京都大学大学院 経済学研究科 再生可能エネルギー経済学講座

ディスカッションペーパー No.41

京都大学大学院経済学研究科
再生可能エネルギー経済学講座
ディスカッションペーパー

Cross-regional Impacts of Renewable Power Generation on the Electricity Market:
Empirical Study on Japan’s Electricity Spot Market

2022 年 5 月
May 2022

京都大学大学院経済研究科 馬騰
Teng Ma, Graduate School of Economics, Kyoto University
京都大学大学院経済研究科 再生可能エネルギー経済学講座 杜依濛*Yimeng Du (Corresponding author), Research Project on Renewable Energy Economics,
Kyoto University
国防科技大学前沿交叉学科学院 徐涛
Tao Xu, College of Advanced Interdisciplinary Studies,
National University of Defense Technology
曁南大学経済学院 陳旺
Wang Chen, School of Economics, Jinan University

ディスカッションペーパー No.41

京都大学大学院 経済学研究科 再生可能エネルギー経済学講座

Cross-regional Impacts of Renewable Power Generation on the Electricity Market:
Empirical Study on Japan’s Electricity Spot Market
京都大学大学院経済研究科

馬騰Teng Ma, Graduate School of Economics, Kyoto University
京都大学大学院経済研究科 再生可能エネルギー経済学講座 杜依濛*Yimeng Du (Corresponding author), Research Project on Renewable Energy Economics,
Kyoto University
国防科技大学前沿交叉学科学院 徐涛
Tao Xu, College of Advanced Interdisciplinary Studies,
National University of Defense Technology
曁南大学経済学院 陳旺
Wang Chen, School of Economics, Jinan University

Abstract:
This study examined the cross-regional impacts of renewable power generation in Tohoku on the electricity spot market
in Tokyo. We use the hourly supply data of renewable power generation, cross-regional transmitted electricity, andtransaction data in the electricity spot market to investigate how renewable power generated in a neigh- boring area affects
the local spot price and market volatility. Our results indicate that the electricity spot market is affected not only by local
renewable power generation, but also by renewable power generation in neighboring areas. The relatively higher pricereduction effect of transmitted power is due to its higher price elasticity under a tight supply-demand balance. Specifically,
our results confirm the cross-regional price stabilization impact of renewable power generation in Tohoku. The negative
impact on realized volatility is due to the relatively more stable renewable electricity supply from Tohoku. Furthermore,
we find that renewable power producers, especially solar power producers, are not incentivized by the fixed-price Feedin Tariff to meet electricity demand during peak hours because of higher marginal costs.
Keywords: Spot Market; Renewable Electricity; Cross-regional effects; Japan京都大学大学院 経済学研究科 再生可能エネルギー経済学講座

ディスカッションペーパー No.41

１．Introduction
The electricity spot market is one of Japan’s wholesale electricity markets, and diverse energy
resources, including renewable energy, are traded on it. Along with deepening the reforms ofmarketization in Japan, renewable power suppliers are expected to play an important role inthis market1. To generate a more competitive market, it is necessary to increase the penetrationof renewable power, as this may reduce the wholesale spot prices. An increase in renewable
power generation can displace marginal generation with high fuel costs (Rintama ̈ki et al., 2017).
In particular, since the marketization reform in April 2016, the trade volume in Japan’s
electricity spot market has been increasing rapidly. Various electricity utilities have entered themarket, stimulating competition and making it possible to trade renewable electricity directlywith the suppliers in the market.
Renewable power plays an important role in the spot market. The existing literature on theeffect of renewable energy power traded on the spot market suggests that the electric- ity spot
market can benefit from increased solar and wind power generation in European countries andthe US (Rintama ̈ki et al., 2017; Martinez-Anido et al., 2016; Woo et al., 2011; Sensfuß et al.,2008; Paraschiv et al., 2014). Ma et al. (2019) examined the impact of renewable power supply
on the electricity spot market in Japan and found that increasing renewable power generation
reduces the spot price, and that stable renewable generation can reduce realized volatility.
Although regional power transactions in the Japanese spot market have been observed since
the full liberalization of the electricity market in 2016, the impact of transmitted renewable
power from neighboring regions on the local spot market remains unclear. To fill this researchgap, this study examines the cross-regional impacts of renewable power generation on the Japan
Electric Power eXchange (JEPX) spot market. Our objective is to examine the indirect impact
of solar and wind power generation in Tohoku on the spot price and volatility in the electricity
spot market of Tokyo. We limited the setting to Tokyo and Tohoku because of thecomplementary supply and demand relationship between these two areas. For example, Tohoku
has huge renewable power potential but less power demand, while Tokyo has more power
demand (Wakiyama and Kuriyama, 2018). Simultaneously, the “Tokyo–Tohoku electricity grid”
is relatively isolated from other areas of Japan. The country is divided between Tokyo andChubu by different mains frequency and between Hokkaido and Tohoku by geographicalisolation (Figure 1).
[Figure 1]
This study makes the following contributions to the existing literature. First, in contrast to the1

The system reforms on electricity market in Japan were implemented by Ministry of Economy, Trade andIndustry (METI) since 1995.

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results of Ma et al. (2019), we found that the electricity spot market is not only affected by local
renewable power generation, but also by renewable power generation in the neighboring areas.
Further, the power generation in Tohoku has higher price reduction effects compared to the
local renewable generation since the cross-regional power from Tohoku is used to addresselectricity shortages in Tokyo. In general, power transmitted to fill an electricity demand gap
has higher price elasticity; thus, it has higher price reduction effects. Second, we estimated theimpacts of renewable power production in neighboring areas on the realized volatility of thelocal electricity market. The result shows that the negative impacts on realized volatility may
be caused by the relatively stable renewable electricity supply from Tohoku. However, thecurrent transmission capacities between Tohoku and Tokyo are insufficient to support thetransmission load (Wakiyama and Kuriyama, 2018). We suggest that further improvement in
the capacity of the interconnection grid can reduce and stabilize the spot prices. Finally, thisstudy investigated whether the impact of transmitted renewable power on the local electricity
market varies with changes in the local electricity demand. The results reveal a higher pricestabilization impact in the off-peak period since power producers tend to avoid higher marginal
costs during peak periods. This result suggests that renewable power producers are notincentivized to supply more electricity in response to increasing electricity demand. The Feedin Tariff (FIT) scheme does not provide an incentive for power producers under the market basedue to the fixed price.
The remainder of this study is organized as follows. Section 2 presents an overview of thetransactions on the JEPX in Tokyo and the combination of power sources and renewableelectricity in Tohoku and Tokyo. In Section 3, we describe the empirical methodology and data
used in the analyses. The estimation results are presented and discussed in Section 4. Finally,
Section 5 presents our conclusions and the implications of this research.

2．BackgroundThe electricity spot market was established in Japan to enable new power retailers to supply
power at low prices, as part of reforms designed to foster competition in the market2. However,owing to the inadequate transfer capabilities for cross-border transmission, the spot prices in
the JEPX are set at different levels for various service regions (Figure 2). A relatively higher
price level is observed in the eastern grid, which includes the regions of Hokkaido, Tohoku, andTokyo, due to the unbalanced electricity supply and demand in this area. Conversely, the areasin the western grid show consistent price levels due to their relatively sufficient cross-regional
electricity transmission. Meanwhile, the lower deviation of the spot price in Tokyo implies a
2 The electricity spot market was established by JEPX in 2005 to ensure fair competition in Japan’s wholesale

electricity market. The spot market is the primary market of the JEPX. Approximately 96.3% of the electricity
trades during the fiscal year (FY) 2018 occurred in the spot market. We calculated this share by dividing the
trading value of the spot market by the total trading value of the JEPX in 2018 (JEPX, n.d. ).

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ディスカッションペーパー No.41

more liberalized market.
[Figure 2]
As an “absolute clean energy”, renewable power is also transacted in the electricity market;
however, only about 1% of the total electricity transactions in the JEPX in 2015 were related torenewable power (METI, 2016). To enhance the proportion of renewable power transactions in
the JEPX, the Japanese government has made efforts to promote renewable power, especiallyafter the Fukushima nuclear accident in March 2011. As the main policy instrument, the FIT
scheme was introduced in July 2012 to replace the Renewable Portfolio Standards system that
had been in force since April 2003 (Sakabe et al., 2020). Moreover, an “Energy Mix” target for
FY2030 was announced by the Japanese government in July 2015, which aimed to increase theshare of renewable energy in Japan’s total power generation from 14.5% in 2016 to 22–24% by
2030 (METI, 2015).
Although the tariff rate under the FIT has continued to decrease in recent years, solar power
generation has increased from 66,133 GWh in 2012 to 626,677 GWh in 2018 (METI, 2019).
Meanwhile, renewable power production also displays regional trends. As shown in Figure 3,
solar power generation is higher in Tokyo, Chubu, and Kyushu, whereas wind power generation
is more prominent in Hokkaido, Tohoku, and Tokyo.
[Figure 3]
We selected Tokyo and Tohoku as the setting for this study due to the special nature of theelectricity grid in Japan (Figure 1). The country is divided into two parts, each at a different
mains frequency. The eastern grids (including Hokkaido, Tohoku, Tokyo, and the eastern partof Chubu) and western grids (including Chubu, Kansai, Chugoku, Shikoku, and a major area of
Kyushu) are connected by high-voltage direct current connections. The frequency is 50 Hz and
60 Hz in the eastern and western grids, respectively.
In the eastern grid, there is limited transmission capacity between Hokkaido and Tohoku due
to the geographic location of Hokkaido, which is isolated from the Japanese mainland. The
Tohoku–Tokyo line is the busiest connection in Japan, as Tokyo accounts for nearly 37% ofJapan’s electricity consumption (OCCTO, 2020). Approximately 27,575 GWh electricity wastransmitted from Tohoku to Tokyo in FY 2019 (Table 1) 3 . Despite efforts to increase thecapacity of interconnection lines among areas, there is frequent disconnection between the spot
markets, especially between Hokkaido–Honshu and Tokyo–Chubu4. Specifically, during thefirst quarter of 2019, the average occurrence rate of spot market disconnection between
3

The transmission values were calculated by the sum of forward transmission and counter transmission on
the interconnection lines.
4 The largest and most populous main island of Japan. The region include all areas in Japan excepting
Hokkaido, Kyushu and Okinawa.

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Hokkaido and Honshu was 94.5%, and that between Tokyo and Chubu even reached 99%(METI, 2020a). During FY 2019, the annual magnitudes of transmitted electricity on the crossregional lines of Hokkaido–Honshu and Tokyo–Chubu were only 2,396 GWh and 4,501 GWh,
respectively. Therefore, the electricity grid in the “Tohoku–Tokyo” area considered in this study
is relatively isolated from the other areas in Japan. For this reason, we focus on these areas tostudy the cross-regional impacts of renewable power generation on the electricity market. The
higher solar and wind power generation in Tokyo is because of the higher electricity demand
there, whereas the higher wind power production in Tohoku and Hokkaido is a result of theseareas’ superior wind resources (Mizuno, 2014).
[Table 1]

3．Methodology and Data
3.1. Empirical model
A traditional two-stage least-squares (2SLS) framework was used to examine the crossregional impact of Tohoku’s renewable power generation on the Tokyo spot market. To consider
the partial autocorrelations in the spot price, realized volatility, and electricity transmission, weused the autocorrelation distributed log model in both stages of the model. Furthermore, both
the renewable generation and electricity transmission were considered exogenous to the spot
price and realized volatility, since the spot price is decided after the bidding in the electricity
spot market. The first stage of the 2SLS model predicts the direct effects of Tohoku’s renewable
power generation on the transmission of electricity from Tohoku to Tokyo.

𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡 = β0 + β1 𝑙𝑎𝑔 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡 + β2 𝑅𝑒(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 + β3 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 + ε𝑡 , (1)where Transmissiont i is the dependent variable and refers to the electricity transmitted fromTohoku to Tokyo in hour t. This net electricity transmission from Tohoku to Tokyo was
calculated by subtracting Transmission (Hokkaido Forward) from Transmission (Tohoku
Forward), owing to the almost zero electricity transmission from Tokyo to Tohoku. lag
Transmissiont shows the lagged terms of the transmitted electricity generations, which includeslag(1hour)Transmissiont, lag(1day)Transmissiont, and lag(1week)Transmissiont for separately
considering the short run dynamic terms by one hour before, one day before, and one week
before. Re(Tohoku)t represents the renewable power generation, including the solar and wind
power sectors in Tohoku (Solar(Tohoku)t and Wind(Tohoku)t, respectively). Controlt includes
control variables indicating other factors that may affect the electricity transmission fromTohoku to Tokyo, including Forecast(Tohoku)t, Forecast(Tokyo)t, and Primary(Tohoku)t.
Forecast(Tohoku)t, Forecast(Tokyo)t are the first differences of hourly electricity demand
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ディスカッションペーパー No.41

forecast in Tohoku and Tokyo, used to represent the expected demand on electricity 5 .
Primary(Tohoku)t is the share of primary energy generation in Tohoku, including thermal,hydroelectric, and nuclear power 6. ε𝑡 is the error term.
The second stage estimates the cross-regional impact of Tohoku’s renewable power generationon the spot price in Tokyo through its impact on electricity transmission between the tworegions.
̂Pricet = γ0 + γ1 lag 𝑃𝑟𝑖𝑐𝑒𝑡 + γ2 𝑅𝑒(𝑇𝑜𝑘𝑦𝑜)𝑡 + γ3 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛
𝑡 + γ4 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 + 𝜀𝑡 , (2)where Pricet is the dependent variable for stage 2 that represents spot price in hour t. lag Pricet
is denoted as the lagged terms of the spot prices, including the price 1 hour before𝑙𝑎𝑔(1 ℎ𝑜𝑢𝑟)𝑃𝑟𝑖𝑐𝑒𝑡 , 1 day before 𝑙𝑎𝑔(1𝑑𝑎𝑦)𝑃𝑟𝑖𝑐𝑒𝑡 , and 1 week before 𝑙𝑎𝑔(1𝑤𝑒𝑒𝑘)𝑃𝑟𝑖𝑐𝑒𝑡 .
𝑅𝑒(𝑇𝑜𝑘𝑦𝑜)𝑡 represents the renewable power generation, including the solar and wind power̂
sectors in Tohoku (𝑆𝑜𝑙𝑎𝑟(𝑇𝑜𝑘𝑦𝑜)𝑡 and 𝑊𝑖𝑛𝑑(𝑇𝑜𝑘𝑦𝑜)𝑡 , respectively). 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛
𝑡 isthe value of 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡 predicted by equition (1), represents the cross-regional impactsof Tohoku's renewable power generation in Tokyo through the interconnection line between
̂
Tohoku and Tokyo in hour 𝑡. 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
𝑡 is used to represent the predicted valueof the impacts of Tohoku's solar power generation on transmission generation, and̂
𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 is for that of wind power. 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 includes control variables
indicating other factors that may affect the electricity transmission from Tohoku to Tokyo,
including 𝐹𝐼𝑇𝑡 , 𝑇𝑟𝑎𝑛𝑠𝐼𝑚𝑝𝑎𝑐𝑡𝑡 , 𝑃𝑟𝑖𝑚𝑎𝑟𝑦(𝑇𝑜𝑘𝑦𝑜)𝑡 , and 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜𝑘𝑦𝑜)𝑡 . 𝐹𝐼𝑇𝑡
represents the tariff rates provided for solar power under the FIT7. 𝑇𝑟𝑎𝑛𝑠𝐼𝑚𝑝𝑎𝑐𝑡𝑡 represents
the other indirect impacts on Tokyo through by the cross-regional interconnection line between
Tohoku and Tokyo in hour $t$, except the renewable power generation. 𝑃𝑟𝑖𝑚𝑎𝑟𝑦(𝑇𝑜𝑘𝑦𝑜)𝑡 is
the share of primary energy generation in Tokyo. By using the log-log specification equation,
our results imply that the estimated coefficient of the logarithmic renewable power generationand other variables represent the elasticity of the day-head spot price on the JEPX, while
𝑃𝑟𝑖𝑚𝑎𝑟𝑦(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝑃𝑟𝑖𝑚𝑎𝑟𝑦(𝑇𝑜𝑘𝑦𝑜)𝑡 are not converted to logarithmic form due totheir special nature.
We also focused on the impacts of renewable power generation on realized volatility. Market
volatility can be explained by the liquidity provision process and reflects market activity andthe security risks (Chordia et al., 2002; Copeland and Galai, 1983; Tauchen and Pitts, 1983). In
this study, daily realized volatility was considered as market activity, which was calculated as
the sum of squared hourly price changes (Andersen et al., 2003). It was defined by the following
First differences were selected due to the Augmented Dickey-Fuller (ADF) unit roots test indicating that theseries is unstationary for hourly electricity demand forecast in Tohoku and Tokyo.
6 The share is calculated by the sum of the generation of baseload supply and mid-range load supply divided
by electricity demand.
7 The tariff rate for solar power is used to represent the overall tariff rate, due to overcome themulticollinearity in our measure.
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equations:𝑃

𝑅𝑒𝑡𝑢𝑟𝑛𝑡 = 𝑙𝑛 (𝑃 𝑡 ),(3)

2
𝑅𝑉𝑑 = √（ ∑24
𝑡=1 𝑅𝑒𝑡𝑢𝑟𝑛𝑡 ）.

(4)

𝑡−1

where Pt illustrates the hourly transaction price in day ahead spot market on hour t, Returnt
represents the hourly change of spot price, which is calculated by taking the natural log of thequotient of the spot price on hour t divided by the price an hour before. RVd is the realized pricevolatility on day d.
Therefore, the first stage of our 2SLS model predicts the direct effects of Tohoku’s renewable
power generation on electricity transmission from Tohoku to Tokyo on a daily basis. The modelto analyze the effect of realized volatility can be described as follows:
𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑑 = β0 + β1 𝑙𝑎𝑔𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑑 + β2 𝑅𝑒(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 + β3 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑑 + ε𝑑, (5)The second stage estimates the cross-regional impact of Tohoku’s renewable power generation on realized volatility through its impact on electricity transmission between the tworegions.
̂
𝑅𝑉𝑑 = γ0 + γ1 𝑙𝑎𝑔𝑅𝑉𝑑 + γ2 𝑅𝑒(𝑇𝑜𝑘𝑦𝑜)𝑑 + γ3 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛
𝑑 + γ4 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑑 + ε𝑑 ,(6)where RVd is the dependent variable for stage 2; it represents realized volatility on day d in theTokyo electricity spot market. The lagged terms lagIndicatord contain the realized volatility 1
day before (RVd − 1) and 1 week before (RVd − 7), indicating the dynamic process. The otherindependent variables were converted from hourly to daily data, and εim is the error term.

3.2. Data
All samples were collected from April 1, 2016, to March 31, 2020. Tokyo’s spot price data
were obtained from the homepage of the JEPX website. Since the spot price is calculated for
48 products traded in 30-minute time intervals on the day before, the hourly price data were
calculated based on the mean of each of the two time interval values in 1 hour (JEPX, n.d.b).
The energy generation data were acquired from the electric power companies in Tohoku andTokyo, covering various electricity sources such as nuclear, thermal, inflow- type hydroelectric,
geothermal, biomass, solar, wind, and pump-storage-type hydroelectric; moreover, regional
power transmission was calculated based on the cross-regional interconnected lines (Tohoku
EPCO, Tokyo EPCO, and Hokkaido EPCO). The tariff rates of FIT were obtained from the
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Agency for National Resources and Energy (ANRE, n.d.a). We also gathered the electricity
demand forecast datasets from the Organization for Cross-regional Coordination ofTransmission Operator, Japan (OCCTO, n.d.).
Descriptive statistics are shown in Table 2. Panel A presents the summary of the hourly dataset.
The sample included all trading hours from April 2016 to March 2020 for Tohoku and Tokyo,
with 35,064 observations for each variable. The average Tokyo spot price was 9.818 Yen/kWh.
The average hourly solar and wind power generation was 1,603 MWh and 106.9 MWh in Tokyoand 575.8 MWh and 277.8 MWh in Tohoku, respectively. As Figures 4 and 5 show, the spot
price is relatively stable, with strong intraday and annual seasonal patterns in Tokyo. The price
was observed to be higher during the daytime, except for the lunch break, and in the spring andsummer seasons each year, suggesting that higher electricity demand occurs during theafternoon, and in spring and summer. The realized volatility also was higher in summer andwinter; however, it has become more stable in recent years, indicating more frequenttransactions (Figure 6). As shown in Figures 7–10, the supply of solar and wind power shows
intraday and seasonal patterns every year. The supply generated from solar power was higher
in Tokyo, while that from wind power was lower. Meanwhile, the average electricity
transmission from Tohoku to Tokyo was 2,544 MWh. Figures 11 and 12 represent the hourly
and monthly trends in the electricity transmission from Tohoku to Tokyo, respectively. The
transmission increased gradually, displaying strong intraday and annual seasonal patterns. Panel
B of Table 2 summarizes the daily dataset. The sample included the trading days during thestudy period for Tohoku and Tokyo, with 1,461 observations for each variable. The daily
electricity generation data, electricity demand forecast data, and gross regional transmission
data were calculated by adding the hourly intraday data. The average realized volatility in Tokyo
was 0.435% during the whole sample period, while the realized volatility was almost always
higher than 2% in February and August (Figure 6).
[Table 2][Figures 4-12]

4. Results
4.1 Spot price
Table 3 reports the impacts of renewable power generation in Tohoku on the electricity
transmission from Tohoku to Tokyo based on equation (1). Column (1) lists the baseline
estimations by eliminating the control variables Controlt. The estimates with the control
variables Controlt are reported in columns (2) and (3); column (3) includes the hourly, monthly,and yearly dummy variables.

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[Table 3]
All the coefficients of the lagged terms (lagTransmissiont) are positive and statisticallysignificant in all columns of Table 3, suggesting that the electricity transmission in any period
strongly depends on the electricity transmission in preceding periods. Solar(Tohoku)t andWind(Tohoku)t are positive and statistically significant in columns (2)–(3), which means that
increasing renewable power generation in Tohoku also increases the electricity transmission
from Tohoku to Tokyo. In particular, the coefficient is 0.011 for Solar(Tohoku)t and 0.006 forWind(Tohoku)t in column (3), suggesting that a 1% increase in solar and wind power generation
leads to a 0.011% and 0.006% increase in electricity transmission, respectively. The coefficientsof Primary(Tohoku)t are positive and statistically significant in columns (2) and (3), meaningthat the base-load and mid-range energy resources lead to an increase in electricity transmission.
D_Forecast(Tohoku)t and D_Forecast(Tokyo)t have opposite and statistically significant signs,
suggesting that higher electricity demand plays an important role in electricity transmission 8.
These findings indicate that renewable power was transmitted from Tohoku to Tokyo to deal
with the unbalanced electricity demand and supply situation in Tokyo.
Along with increasing renewable power generation in Tohoku, the cross-regional transmission of renewable power also increased. However, the relatively low coefficients ofSolar(Tohoku)t and Wind(Tohoku)t suggest that only a small share of renewable power can betransmitted through the Tohoku–Tokyo interconnection line. This result indicates the need forreinforcement of the interconnection line to increase transmission capacity and capability on
responding to variable renewable energy generation.
The regression results on the impacts of renewable power generation on the electricity spot
market in Tokyo are presented in Table 4. The results listed in column (1) are based on themodel that only focuses on local power generation. Column (2) reports the results after adding
transmitted power from Tohoku. The estimation results with control variables Controlt andinteraction terms Interactiont are shown in columns (3)–(5).
[Table 4]
All the lagged terms of spot price 𝑙𝑎𝑔𝑃𝑟𝑖𝑐𝑒𝑡 have positive and statistically significant
coefficients in all columns, which means that the spot price in Tokyo's electricity spot market
can also be determined by the previous prices. Both the 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜𝑘𝑦𝑜)𝑡 and 𝑊𝑖𝑛𝑑(𝑇𝑜𝑘𝑦𝑜)𝑡coefficients are negative and statistically significant, suggesting that increasing local renewable
power generation helps to reduce the spot price in the Tokyo's market. For example, thecoefficient is -0.006 for 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜𝑘𝑦𝑜)𝑡 and -0.002 for 𝑊𝑖𝑛𝑑(𝑇𝑜𝑘𝑦𝑜)𝑡 in column (3),8

𝐷_𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝐷_𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜𝑘𝑦𝑜)𝑡 represent the first-order difference terms of
𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜𝑘𝑦𝑜)𝑡 because the null hypothesis was not rejected by the ADF unit root
test, indicating that the series was not stationary for these variables.

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suggesting that a 1% increase in local solar and wind power generation leads to a 0.006% and0.002% decline in spot price, respectively. The findings are consistent with the results of Ma etal., (2019), who report that a 1% solar and wind power generation leads to a 0.014% and 0.001%
̂
reduction in the spot price for the entire country. 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
and𝑡̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 are negative and statistically significant in columns (2) and (3), whichmeans that increasing renewable power generation in Tohoku can also help reduce theelectricity spot price in Tokyo’s spot market. The results suggest that a 1% increase in the crossregional impacts of solar and wind power generation in Tohoku induces a 0.013% and 0.003%
decline, respectively, in Tokyo’s spot price 9 . These results confirm the existence of crossregional impacts of Tohoku’s renewable power on Tokyo’s spot market. The cross-regional
impact of solar power generation was higher than that of wind power generation, as there islower generation of wind power compared to solar power in Tohoku (Figure 3). FITt is negativeand statistically significant in columns (3)— (5), which suggests that the introduction of the
FIT system has significantly reduced the spot price in Tokyo’s spot market. This may be because
the FIT scheme sufficiently encourages the promotion and market participation of renewable
power in Tokyo. Our results contrast with Böhringer et al. (2017) which focused on Germany
electricity market indicates that the feed-in tariffs increase the average household electricity
price in Germany. We consider that it is due to the lower renewable generation in Japan thatcompare with Germany. Primary(Tokyo)t had a positive and statistically significant coefficient,which means that the base-load and mid- range energy resources can lead to an increase inTokyo’s electricity spot price because of the higher marginal cost of base-load energy sources
compared to renewable energy (Antweiler and Muesgens, 2021).
Moreover, to investigate whether the price reduction impact of renewable power generation
̂can be differed by differences in electricity demand, the interaction terms of 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡and period dummy variables 𝑃𝑒𝑟𝑖𝑜𝑑𝑡 are added to Equation (2) as follows:̂𝑃𝑟𝑖𝑐𝑒𝑡 = γ0 + γ1 𝑙𝑎𝑔𝑃𝑟𝑖𝑐𝑒𝑡 + γ2 𝑅𝑒𝑇𝑜𝑘𝑦𝑜𝑡 + γ3 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛
𝑡 + γ4 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 +̂
γ5 𝑃𝑒𝑟𝑖𝑜𝑑𝑡 + γ5 𝑃𝑒𝑟𝑖𝑜𝑑𝑡 × 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡 + ε𝑡 ,(7)

𝑃𝑒𝑟𝑖𝑜𝑑𝑡 including dummy variables 𝑃𝑒𝑎𝑘𝑡 and 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 , where 𝑃𝑒𝑎𝑘𝑡 indicates thepeak period of electricity demand in spot market, that equal to 1 during 1:00 p.m. -- 4:00 p.m,
and 0 otherwise. 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 is used to represent the daytime of a day. The variable equals to 1
during 8:00 a.m. -10:00 p.m, and 0 otherwise. The summarization of coefficients γ3 and γ5
9The coefficients of the indirect impacts of Tohoku's renewable power generations were calculated by the
products of the coefficients of 𝑅𝑒(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 ( 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 ) and the coefficients of̂̂̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡 (𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 ). For example, the coefficients of the indirect
impacts of Tohoku's solar power generation was calculated by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡̂and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 : 0.011 × -1.226 that equal -0.013, and the coefficients of the indirect impacts of
Tohoku's wind power generation was calculated by product of the coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 : 0.006 × -0.472 that equal -0.003.

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represents the cross-regional impact of Tohoku's renewable power generation on spot priceduring the peaktime/daytime, and γ3 shows the impact during the off-peak/nighttime.
The estimation results are presented in columns (4) and (5) of Table 4. The coefficients of
𝑃𝑒𝑎𝑘𝑡 and 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 are positive and statistically significant, which suggests that the spot̂
price was higher in periods with higher electricity demand. 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
𝑡 isnegativeand statistically significant. By contrast, the cross-term 𝑃𝑒𝑎𝑘𝑡 ×̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 positive and statistically significant in column (4), which suggeststhat the cross-regional price reduction impacts of Tohoku’s solar power generation were higher
during the off-peak periods.
̂̂
By contrast, both 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 and 𝑃𝑒𝑎𝑘𝑡 × 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 are
negative and statistically significantly correlated with the spot price, meaning that the impacts
on the spot price of transmitted wind power were notable during the peak period. The
coefficients indicate that a 1% increase in the solar and wind power generation in Tohoku
induces about a 0.0008% and 0.009% reduction, respectively, in the spot price during peak time.
During the off-peak time, the price reduction impact of transmitted solar and wind power
generation is 0.014% and 0.002%, respectively 10.
̂From the daytime sub-sample, we find that the coefficients of 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 and̂
the cross-term 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 × 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 are negative and statistically significant
in column (5), meaning that the cross-regional price reduction impacts of solar power̂
generation w ere higher during daytime. Meanwhile, coefficients of 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡̂and the cross-terms 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 × 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 show opposite sign in column (5),suggesting that the cross-regional impacts of Tohoku's wind power generation were higher
during the nighttime. For example, a 1% increase in Tohoku's solar and wind power generations
induces a 0.017% and 0.007% reduction, respectively, in the spot price during daytime. The
cross regional price reduction impact during nighttime of solar power is 0.010% and that ofwind power is 0.004%11.
10

The coefficients of the indirect impacts of Tohoku's solar power generation during peak time period waŝcalculated by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and ( 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 + 𝑃𝑒𝑎𝑘𝑡 ×̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 ): 0.011 × (-1.316+1.242) = -0.0008, and the coefficients of the indirect impacts of
Tohoku's wind power generation during same period was calculated by product of the coefficients of̂̂𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and (𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 + 𝑃𝑒𝑎𝑘𝑡 × 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 ): 0.006 × (-0.339-1.092) = -0.009.
Meanwhile, the coefficients of the indirect impacts of Tohoku's solar power generation during off-peak timê
period was calculated by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 : 0.011 × 1.316 = -0.014, and the coefficients of the indirect impacts of Tohoku's wind power generation during samê
period was calculated by product of the coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 : 0.006 × 0.339 = -0.002.
11 The coefficients of the indirect impacts of Tohoku's solar power generation during daytime period waŝcalculated by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and ( 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 + 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 ×̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 ): 0.011 × (-0.933-0.587) = -0.017, and the coefficients of the indirect impacts of Tohoku's
wind power generation during same period was calculated by product of the coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡̂̂and ( 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 + 𝐷𝑎𝑦𝑡𝑖𝑚𝑒𝑡 × 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟) 𝑡 ): 0.006 × (674-1.847) = -0.007. The
coefficients of the indirect impacts of Tohoku's solar power generation during nighttime period was calculated̂by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢) 𝑡 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑡 : 0.011 × -0.933 = -0.010, and thecoefficients of the indirect impacts of Tohoku's wind power generation during same period was calculated bŷ
product of the coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑡 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑡 : 0.006 × 0.674 = 0.004.

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In summary, the estimation results of the second stage model suggest that both the local
renewable power generation and the renewable power transmitted from Tohoku via the crossregional interconnection lines can help reduce the electricity spot price in Tokyo. Higher pricereduction impacts were observed for the transmitted power. We consider that this is due to theelectricity transmission from a neighboring area, which helped address the electricity shortagein the local market. The transmitted renewable power has a relatively higher price elasticity,
further increasing the price reduction impact.
Moreover, the results indicate that the cross-regional price reduction impact of wind power
generation was higher that of solar power generation during peak time, while opposite impacts
were found during off-peak time. We interpret this to mean that the wind power suppliers in
Tohoku are more actively providing electricity to match higher demand, and thus, induce ahigher price reduction impact during peak times (Figure 10). Further, although Tohoku hastremendous wind power resources, the share of wind power generation in the electricity demandof the area is still relatively small12, Such insufficient power generation leads to a comparatively
small impact on price reduction. Meanwhile, the cross-regional price reduction impact of solar
power is higher during off-peak time. We consider this to be a result of solar power suppliers’
behaviors; they tend to avoid trading during peak time owing to the higher marginal costs.

4.2 Realized Volatility
We also investigated the impact of Tohoku’s renewable power generation on realized volatilityin Tokyo’s electricity spot market. The 2SLS framework was used to estimate the cross-regional
impacts on realized volatility in the JEPX, using the same procedure as before. The regression
results for the impacts of Tohoku’s renewable power generation on the electricity transmission
from Tohoku to Tokyo are presented in Table 5. The structure of Table 5 is the same as in Table
3.
[Table 5]
All the coefficients of the lagged terms of 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑑 are positive and statisticallysignificant in all columns of Table 3. The daily transmitted electricity is strongly dependent on
previous electricity transmission. 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 are positive andstatistically significant in columns (2) are (3), which means that increasing the renewable power
generation in Tohoku leads to an increase in the electricity trans- mission from Tohoku to Tokyo.
In particular, the coefficient is 0.080 for 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 0.032 for 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑in column (3), which suggests that a 1% increase in daily solar and wind power generation leads
to a 0.080% and 0.032% increase in electricity transmission, respectively. The coefficients
𝑃𝑟𝑖𝑚𝑎𝑟𝑦(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 , 𝐷_𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 𝐷_𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡(𝑇𝑜𝑘𝑦𝑜)𝑑 are similar to
12

The share was 3.481% during peak time and 3.523% during off-peak time.

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the results in Table 3, demonstrating the consistency of the hourly and daily estimations. The
regression results for the second stage model based on equation (4) illustrate the impacts ofrenewable power generation on the realized volatility (Table 6). In contrast to column (3), theresults for the estimation without controls are listed in columns (1) and (2).
[Table 6]
All the lagged terms of realized volatility 𝑅𝑉𝑑 have positive and statistically significant
coefficients in all columns. The results mean that the volatility in Tokyo’s electricity spot market
can also be determined by its previous values. The coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜𝑘𝑦𝑜)𝑑 are positiveand statistically significant in columns (2) and (3), which suggests that local solar power
generation leads to an increase in the realized volatility in Tokyo’s market. For example, thecoefficient is 0.044 for Solar(Tokyo)d in column (3), which suggests that a 1% increase in local
solar power generation leads to a 0.044% increase in realized volatility. This result is consistent
with Rintamä ki et al. (2017), Ketterer (2014), and Ma et al. (2019), namely, that renewable
power generation increases the realized volatility in the local electricity spot market. Meanwhile,̂̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)𝑑 (columns (2) and (3)) and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑑 (column (2))
are negatively and significantly correlated with the realized volatility. These results show that
increasing the renewable power transmitted from Tohoku reduces the realized volatility inTokyo’s spot market. In particular, a 1% increase in the indirect impacts of Tohoku’s solar andwind power generation induces a 0.034% and 0.019% decline in the change in realized volatility,
respectively13. We consider that lower impacts on wind power is owing to the higher generationof wind power is transmitted from Tohoku that compare with solar power. The coefficient ofPrimary(Tohoku)d has a positive and statistically significant sign in column (3), showing thathigher base-load and mid-range power leads to increased realized volatility. TransImpactd is
positive and statistically significant in columns (3), which suggests that the electricity
transmission excluding renewable power from Tohoku to Tokyo leads to an increase in therealized volatility in Tokyo’s spot market. Our findings show opposite impacts of local andtransmitted renewable power on the realized volatility in Tokyo. We suggest that the positive
impacts of local renewable power generation is due to the relatively unstable supply of the local
renewable power, limited access to regulating power, and insufficient transmission capacity of
the interconnection line. Rintamä ki et al. (2017) concluded that access to flexible capacity
through adequate transmission capacity could reduce short-term volatility. We consider that thevolatility reduction impact of trans- mitted renewable power is due to the more stable renewableelectricity supply from Tohoku (Ma et al., 2019). These results also indicate that the13

The coefficients of the indirect impacts of Tohoku's solar power generation was calculated by product of thê
coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
𝑑 : 0.080 × -0.218 that equal -0.017, and thecoefficients of the indirect impacts of Tohoku's wind power generation was calculated by product of thê
coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑑 : 0.032 × -0.333 that equal -0.010.

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enhancement of transmission capacity has the potential to increase the stability of the Tokyo
market.
Furthermore, we also estimated the impacts on the realized volatility during different intraday
periods. The intraday periods were classified into peak time (1:00 p.m.–4:00 p.m.), off-peaktime (12:00 a.m.–12:00 p.m. and 5:00 p.m.–12:00 a.m.), daytime (8:00 a.m.–10:00 p.m.) andnighttime (11:00 p.m.–7:00 a.m.). The realized volatility was recalculated based on these subsamples according to equations (3) and (4) 14. The results for the first and second stages areshown in Tables 7 and 8, respectively. The results for the estimations during the peak period arelisted in column (1) and those for the off-peak period are listed in column (2). Columns (3) and(4) report the results for the estimations during the daytime period and nighttime period,
respectively. The coefficients of Solar(Tohoku)d and Wind(Tohoku)d in Table 7 suggest thatthe transmitted power generation from Tohoku can be varied with changes in electricity demandin Tokyo.
[Table 7]
The coefficients of Solar(Tokyo)d show a positive and statistically significant sign in columns(2) and (3) in Table 8. Wind(Tokyo)d is positive and statistically significant sign in column (2).
The results suggest that local renewable power generation increases realized volatility during
the daytime and off-peak periods. In particular, a 1% increase in solar and wind power
generation leads to a 0.034% and 0.012% increase, respectively, in the realized volatility in̂Tokyo’s
electricity
spot
market.
Meanwhile,
𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
and𝑑̂𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑑 have negative and statistically significant signs in columns (2) and(3). The result indicates that the volatility reduction impact of transmitted renewable power also
can be observed during the daytime and off-peak time. For instance, a 1% increase in solar andwind power transmitted from Tohoku leads to a 0.018% and 0.010% decline in realized
volatility during off-peak time, respectively 15. On the other hand, a 1% increase in solar andwind power transmitted from Tohoku leads to a 0.026% and 0.010% decline in realized
volatility during daytime, respectively 16 . Conversely, the insignificant coefficients of therenewable power generation indicators shown in column (4) in Table 8 indicate that both local
and transmitted renewable energy do not exert an impact on the realized volatility during the14

2
For instance, realized volatility during the peak time is calculated as 𝑅𝑉(𝑝𝑒𝑎𝑘𝑡𝑖𝑚𝑒)𝑑 = ∑15
𝑡=13 𝑅𝑒𝑡𝑢𝑟𝑛𝑡 .
The coefficients of the indirect impacts of Tohoku's solar power generation during the off-peak time period
was calculated by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 in column (2) of Table 7 and̂
𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
𝑑 in column (2) of Table 8: 0.056 × -0.314 that equal -0.018, and the coefficients of theindirect impacts of Tohoku's wind power generation during same period was calculated by product of thê
coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑑 : 0.031 × -0.318 that equal -0.010.
16 The coefficients of the indirect impacts of Tohoku's solar power generation during the off-peak time period
was calculated by product of the coefficients of 𝑆𝑜𝑙𝑎𝑟(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 in column (2) of Table 7 and̂
𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑆𝑜𝑙𝑎𝑟)
𝑑 in column (2) of Table 8: 0.138 × -0.188 that equal -0.026, and the coefficients of theindirect impacts of Tohoku's wind power generation during same period was calculated by product of thê
coefficients of 𝑊𝑖𝑛𝑑(𝑇𝑜ℎ𝑜𝑘𝑢)𝑑 and 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛(𝑊𝑖𝑛𝑑)𝑑 : 0.030 × -0.328that equal -0.010.

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nighttime.
[Table 8]
These results on realized volatility in different periods indicate that both local renewable andtransmitted renewable power generation affect realized volatility during the period with lower
electricity demand 17. The renewable power suppliers have not been incentivized by the FIT
scheme to increase production during the period with higher electricity demand (METI, 2020b).
This result complements the findings of Schmidt et al. (2013) that FIT does not provide an
incentive to match electricity production with the marginal costs of electricity production. The
actual marginal cost during peak periods has been higher, and during off-peak periods it has
been below average. High marginal cost power plants have been built to operate only during
peak periods, with substantial unused capacity during the off-peak (Pikk and Viiding, 2013).
Renewable electricity suppliers tend to trade during periods with lower demand to avoid thehigher marginal cost and consequently the higher financial risk during peak times.

5. Conclusions
This study examined the impacts of cross-region transmission from Tohoku on the electricity
spot market in Tokyo. Our findings indicate that, by providing flexibility in the energy capacityof the local market, the renewable power from neighboring areas plays an important role in thelocal electricity market. In the study areas, the renewable power power from Tohoku has higher
price reduction effects compared to local renewable power generation in Tokyo. The relatively
higher effects could be due to higher price elasticity under a tight supply–demand balance. In
particular, cross-regional wind power generation has a higher price reduction effect during peaktimes compared to distributed solar power. The wind power generation can meet the higher
electricity demand in the spot market. Therefore, we suggest that increasing investment in thewind industry can help balance the peak time electricity supply–demand. Meanwhile, ourresults confirm the cross-regional price stabilization impact of renewable power generation in
Tohoku. The negative impact on realized volatility is due to the relatively more stable renewableelectricity transmitted from Tohoku. However, the current transmission capacity between
Tohoku and Tokyo is still limited; this restricts the potential for transmission of renewable
power from Tohoku to Tokyo. Therefore, increasing the capacity of the interconnections
between Tohoku and Tokyo is a key factor in increasing renewable electricity deployment inTokyo, leading to further price reductions and stabilization in the spot market.
Furthermore, our results indicate that renewable power producers, especially solar power
17

According to results in column (2) and (4) in Table 8, a statistically significant impact can be found during
the off-peak period except nighttime, which means the period of 8:00 a.m. - 1:00 p.m. and 4:00 p.m. -10:00p.m..

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ディスカッションペーパー No.41

producers, are not incentivized by the fixed-price FIT to meet electricity demand during peaktimes because of the higher marginal costs. Implementing the feed-in premiums (FIP) scheme
can help facilitate demand-based power supply since the premium that producers receive isbased on the market price (Marques et al., 2019). Further, the FIP is deemed to provide an
incentive to better match renewable power output with marginal production costs. In fact, toachieve the carbon-neutral goal for 2050, the sliding FIP system will be implemented in theJapanese market from April 2022 (METI, 2021). The government expects that renewable power
producers will be incentivized to react based on market price signals once the FIT has been
replaced with the FIP. However, the government also need to beware the FIP retarding theinvestment in renewable power technologies 18 . Moreover, we suggest that increasing theusage of aggregator services can increase the stability of renewable power output and thus
induce further price reduction effects during peak periods.

18

Du and Ma (2021) discuss the implement of FIP in Germany and indiating that FIP achieved its objectiveof encouraging the market integration of wind energy but retard the investment in solar power technologies
in Germany.

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Acknowledgement
Earlier version of this paper was presented at the 2021 annual meeting of the Society for
Environmental Economics and Policy Studies. Authors express the gratitude to not only helpfulcomments from Kimio Yamaka, Yoh Yasuda, Toru Morotomi, but also the support by Research
Project on Renewable Energy Economics of Kyoto University. This study was funded by the
JSPS KAKENHI Grant Number 20K13481.

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Table 1: Annual utilization of cross-regional interconnection lines for regional
service areas in fiscal years (50Hz areas)

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ディスカッションペーパー No.41

Table 2: Descriptive statistics

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Table 3: The impact of renewable power generation on cross-regional
interconnection line (hourly)

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Table 4: The impact of renewable power generation on electricity spot price2022 年 5 月

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Table 5: The impact of renewable power generation on cross-regional
interconncetion line (daily)

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Table 6: The impact of renewable power generation on realized volatility

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Table 7: The impact of renewable power generation on cross-regional
interconnection line (divided by periods)

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Table 8: The impact of renewable power generation on realized volatility(divided by periods)

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Hokkaido
Area
Interconnection Lines
AC-DC Converter Equipment

KyusyuArea

Hokuriku
Area

Tohoku
Area

Chubu
Area

Tokyo
Area

ChugokuArea
KansaiArea
ShigokuArea

The object areasof this study

Okinawa
Area
60Hz

50Hz

Figure 1: The object areas in this study

Figure 2: Electricity spot price in nine regions (FY2016–FY2019)28

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Figure 3: Renewable power generation in FY 2019

Figure 4: Trends in daily system spot price in Tokyo's electricity spot market(FY2016–FY2019)2022 年 5 月

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Figure 5: Trends in hourly system spot price in Tokyo's electricity spot market(FY2016–FY2019)

Figure 6: Trends in daily realized volatility in the Tokyo's spot market(FY2016–FY2019)30

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Figure 7: Trends in monthly solar power generation (FY2016–FY2019)

Figure 8: Trends in monthly wind power generation (FY2016–FY2019

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Figure 9: Trends in hourly solar power generation (FY2016–FY2019)

Figure 10: Trends in hourly wind power generation (FY2016–FY2019)32

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Figure 11: Trends in monthly electricity transmission from Tohoku to Tokyo(FY2016–FY2019)

Figure 12: Trends in hourly electricity transmission from Tohoku to Tokyo(FY2016–FY2019)2022 年 5 月

33