Welfare Effects of Implicit Auction on Interconnector Capacity: Evidence from Japan

Welfare Effects of Implicit Auction on Interconnector Capacity: Evidence from Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Welfare Effects of Implicit Auction on Interconnector Capacity: Evidence from Japan Kota Sugimoto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2874856/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study examines the welfare impact of implicit auction on the interconnector transmission capacity in Japan. The first-come-first-served rule allows incumbent generation or retail firms to exercise vertical market power by withholding the interconnector capacity and create congestion in the day-ahead market. The implicit auction allocates all the capacity simultaneously with energy in the day-ahead market. It prevents them from strategically reserving their physical transmission capacity ex ante and increases cross-zonal trade volumes in the day-ahead market. Increased trade reduces the price gap between the import and export zones. I use machine-learning methods to estimate the welfare impact of implicit auction. I predict the counterfactual market outcomes without implicit auction, and find that the trade effect of implicit auction is over $ 200 million per year. The study also finds that implicit auction does not necessarily reduce the price gap between export and import zone because it not only has a trade effect but also a counteracting volume effect. JEL codes: L94, Q41 transmission right market power congestion management interconnector implicit auction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. INTRODUCTION Transmission lines play a crucial role in an electric power network. They contribute to balancing demand and supply in real-time, guaranteeing efficient merit-order dispatch in wider geographic areas, and integrating intermittent renewable energy. In addition, transmission capacity increases competition in the generation and retail sectors by creating wider markets. However, interconnector transmission capacity is a scarce resource because, historically, each vertically integrated utility is required to supply all customers in its area under a regional monopoly and does not depend on imports across interconnectors. Hence, the efficient allocation of limited capacity is important, especially after market liberalization. Allocations are done through either market or non-market-based congestion management methods (European Transmission System Operators, 2004). What is the economic impact of market-based congestion management methods? This study empirically evaluates the welfare impact of implicit auction relative to first-come-first-served (FCFS) allocations in Japan. Implicit auction simultaneously allocates interconnector capacity with energy in the day-ahead market, while the FCFS rule allows incumbents to reserve the capacity before the energy market. The FCFS rule effectively allocates it as a free non-tradable physical transmission right (PTR), which provides the right holders with an instrument to exercise vertical market power (Bushnell, 1999 ). They can withhold PTR from the subsequent day-ahead market to create market-splitting and sell at higher market prices in the import constraint zone by intentionally overestimating their capacity needs. There is a penalty surcharge for canceling the reservation; however, the size of the surcharge is limited, and capacity holders can reduce the reservation even after the day-ahead market. Implicit auction prohibits interconnector capacity reservations in advance, and all capacity is released to the day-ahead market, thereby enabling more cross-zonal trade in the energy market. Increased trade reduces the price gap between the import and export zones. Implicit auction also improves the liquidity of the day-ahead market, because firms that previously reserved interconnector capacity for bilateral contracts outside the energy market are required to bid in the market. I focus on two interconnectors where congestion was severe: Hokkaido-Honshu (HH) interconnectors connecting the Hokkaido and East zones, and Kanmon interconnectors connecting the West and Kyushu zones. The empirical challenge is that market outcomes are unobservable if FCFS has not been abolished. I use several machine-learning methods to predict counterfactual market outcomes in the post-treatment period. By comparing the loss functions, I evaluate the predictive performance and choose the best model to predict the counterfactuals. Moreover, by comparing actual and counterfactual market outcomes, I estimate the welfare impact of implicit auctions. I find that the implicit auction improved the efficient allocation of interconnector capacity and prevent market power, relative to the FCFS rule. Furthermore, I find that the implicit auction has an economically significant impact on welfare. The trade effect is approximately $ 40 million for the Hokkaido-Honshu interconnector and $ 60 million for the Kanmon interconnector over the six months of implementation. The annual gain is over $ 200 million/year. My contributions are three-fold. First, this study presents evidence of the impact of an implicit auction, compared to the non-market-based FCFS rule. Numerous studies have examined the inefficiency of an explicit auction, another market-based method in comparison to implicit auction in Europe (e.g., Bunn and Zachmann, 2010 ; Brunekreeft et al., 2005 ; Creti et al., 2010 ; Ehrenmann and Neuhoff, 2009 ; Füss et al., 2020 ; Gugler et al., 2018 ; Keppler et al., 2016; Newbery et al., 2016 ). Implicit auction can avoid the economically inefficient use of interconnector capacity, that is, export from high-price zones to low-price zones, attributable to the explicit auction. However, this study shows that implicit auction can also reduce the underutilization of interconnector transmission capacity caused by the cancellation of reserved interconnector capacity under FCFS. Moreover, I show that implicit auction has not only a price-decreasing effect (trade effect) on the day-ahead market price gap through releasing the interconnector capacity previously reserved under FCFS, but also a price-increasing effect (volume effect) by forcing those who reserved the capacity to participate in the market. Second, I use empirical evidence to show that, the FCFS rule allowed PTR holders to exercise vertical market power, as predicted by the theoretical literature (e.g., Joskow and Tirole, 2000 ; Bushnell, 1999 ), and how an implicit auction could prevent this by simultaneously allocating the PTR in the day-ahead market. Third, I avoid the potential omitted variable bias that can generally occur when studying two countries in Europe (e.g., Germany and France), which are connected by other neighboring countries and whose relevant data are often not available. By contrast, Japan is not connected to other countries, and all zonal data are available. An additional advantage is that its network is effectively radial and the analysis does not suffer from loop-flow issues. The remainder of this paper is organized as follows. Section 2 describes the wholesale market structure, and section 3 explains interconnector congestion management methods in Japan. Section 4 defines the two effects of an implicit auction: trade and volume effects. Section 5 presents the research design, and Section 6 explains the data. Section 7 presents the estimates of the trade effect. Section 8 presents the counterfactual simulations and the welfare impact of implicit auctions and section 9 concludes the paper. 2. WHOLESALE MARKET STRUCTURE IN JAPAN Figure 1 shows that the Japanese day-ahead market comprises nine fixed areas connected by interconnectors. Each area is equivalent to the control area in which the former vertically integrated monopoly operates. I classify the nine areas into four zones: Hokkaido, East, West, and Kyushu. Hokkaido is located in the northeastern part of Japan and has the highest day-ahead market price during the study period, partly because it still uses oil-fueled thermal power plants. The East zone includes Tokyo and Tohoku. The Hokkaido and East zones are connected by a high-voltage direct current HH interconnector. I defined the West zone as the compilation of Chubu, Kansai, Hokuriku, Shikoku, and Chugoku areas and regarded it as a single zone, as there is little congestion within the zone, and the market price is almost the same. The East and West zones are connected by high-voltage direct current frequency converters 1 . Kyushu is located in the southwestern part of Japan. The West and Kyushu zones are connected by a high-voltage alternate current Kanmon interconnector. These three interconnectors experienced frequent congestion. The remaining interconnectors are mostly uncongested during the study period. Although there is an intraday market, almost all energy is traded in the day-ahead market. [1] Eastern and western Japan use 50 Hz and 60 Hz, respectively. 3. CONGESTION MANAGEMENT METHODS: FCFS AND IMPLICIT AUCTION In Japan, the day-ahead market uses a single-price auction and a market-splitting method. The market-splitting algorithm allocates all net available interconnector capacity (NAIC) to market participants who successfully contract in the auction (Marmiroli et al., 2009 ). Before October 2018, the FCFS rule allowed incumbents to reserve their capacity for free before the start of the day-ahead auction at 10 am. Under FCFS, the NAIC in the day-ahead market is calculated as NAIC = Operating capacity – Margin – Reserved capacity. Operating capacity is the maximum amount of energy that can flow through an interconnector. The limit is the minimum value that satisfies all four constraints: thermal capacity, synchronization stability, voltage stability, and frequency. Margin is the reserved capacity to import or export energy from other areas to maintain system security during an emergency. The operating capacity and margin are calculated annually by the organization for cross-regional coordination of transmission operators (OCCTO), an association of regulators and transmission system operators. The operating capacity is stable except during planned maintenance. The margin is set to be constant for HH and zero for Kanmon. It should be noted that an implicit auction was already used in the market under the FCFS rule, but it can only be applied to the remaining NAIC. FCFS-based reservation is allowed for up to 10 years and could also be exclusively renewed. The reservation was effectively a non-tradable PTR (Joskow and Tirole, 2000 ; Bushnell, 1999 ; Gilbert et al., 2004 ). The market-split algorithm provides incumbents with an incentive to create congestion strategically and increase market revenue by selling at higher prices in an importing zone. The FCFS rule can give PTR holders the ability to exercise market power and avoid the risk of market price divergence between zones (Twomey et al., 2005 ). They can withhold interconnector capacity from the energy market by nominating it as a scheduled flow. Under the FCFS rule, most interconnector capacities were used for bilateral contracts outside the wholesale energy market. Figure 2 shows the 70–100 TWh/year energy flow interconnectors for bilateral contracts, until 2017. Figure 2 corresponds to approximately 7–10% of annual national power consumption. After the implicit auction was implemented in October 2018, the amount of trade via bilateral contracts dropped substantially to approximately 0.25 TWh/year. Meanwhile, the amount of energy traded in the day-ahead market skyrocketed, implying that the energy for bilateral contracts now flows into the day-ahead market. Figure 3 shows the daily average reservation rates for HH (Panel A) and Kanmon (Panel B) from October 2016 to September 2018. The red circle shows the reservation rate seven days before delivery, and the black dot is the reservation rate two days before delivery. The direction of the reserved flow is from East to Hokkaido in Panel A, and from Kyushu to West in Panel B. Both interconnectors are reserved in one direction. As expected, the reservation rate in both interconnectors is almost 100% seven days before delivery, implying that there is no available transmission capacity for the day-ahead market. While some of the capacity is released two days before delivery for HH, approximately 80% of capacity is already allocated before the day-ahead market. The difference between the reservation rate on the second and seventh day is very small in the case of Kanmon. This figure shows that the interconnector capacity available in the day-ahead market is limited for both interconnectors under the FCFS rule. Those who could reserve interconnector capacity could also cancel the reservation even after day-ahead market clearing, leaving an unused capacity. It should be noted that they could only cancel (i.e., reduce) the amount of reserved capacity, but could not increase it under FCFS. The cancellation does not mean exercising market power itself, but it implies that they could initially reserve more than they required, aggravating market power concerns. Those who canceled the reservation had to pay a penalty to prevent strategic overestimation of capacity needs. The surcharge was set at 0.01 yen/kWh of canceled capacity when they canceled more than 10% of the initial reserved capacity (OCCTO, 2017 ). There was no surcharge for the capacity cancellation within 10% of the initial reservation. The low penalty implies that they could still strategically withhold unnecessary interconnector capacity from the market. The right to cancel also helps the PTR holders to reduce the surplus imbalance. Figure 4 shows how often reserved capacity was canceled after the day-ahead market. Panel A plots the relationship between the market-price gap between Hokkaido and the East zones in the day-ahead market and the reservation rate of the HH interconnector at 5 pm on the day before delivery. A negative reservation rate indicates that the interconnector capacity is reserved to transmit energy from East to Hokkaido. The two graphs on the left in each panel show the two-year observations from October 2016 to September 2018 under FCFS, and the two graphs on the right show the two-year observations after the full implicit auction was implemented from October 2018 to September 2020. Under FCFS, the reservation rate of the HH interconnector was greater than 100% at 5 pm in numerous instances, even though the day-ahead market price in Hokkaido was higher than that in the East zone. The positive price gaps imply that congestion occurred at the HH interconnector during day-ahead market clearing, and all available capacity was allocated to the market, making a 100% reservation rate at 10 am. Hence, this figure confirms that the reserved interconnector capacity was frequently canceled after day-ahead market-splitting. From October 2018, reservations before the day-ahead market were prohibited, and the NAIC was equal to the operating capacity minus the margin. Under a full implicit auction, incumbents could no longer reserve capacity, and all available capacity was allocated to the day-ahead market. The two graphs on the right show that most observations clustered at the dotted red lines after the implicit auction, indicating that they could neither reserve nor cancel the interconnector capacity. Panel B of Fig. 4 also shows that the market price in the West was higher than that in Kyushu for some hours, but the Kanmon interconnector capacity was not fully allocated at 5 pm. In sum, the FCFS rule allowed FTR holders to reduce reservations after the day-ahead market in both interconnectors, while implicit auction succeeded in fully allocating it in an economically efficient direction. 4. EFFECTS OF IMPLICIT AUCTION I argue that a full implicit auction (i.e., abolishing the FCFS rule) has two effects on the day-ahead market in Japan. The first is the trade effect. The implicit auction releases the entire interconnector capacity previously reserved for the energy market. This increases the NAIC and the trade quantity across market zones, similar to an interconnector capacity upgrade investment. Consequently, it will reduce the market price gap between the import and export zones. Figure 5 shows how an increase in the NAIC reduces the price gap between zones in the day-ahead market. \({P}_{Autarky}^{export}\) ( \({P}_{Autarky}^{import}\) ) is the price in the export (import) zone under autarky. A price gap exists between \({P}_{Autarky}^{export}\) and \({P}_{Autarky}^{import}\) . As the trade quantity increases, the price in the export zone increases to \({P}_{Trade}^{export}\) and the price in zone B decreases to \({P}_{Trade}^{import}\) , converging the price gap. The second is the volume effect. The full implicit auction prohibits reserving interconnector capacity for bilateral contracts across zones and thus increases the volume of the day-ahead market. After the full implicit auction was introduced in October 2018, those who used to reserve the interconnector capacity for bilateral contracts were required to bid in the day-ahead market to obtain the interconnector capacity to trade energy across zones. Moreover, the regulator gave them temporary financial transmission rights to alleviate the unexpected risk of a price gap derived from market-splitting. This transitional measure allowed retailers who could successfully bid a day ahead to recover the price gap by March 2026. Thus, incumbents have a strong incentive to bid in the day-ahead market. Consequently, the full implicit auction increased the volume of both selling and buying quantities in the day-ahead market. Figure 6 shows both the volume of selling and buying increased discontinuously after October 2018. This increase in the market volume may have affected the market price gap. When the additional offer price in the export zone is lower than the marginal cost of the marginal generator, the additional offer lowers the resulting zonal price. Otherwise, it does not affect the prices in the export zone. The left panel of Fig. 7 depicts the former scenario. Similarly, if the additional bid price in the import zone is higher than the former equilibrium price, the addition raises the market price in the import zone. Otherwise, it does not affect the price in the import zone. The right panel of Fig. 7 depicts the former scenario. In sum, the volume effect can widen the price gap between the import and export zones. 5. EMPIRICAL STRATEGY To estimate the trade effect, I model the day-ahead market price gap between the import and export zones as a function of the NAIC, volume added to the market after the implicit auction, supply controls, demand controls, and fixed effects. The following equation is used: $$\varDelta {{p}}_{{D}{A}, {t}}={{\alpha }}_{0}+{{\beta }}_{1}{{N}{A}{I}{C}}_{{t}}+{{\beta }}_{2}{{V}{o}{l}{u}{m}{e}}_{{t}}+{{\beta }}_{3}{S}{u}{p}{p}{l}{{y}}_{{t}}+{{\beta }}_{4}{D}{e}{m}{a}{n}{{d}}_{{t}}+{{F}{E}}_{{t}} +{{\epsilon }}_{{t}} \left(1\right).$$ \(\varDelta {p}_{\text{D}\text{A}, \text{t}}\) is the day-ahead market price difference between the import and export zones (Hokkaido and East for HH and West and Kyushu for Kanmon). NAIC is the interconnector capacity available in the day-ahead market. The NAIC has two different values depending on the flow direction. I use the NAIC from the export zone to the import zone. \({\beta }_{1}\) is the parameter used to measure the size of the trade effect. Volume is the bid quantity added to the market after an implicit auction. As this variable is not observable, I use the amount of counterfactual reserve capacity as a proxy, assuming that the added bid quantity would be equal to the volume of counterfactual reserved capacity if the FCFS rule was maintained. This assumption seems plausible because those who tended to reserve capacity were not only required to bid in the market to obtain it but were also given the transitional financial transmission right conditional on making a successful bid in the market, as discussed in the previous section. I include the supply and demand shocks that may affect the outcome and NAIC. Supply control variables include photovoltaic (PV) and wind generation in each zone. Nuclear power generation is supplied as a baseload and included as an exogenous variable. To control fossil fuel costs, daily coal prices, monthly liquefied natural gas (LNG) import prices, and daily Brent oil prices are used. Demand variables are the actual hourly electricity consumption in each zone. These are proxies for forecasted demand and are assumed to be exogenous in the short term. FE t includes hour, day of the week, month, and fiscal year fixed effects. I also include a month-year fixed effect to control for unobservable progress of the retail competition and the “gross bidding” policy, which started in April 2017 to encourage the incumbent generation or retail firms to voluntarily bid some of their internal contracts into the day-ahead market. Hour-month fixed effects are also added to control for prediction errors in renewable energy generation and demand. Standard errors are clustered by day to address autocorrelation. 6. DATA The JEPX publishes half-hourly, day-ahead market price data for each area. I calculate the average hourly market prices (yen/kWh) to merge with the hourly covariates. The data on operating capacity, margin, and reserved interconnector capacity are available at OCCTO. I use the reserved interconnector capacity at 3 pm, two days before delivery, as the reserved interconnector capacity under the FCFS rule. This implicitly assumes that incumbents could have canceled the reservation after the day-ahead market on day t-1, but did not cancel from 3 pm on day t-2 to 10 am on day t-1. This assumption seems plausible because they had the incentive to withhold the capacity at least until the day-ahead market clearing to strategically create congestion and market splits. The volume variable is the predicted counterfactual reserved capacity, which is estimated in Section 8 . Supply and demand variables are gathered from transmission system operators’ websites. The daily coal price data are obtained from the globalCOAL NEWC Index, while the daily Brent oil price is assembled by the Energy Information Administration. The monthly LNG import price is calculated based on the Trade Statistics of Japan in the Ministry of Finance. The study period was from midnight on June 4, 2016 to 3 pm on March 28, 2019. I exclude the data afterward because the operating capacity of the HH interconnector was upgraded from 600 MW to 900 MW at 3 pm on March 28, 2019, and the financial transmission rights market was introduced on April 1, 2019, which may have changed the bidding behavior of the market participants. Table 1 presents the descriptive statistics. The NAIC of the Kanmon interconnector is much larger than that of the HH interconnector. The Kanmon interconnector has an operating capacity of 2800 MW, whereas the HH interconnector has an operating capacity of 600 MW. Solar power generation in Japan is much greater than wind power generation. Nuclear power plants were present in all four zones but stopped operating after the nuclear accident in Fukushima in 2011. Only several nuclear power plants in the West and Kyushu zones were operational during the study period. Table 1 Descriptive Statistics (06/04/2016 ~ 03/28/2019) Variable Unit Obs Mean Std. Dev. Min Max Price gap between Hokkaido and East zones yen/kWh 24,183 3.290603 4.370945 -39.51 39.115 Price gap between the West and Kyushu zones yen/kWh 24,663 0.28956 1.160589 0 52.68 NAIC of HH MW 24,663 46.67206 47.14436 0 200.496 NAIC of Kanmon MW 24,663 710.1228 825.4094 0 2980 Volume for HH MW 24,663 19.20276 42.91469 0 149.5973 Volume for Kanmon MW 24,663 332.1523 726.4957 0 2301.543 Solar in Hokkaido MWh 24,663 157.3206 236.4576 0 1103 Solar in the East MWh 24,663 2016.408 3078.68 0 13901 Solar in the West MWh 24,663 2481.806 3656.362 0 16161 Solar in Kyushu MWh 24,663 1000.39 1532.196 0 6656 Wind in Hokkaido MWh 24,663 99.80189 70.19188 0 317 Wind in the East MWh 24,663 368.4876 281.3567 0 1338 Wind in the West MWh 24,663 224.6692 165.2895 3 832 Wind in Kyushu MWh 24,663 63.0356 58.8241 0 316 Nuclear in the West zone MWh 24,663 2230.038 1463.747 0 5025 Nuclear in Kyushu MWh 24,663 2127.796 1136.346 634 4151 Demand in Hokkaido MWh 24,663 3568.859 635.8973 0 5422 Demand in the East MWh 24,663 42478.48 7755.905 26482 69346 Demand in the West MWh 24,663 46223.74 8358.808 28290 75471 Demand in Kyushu MWh 24,663 10029.18 1706.838 6453 16011 Coal price $/ton 24,663 93.44434 15.71042 50.46 122.89 LNG price 1000 yen/ton 24,663 49.16741 9.223131 32.87843 65.38621 Oil price $/barrel 24,663 59.56472 10.92182 40 86.07 7. RESULTS Table 2 presents the estimation results of Eq. (1). Column (1) is the baseline estimation with the hour, week, month, and fiscal year fixed effects. This indicates that a 1 MW increase in the NAIC of the HH line reduces the day-ahead market price gap between Hokkaido and the East zone by 0.014 yen/kWh (14 yen/MWh). The other control variables have expected signs and sizes. The volume variable is positively correlated with the outcome. Solar power generation in Hokkaido significantly reduces the market price gap by lowering the market price in Hokkaido. Wind generation remains mostly insignificant, partly because there is less variation than that in solar generation. Demand in Hokkaido is positively associated with the outcome, whereas demand in the East is negatively associated with the outcome. LNG prices are negatively associated with the market price gap. It is notable that, there was no LNG-fired power plant in Hokkaido during the study period; thus, the LNG price did not affect the market price in Hokkaido. Meanwhile, the rise in the LNG price increases the market price in the East and consequently reduces the price gap. Columns (2) and (4) introduce additional fixed effects. Column (2) includes month×year fixed effects to flexibly control for the unobservable supply or demand shocks that may differ by month in the sample period. Column (3) includes hour×month fixed effects. Column (4) includes both month×year and hour×month fixed effects and is our preferred specification. Table 2 Trade effect of the HH interconnector Variable 1 2 3 4 NAIC -0.014** -0.024*** -0.015** -0.026*** [0.006] [0.007] [0.007] [0.007] Volume 0.042*** 0.080** 0.042*** 0.070** [0.013] [0.030] [0.013] [0.027] Solar in Hokkaido -0.004*** -0.004*** -0.002*** -0.002*** [0.001] [0.001] [0.001] [0.001] Solar in the East 0 0.000* 0 0 [0.000] [0.000] [0.000] [0.000] Solar in the West 0 0 0 0 [0.000] [0.000] [0.000] [0.000] Solar in Kyushu -0.000** -0.000** 0 0 [0.000] [0.000] [0.000] [0.000] Wind in Hokkaido 0 0 -0.001 0 [0.001] [0.001] [0.001] [0.001] Wind in the East 0 0 0 0 [0.000] [0.000] [0.000] [0.000] Wind in the West 0 0 0 0 [0.001] [0.001] [0.001] [0.001] Wind in Kyushu 0 -0.001 0.001 0 [0.001] [0.001] [0.001] [0.001] Nuclear in the West 0 0 0 0 [0.000] [0.000] [0.000] [0.000] Nuclear in Kyushu -0.001*** 0 -0.001*** 0 [0.000] [0.000] [0.000] [0.000] Demand in Hokkaido 0.001*** 0.002*** 0.002*** 0.002*** [0.000] [0.000] [0.001] [0.000] Demand in the East -0.000*** -0.000*** -0.000*** -0.000*** [0.000] [0.000] [0.000] [0.000] Demand in the West 0 0.000** 0 0 [0.000] [0.000] [0.000] [0.000] Demand in Kyushu -0.000*** -0.001*** 0 0 [0.000] [0.000] [0.000] [0.000] Coal price -0.018 0.044*** -0.022* 0.045*** [0.011] [0.013] [0.011] [0.013] LNG price -0.192*** -4.834*** -0.172*** -5.157*** [0.031] [1.568] [0.030] [1.522] Oil price -0.082*** -0.045 -0.078*** -0.036 [0.021] [0.028] [0.021] [0.029] Month×Year FE No Yes No Yes Hour×Month FE No No Yes Yes Adj-R-squared 0.285 0.355 0.312 0.38 Day-cluster robust standard errors are shown in parentheses. All the specifications include hour, week, month, and fiscal year fixed effects. The total number of observations is 24,183. *** p < 0.01, ** p < 0.05, * p < 0.1 Table 3 shows the estimation results for the Kanmon interconnector across the West and Kyushu zones. A 1 MW increase in the NAIC of the Kanmon interconnector decreases the market price gap by 0.0004 yen/kWh (0.4 yen/MWh) between the West and Kyushu zones across all specifications. Column (4) is my preferred specification and is used for the welfare analysis in the next section. Table 3 Trade effect of Kanmon interconnector Variable 1 2 3 4 NAIC -0.0004*** -0.0004*** -0.0004*** -0.0004*** [0.0001] [0.0001] [0.0001] [0.0001] Volume 0.0006*** -0.0012** 0.0006*** -0.0012** [0.0002] [0.0005] [0.0002] [0.0005] PV in Hokkaido 0.0002 0.0003 0.0003 0.0003 [0.0002] [0.0002] [0.0002] [0.0002] PV in the East -0.0000* -0.0000** -0.0000** -0.0000** [0.0000] [0.0000] [0.0000] [0.0000] PV in the West 0 0 0 0 [0.0000] [0.0000] [0.0000] [0.0000] PV in Kyushu 0.0001*** 0.0001*** 0.0001*** 0.0001*** [0.0000] [0.0000] [0.0000] [0.0000] Wind in Hokkaido -0.0004 -0.0005 -0.0004 -0.0005 [0.0004] [0.0004] [0.0004] [0.0004] Wind in the East 0 0.0001 0 0.0001 [0.0001] [0.0001] [0.0001] [0.0001] Wind in the West 0.0001 0 0.0001 0 [0.0001] [0.0001] [0.0001] [0.0001] Wind in Kyushu -0.0009*** -0.0008*** -0.0009*** -0.0008*** [0.0002] [0.0002] [0.0002] [0.0002] Nuclear in the West -0.0003*** -0.0001* -0.0003*** -0.0001* [0.0001] [0.0001] [0.0001] [0.0001] Nuclear in Kyushu 0.0002*** 0.0002** 0.0002*** 0.0002** [0.0001] [0.0001] [0.0001] [0.0001] Demand in Hokkaido -0.0002** -0.0002** -0.0001* -0.0001** [0.0001] [0.0001] [0.0001] [0.0001] Demand in the East 0.0000** 0 0.0000** 0 [0.0000] [0.0000] [0.0000] [0.0000] Demand in the West 0.0000* 0.0000** 0.0000* 0.0000* [0.0000] [0.0000] [0.0000] [0.0000] Demand in Kyushu -0.0001** -0.0001 -0.0001 0 [0.0000] [0.0000] [0.0000] [0.0001] Coal price 0.0121*** 0.0079*** 0.0121*** 0.0079*** [0.0026] [0.0023] [0.0026] [0.0023] LNG price 0.0262*** 0.5888* 0.0273*** 0.5836* [0.0085] [0.2972] [0.0087] [0.3000] Oil price 0.0095* -0.0200** 0.0096* -0.0194** [0.0054] [0.0089] [0.0055] [0.0090] Month×Year FE No Yes No Yes Hour×Month FE No No Yes Yes Adj-R-squared 0.1344 0.1636 0.1417 0.1712 Day-cluster robust standard errors are shown in parentheses. All the specifications include hour, week, month, and fiscal year fixed effects. The total number of observations is 24,663. *** p < 0.01, ** p < 0.05, * p < 0.1 8. WELFARE ANALYSIS 8.1 Welfare effect of implicit auction Figure 8 illustrates the welfare impact of implicit auction. Trapezium ABCD is the welfare gain from trade in the day-ahead market under implicit auction relative to autarky. The dotted and colored trapezium EFGH is the welfare gain from trade if the FCFS is maintained relative to autarky. \({\widehat{q}}_{FCFS}\) is the counterfactual trade quantity under FCFS during the post-treatment period. The welfare impact of the implicit auction is equal to the difference between the area of trapezium ABCD and that of trapezium EFGH. The implicit auction increases the trade quantity \({q}_{IA}\) relative to the counterfactual trade quantity \({\widehat{q}}_{FCFS}\) under FCFS. This translates into greater economic welfare. The difference between \({\varDelta \widehat{p}}_{IA}^{Autarky}\) and \({\varDelta \widehat{p}}_{FCFS}^{Autarky}\) reflects the volume effect of the implicit auction; it can increase the price gap by increasing the amount of the bid/offer even in the absence of trade. Welfare gain of implicit auction relative to the FCFS rule is calculated as \(\varDelta W=\frac{1}{2}\left(\varDelta {p}_{IA}+ {\varDelta \widehat{p}}_{IA}^{Autarky}\right)\times {q}_{IA}-\frac{1}{2}\left(\varDelta {\widehat{p}}_{FCFS}+ {\varDelta \widehat{p}}_{FCFS}^{Autarky}\right)\times {\widehat{q}}_{FCFS}\) (2). This is interpreted as production cost savings resulting from more efficient merit order dispatch across zones (Mansur and White, 2012). Moreover, it prevents PTR holders from withholding interconnector capacity from the day-ahead market, which potentially comes from exercising market power. 8.2 Counterfactual prediction To estimate the welfare gain of the implicit auction, I predict a counterfactual market price gap \({\varDelta \widehat{p}}_{IA}^{Autarky}\) , \(\varDelta {\widehat{p}}_{FCFS}\) , \({\varDelta \widehat{p}}_{FCFS}^{Autarky},\) and trade quantity \({\widehat{q}}_{FCFS}\) if the full implicit auction had not replaced FCFS after October 2018. To obtain the best predictive model, I use the least absolute shrinkage and selection operator (LASSO), random forest (RF), deep neural network (DNN), and linear regression to compare the mean square error (MSE) of the predicted counterfactuals. These machine-learning methods can flexibly approximate the conditional mean of the dependent variable, and predict counterfactuals better than linear regression. LASSO adds a regularizer of the sum of the absolute values of the coefficients to the linear regression so that the model can avoid in-sample overfitting (Mullainathan and Spiess, 2017 ). I use 10-fold cross-validation to select the optimal value of the tuning parameter. To train the model, I use the R package “gmnnet.” RF combines a regression tree model with bagging. The regression tree sequentially splits the sample based on the threshold value of an explanatory variable and predicts an outcome variable by splitting sub-samples. The RF takes the average of several hundred different trees constructed by bootstrapping the training sample with randomly chosen subsets of explanatory variables. One of the advantages of RF is that it requires relatively little tuning (Athey and Imbens, 2019 ). To train the model, I use the R package “randomForest.” For LASSO and RF, I divide the pretreatment sample into training and test data in a ratio of eight to two. The sample is not randomly split to ensure that the training data are always older than the test data. Pretreatment data are collected from June 4, 2016, to September 30, 2019. I use the training data to estimate the model by minimizing the MSE. The test data are used to evaluate the predictive performance of the models with the loss function (MSE). As DNN requires hyperparameter tuning, the sample is divided into training, validation, and test data in a ratio of six to two to two, respectively. Again, the sample is split nonrandomly. The validation data are used to select the optimal hyperparameters to avoid overfitting. The test data are reserved for evaluating the model performance. The training data are standardized by extracting the mean and dividing it by the standard deviation. The validation and test data use the same mean and standard deviation values for standardization. A deep neural network is trained using training data with a rectified linear unit activation function and the Adam optimizer, which is a stochastic gradient descent algorithm. Hyperparameters include several hidden layers, number of units, dropout rate, and learning rate. To find the best set of hyperparameters efficiently, a “hyperband” is used (Li et al., 2018 ). The number of epochs is set to 800, and the batch size is 64. To avoid overfitting, early stopping is introduced, which completes training when the MSE does not decline consecutively over five epochs. The model is trained using the “TensorFlow” and “Keras” libraries in Python. Table 4 lists the combinations of the search space of the hyperparameters and selected values. Table 4 Hyperparameters for tuning Hyperparameter Search space Number of hidden layers 1, 2, 3 Number of units 32, 64, 96, 128 Dropout rate 0, 0.25, 0.5 Learning rate 0.0001 to 0.001 (log sampling) To calculate these counterfactuals, I first predict the counterfactual reserved interconnector capacity if the FCFS was maintained. I model the reserved interconnector capacity under the FCFS rule as a function of the gross available interconnector capacity (GAIC) and time-fixed effects: $${q}_{reserve,t}^{}={\alpha }_{0}+{\beta }_{1}{GAIC}_{t}+{FE}_{t}+{e}_{t} \left(3\right)$$ , where \({q}_{reserve,t}^{}\) is the reserved interconnector capacity under FCFS. G AIC is operating capacity minus the margin. This equation implies that the reserved interconnector capacity is restricted by G AIC . FE t denotes the hour, week, and month effects. Table 5 summarizes the predictive performance of each model. The first column is the result of the linear regression, second column, the LASSO, third column uses RF, and fourth column, the DNN. As the RF model has the smallest test MSE for both interconnectors, I predict the counterfactual reserved interconnector capacity if the implicit auction was not introduced by RF. Table 5 Test MSE of reserved interconnector capacity (1) (2) (3) (4) Model OLS LASSO RF DNN HH 509.80 512.48 450.57 668.65 Kanmon 147977.1 147337.4 130437.5 229355.3 Note: In the RF, six variables are randomly sampled as candidates for each split. Based on the parameters estimated by RF, I predict a counterfactual reserved interconnector capacity of HH and Kanmon, if the FCFS rule was maintained after October 2018, using the data in the post-treatment period: \({\widehat{q}}_{reserve, t}^{post}\) . Figure 9 shows the observed and counterfactual reserved interconnector capacity. The black dots indicate the actual reserved capacity before the implicit auction, and the red dots show the counterfactual reserved capacity after October 2018. Next, I predict the counterfactual price gap if the FCFS was not replaced by an implicit auction, as follows: $$\varDelta {{p}}_{{D}{A}, {t}}={{\alpha }}_{0}+{{\beta }}_{1}{{N}{A}{I}{C}}_{{t}}+{{\beta }}_{2}{S}{u}{p}{p}{l}{{y}}_{{t}}+{{\beta }}_{3}{D}{e}{m}{a}{n}{{d}}_{{t}}+{{F}{E}}_{{t}} +{{\epsilon }}_{{t}} \left(4\right).$$ Equation (4) is the same as Eq. (1), except that there is no volume variable in the model, and I only use data from the pretreatment period (i.e., before October 2018). Table 6 shows that RF can predict the day-ahead market price gap between Hokkaido and the East with the lowest MSE, while OLS produces the lowest MSE for the West and Kyushu compared to other machine-learning methods. Table 6 Test MSE of day-ahead market price gaps (1) (2) (3) (4) Model OLS LASSO RF DNN Spread between Hokkaido and the East 21.29 21.50 19.55 20.70 Spread between the West and Kyushu 5.28 5.34 5.29 5.41 Note: In the RF, seven variables are randomly sampled as candidates at each split. Thus, I use RF to predict the counterfactual price gap between Hokkaido and the East, and OLS to predict that of the West and Kyushu in the post-treatment period. Figure 10 presents the observed and counterfactual market price gaps. The black dots show the daily-mean actual price gap, and the circles represent the daily-mean counterfactual price gap, after October 2018. I then estimate the counterfactual trade quantity \({\widehat{q}}_{FCFS}\) allocated in the day-ahead market if the FCFS rule was not abolished as follows: $${\widehat{q}}_{FCFS}={\widehat{NAIC}}_{t} if \varDelta {\widehat{p}}_{DA,t}\ne 0$$ 5 Note that the estimated \(\widehat{NAI{C}_{t}}\) and counterfactual price gap \(\varDelta {\widehat{p}}_{DA,t}\) are used. \({\widehat{q}}_{FCFS}\) is equal to \(\widehat{NAI{C}_{t}}\) as long as the market price gap is non-zero. This relationship holds because the market-splitting algorithm allocates all the remaining capacity in the wake of congestion (Marmiroli et al., 2009 ). Next, I calculate the counterfactual day-ahead market price under autarky \(\varDelta {p}_{DA,t}^{autarky}\) as follows: $$\varDelta {p}_{DA,t}^{autarky}=\left\{\begin{array}{c}\varDelta {p}_{DA,t}-\widehat{{{\beta }}_{1}}{NAIC}_{t} under full implicit auction\\ {\varDelta \widehat{p}}_{\text{D}\text{A},\text{t}}-\widehat{{{\beta }}_{1}}{\widehat{NAIC}}_{t} under FCFS\end{array}\right.$$ \({\widehat{p}}_{DA,t}\) is the estimated market price gap under FCFS. \(\widehat{{\beta }_{1}}\) is the estimated coefficient of trade effect by Eq. (1). \(\widehat{NAIC}\) is predicted counterfactual, if the FCFS was maintained in the post-treatment period. 8.3 Welfare Analysis Finally, a welfare analysis is conducted according to Eq. (2). Table 7 summarizes the welfare effects of a full implicit auction. The estimated average price gap between Hokkaido and the East under autarky is 3.93 yen/kWh between April and September 2018, and 9.52 yen/kWh between October 2018 and March 2019. The estimated average price gap between the West and Kyushu under autarky is 0.96 yen/kWh before the full implicit auction and 1.39 yen/kWh after October 2018. I estimate that the counterfactual average price gap between Hokkaido and the East zones under the FCFS after October 2018 would be 3.19 yen/kWh, compared with 3.11 yen/kWh before October 2018. I also find that the counterfactual average price gap between the West and Kyushu zones under the FCFS after October 2018 would be 0.86 yen/kWh for Kanmon, compared to 0.63 yen/kWh before October 2018. The actual average price gap after October 2018 is 6.08 yen/kWh between Hokkaido and the East zones, and 0.43 yen/kWh between the West and Kyushu zones. This implies that if the FCFS rule continued, the average price gaps would be lower than the observed price gap under the implicit auction by 2.89 yen/kWh for HH and higher than that by 0.43 yen/kWh for Kanmon. This is an important implication: implicit auction does not necessarily reduce the price gap because it not only has a trade effect, but because it also has a counteracting volume effect. The actual average trade quantity under the FCFS is 38.26 MW for HH and 31.00 MW for Kanmon. The counterfactual average trade quantity after October 2018 is 23.83 MW for HH and 436.51 MW for Kanmon. The actual average trade quantity after implicit auction increased to 123.55 MW for HH and 1967.56 MW for Kanmon, respectively. Thus, I estimate that implicit auction increased trade quantity on average by 99.72 MW for HH and by 1531.05 MW for Kanmon, relative to the FCFS. Finally, using Eq. (2), I estimate the implicit auction produced an economic gain of 4.07 billion yen ($ 40 million) across HH and 6.36 billion yen ($63 million) across Kanmon after the six-month implementation. The larger gain in Kanmon indicates that it has a larger GAIC. The total annual welfare gain was about 20.86 billion yen (USD 208 million). This welfare gain was much larger than the one-time implementation cost of the implicit auction, which, according to a member of the JEPX, is 20–30 million yen. Table 7 Welfare impact of trade under FCFS and implicit auction HH Kanmon Price gap between zones (yen/kWh) Autarky price gap (counterfactual) FCFS 3.93 0.96 Implicit auction 9.52 1.39 FCFS Pre 2018/10 (actual) 3.11 0.63 Post 2018/10 (counterfactual) 3.19 0.86 Implicit auction Post 2018/10 (actual) 6.08 0.43 Difference between implicit auction and FCFS \(\varDelta\) 2.89 \(\varDelta\) -0.43 Trade Quantity (MWh) FCFS Pre 2018/10 (actual) 38.26 31.00 Post 2018/10 (counterfactual) 23.83 436.51 Implicit auction Post 2018/10 (actual) 123.55 1967.56 Difference between implicit auction and FCFS \(\varDelta\) 99.72 \(\varDelta\) 1531.05 Gain from trade (billion yen/ 6 months) Difference between implicit auction and FCFS (Billion yen/ 5 months) 4.07 6.36 9. CONCLUSION This study examines one of the market-based congestion management methods of interconnector transmission capacity: the implicit auction. Implicit auction has two conflicting effects on the day-ahead market, the trade and volume effects. The trade effect arises from the increased trade through interconnectors. Implicit auction increases the interconnector capacity allocated in the day-ahead market and allows for more trade between the export and import zones. The volume effect implies that the implicit auction increases the number of electricity bids in the day-ahead market by prohibiting incumbents from reserving interconnector capacity for their bilateral contracts. To simulate market outcomes without implicit auction, counterfactual market outcomes are predicted by comparing linear regression with machine learning methods. I find that the RF generally performs better than linear regression. The welfare impact of the implicit auction is estimated to be approximately $ 40 million for HH and $ 60 million for the Kanmon interconnector after six months of implementation. The annual national production cost savings owing to the trade effect were approximately $ 200 million. This study highlights the advantages of implicit auction relative to FCFS. Implicit auction not only prevents incumbents from exercising vertical market power but also promotes efficient resource allocation in the day-ahead market. However, it is important to note that congestion persists after the implicit auction, particularly at the HH interconnector. It reflects the fact that there is a volume effect and the interconnector capacity of the HH was only 0.6 GW by the end of February 2019. With an increase in the amount of variable renewable energy generation, additional investment in interconnectors further increases the benefits of the implicit auction. There are two limitations to this study due to the lack of available data. First, there is neither interconnector capacity reservation data by company, nor bidding information by company in the day-ahead market. Therefore, it is not possible to verify further whether incumbents exercise market power by using PTR. Second, I cannot estimate the effect of the implicit auction on the reduction of CO2 emission, because transmission system operators only publish aggregate fossil fuel generation data. Implicit auctions may reduce CO2 emissions by decreasing generation from oil and gas-fired power plants that have high marginal costs in an import zone while increasing the CO2 emission by dispatching cheaper coal-fired power plants more frequently in an export zone. Declarations Competing 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. Data availability statements: The datasets analysed during the current study are not publicly available due to confidential company data by globalCOAL. Acknowledgements: The author is grateful to very helpful comments and suggestions from members of Research Project on Renewable Energy Economics, Kyoto University. The author received funding from the employer, Tokyo Foundation for Policy Research (https://www.tkfd.or.jp/en/). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics , 11 (1), 685–725. https://doi.org/10.1146/annurev-economics-080217-053433 Brunekreeft, G., Neuhoff, K., & Newbery, D. (2005). Electricity transmission: An overview of the current debate. Utilities Policy , 13 (2) (2 SPEC. ISS.), 73–93. https://doi.org/10.1016/j.jup.2004.12.002 Bunn, D., & Zachmann, G. (2010). 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Nov, 2023 Reviews received at journal 06 Sep, 2023 Reviews received at journal 20 Jun, 2023 Reviewers agreed at journal 24 May, 2023 Reviewers agreed at journal 15 May, 2023 Reviewers invited by journal 09 May, 2023 Editor assigned by journal 02 May, 2023 Submission checks completed at journal 02 May, 2023 First submitted to journal 28 Apr, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2874856","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":197035359,"identity":"aa781cf9-0cca-4a25-8296-30ea32ce5733","order_by":0,"name":"Kota Sugimoto","email":"data:image/png;base64,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","orcid":"","institution":"Yokohama National University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kota","middleName":"","lastName":"Sugimoto","suffix":""}],"badges":[],"createdAt":"2023-04-29 01:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2874856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2874856/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36659344,"identity":"30f2ec82-ad2c-4d5c-a425-42a8190ecf8c","added_by":"auto","created_at":"2023-05-05 21:33:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89490,"visible":true,"origin":"","legend":"\u003cp\u003eDay-ahead market price zones in Japan\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/8750265290f819834d7a3811.jpeg"},{"id":36659690,"identity":"3676db44-0f61-4ecf-85e4-096d98d103ea","added_by":"auto","created_at":"2023-05-05 21:41:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58065,"visible":true,"origin":"","legend":"\u003cp\u003eCross-zonal trade volume via interconnectors by trading methods (TWh/ Fiscal year). Source: OCCTO\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/c12c1e691b714e42bbbb8b5f.png"},{"id":36659343,"identity":"1a62ade3-556a-4b88-ae0a-dd39ee2552b0","added_by":"auto","created_at":"2023-05-05 21:33:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":359100,"visible":true,"origin":"","legend":"\u003cp\u003eDaily average reservation rate under FCFS\u003c/p\u003e\n\u003cp\u003eNote: Red circles show the reservation rate seven days before delivery, and black dots are the reservation rate two days before delivery. Source: OCCTO.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/289491d0ab02555fdb55e384.jpg"},{"id":36659347,"identity":"2f5142be-5a64-47be-9dc4-8ecb9f126067","added_by":"auto","created_at":"2023-05-05 21:33:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335281,"visible":true,"origin":"","legend":"\u003cp\u003eMarket price gap and interconnector capacity reservation rate\u003c/p\u003e\n\u003cp\u003eNote: The two graphs on the left in each panel show the observations from October 2016 to September 2018, under the FCFS, and the two graphs on the right show the observations from October 2018 to September 2020 after the full implicit auction was implemented. A negative reservation rate indicates that the capacity is reserved for transmitting energy from the East zone to Hokkaido in Panel A and from Kyushu to the West zone in Panel B. Source: Japan Electric Power Exchange (JEPX) and OCCTO.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/4620bb28dea7585bc7c01976.jpg"},{"id":36660001,"identity":"9078da88-ce58-485c-b4d0-f5af7197b683","added_by":"auto","created_at":"2023-05-05 21:49:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38350,"visible":true,"origin":"","legend":"\u003cp\u003eTrade Effect\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/fabcd0560e0b74041fc81be8.png"},{"id":36659693,"identity":"5f7cf40a-7bcf-4304-85da-c530ce6bf9c5","added_by":"auto","created_at":"2023-05-05 21:41:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":247595,"visible":true,"origin":"","legend":"\u003cp\u003eThe volume of bidding and offer in the day-ahead market.\u003c/p\u003e\n\u003cp\u003eNote: The solid line is the daily moving average of the volume of selling, and the dotted line is the daily moving average of the volume of buying in the day-ahead market. Source: JEPX.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/ff8a7fc4d29a9de682cffdf8.png"},{"id":36659999,"identity":"80ad9022-e669-434e-9c0f-de4c502b3a20","added_by":"auto","created_at":"2023-05-05 21:49:19","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":78661,"visible":true,"origin":"","legend":"\u003cp\u003eVolume effect\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/17e9dcd627de834c8b5626db.jpeg"},{"id":36660537,"identity":"a9b2bee2-a292-4d58-a62e-7a0c7baf241d","added_by":"auto","created_at":"2023-05-05 21:57:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38100,"visible":true,"origin":"","legend":"\u003cp\u003eWelfare effect of implicit auction\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/9332c7557bec442ae99c4878.png"},{"id":36659352,"identity":"91295f7f-2aa7-40a5-a431-431c0e6875fb","added_by":"auto","created_at":"2023-05-05 21:33:19","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":342711,"visible":true,"origin":"","legend":"\u003cp\u003eReserved interconnector capacity under FCFS\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/438998b0be3448bea9983c03.jpg"},{"id":36659349,"identity":"d1b77749-cc6b-4226-a964-d64f6a38df1f","added_by":"auto","created_at":"2023-05-05 21:33:19","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":348299,"visible":true,"origin":"","legend":"\u003cp\u003eDay-ahead market price spread\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/774ab31259d75ceddbefb4ea.jpg"},{"id":36660543,"identity":"b511542b-aff9-46a8-99c2-ec21dc78039e","added_by":"auto","created_at":"2023-05-05 21:57:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1458439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2874856/v1/19a588ac-fd41-4ec6-88e1-d3e97b084b1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Welfare Effects of Implicit Auction on Interconnector Capacity: Evidence from Japan","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eTransmission lines play a crucial role in an electric power network. They contribute to balancing demand and supply in real-time, guaranteeing efficient merit-order dispatch in wider geographic areas, and integrating intermittent renewable energy. In addition, transmission capacity increases competition in the generation and retail sectors by creating wider markets. However, interconnector transmission capacity is a scarce resource because, historically, each vertically integrated utility is required to supply all customers in its area under a regional monopoly and does not depend on imports across interconnectors. Hence, the efficient allocation of limited capacity is important, especially after market liberalization. Allocations are done through either market or non-market-based congestion management methods (European Transmission System Operators, 2004). What is the economic impact of market-based congestion management methods?\u003c/p\u003e \u003cp\u003eThis study empirically evaluates the welfare impact of implicit auction relative to first-come-first-served (FCFS) allocations in Japan. Implicit auction simultaneously allocates interconnector capacity with energy in the day-ahead market, while the FCFS rule allows incumbents to reserve the capacity before the energy market. The FCFS rule effectively allocates it as a free non-tradable physical transmission right (PTR), which provides the right holders with an instrument to exercise vertical market power (Bushnell, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). They can withhold PTR from the subsequent day-ahead market to create market-splitting and sell at higher market prices in the import constraint zone by intentionally overestimating their capacity needs. There is a penalty surcharge for canceling the reservation; however, the size of the surcharge is limited, and capacity holders can reduce the reservation even after the day-ahead market. Implicit auction prohibits interconnector capacity reservations in advance, and all capacity is released to the day-ahead market, thereby enabling more cross-zonal trade in the energy market. Increased trade reduces the price gap between the import and export zones. Implicit auction also improves the liquidity of the day-ahead market, because firms that previously reserved interconnector capacity for bilateral contracts outside the energy market are required to bid in the market.\u003c/p\u003e \u003cp\u003eI focus on two interconnectors where congestion was severe: Hokkaido-Honshu (HH) interconnectors connecting the Hokkaido and East zones, and Kanmon interconnectors connecting the West and Kyushu zones. The empirical challenge is that market outcomes are unobservable if FCFS has not been abolished. I use several machine-learning methods to predict counterfactual market outcomes in the post-treatment period. By comparing the loss functions, I evaluate the predictive performance and choose the best model to predict the counterfactuals. Moreover, by comparing actual and counterfactual market outcomes, I estimate the welfare impact of implicit auctions.\u003c/p\u003e \u003cp\u003eI find that the implicit auction improved the efficient allocation of interconnector capacity and prevent market power, relative to the FCFS rule. Furthermore, I find that the implicit auction has an economically significant impact on welfare. The trade effect is approximately \u003cspan\u003e$\u003c/span\u003e 40\u0026nbsp;million for the Hokkaido-Honshu interconnector and \u003cspan\u003e$\u003c/span\u003e 60\u0026nbsp;million for the Kanmon interconnector over the six months of implementation. The annual gain is over \u003cspan\u003e$\u003c/span\u003e 200\u0026nbsp;million/year.\u003c/p\u003e \u003cp\u003eMy contributions are three-fold. First, this study presents evidence of the impact of an implicit auction, compared to the non-market-based FCFS rule. Numerous studies have examined the inefficiency of an explicit auction, another market-based method in comparison to implicit auction in Europe (e.g., Bunn and Zachmann, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Brunekreeft et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Creti et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ehrenmann and Neuhoff, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; F\u0026uuml;ss et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gugler et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Keppler et al., 2016; Newbery et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Implicit auction can avoid the economically inefficient use of interconnector capacity, that is, export from high-price zones to low-price zones, attributable to the explicit auction. However, this study shows that implicit auction can also reduce the underutilization of interconnector transmission capacity caused by the cancellation of reserved interconnector capacity under FCFS. Moreover, I show that implicit auction has not only a price-decreasing effect (trade effect) on the day-ahead market price gap through releasing the interconnector capacity previously reserved under FCFS, but also a price-increasing effect (volume effect) by forcing those who reserved the capacity to participate in the market.\u003c/p\u003e \u003cp\u003eSecond, I use empirical evidence to show that, the FCFS rule allowed PTR holders to exercise vertical market power, as predicted by the theoretical literature (e.g., Joskow and Tirole, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bushnell, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and how an implicit auction could prevent this by simultaneously allocating the PTR in the day-ahead market.\u003c/p\u003e \u003cp\u003eThird, I avoid the potential omitted variable bias that can generally occur when studying two countries in Europe (e.g., Germany and France), which are connected by other neighboring countries and whose relevant data are often not available. By contrast, Japan is not connected to other countries, and all zonal data are available. An additional advantage is that its network is effectively radial and the analysis does not suffer from loop-flow issues.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the wholesale market structure, and section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e explains interconnector congestion management methods in Japan. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e defines the two effects of an implicit auction: trade and volume effects. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the research design, and Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e6\u003c/span\u003e explains the data. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the estimates of the trade effect. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the counterfactual simulations and the welfare impact of implicit auctions and section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e9\u003c/span\u003e concludes the paper.\u003c/p\u003e"},{"header":"2. WHOLESALE MARKET STRUCTURE IN JAPAN","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the Japanese day-ahead market comprises nine fixed areas connected by interconnectors. Each area is equivalent to the control area in which the former vertically integrated monopoly operates. I classify the nine areas into four zones: Hokkaido, East, West, and Kyushu. Hokkaido is located in the northeastern part of Japan and has the highest day-ahead market price during the study period, partly because it still uses oil-fueled thermal power plants. The East zone includes Tokyo and Tohoku.\u003c/p\u003e\n\u003cp\u003eThe Hokkaido and East zones are connected by a high-voltage direct current HH interconnector. I defined the West zone as the compilation of Chubu, Kansai, Hokuriku, Shikoku, and Chugoku areas and regarded it as a single zone, as there is little congestion within the zone, and the market price is almost the same. The East and West zones are connected by high-voltage direct current frequency converters\u003csup\u003e1\u003c/sup\u003e. Kyushu is located in the southwestern part of Japan. The West and Kyushu zones are connected by a high-voltage alternate current Kanmon interconnector. These three interconnectors experienced frequent congestion. The remaining interconnectors are mostly uncongested during the study period. Although there is an intraday market, almost all energy is traded in the day-ahead market.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e[1]\u003c/sup\u003e Eastern and western Japan use 50 Hz and 60 Hz, respectively.\u003c/p\u003e"},{"header":"3. CONGESTION MANAGEMENT METHODS: FCFS AND IMPLICIT AUCTION","content":"\u003cp\u003eIn Japan, the day-ahead market uses a single-price auction and a market-splitting method. The market-splitting algorithm allocates all net available interconnector capacity (NAIC) to market participants who successfully contract in the auction (Marmiroli et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Before October 2018, the FCFS rule allowed incumbents to reserve their capacity for free \u003cem\u003ebefore\u003c/em\u003e the start of the day-ahead auction at 10 am. Under FCFS, the NAIC in the day-ahead market is calculated as\u003c/p\u003e\n\u003cp\u003eNAIC\u0026thinsp;=\u0026thinsp;Operating capacity \u0026ndash; Margin \u0026ndash; Reserved capacity.\u003c/p\u003e\n\u003cp\u003eOperating capacity is the maximum amount of energy that can flow through an interconnector. The limit is the minimum value that satisfies all four constraints: thermal capacity, synchronization stability, voltage stability, and frequency. Margin is the reserved capacity to import or export energy from other areas to maintain system security during an emergency. The operating capacity and margin are calculated annually by the organization for cross-regional coordination of transmission operators (OCCTO), an association of regulators and transmission system operators. The operating capacity is stable except during planned maintenance. The margin is set to be constant for HH and zero for Kanmon. It should be noted that an implicit auction was already used in the market under the FCFS rule, but it can only be applied to the remaining NAIC.\u003c/p\u003e\n\u003cp\u003eFCFS-based reservation is allowed for up to 10 years and could also be exclusively renewed. The reservation was effectively a non-tradable PTR (Joskow and Tirole, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bushnell, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Gilbert et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The market-split algorithm provides incumbents with an incentive to create congestion strategically and increase market revenue by selling at higher prices in an importing zone. The FCFS rule can give PTR holders the ability to exercise market power and avoid the risk of market price divergence between zones (Twomey et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). They can withhold interconnector capacity from the energy market by nominating it as a scheduled flow.\u003c/p\u003e\n\u003cp\u003eUnder the FCFS rule, most interconnector capacities were used for bilateral contracts outside the wholesale energy market. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the 70\u0026ndash;100 TWh/year energy flow interconnectors for bilateral contracts, until 2017. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e corresponds to approximately 7\u0026ndash;10% of annual national power consumption. After the implicit auction was implemented in October 2018, the amount of trade via bilateral contracts dropped substantially to approximately 0.25 TWh/year. Meanwhile, the amount of energy traded in the day-ahead market skyrocketed, implying that the energy for bilateral contracts now flows into the day-ahead market.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the daily average reservation rates for HH (Panel A) and Kanmon (Panel B) from October 2016 to September 2018. The red circle shows the reservation rate seven days before delivery, and the black dot is the reservation rate two days before delivery. The direction of the reserved flow is from East to Hokkaido in Panel A, and from Kyushu to West in Panel B. Both interconnectors are reserved in one direction. As expected, the reservation rate in both interconnectors is almost 100% seven days before delivery, implying that there is no available transmission capacity for the day-ahead market. While some of the capacity is released two days before delivery for HH, approximately 80% of capacity is already allocated before the day-ahead market. The difference between the reservation rate on the second and seventh day is very small in the case of Kanmon. This figure shows that the interconnector capacity available in the day-ahead market is limited for both interconnectors under the FCFS rule.\u003c/p\u003e\n\u003cp\u003eThose who could reserve interconnector capacity could also cancel the reservation even after day-ahead market clearing, leaving an unused capacity. It should be noted that they could only cancel (i.e., reduce) the amount of reserved capacity, but could not increase it under FCFS. The cancellation does not mean exercising market power itself, but it implies that they could initially reserve more than they required, aggravating market power concerns. Those who canceled the reservation had to pay a penalty to prevent strategic overestimation of capacity needs. The surcharge was set at 0.01 yen/kWh of canceled capacity when they canceled more than 10% of the initial reserved capacity (OCCTO, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). There was no surcharge for the capacity cancellation within 10% of the initial reservation. The low penalty implies that they could still strategically withhold unnecessary interconnector capacity from the market. The right to cancel also helps the PTR holders to reduce the surplus imbalance.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows how often reserved capacity was canceled after the day-ahead market. Panel A plots the relationship between the market-price gap between Hokkaido and the East zones in the day-ahead market and the reservation rate of the HH interconnector at 5 pm on the day before delivery. A negative reservation rate indicates that the interconnector capacity is reserved to transmit energy from East to Hokkaido. The two graphs on the left in each panel show the two-year observations from October 2016 to September 2018 under FCFS, and the two graphs on the right show the two-year observations after the full implicit auction was implemented from October 2018 to September 2020.\u003c/p\u003e\n\u003cp\u003eUnder FCFS, the reservation rate of the HH interconnector was greater than 100% at 5 pm in numerous instances, even though the day-ahead market price in Hokkaido was higher than that in the East zone. The positive price gaps imply that congestion occurred at the HH interconnector during day-ahead market clearing, and all available capacity was allocated to the market, making a 100% reservation rate at 10 am. Hence, this figure confirms that the reserved interconnector capacity was frequently canceled after day-ahead market-splitting.\u003c/p\u003e\n\u003cp\u003eFrom October 2018, reservations before the day-ahead market were prohibited, and the NAIC was equal to the operating capacity minus the margin. Under a full implicit auction, incumbents could no longer reserve capacity, and all available capacity was allocated to the day-ahead market. The two graphs on the right show that most observations clustered at the dotted red lines after the implicit auction, indicating that they could neither reserve nor cancel the interconnector capacity.\u003c/p\u003e\n\u003cp\u003ePanel B of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e also shows that the market price in the West was higher than that in Kyushu for some hours, but the Kanmon interconnector capacity was not fully allocated at 5 pm. In sum, the FCFS rule allowed FTR holders to reduce reservations after the day-ahead market in both interconnectors, while implicit auction succeeded in fully allocating it in an economically efficient direction.\u003c/p\u003e"},{"header":"4. EFFECTS OF IMPLICIT AUCTION","content":"\u003cp\u003eI argue that a full implicit auction (i.e., abolishing the FCFS rule) has two effects on the day-ahead market in Japan. The first is the trade effect. The implicit auction releases the entire interconnector capacity previously reserved for the energy market. This increases the NAIC and the trade quantity across market zones, similar to an interconnector capacity upgrade investment. Consequently, it will reduce the market price gap between the import and export zones. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows how an increase in the NAIC reduces the price gap between zones in the day-ahead market. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{Autarky}^{export}\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{Autarky}^{import}\\)\u003c/span\u003e\u003c/span\u003e) is the price in the export (import) zone under autarky. A price gap exists between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{Autarky}^{export}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{Autarky}^{import}\\)\u003c/span\u003e\u003c/span\u003e. As the trade quantity increases, the price in the export zone increases to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{Trade}^{export}\\)\u003c/span\u003e\u003c/span\u003e and the price in zone B decreases to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{Trade}^{import}\\)\u003c/span\u003e\u003c/span\u003e, converging the price gap.\u003c/p\u003e\n\u003cp\u003eThe second is the volume effect. The full implicit auction prohibits reserving interconnector capacity for bilateral contracts across zones and thus increases the volume of the day-ahead market. After the full implicit auction was introduced in October 2018, those who used to reserve the interconnector capacity for bilateral contracts were required to bid in the day-ahead market to obtain the interconnector capacity to trade energy across zones. Moreover, the regulator gave them temporary financial transmission rights to alleviate the unexpected risk of a price gap derived from market-splitting. This transitional measure allowed retailers who could successfully bid a day ahead to recover the price gap by March 2026. Thus, incumbents have a strong incentive to bid in the day-ahead market. Consequently, the full implicit auction increased the volume of both selling and buying quantities in the day-ahead market. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows both the volume of selling and buying increased discontinuously after October 2018.\u003c/p\u003e\n\u003cp\u003eThis increase in the market volume may have affected the market price gap. When the additional offer price in the export zone is lower than the marginal cost of the marginal generator, the additional offer lowers the resulting zonal price. Otherwise, it does not affect the prices in the export zone. The left panel of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the former scenario. Similarly, if the additional bid price in the import zone is higher than the former equilibrium price, the addition raises the market price in the import zone. Otherwise, it does not affect the price in the import zone. The right panel of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the former scenario. In sum, the volume effect can widen the price gap between the import and export zones.\u003c/p\u003e"},{"header":"5. EMPIRICAL STRATEGY","content":"\u003cp\u003eTo estimate the trade effect, I model the day-ahead market price gap between the import and export zones as a function of the NAIC, volume added to the market after the implicit auction, supply controls, demand controls, and fixed effects. The following equation is used:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\varDelta {{p}}_{{D}{A}, {t}}={{\\alpha }}_{0}+{{\\beta }}_{1}{{N}{A}{I}{C}}_{{t}}+{{\\beta }}_{2}{{V}{o}{l}{u}{m}{e}}_{{t}}+{{\\beta }}_{3}{S}{u}{p}{p}{l}{{y}}_{{t}}+{{\\beta }}_{4}{D}{e}{m}{a}{n}{{d}}_{{t}}+{{F}{E}}_{{t}} +{{\\epsilon }}_{{t}} \\left(1\\right).$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\varDelta {p}_{\\text{D}\\text{A}, \\text{t}}\\)\u003c/span\u003e \u003c/span\u003e is the day-ahead market price difference between the import and export zones (Hokkaido and East for HH and West and Kyushu for Kanmon). NAIC is the interconnector capacity available in the day-ahead market. The NAIC has two different values depending on the flow direction. I use the NAIC from the export zone to the import zone. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e is the parameter used to measure the size of the trade effect.\u003c/p\u003e\n\u003cp\u003eVolume is the bid quantity added to the market after an implicit auction. As this variable is not observable, I use the amount of counterfactual reserve capacity as a proxy, assuming that the added bid quantity would be equal to the volume of counterfactual reserved capacity if the FCFS rule was maintained. This assumption seems plausible because those who tended to reserve capacity were not only required to bid in the market to obtain it but were also given the transitional financial transmission right conditional on making a successful bid in the market, as discussed in the previous section.\u003c/p\u003e\n\u003cp\u003eI include the supply and demand shocks that may affect the outcome and NAIC. Supply control variables include photovoltaic (PV) and wind generation in each zone. Nuclear power generation is supplied as a baseload and included as an exogenous variable. To control fossil fuel costs, daily coal prices, monthly liquefied natural gas (LNG) import prices, and daily Brent oil prices are used. Demand variables are the actual hourly electricity consumption in each zone. These are proxies for forecasted demand and are assumed to be exogenous in the short term.\u003c/p\u003e\n\u003cp\u003eFE\u003csub\u003et\u003c/sub\u003e includes hour, day of the week, month, and fiscal year fixed effects. I also include a month-year fixed effect to control for unobservable progress of the retail competition and the \u0026ldquo;gross bidding\u0026rdquo; policy, which started in April 2017 to encourage the incumbent generation or retail firms to voluntarily bid some of their internal contracts into the day-ahead market. Hour-month fixed effects are also added to control for prediction errors in renewable energy generation and demand. Standard errors are clustered by day to address autocorrelation.\u003c/p\u003e"},{"header":"6. DATA","content":"\u003cp\u003eThe JEPX publishes half-hourly, day-ahead market price data for each area. I calculate the average hourly market prices (yen/kWh) to merge with the hourly covariates. The data on operating capacity, margin, and reserved interconnector capacity are available at OCCTO. I use the reserved interconnector capacity at 3 pm, two days before delivery, as the reserved interconnector capacity under the FCFS rule. This implicitly assumes that incumbents could have canceled the reservation \u003cem\u003eafter\u003c/em\u003e the day-ahead market on day t-1, but did not cancel from 3 pm on day t-2 to 10 am on day t-1. This assumption seems plausible because they had the incentive to withhold the capacity at least until the day-ahead market clearing to strategically create congestion and market splits. The volume variable is the predicted counterfactual reserved capacity, which is estimated in Section \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eSupply and demand variables are gathered from transmission system operators\u0026rsquo; websites. The daily coal price data are obtained from the globalCOAL NEWC Index, while the daily Brent oil price is assembled by the Energy Information Administration. The monthly LNG import price is calculated based on the Trade Statistics of Japan in the Ministry of Finance. The study period was from midnight on June 4, 2016 to 3 pm on March 28, 2019. I exclude the data afterward because the operating capacity of the HH interconnector was upgraded from 600 MW to 900 MW at 3 pm on March 28, 2019, and the financial transmission rights market was introduced on April 1, 2019, which may have changed the bidding behavior of the market participants.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics. The NAIC of the Kanmon interconnector is much larger than that of the HH interconnector. The Kanmon interconnector has an operating capacity of 2800 MW, whereas the HH interconnector has an operating capacity of 600 MW. Solar power generation in Japan is much greater than wind power generation. Nuclear power plants were present in all four zones but stopped operating after the nuclear accident in Fukushima in 2011. Only several nuclear power plants in the West and Kyushu zones were operational during the study period.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive Statistics (06/04/2016\u0026thinsp;~\u0026thinsp;03/28/2019)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnit\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. Dev.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrice gap between Hokkaido and East zones\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyen/kWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.290603\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.370945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-39.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.115\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrice gap between the West and Kyushu zones\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyen/kWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28956\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.160589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNAIC of HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.67206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.14436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200.496\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNAIC of Kanmon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e710.1228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e825.4094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2980\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume for HH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.20276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.91469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.5973\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume for Kanmon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e332.1523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e726.4957\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2301.543\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e157.3206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e236.4576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1103\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2016.408\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3078.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13901\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2481.806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3656.362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16161\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1000.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1532.196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6656\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e99.80189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70.19188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e317\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e368.4876\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e281.3567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1338\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e224.6692\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e165.2895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e832\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e63.0356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58.8241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e316\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNuclear in the West zone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2230.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1463.747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNuclear in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2127.796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1136.346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e634\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4151\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3568.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e635.8973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5422\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42478.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7755.905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69346\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46223.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8358.808\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75471\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10029.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1706.838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoal price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e$/ton\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93.44434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.71042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLNG price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1000 yen/ton\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.16741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.223131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.87843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.38621\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOil price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e$/barrel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24,663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59.56472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.92182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"7. RESULTS","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the estimation results of Eq.\u0026nbsp;(1). Column (1) is the baseline estimation with the hour, week, month, and fiscal year fixed effects. This indicates that a 1 MW increase in the NAIC of the HH line reduces the day-ahead market price gap between Hokkaido and the East zone by 0.014 yen/kWh (14 yen/MWh). The other control variables have expected signs and sizes. The volume variable is positively correlated with the outcome. Solar power generation in Hokkaido significantly reduces the market price gap by lowering the market price in Hokkaido. Wind generation remains mostly insignificant, partly because there is less variation than that in solar generation. Demand in Hokkaido is positively associated with the outcome, whereas demand in the East is negatively associated with the outcome. LNG prices are negatively associated with the market price gap. It is notable that, there was no LNG-fired power plant in Hokkaido during the study period; thus, the LNG price did not affect the market price in Hokkaido. Meanwhile, the rise in the LNG price increases the market price in the East and consequently reduces the price gap.\u003c/p\u003e\n\u003cp\u003eColumns (2) and (4) introduce additional fixed effects. Column (2) includes month\u0026times;year fixed effects to flexibly control for the unobservable supply or demand shocks that may differ by month in the sample period. Column (3) includes hour\u0026times;month fixed effects. Column (4) includes both month\u0026times;year and hour\u0026times;month fixed effects and is our preferred specification.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTrade effect of the HH interconnector\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNAIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.014**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.024***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.015**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.026***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.006]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.007]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.007]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.007]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.080**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.070**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.013]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.030]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.013]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.027]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNuclear in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNuclear in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoal price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.022*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.011]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.013]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.011]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.013]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLNG price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.192***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.834***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.172***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.157***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.031]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[1.568]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.030]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[1.522]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOil price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.082***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.078***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.021]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.028]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.021]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.029]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonth\u0026times;Year FE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHour\u0026times;Month FE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdj-R-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDay-cluster robust standard errors are shown in parentheses. All the specifications include hour, week, month, and fiscal year fixed effects. The total number of observations is 24,183. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the estimation results for the Kanmon interconnector across the West and Kyushu zones. A 1 MW increase in the NAIC of the Kanmon interconnector decreases the market price gap by 0.0004 yen/kWh (0.4 yen/MWh) between the West and Kyushu zones across all specifications. Column (4) is my preferred specification and is used for the welfare analysis in the next section.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTrade effect of Kanmon interconnector\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNAIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0004***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0006***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0012**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0006***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0012**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0005]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0005]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePV in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePV in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0000*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePV in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePV in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0004]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0004]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0004]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0004]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWind in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0009***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0008***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0009***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0008***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0002]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNuclear in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0003***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0003***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNuclear in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0002***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0002***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0002**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in Hokkaido\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in the West\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemand in Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0000]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0001]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoal price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0121***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0079***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0121***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0079***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0026]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0023]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0026]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0023]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLNG price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0262***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5888*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0273***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5836*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0085]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.2972]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0087]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.3000]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOil price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0095*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0200**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0096*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0194**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0054]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0089]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0055]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.0090]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonth\u0026times;Year FE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHour\u0026times;Month FE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdj-R-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1417\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1712\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDay-cluster robust standard errors are shown in parentheses. All the specifications include hour, week, month, and fiscal year fixed effects. The total number of observations is 24,663. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e"},{"header":"8. WELFARE ANALYSIS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e8.1 Welfare effect of implicit auction\u003c/h2\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the welfare impact of implicit auction. Trapezium ABCD is the welfare gain from trade in the day-ahead market under implicit auction relative to autarky. The dotted and colored trapezium EFGH is the welfare gain from trade if the FCFS is maintained relative to autarky. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{q}}_{FCFS}\\)\u003c/span\u003e\u003c/span\u003e is the counterfactual trade quantity under FCFS during the post-treatment period. The welfare impact of the implicit auction is equal to the difference between the area of trapezium ABCD and that of trapezium EFGH. The implicit auction increases the trade quantity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({q}_{IA}\\)\u003c/span\u003e\u003c/span\u003e relative to the counterfactual trade quantity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{q}}_{FCFS}\\)\u003c/span\u003e\u003c/span\u003e under FCFS. This translates into greater economic welfare. The difference between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta \\widehat{p}}_{IA}^{Autarky}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta \\widehat{p}}_{FCFS}^{Autarky}\\)\u003c/span\u003e\u003c/span\u003e reflects the volume effect of the implicit auction; it can increase the price gap by increasing the amount of the bid/offer even in the absence of trade.\u003c/p\u003e\n\u003cp\u003eWelfare gain of implicit auction relative to the FCFS rule is calculated as\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\varDelta W=\\frac{1}{2}\\left(\\varDelta {p}_{IA}+ {\\varDelta \\widehat{p}}_{IA}^{Autarky}\\right)\\times {q}_{IA}-\\frac{1}{2}\\left(\\varDelta {\\widehat{p}}_{FCFS}+ {\\varDelta \\widehat{p}}_{FCFS}^{Autarky}\\right)\\times {\\widehat{q}}_{FCFS}\\)\u003c/span\u003e \u003c/span\u003e (2).\u003c/p\u003e\n\u003cp\u003eThis is interpreted as production cost savings resulting from more efficient merit order dispatch across zones (Mansur and White, 2012). Moreover, it prevents PTR holders from withholding interconnector capacity from the day-ahead market, which potentially comes from exercising market power.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e8.2 Counterfactual prediction\u003c/h2\u003e\n\u003cp\u003eTo estimate the welfare gain of the implicit auction, I predict a counterfactual market price gap \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta \\widehat{p}}_{IA}^{Autarky}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {\\widehat{p}}_{FCFS}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta \\widehat{p}}_{FCFS}^{Autarky},\\)\u003c/span\u003e\u003c/span\u003eand trade quantity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{q}}_{FCFS}\\)\u003c/span\u003e\u003c/span\u003e if the full implicit auction had not replaced FCFS after October 2018. To obtain the best predictive model, I use the least absolute shrinkage and selection operator (LASSO), random forest (RF), deep neural network (DNN), and linear regression to compare the mean square error (MSE) of the predicted counterfactuals. These machine-learning methods can flexibly approximate the conditional mean of the dependent variable, and predict counterfactuals better than linear regression.\u003c/p\u003e\n\u003cp\u003eLASSO adds a regularizer of the sum of the absolute values of the coefficients to the linear regression so that the model can avoid in-sample overfitting (Mullainathan and Spiess, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). I use 10-fold cross-validation to select the optimal value of the tuning parameter. To train the model, I use the R package \u0026ldquo;gmnnet.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eRF combines a regression tree model with bagging. The regression tree sequentially splits the sample based on the threshold value of an explanatory variable and predicts an outcome variable by splitting sub-samples. The RF takes the average of several hundred different trees constructed by bootstrapping the training sample with randomly chosen subsets of explanatory variables. One of the advantages of RF is that it requires relatively little tuning (Athey and Imbens, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). To train the model, I use the R package \u0026ldquo;randomForest.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eFor LASSO and RF, I divide the pretreatment sample into training and test data in a ratio of eight to two. The sample is not randomly split to ensure that the training data are always older than the test data. Pretreatment data are collected from June 4, 2016, to September 30, 2019. I use the training data to estimate the model by minimizing the MSE. The test data are used to evaluate the predictive performance of the models with the loss function (MSE).\u003c/p\u003e\n\u003cp\u003eAs DNN requires hyperparameter tuning, the sample is divided into training, validation, and test data in a ratio of six to two to two, respectively. Again, the sample is split nonrandomly. The validation data are used to select the optimal hyperparameters to avoid overfitting. The test data are reserved for evaluating the model performance. The training data are standardized by extracting the mean and dividing it by the standard deviation. The validation and test data use the same mean and standard deviation values for standardization. A deep neural network is trained using training data with a rectified linear unit activation function and the Adam optimizer, which is a stochastic gradient descent algorithm. Hyperparameters include several hidden layers, number of units, dropout rate, and learning rate. To find the best set of hyperparameters efficiently, a \u0026ldquo;hyperband\u0026rdquo; is used (Li et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The number of epochs is set to 800, and the batch size is 64. To avoid overfitting, early stopping is introduced, which completes training when the MSE does not decline consecutively over five epochs. The model is trained using the \u0026ldquo;TensorFlow\u0026rdquo; and \u0026ldquo;Keras\u0026rdquo; libraries in Python. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e lists the combinations of the search space of the hyperparameters and selected values.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHyperparameters for tuning\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHyperparameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSearch space\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of hidden layers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1, 2, 3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of units\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32, 64, 96, 128\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDropout rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0, 0.25, 0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLearning rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001 to 0.001 (log sampling)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eTo calculate these counterfactuals, I first predict the counterfactual reserved interconnector capacity if the FCFS was maintained. I model the reserved interconnector capacity under the FCFS rule as a function of the gross available interconnector capacity (GAIC) and time-fixed effects:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$${q}_{reserve,t}^{}={\\alpha }_{0}+{\\beta }_{1}{GAIC}_{t}+{FE}_{t}+{e}_{t} \\left(3\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e,\u003c/p\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({q}_{reserve,t}^{}\\)\u003c/span\u003e\u003c/span\u003e is the reserved interconnector capacity under FCFS. G\u003cem\u003eAIC\u003c/em\u003e is operating capacity minus the margin. This equation implies that the reserved interconnector capacity is restricted by G\u003cem\u003eAIC\u003c/em\u003e. FE\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e denotes the hour, week, and month effects.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the predictive performance of each model. The first column is the result of the linear regression, second column, the LASSO, third column uses RF, and fourth column, the DNN. As the RF model has the smallest test MSE for both interconnectors, I predict the counterfactual reserved interconnector capacity if the implicit auction was not introduced by RF.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTest MSE of reserved interconnector capacity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOLS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLASSO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDNN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e509.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e512.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e450.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e668.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKanmon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147977.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147337.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130437.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229355.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: In the RF, six variables are randomly sampled as candidates for each split.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the parameters estimated by RF, I predict a counterfactual reserved interconnector capacity of HH and Kanmon, if the FCFS rule was maintained after October 2018, using the data in the post-treatment period: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{q}}_{reserve, t}^{post}\\)\u003c/span\u003e\u003c/span\u003e. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e shows the observed and counterfactual reserved interconnector capacity. The black dots indicate the actual reserved capacity before the implicit auction, and the red dots show the counterfactual reserved capacity after October 2018.\u003c/p\u003e\n\u003cp\u003eNext, I predict the counterfactual price gap if the FCFS was not replaced by an implicit auction, as follows:\u003c/p\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\varDelta {{p}}_{{D}{A}, {t}}={{\\alpha }}_{0}+{{\\beta }}_{1}{{N}{A}{I}{C}}_{{t}}+{{\\beta }}_{2}{S}{u}{p}{p}{l}{{y}}_{{t}}+{{\\beta }}_{3}{D}{e}{m}{a}{n}{{d}}_{{t}}+{{F}{E}}_{{t}} +{{\\epsilon }}_{{t}} \\left(4\\right).$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eEquation (4) is the same as Eq.\u0026nbsp;(1), except that there is no volume variable in the model, and I only use data from the pretreatment period (i.e., before October 2018). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows that RF can predict the day-ahead market price gap between Hokkaido and the East with the lowest MSE, while OLS produces the lowest MSE for the West and Kyushu compared to other machine-learning methods.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTest MSE of day-ahead market price gaps\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOLS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLASSO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDNN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpread between Hokkaido and the East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpread between the West and Kyushu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: In the RF, seven variables are randomly sampled as candidates at each split.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThus, I use RF to predict the counterfactual price gap between Hokkaido and the East, and OLS to predict that of the West and Kyushu in the post-treatment period. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e presents the observed and counterfactual market price gaps. The black dots show the daily-mean actual price gap, and the circles represent the daily-mean counterfactual price gap, after October 2018.\u003c/p\u003e\n\u003cp\u003eI then estimate the counterfactual trade quantity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{q}}_{FCFS}\\)\u003c/span\u003e\u003c/span\u003e allocated in the day-ahead market if the FCFS rule was not abolished as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$${\\widehat{q}}_{FCFS}={\\widehat{NAIC}}_{t} if \\varDelta {\\widehat{p}}_{DA,t}\\ne 0$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eNote that the estimated \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{NAI{C}_{t}}\\)\u003c/span\u003e\u003c/span\u003e and counterfactual price gap \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {\\widehat{p}}_{DA,t}\\)\u003c/span\u003e\u003c/span\u003e are used. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{q}}_{FCFS}\\)\u003c/span\u003e\u003c/span\u003e is equal to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{NAI{C}_{t}}\\)\u003c/span\u003e\u003c/span\u003e as long as the market price gap is non-zero. This relationship holds because the market-splitting algorithm allocates all the remaining capacity in the wake of congestion (Marmiroli et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNext, I calculate the counterfactual day-ahead market price under autarky \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {p}_{DA,t}^{autarky}\\)\u003c/span\u003e\u003c/span\u003e as follows:\u003c/p\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\varDelta {p}_{DA,t}^{autarky}=\\left\\{\\begin{array}{c}\\varDelta {p}_{DA,t}-\\widehat{{{\\beta }}_{1}}{NAIC}_{t} under full implicit auction\\\\ {\\varDelta \\widehat{p}}_{\\text{D}\\text{A},\\text{t}}-\\widehat{{{\\beta }}_{1}}{\\widehat{NAIC}}_{t} under FCFS\\end{array}\\right.$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\widehat{p}}_{DA,t}\\)\u003c/span\u003e \u003c/span\u003e is the estimated market price gap under FCFS. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{{\\beta }_{1}}\\)\u003c/span\u003e\u003c/span\u003e is the estimated coefficient of trade effect by Eq.\u0026nbsp;(1). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{NAIC}\\)\u003c/span\u003e\u003c/span\u003e is predicted counterfactual, if the FCFS was maintained in the post-treatment period.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e8.3 Welfare Analysis\u003c/h2\u003e\n\u003cp\u003eFinally, a welfare analysis is conducted according to Eq.\u0026nbsp;(2). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the welfare effects of a full implicit auction. The estimated average price gap between Hokkaido and the East under autarky is 3.93 yen/kWh between April and September 2018, and 9.52 yen/kWh between October 2018 and March 2019. The estimated average price gap between the West and Kyushu under autarky is 0.96 yen/kWh before the full implicit auction and 1.39 yen/kWh after October 2018. I estimate that the counterfactual average price gap between Hokkaido and the East zones under the FCFS after October 2018 would be 3.19 yen/kWh, compared with 3.11 yen/kWh before October 2018. I also find that the counterfactual average price gap between the West and Kyushu zones under the FCFS after October 2018 would be 0.86 yen/kWh for Kanmon, compared to 0.63 yen/kWh before October 2018. The actual average price gap after October 2018 is 6.08 yen/kWh between Hokkaido and the East zones, and 0.43 yen/kWh between the West and Kyushu zones. This implies that if the FCFS rule continued, the average price gaps would be lower than the observed price gap under the implicit auction by 2.89 yen/kWh for HH and higher than that by 0.43 yen/kWh for Kanmon. This is an important implication: implicit auction does not necessarily reduce the price gap because it not only has a trade effect, but because it also has a counteracting volume effect.\u003c/p\u003e\n\u003cp\u003eThe actual average trade quantity under the FCFS is 38.26 MW for HH and 31.00 MW for Kanmon. The counterfactual average trade quantity after October 2018 is 23.83 MW for HH and 436.51 MW for Kanmon. The actual average trade quantity after implicit auction increased to 123.55 MW for HH and 1967.56 MW for Kanmon, respectively. Thus, I estimate that implicit auction increased trade quantity on average by 99.72 MW for HH and by 1531.05 MW for Kanmon, relative to the FCFS.\u003c/p\u003e\n\u003cp\u003eFinally, using Eq.\u0026nbsp;(2), I estimate the implicit auction produced an economic gain of 4.07\u0026nbsp;billion yen ($ 40\u0026nbsp;million) across HH and 6.36\u0026nbsp;billion yen ($63\u0026nbsp;million) across Kanmon after the six-month implementation. The larger gain in Kanmon indicates that it has a larger GAIC. The total annual welfare gain was about 20.86\u0026nbsp;billion yen (USD 208\u0026nbsp;million). This welfare gain was much larger than the one-time implementation cost of the implicit auction, which, according to a member of the JEPX, is 20\u0026ndash;30\u0026nbsp;million yen.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eWelfare impact of trade under FCFS and implicit auction\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eKanmon\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePrice gap between zones (yen/kWh)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAutarky price gap (counterfactual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCFS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImplicit auction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCFS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre 2018/10 (actual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost 2018/10 (counterfactual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImplicit auction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost 2018/10 (actual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDifference between implicit auction and FCFS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e2.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e-0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTrade Quantity (MWh)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCFS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre 2018/10 (actual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost 2018/10 (counterfactual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e436.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImplicit auction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost 2018/10 (actual)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e123.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1967.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDifference between implicit auction and FCFS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e99.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e1531.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGain from trade (billion yen/ 6 months)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDifference between implicit auction and FCFS\u003c/p\u003e\n\u003cp\u003e(Billion yen/ 5 months)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"9. CONCLUSION","content":"\u003cp\u003eThis study examines one of the market-based congestion management methods of interconnector transmission capacity: the implicit auction. Implicit auction has two conflicting effects on the day-ahead market, the trade and volume effects. The trade effect arises from the increased trade through interconnectors. Implicit auction increases the interconnector capacity allocated in the day-ahead market and allows for more trade between the export and import zones. The volume effect implies that the implicit auction increases the number of electricity bids in the day-ahead market by prohibiting incumbents from reserving interconnector capacity for their bilateral contracts. To simulate market outcomes without implicit auction, counterfactual market outcomes are predicted by comparing linear regression with machine learning methods. I find that the RF generally performs better than linear regression. The welfare impact of the implicit auction is estimated to be approximately \u003cspan\u003e$\u003c/span\u003e 40\u0026nbsp;million for HH and \u003cspan\u003e$\u003c/span\u003e 60\u0026nbsp;million for the Kanmon interconnector after six months of implementation. The annual national production cost savings owing to the trade effect were approximately \u003cspan\u003e$\u003c/span\u003e 200\u0026nbsp;million.\u003c/p\u003e \u003cp\u003eThis study highlights the advantages of implicit auction relative to FCFS. Implicit auction not only prevents incumbents from exercising vertical market power but also promotes efficient resource allocation in the day-ahead market. However, it is important to note that congestion persists after the implicit auction, particularly at the HH interconnector. It reflects the fact that there is a volume effect and the interconnector capacity of the HH was only 0.6 GW by the end of February 2019. With an increase in the amount of variable renewable energy generation, additional investment in interconnectors further increases the benefits of the implicit auction.\u003c/p\u003e \u003cp\u003eThere are two limitations to this study due to the lack of available data. First, there is neither interconnector capacity reservation data by company, nor bidding information by company in the day-ahead market. Therefore, it is not possible to verify further whether incumbents exercise market power by using PTR. Second, I cannot estimate the effect of the implicit auction on the reduction of CO2 emission, because transmission system operators only publish aggregate fossil fuel generation data. Implicit auctions may reduce CO2 emissions by decreasing generation from oil and gas-fired power plants that have high marginal costs in an import zone while increasing the CO2 emission by dispatching cheaper coal-fired power plants more frequently in an export zone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available due to confidential company data by globalCOAL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author is grateful to very helpful comments and suggestions from members of Research Project on Renewable Energy Economics, Kyoto University. The author received funding from the employer, Tokyo Foundation for Policy Research (https://www.tkfd.or.jp/en/). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAthey, S., \u0026amp; Imbens, G. W. (2019). Machine learning methods that economists should know about. \u003cem\u003eAnnual Review of Economics\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 685\u0026ndash;725. https://doi.org/10.1146/annurev-economics-080217-053433\u003c/li\u003e\n\u003cli\u003eBrunekreeft, G., Neuhoff, K., \u0026amp; Newbery, D. (2005). Electricity transmission: An overview of the current debate. \u003cem\u003eUtilities Policy\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2) (2 SPEC. ISS.), 73\u0026ndash;93. https://doi.org/10.1016/j.jup.2004.12.002\u003c/li\u003e\n\u003cli\u003eBunn, D., \u0026amp; Zachmann, G. (2010). Inefficient arbitrage in inter-regional electricity transmission. \u003cem\u003eJournal of Regulatory Economics\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(3), 243\u0026ndash;265. https://doi.org/10.1007/s11149-009-9104-5\u003c/li\u003e\n\u003cli\u003eBushnell, J. (1999). Transmission rights and market power. \u003cem\u003eThe Electricity Journal\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(8), 77\u0026ndash;85. https://doi.org/10.1016/S1040-6190(99)00074-3\u003c/li\u003e\n\u003cli\u003eCreti, A., Fumagalli, E., \u0026amp; Fumagalli, E. (2010). Integration of electricity markets in Europe: Relevant issues for Italy. \u003cem\u003eEnergy Policy\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(11), 6966\u0026ndash;6976. https://doi.org/10.1016/j.enpol.2010.07.013\u003c/li\u003e\n\u003cli\u003eEhrenmann, A., \u0026amp; Neuhoff, K. (2009). 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A review of the monitoring of market power: The possible roles of TSOs in monitoring for market power issues in congested transmission systems \u003cem\u003eCMI Working Paper 71\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-regulatory-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rege","sideBox":"Learn more about [Journal of Regulatory Economics](http://link.springer.com/journal/11149)","snPcode":"11149","submissionUrl":"https://submission.nature.com/new-submission/11149/3","title":"Journal of Regulatory Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"transmission right, market power, congestion management, interconnector, implicit auction","lastPublishedDoi":"10.21203/rs.3.rs-2874856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2874856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the welfare impact of implicit auction on the interconnector transmission capacity in Japan. The first-come-first-served rule allows incumbent generation or retail firms to exercise vertical market power by withholding the interconnector capacity and create congestion in the day-ahead market. The implicit auction allocates all the capacity simultaneously with energy in the day-ahead market. It prevents them from strategically reserving their physical transmission capacity ex ante and increases cross-zonal trade volumes in the day-ahead market. Increased trade reduces the price gap between the import and export zones. I use machine-learning methods to estimate the welfare impact of implicit auction. I predict the counterfactual market outcomes without implicit auction, and find that the trade effect of implicit auction is over $ 200\u0026nbsp;million per year. The study also finds that implicit auction does not necessarily reduce the price gap between export and import zone because it not only has a trade effect but also a counteracting volume effect.\u003c/p\u003e\n\u003cp\u003eJEL codes: L94, Q41\u003c/p\u003e","manuscriptTitle":"Welfare Effects of Implicit Auction on Interconnector Capacity: Evidence from Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-05 21:33:14","doi":"10.21203/rs.3.rs-2874856/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-11-03T17:42:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-06T06:25:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-20T11:11:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17ef4d74-5583-4ffb-927e-1b6f1f398574","date":"2023-05-24T18:34:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"de350772-e083-46b0-bb8e-e13ae97fed76","date":"2023-05-15T09:16:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-10T02:27:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-05-03T02:40:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-05-03T02:40:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Regulatory Economics","date":"2023-04-29T01:34:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-regulatory-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rege","sideBox":"Learn more about [Journal of Regulatory Economics](http://link.springer.com/journal/11149)","snPcode":"11149","submissionUrl":"https://submission.nature.com/new-submission/11149/3","title":"Journal of Regulatory Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b8815a13-1a22-4c5d-bf52-1c54149c95e1","owner":[],"postedDate":"May 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-04T15:54:05+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-05 21:33:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2874856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2874856","identity":"rs-2874856","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}