introduction and utilization efforts of renewable energy.

15

The Government of Japan has established measures to promote renewable energy.

16

The feed-in-tariffs (FIT) scheme was launched in July 2017, and the introduction of

17

renewables accelerated in all sectors, including governments, local authorities,

18

businesses, as well as individuals (METI, 2018 [5]). Installed photovoltaics (PV)

19

capacity increased from 3,618 MW in 2010 to 49,040 MW in 2017. Onshore wind

20

power installed capacity increased from 2,269 MW in 2010 to 3,358 MW in 2017

21

(from the IRENA [6]).

22

Among the renewables, energy and/or electricity outputs from PV and wind

23

turbines are limited by meteorological conditions, such as solar radiation, cloud

24

cover, wind direction, and wind speed. PV output changes according to precipitation

25

and cloud cover, and wind power output changes according to wind direction and

26

wind speed as well as the distribution of areas suited to wind turbines (IPCC, 2011

27

[7]). Future climate change will modify these meteorological conditions. Some will

28

increase and some will decrease, and future renewable outputs will vary in line with

29

these changes. In this context, at the same time that renewables can mitigate climate

30

change, they also suffer the consequences of climate change. These impacts on

31

renewables will be unavoidable to a certain level, even with stringent mitigation

32

policies (IPCC, 2014a [8]). Given this situation, assessing impacts of climate change 2

1

on renewable outputs will provide essential background to design and realize low and

2

decarbonized energy systems with renewables as the core components of long-term

3

energy supply.

4

Considering the climate change impacts upon the energy sector, the National Plan

5

for Adaptation to the Impacts of Climate Change formulated by the Japanese

6

government in 2015 mentions the importance of adapting policies in the relevant

7

fields (Japan, 2015 [9]). Furthermore, the Climate Change Adaptation Act was

8

promulgated in Japan in June 2018, and came into effect in December 2018 (Japan,

9

2018 [10]). In this context, while changes arising from climate change are

10

unavoidable, statutory preparations are underway regarding the planning and

11

introduction of adaptive policies. However, there is still not an adequate amount of

12

knowledge regarding adaptive policies in the energy field. In certain nations outside

13

of Japan, the consideration and planning of adaptive policies in the energy field are

14

already underway (France, 2013 [11]; Germany, 2008 [12]). It is hoped that the

15

examination and introduction of adaptation policies based on the ascertainment of the

16

climate change impacts on the energy sector will henceforth take place in Japan.

17

This paper is organized as follows: in Section 2, the methodology to evaluate the

18

potential PV energy generations are described; in Section 3, the estimation results

19

are summarized; discussions and future issues are presented in Section 4. Finally,

20

conclusions are given in Section 5.

21 22 23

2. Material and methods 2.1 Study area

24

The potential of renewable resources has been investigated in a national and

25

regional scale in Japan. CRIEPI (2012) [13] estimated the potential of PV and wind

26

power in an hourly basis by using data from the Automated Meteorological Data

27

Acquisition Data System (AMeDAS) [14]. AMeDAS is a surface observation network

28

developed by the Japan Meteorological Agency (JMA) for gathering regional weather

29

data and comprises 1,300 stations with rain gauges throughout Japan [15]. Among

30

these, around 840 stations observe not only precipitation, but also wind speed and

31

direction, temperature, and duration of sunshine, with an average separation of 21 km.

32

Japan's Ministry of the Environment (MOE) has developed the Map of Renewable 3

1

Energy Potential in Japan [16], which provides the annual potential energy

2

production

3

meteorological observation data. Evaluation of PV output often relies on the Solar

4

Radiation Database [17], which was developed by the New Energy and Industrial

5

Technology Organization (NEDO) and covers the hourly solar radiation data

6

observed at the weather stations and AMeDAS locations. These meteorological

7

observation data and their deliverables represent current and/or historical situations.

8

However, past research and experiments on estimation of PV only provide snapshots

9

of current potentials. To the best of our knowledge, there is no research on the effect

10

of different GHG emission scenarios and climate models on future renewable

11

energies production and its uncertainties in Japan. However, it is noteworthy that

12

there has been considerable research addressing this issue with target areas such as

13

all of Europe, all of Africa, individual European or African countries, and the USA

14

(see Solaun & Gerda (2019) [18] for the latest review and references therein).

by PV,

wind

power,

and

other

types

of

renewables

based

on

15

Therefore, this study aims to evaluate the future potential of PV power in Japan

16

and their uncertainties. For that, multiple GHG emission scenarios and climate

17

models are used. The long-term implications of climate change on the establishment

18

of sustainable energy systems using renewable resources are discussed. This study

19

develops a series of methods that can examine current and future PV potentials on an

20

hourly and an annual basis by using Global Climate Models (GCM) outputs spatially

21

downscaled to 1-km mesh. The target area is Fukushima Prefecture, located in the

22

northeast region of Japan (see Fig. 1). Fig. 1 also shows the population distribution

23

in Fukushima Prefecture with 1-km mesh. Please note that the data is based on the

24

population of 2010, before the 2011 Tohoku earthquake and tsunami. In this study,

25

the year 2010 was set as the "base year” for the following reasons: 1) the latest

26

population distribution data provided by the government is 2010; 2) the annual

27

duration of sunshine in 2010 was closest to “normal” or “average” levels seen in East

28

Japan (JWA, 2011 [19]). We have used the AMeDAS observation data in 2010 for the

29

base year data.

4

1 2

Fig. 1

3

2010 with 1-km mesh (before the 2011 Tohoku earthquake and tsunami). Population

4

data is from the MLIT [20].

Location of Fukushima Prefecture in Japan, with population distribution in

5 6

Renewable energies do not provide constant levels of output. For example, PV

7

mainly generate power during the daytime. In addition, these factors also differ

8

according to the location of the facilities. To proceed with the construction of a

9

sustainable and low-carbon energy system, estimations of power generation on an

10

hourly basis with a high spatial resolution that consider peculiarities in land use are

11

desirable. Therefore, an evaluation method was developed by employing high

12

temporal resolution with hourly basis and high spatial resolution with 1-km mesh to

13

evaluate the impact of climate change on PV power generation.

14

The developed methodology consists of three steps: 1) Spatial interpolation (i.e.

15

downscaling) of hourly basis meteorological observation data to 1-km mesh; 2)

16

Downscale and bias correction of GCM outputs from climate models using the

17

meteorological observation data; and 3) Estimation of PV power generation based on

18

the downscaled and bias corrected GCM outputs (i.e., climate scenario data). Details

19

of the methodology and the correct data are described below. It should be noted that,

20

since this study aimed to enable the application of the developed method in any part 5

1

of Japan, data which are available nationwide were employed.

2 3

2.2 Meteorological data

4

To estimate the power that can be generated by PV, data on solar radiation,

5

temperature, and wind speed are required. This study used the AMeDAS data [14] for

6

temperature and wind speed published by JMA as the base year data. The JMA

7

provides solar radiation data from their limited observation stations. This study uses

8

the model coupled crop-meteorological database (MeteoCrop DB) Ver. 2 [21],

9

developed by the National Institute for Agro-Environmental Science (NIAES) as an

10

additional data source for solar radiation. The MeteoCrop DB collects and prepares

11

meteorological observation data for rice cultivation, and it includes estimations at

12

AMeDAS locations and weather stations. The MeteoCrop DB provides data on

13

duration of sunshine and solar radiation at the same observational locations of

14

temperature and wind speed/direction. Data specifications are illustrated in Table. 1.

15 16

Table. 1 Data sources of meteorological factors Po wer generation type

Meteorological factor Temperature, wind speed

PV po wer Solar radiation

Data source Japan Meteorological Agency “Auto mated Meteorological Data Acquisition System ( AMeDAS)” [14] National Institute for Agro-Environmental Sciences “Model coupled crop-meteorological database Ver.2” [21]

17 18

More than 40 GCM outputs were used in IPCC (2013) [22], and more than 20 in

19

IPCC (2018). To minutely analyze the impacts of climate change in Japan, the present

20

study employs the climate models used in the Comprehensive Study on Impact

21

Assessment and Adaptation for Climate Change (S-8) [23] and the Integrated Climate

22

Assessment – Risks, Uncertainties and Society (S-10) [24], both implemented by the

23

Environment Research and Technology Development Fund of MOE.

24

As mentioned above, the forecast of meteorological conditions depends not only

25

on climate models but also on GHGs emission scenarios. Therefore, this study

26

focuses on the Representative Concentration Pathway (RCP) used in IPCC (2013) 6

1

[22]. Three RCP scenarios are used: the RCP8.5 scenario, in which the current

2

changes are maintained and the slowest global warming countermeasures are

3

assumed; the RCP2.6 scenario, with the fastest countermeasures; and the RCP4.5

4

scenario, which lies midway between the RCP8.5 and RCP2.6 scenarios. Regarding

5

the GCM outputs of the climate models utilized in the S-8 and S-10 projects, those

6

that embrace all three RCP scenarios were employed. Seven climate models were

7

employed, as shown in Table. 2. Then, an assessment using the GCM outputs

8

produced by the employed climate models are conducted. Table. 2 shows the

9

characteristics of the climate scenarios, such as time periods, temporal resolution,

10

and indicators.

11 12

Table. 2 Data characteristics of the climate scenarios Item Climate models

Details GFDL-CM3, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5 A-LR, MIROC5h, MIROC-ESM-CHEM, MRI-CGCM3.0

Representative concentration scenarios

RCP2.6, RCP4.5, RCP8.5

Time periods

Base year (2010), 2030 (average between 2021–2040), 2050 (average between 2041–2060), 2070 (average between 2061–2080)

Temporal resolution

Monthly

Indicators

Near-surface air temperature (tas), surface do wnwelling shortwave radiation (rsds), near-surface wind speed (sfcWind)

13 14

2.3 Data spatial interpolation

15

Meteorological observation data were acquired from weather stations and

16

AMeDAS observation points (at intervals of about 21 km). In addition, the spatial

17

resolution of the GCM outputs is in principle around 100 km (1° x 1°). To obtain data

18

with a spatial resolution of 1-km mesh, it is necessary to spatially interpolate

19

(downscaling) both data on each mesh. In this study, based on the research by

20

Yokozawa et al. (2003) [25], the inverse distance weighting method was adopted, in

21

which four locations of the meteorological observation data (or GCM outputs) in the

22

vicinity of a target 1-km mesh were sampled. After that, they were downscaled by

23

weighting them in terms of distance:

24

7

=

∑

1

∑



(1)

1 2

where,

3

the vicinity of the target 1-km mesh,

4

(or GCM outputs) of the four locations in the vicinity of the target 1-km mesh, and

5 6

represents the target 1-km mesh data, N=4 is the number of locations in

to

to

the meteorological observation data

the distances between the target 1-km mesh and the four meteorological

observation data (or GCM output) locations.

7

Climate scenario data have an unavoidable bias, and it is necessary to correct this

8

bias to properly evaluate the future changes in renewable energy output. In this study

9

the bias correction shown below was implemented, according to Yokozawa et al.

10

(2003) [25].

11 12

13 14

・ Temperature , , ,

=

, , ,

+ (

−

)

(2)

・ Solar radiation, wind speed , , ,

=

, , ,

×

(3)

15 16

where

17

, , ,

, , ,

are the future climate factors after bias correction on an hourly basis,

are the current climate factors from meteorological observations also on an

18

hourly basis,

19

and

20

d, and t are year, month, day, and time, respectively. Using this methodology, the

21

average monthly amount of change due to climate change is reflected by the time

22

sequence of meteorological observations.

is the future average monthly climate factor from GCM outputs,

is the current average monthly climate factor from GCM outputs, and y, m,

23

Fig. 2 shows the predicted changes in the average annual solar radiation and wind

24

speed (the rate of change from the base year), as well as the predicted changes in the

25

average annual temperature (increase from the base year) in Fukushima Prefecture.

26

The rates of change from the base year for solar radiation and wind speed are 8

1 2 3

calculated as follows: first, the second term on the right-hand side of the eq 3 (i.e.,

(

/

)) are summed up for 365 days for each 1-km mesh, and the result is then

divided by 365 days. Finally, the obtained numbers are averaged between all 1-km

4

mesh in Fukushima Prefecture. The increase from the base year for temperature is

5

also calculated in the same manner. The "base year” represents the meteorological

6

observation data for the year 2010, in which the annual duration of sunshine was

7

closest to average levels in East Japan (JWA, 2011 [19]). Looking at the rate of

8

change of the average annual solar radiation, it can be seen that the increase is

9

approximately 1.8% [0.5%–3.5%] in 2030, 4.0% [0.5%–7.0%] in 2050, and 5.1%

10

[1.3%–10.6%] in 2070, indicating a trend that the further into the future, the larger

11

the increase. Changes in precipitation and cloud cover due to climate change exert an

12

impact upon solar radiation. Considering the rate of change of average annual wind

13

speeds, it can be seen that the changes are approximately -1.5% [-4.1%–1.6%] in

14

2030, -1.8% [-5.2%–2.7%] in 2050, and -1.9% [-6.6%–2.9%] in 2070, and while the

15

overall trend is downward, the dispersion amongst climate models is considerable.

16

Considering the change of average annual temperature, it can be seen that the

17

increases are approximately 1.42 °C [0.35–2.38 °C] in 2030, 2.15 °C [0.70–3.68 °C]

18

in 2050, and 2.74 °C [0.81–5.27 °C] in 2070, indicating a trend that the further into

19

the future, the larger the increase.

20

In this study, we have used the AMeDAS observation data as the meteorological

21

observation data of the base year. The spatially interpolated data using the AMeDAS

22

observation data require examination for accuracy with surface observation data

23

other than AMeDAS, but there are currently no such observations with high accuracy.

24

The National Agriculture and Food Research Organization (NARO) provides “the

25

Agro-Meteorological Grid Square Data” [26], which provides daily climate values

26

with a spatial resolution of 1-km mesh by interpolating AMeDAS data such as

27

altitude (Seino 1993 [27]). For reference, our interpolated hourly data was

28

aggregated into daily values, and then compared with the NARO data by calculating

29

the linear regression for the two data with only-1-km meshes in which it would be

30

possible to install PV arrays (7,224 meshes among 13,543 meshes in Fukushima

31

Prefecture; please refer to Table 4 for PV array installation). Table 3 summarizes the

32

relations between spatially interpolated AMeDAS data and the NARO data by linear 9

1

regression. For temperature and solar radiation, strong correlations between the two

2

sets of data can be found. For wind speed, on the other hand, correlation cannot be

3

found, which shows the method of interpolation affects resulting values of wind

4

speed. However, we note that wind speed does not seriously affect the amount of PV

5

power generation since it only affects the array temperature (see eq. 10). The amount

6

of PV power generation is strongly dependent on the amount of solar radiation (see

7

Fig. 4)

8

Although the accuracy of interpolation affects the amount of PV power generation

9

itself, the accuracy of interpolation does not seriously affect the ratio between the

10

amount of PV power generations of the base year and that of the future, because the

11

changes in temperature, solar radiation, and wind speed from climate change are

12

determined by the difference (for temperature) or ratio (for solar radiation and wind

13

speed) between the base year and the future as shown in eq 2-3. Furthermore, since

14

the spatial resolution of GCM outputs is around 100 km, which is coarser than that of

15

AMeDAS (at intervals of about 21 km), the interpolation of GCM outputs will not

16

seriously affect the amount of PV power generation compared to that of AMeDAS

17

data.

18 19

Table. 3 Relations between spatially interpolated AMeDAS data and the NARO data

20

obtained by linear regression Indicators Temperature Solar radiation Wind speed

Slope 0.990 0.982 0.905

Intercept 0.528 144 0.174

R2 0.991 0.946 0.018

21 22

As described in Section 2.2, the meteorological observation data used for the bias

23

correction has a spatial resolution at intervals of about 21 km. Therefore, to conduct

24

more spatially precise bias correction, there is a need for more spatially finer

25

meteorological observations. In addition, the spatial resolution of GCM outputs is

26

around 100 km (1° x 1°). Therefore, GCM outputs with spatially finer resolution are

27

required to account for geographical local conditions, which in turn affect local

28

meteorological conditions. The direct use of dynamically downscaled climate data by

29

Regional Climate Models (RCM) is a strategy to account for geographical local

10

1

conditions, despite the need for bias correction. These are future issues that need to

2

be addressed.

3

(a) Predicted changes in annual average solar radiation

(b) Predicted changes in annual average wind speed

11

(c) Predicted changes in annual average temperature 1

Fig. 2. Predicted changes in (a) annual average solar radiation, (b) annual average

2

wind speed, and (c) annual average temperature relative to the base year under seven

3

climate models in Fukushima Prefecture.

4

The upper limits of the horizontal bars show the maximum prediction value, while

5

the lower limits show the minimum prediction value. The tops of the boxes represent

6

the 75th percentile, and the bottoms the 25th percentile. The black diamonds show

7

the average values. The predictive results of the three climate models developed in

8

Japan (i.e., MIROC5h, MRI-CGCM3.0, and MIROC-ESM-CHEM) are shown as

9

circles as a reference.

10 11

2.4 PV power generation

12

The amount of PV power generation is estimated by multiplying the total solar

13

radiation by the installed capacity. The solar radiation considers inclined surfaces

14

per unit of surface area, and the azimuthal angle direction. The installed capacity was

15

obtained from the surface area suitable for installations.

16

Since the PV arrays installed on buildings have a constant azimuthal angle with the

17

ground, the solar radiation absorbed by the arrays changes according to that

18

inclination. It is, therefore, necessary to estimate the solar radiation on inclined

19

surfaces of any inclined direction surfaces. The following items explain the 12

1

methodology to calculate total solar radiation on inclined surfaces.

2

First, the total solar radiation on horizontal surfaces is separated into normal

3

direct radiation and diffuse solar radiation on horizontal surfaces (direct/diffuse). To

4

calculate the diffuse solar radiation on horizontal surfaces, the Erbs model (Erbs et

5

al., 1982 [28]) was used as given by eq. 4. According to Soga (1999) [29], results

6

using the Perez ([30]), the Erbs, the Chandrasekaran ([31]), and the Reindle ([32])

7

models are found to produce relatively good estimations compared to such as the

8

Skartveit model ([33]). Although the Perez and the Reilde models take relative

9

humidity as an input, only two stations of AMeDAS in Fukushima Prefecture observe

10

relative humidity, and therefore it is not feasible to use the Perez and Reilde models.

11

Thus, we have used the Erbs model, which is not state-of-the-art, but produces

12

relatively good estimations and has been well-utilized in Japan.

13 14

15

#

17 18 19

=

( ⋯ (0 ≦ 0.22) ' # , , , × )1.0－0.09 × K. ( % # , , , & × )0.9511 − 0.1604 × 0＋4.388 × 02－16.638 × 03＋12.336 × 04. ⋯ )0.22 ＜ 0 ≦ 0.80. % ( ⋯ )0.80 ＜ 0. $ # , , , × 0.1651

# 16

, , ,

, , ,

=

# (,

where, #

, ,

, , ,

－# sinℎ

, , ,

(5)

[kW/m 2 ] is the diffuse solar radiation on horizontal surfaces, # (,

[kW/m ] is the total solar radiation on horizontal surfaces, # 2

, , ,

2

, ,

[kW/m ] is the

normal direct radiation, K [-] is the clearness index (calculated by dividing the total

20

solar radiation on horizontal surfaces by the extraterrestrial solar radiation on

21

horizontal surfaces), and h [rad] is the solar elevation angle, and y, m, d, and t are

22

year, month, day, and time, respectively.

23

Referring to NEDO (2012) [34], the total solar radiation on inclined surfaces was

24

calculated from the diffuse solar radiation on horizontal surfaces, normal direct

25

radiation, and global solar radiation on horizontal surfaces obtained from the

26

previous calculation. Eq. 8 is the isotropic model (Liu & Jordan, 1963 [35])

27

employed in the analysis. In NEDO (2012) [34], albedo is set as 0.7 for snow and 0.2 13

(4)

1

for no-snow. However, the study did not consider the changes in albedos by snowfall.

2 # <,,

# <,, # <,,

3 4 5 6 7 8

, , , , , ,

# <,( , ,

,

= # (, =# =#

, , , , , ,

= # <,,

where, # <,,

, ,

, , , ,

× = ×

1－ cos@ 2

× cosA ×

1＋ cos@ 2

+ # <,,

, ,

(6) (7) (8)

+ # <,,

, ,

(9)

[kW/m 2 ] is the ground reflection component, # <,,

direct component, # <,,

, ,

, ,

[kW/m 2 ] is the diffuse component, # <,( , ,

[kW/m 2 ] is the ,

[kW/m 2 ] is the

total solar radiation on inclined surfaces, = [-] is the albedo，@ [rad] is the tilt angle

of inclined surface from horizontal, and A [rad] is the solar incident angle on inclined surfaces.

9

To estimate the hourly amount of PV power generation for each 1-km mesh using

10

the climate scenario data developed, it is necessary to configure the type of buildings

11

in which the PV arrays are installed and the installation conditions of the arrays. It is

12

also necessary to estimate the surface area where installation is possible and the

13

amount of solar radiation on inclined surfaces.

14

In this study, PV arrays are installed in detached houses, complex housing

15

(condominiums, apartments, etc.), and public sector buildings (public, educational,

16

medical, and welfare usage buildings). Building and surface area types were derived

17

from Gomi et al. (2017) [36]. It should be noted that, in the present study, future

18

transitions of buildings were not considered, they were kept the same during the

19

analysis.

20

There is also a need to configure the placement of the PV arrays in the buildings.

21

For detached houses and complex housing, it was assumed that PV arrays would be

22

installed on roofs facing every direction (with the exception of detached houses with

23

flat roofing), according to the configuration method examined by the MOE (2014)

24

[37]. In the case of public sector buildings, it was assumed that PV arrays would be

25

installed only on the roofs, according to METI (2010) [38].

14

1

The configuration details for the installation of PV arrays have been compiled in

2

Table. 4. These configurations were based on MOE (2014) [37], METI (2010) [38],

3

and CRIEPI (2012). It should be noted that since none of the above references

4

establish the configuration of the azimuthal angle for complex housing, they were

5

configured in the same manner as public buildings. With regard to public buildings,

6

physical restrictions due to the security of safety spaces were extrapolated.

7 8

Table. 4 Various conditions for installation of PV arrays (data from MOE (2014) [37],

9

METI (2010) [38], and CRIEPI (2012) [13]) Building type

Item Angle of inclination

Installation conditions 30° All fo ur directions, east, west, south, and north *Flat roofs configured as being south-facing

Azimuthal angle

Detached houses

Roof type (installation coefficients = roof· rooftop surface area/building sur face area) PV array surface area against PV po wer generation system surface area Type of PV array

Co mplexes housing / Public buildings

Gabled roofs: 37% (0.60), hipped roofs 58.4% (0.68), flat roofs: 4.6% (0.34) *For Fukushima Prefecture 0.578 (30° angle of inclination) *Assumed the same as for housing co mplexes and public buildings Polycr ystalline silicon ( mid-range temperature characteristics) 30° All flat roofs ・ Co mplexes housing: 0.14 ・ Public sector buildings: public 0.3, educational 0.4, medical/welfare 0.35

Angle of inclination Roof shape Installation coefficients (roof· rooftop area/total surface area) Physically restrictive ・ Public sector buildings: 0.499 conditions Azimuthal angle South-facing PV array surface area against PV po wer generation system 0.578 (angle of inclination 30°) surface area Polycr ystalline silicon Type of PV array ( mid-range temperature characteristics)

10 11

To estimate the PV power generation per unit of surface area on each 1-km mesh,

12

values for solar radiation per surface area unit were assigned to the inclined surfaces,

13

considering the azimuthal angle direction. Eq. 10–13 were used to estimate the power

14

generation, as illustrated below. Eq. 10 was based on JIS (2005) [39], and eq. 11, eq.

15

12, and eq. 13 on CRIEPI (2012) [13]. The indicators used to estimate power

16

generation are summarized in Table. 5. 15

1 BC KL

2

, , ,

4 5 6 7 8 9 10

, , ,

=

, , ,

BC

, , ,

46

0.41 × (FG

, , ,

10

K × KL

where, BC FG

+E

, , ,

= M1 +

OPQ = R × 0 STC

3

= BD

, , ,

, , ,

− 25

×

, , ,

+ 2J × # <,( , ,

BC (−4) N × M1 + 100

× OPQ × # <,( , ,

R

)H.I + 1

, , ,

10

− 25

−2

,

×

(10)

0.8 N 100

(11) (12)

,

(13)

[℃ ] is the array temperature, BD

[m/s] is the wind speed, KL

, , ,

, , ,

[℃ ] is the temperature,

[-] is the temperature correction

coefficient for the system output, OPQ [kW/m 2 ] is the system output, R [kW/m 2 ] is

the solar radiation at standard test conditions, 0 [ %] is the conversion efficiency,

STC

, , ,

[kW/m 2 ] is the amount of electric power generated per unit of surface area,

K [-] is the basic design coefficients, and y, m, d, and t are year, month, day, and time,

respectively.

11

By multiplying the power generated for each building type and direction, by the

12

surface area in which it would be possible to install PV arrays, the hourly

13

productions of PV power are estimated for each 1-km mesh.

14 15

Table. 5. Information regarding the indicators used to estimate PV power generation Indicator

Symbols in

eqs. 11 to 13 K

Basic design coefficients

Temperature correction coefficients System outp ut

KL

, , ,

OPQ

Explanation The product of the correction coefficients regarding various equipment issues, such as age deterioration of PV arrays, conversion efficiency of po wer conditioners, etc. Correction coefficients considering the drops in production efficiency due to temperature. The potential output of the system as a whole.

16

Assigned values

0.756 [-] ( when using polycr ystalline arrays)

Calculated as per eq. 11 Calculated as per eq. 12

Indicator Solar radiation at standards test conditions [kW/m 2 ]

Conversion efficiency [%]

Symbols in

Explanation

eqs. 11 to 13

Assigned values

R

Standard test conditions consider solar radiation of 1 kW/m 2 , air mass of 1.5, and reference array temperatures of 25±2 ℃ .

1 [kW/m 2 ]

0

The value obtained by dividing the maximum o utput of PV arrays b y the amount of solar radiation absorbed by array surfaces.

2010: 18.5% 2030: 25% (practical module) 2050: 40% *fro m NEDO (2004) [40]

1 2 3

3. Results 3.1 Impacts on PV output

4

The methodology developed in this study enables the estimation of energy

5

generation on an hourly basis. However, because this study focuses on the impact of

6

climate change on annual power generation, the hourly results are shown as

7

aggregated annual results.

8

The "base year” represents results based on the meteorological observation data

9

for the year 2010, in which the annual duration of sunshine was at the most normal

10

levels in East Japan (JWA, 2011 [19]).

11 12

3.1.1 Impacts of climate change on PV output

13

To analyze the impacts of climate change alone, first we evaluated future PV

14

outputs with current efficiency of 18.5%. Fig. 3 shows the rate of change of annual

15

PV power generation compared to the base year level. In addition to the base year,

16

three time periods, each including all three RCP classifications, were analyzed. Here,

17

PV power generation of each 1-km mesh in Fukushima Prefecture are first summed

18

up, then the rate of change between the base year and future are calculated. The rate

19

of change of PV power generation relative to the base year are shown on an annual

20

basis. The generation potential increases in most cases. In all RCP scenarios, the rate

21

of increase becomes larger with time. However, the deviation among climate models

22

in the summer and winter of 2030, 2050, and 2070 are comparatively large. This

23

behavior is consistent with the rate of change in solar radiation. Considering annual

24

rates of change, it can be seen that the increases are approximately 1.7% [0.3%–

17

1

3.6%] in 2030, 3.9% [0.5%–7.0%] in 2050, and 4.9% [1.5%–9.6%] in 2070,

2

indicating a trend that the further into the future, the larger the increase. According

3

to the results above, despite the few cases where a decline is predicted, climate

4

change is estimated to lead to an increase in the potential of PV power generation.

5

Fig. 4 shows the correlation between the predicted change in annual solar radiation

6

(shown in Fig. 2(a)) and predicted PV power generation in Fukushima Prefecture for

7

the fixed conversion efficiency case (18.5%) under seven climate models for each

8

RCP scenario (RCP2.6, RCP4.5, RCP6.0) and each time periods (the base year, 2030,

9

2050, and 2070). The squares show the predicted PV power generation when the

10

maximum changes among the seven climate models are considered for each RCP

11

scenario and each time period; triangles show when that of the median changes are

12

considered; circles show when that of the minimum changes are considered. We can

13

find a clear correlation between the predicted change in annual solar radiation and

14

the predicted PV power generation.

15

16

Fig. 3

Annual PV power generation in Fukushima Prefecture for the fixed

17

conversion efficiency case (18.5%) under seven climate models.

18

For the descriptions of horizontal bars, boxes, and the black diamonds, see Fig. 2.

19

The predictive results of the three climate models developed in Japan (i.e., MIROC5h,

20

MRI-CGCM3.0, and MIROC-ESM-CHEM) are shown as circles as a reference. 18

1

2

Fig. 4

Correlation between the predicted change in annual solar radiation and the

3

predicted PV power generation in Fukushima Prefecture for the fixed conversion

4

efficiency case (18.5%) under seven climate models for each RCP scenario (RCP2.6,

5

RCP4.5, RCP6.0) and each time period (the base year, 2030, 2050, and 2070).

6

The squares show the predicted PV power generation when the maximum changes

7

among the seven climate models are considered for each RCP scenario and each time

8

period; triangles show when that of the median changes are considered; circles show

9

when that of the minimum changes are considered.

10 11

3.1.2 Impacts of PV efficiency improvement

12

The following analysis considers the improvement of PV efficiency. The

13

conversion efficiency is assumed as 18.5% in the base year, 25% in 2030, and 40% in

14

2050, referring to NEDO (2004). Because no data for 2070 was available from NEDO

15

(2004), we excluded all 2070 cases in this analysis. Fig. 5 shows the rate of change 19

1

of PV power generation relative to the base year. Only the winter and annual results

2

are presented here. Regardless of climate change impacts (Fig. 3), PV power

3

generation is expected to increase by approximately 1.4 times in 2030, and 2.3 times

4

in 2050 due to improvements in conversion efficiency. Based on these results, a

5

significant rise in PV power generation was estimated due to both climate change

6

impact and improved conversion efficiency. It was also estimated that PV power will

7

be an important source of renewable energy in the future. Therefore, the gradual

8

introduction of PV power generation is a suitable option for energy system renewal,

9

and it can be considered effective regarding impacts of climate change.

10

11

Fig. 5

12

efficiency improvement case (25% in 2030, and 40% in 2050) under seven climate

13

models

14

For the descriptions of horizontal bars, boxes, and the black diamonds, see Fig. 2.

15

The predictive results of the three climate models developed in Japan (i.e., MIROC5h,

16

MRI-CGCM3.0, and MIROC-ESM-CHEM) are shown as circles as a reference.

Annual PV power generation in Fukushima Prefecture for the conversion

17 18

3.2 Distribution of power generation

19

Fig. 6 shows the power generation for the base year and future (2050) conditions

20

considering both fixed and improved conversion efficiency cases of MIROC5h and 20

1

RCP8.5 to show the two-dimensional change as a reference. Each cell shows the

2

amount of PV power generation for each 1-km mesh calculated in Section 2.4.

3

Densely populated areas such as Koriyama city (central area of the map) (see Fig. 1)

4

show higher power generation. As shown in Fig. 5, there is an increased power

5

generation in the improved conversion efficiency case. Fig. 7 shows the change in

6

power generation relative to the base year for both fixed and improved conversion

7

efficiency cases of MIROC5h and RCP8.5. In both cases, increases in the amount of

8

PV power generated compared to the base year were observed. The increase reached

9

0.45 GWh/yr/km 2 for the fixed conversion efficiency case, and 14 GWh/yr/km 2 for

10

the improved conversion efficiency case.

11

(a) Base year: Fixed conversion efficiency rate of 18.5%

21

(b) Future (2050): Fixed conversion efficiency of 18.5% (MIROC5h; RCP8.5)

(c) Future (2050): Improved conversion efficiency of 40% (MIROC5h; RCP8.5) 1

Fig. 6 Annual power generation in Fukushima Prefecture (GWh/yr/km 2 ): (a) for base

2

year; (b) for the fixed conversion efficiency case in future (2050), and (c) for the

3

improved conversion efficiency case (40%) in future (2050)

4

22

(a) Future (2050): Fixed conversion efficiency of 18.5%

(b) Future (2050): Improved conversion efficiency of 40% 1

Fig. 7 Change in annual power generation in future (2050) relative to the base year in

2

Fukushima Prefecture (GWh/yr/km 2 ) (MIROC5h; RCP8.5): (a) for fixed conversion

3

efficiency of 18.5%; (b) for improved conversion efficiency of 40% 23

1 2

4. Discussion

3

Limitations and future issues derived from the present study are presented as

4

follows. In this study, the AMeDAS observation data (at intervals of about 21 km) are

5

spatially interpolated to be the meteorological observation data of the base year with

6

spatial resolution of 1 km. The accuracy of interpolation is required to be examined

7

with surface observation data other than AMeDAS, but there are no such observations.

8

Thus, we could not fully examine the accuracy. Furthermore, according to the bias

9

correction methodology described in Section 2.2, data for climate scenarios were

10

developed based on meteorological observation data. This methodology attributes the

11

same dispersion pattern of the meteorological observation data (temperature, solar

12

radiation, and wind speed) to the developed climate scenario, since only the average

13

monthly change is reflected in the time sequence of meteorological observations (see

14

eq. 2 and eq. 3). However, dispersion patterns are predicted to change in the future

15

due to climate change (IPCC, 2012 [41]). Thus, it should be noted that climate

16

scenarios are influenced by the features of the bias correction methodology employed.

17

It would be desirable to develop climate scenario data with dispersion pattern due to

18

climate change and the least amount of influence from bias correction. Recently,

19

regional climate scenario data with high spatial resolution on Japan was developed

20

by dynamically downscaling GCM output. Such examples include the Program for

21

Risk Information on Climate Change [42] and the Integrated Research Program for

22

Advancing Climate Models (TOUGOU) [43], both coming from the Ministry of

23

Education, Culture, Sports, Science and Technology. They provide climate scenario

24

data with 5-km and 2-km spatial resolution, respectively. A future analysis of power

25

generated by renewable energies should be conducted by directly using these high

26

spatial resolution climate scenario data, and considering the changes in dispersion

27

patterns due to climate change, having removed the influence of bias correction

28

(though bias would still exist).

29

Extreme weather events due to climate change are projected to increase (IPCC

30

2014a [8]). These would lead not only to impacts on power generation, but also to

31

potential physical impacts upon PV panels. PV panels could become obsolete more

32

quickly due to sustained high temperatures, while airborne debris caused by strong 24

1

winds could also damage them (IPCC, 2014a [8]). Therefore, it is essential that

2

possible impacts and responsive policies against such extreme meteorological events

3

are examined.

4

In this study, it was assumed that PV panels would be installed on houses/buildings,

5

and wind turbines would be installed considering the conditions that render their

6

development possible. However, it should be noted that Japan is facing a period of

7

population decline (IPSS, 2017 [44]), which is considered a significant factor for

8

changes in land usage patterns (MLIT, 2008 [45]). It is envisaged that this sort of

9

change will, in addition to the impacts of climate change, exert a major influence on

10

regional energy supply and demand (Ashina et al., 2017 [46]). In this study land

11

usage was hypothesized as remaining constant in the future. However, for the

12

formulation of detailed adaptation policies at local level, it would be desirable to

13

consider the socioeconomic changes in population and land use in the region being

14

studied.

15

In the light of temporal changes in service demand for electric power, it is

16

essential that hourly amounts of renewable generation are assessed. To formulate

17

plans for a low-carbon society that reflects local characteristics and contribute to

18

their implementation, an analysis including the above-mentioned socioeconomic

19

changes should be considered, and a model that can evaluate hourly energy demand

20

patterns and technological suitability should be used (technological evaluation

21

model) (Shiraki et al., 2016 [47]).

22 23

5. Conclusions

24

Assessing the influence exerted by climate change upon the generation of PV

25

energy provides extremely important information for the introduction of these energy

26

systems from a long-term perspective. For this reason, this study developed a

27

methodology for hourly 1-km mesh estimation of the climate change impact on PV

28

power generation. Using the developed methodology, the future impact of climate

29

change in Fukushima Prefecture was evaluated.

30

Considering annual analyses, PV power generation is predicted to increase in

31

nearly all cases due to the impact of climate change. The annual increases are, on

32

average, 1.7% in 2030, 3.9% in 2050, and 4.9% in 2070. Moreover, it was predicted 25

1

that future improvements in conversion efficiency will lead to a significant increase

2

in PV power generation, and that these annual increases are approximately 1.4x in

3

2030, and 2.3x in 2050. However, we note that, in this study, the year 2010 is set to

4

the “base year.” Thus, when a different year is set to the base year, the ratios of the

5

increase in future PV generation relative to the base year will be different. It is hoped

6

that a systematic introduction of renewable energies occurs in Japan considering

7

these climate change-induced impacts.

8 9

Acknowledgements

10

This study was conducted under the Climate Change Adaptation Research Program

11

from the National Institute for Environmental Studies, the Environment Research and

12

Technology Development Fund 2-1711 from MOE & Environmental Restoration and

13

Conservation Agency, and the MOE’s 2017 Project to Promote CO 2 Technology

14

Assessment. The authors also benefitted from the support of Dr. Minoru Yoshikawa

15

and Masayuki Takano from Mizuho Information and Research Inc. We would like to

16

express our sincere gratitude to all those who helped to make this study possible.

17

26

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Highlights ・ Climate change impacts on future PV energy generation in Japan were investigated. ・ Uncertainty of future climate change was evaluated using multiple GHG emission scenarios and GCMs. ・ Annual PV output increases on average by 1.7% in 2030, 3.9% in 2050, and 4.9% in 2070.

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Declaration of interests ☒ 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. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: