Japan Electric Power Exchange Research Articles

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

Hybrid local energy markets: Incorporating utility function-based peer risk attributes, renewable energy integration, and grid constraints analysis

This study proposes a novel hybrid Local Energy Market (LEM) trading framework meticulously designed for efficient energy trading among distribution network participants. The proposed LEM hybrid modeling framework aims to maximize social welfare by considering the behavior of market participants and incorporating network constraints into the transactional framework. To address the consumption behavior of market participants, the concept of the utility function with peer risk attributes is employed, differentiating between flexible and inflexible demands. Additionally, it adeptly manages various generation types, including diesel generators, wind and solar generators, and storage, each characterized by a unique cost function. The investigation extends to the analysis of the Japan Electric Power Exchange market (JEPX), taking into account the residential electricity demand in Tokyo, Japan, by comparing with community-based (CB) and peer-to-peer (P2P) markets and scrutinizing market player behavior across multiple scenarios. These scenarios include comparisons of different market structures, variations in prices, considerations of grid constraints, and diverse weather scenarios. The proposed framework undergoes rigorous evaluation utilizing the IEEE 33-bus and 69-bus standard test systems, with results demonstrating its practicality, superiority of the proposed market compared to the CB and P2P markets, and providing valuable insights into the intricate interplay between market players’ behavior and the dynamics of diverse market structures, price fluctuations, grid constraints, and weather scenarios.

Analyzing the influence of web search behavior on electricity market price: a case study of Japan electric power exchange

The Japan Electric Power Exchange (JEPX) has introduced a feed-in premium to promote the trading of renewable energy electricity in the market; thus, the exchange has become increasingly important for RE companies to maintain profitability in market trading. However, electricity prices are not only affected by directly measurable factors such as electricity demand, fuel prices, and weather but also by corporate bidding strategies, social conditions, and other human behaviors, making it difficult to predict electricity prices. Given that electricity demand is related to human behavior, this study focuses on web search behavior and clarifies the relationship between keyword search volumes and electricity market prices in Japan. Correlation and vector autoregression analyses results show a moderately strong positive correlation between the logarithmic difference of the keyword search volume and that of the electricity price. In addition, we find that the logarithmic difference of the electricity price tends to increase when that of the keyword search volume on the previous day increases. These results suggest that search volumes of specific keywords can be effective explanatory variables for area price prediction models and can help identify signs of price spikes.

GIS based JEPX spot prices forecasting system using solar power generation focusing on lowest prices

In recent years, the proportion of solar power generation in Japan's power generation facilities has been increasing. The amount of solar power generation affects the JEPX (Japan Electric Power Exchange) spot prices. When the amount of solar power generation is excessive during the low demand period, the lowest prices sometimes appear in the spot market. On the other hand the existing forecasting method cannot forecast the lowest prices; hence the forecasting error of the existing method is large. In this study, the authors focused on the appearance of the lowest prices and analyzed the conditions under which the lowest prices are likely to appear. As a result, the authors identified the conditions where the lowest price is likely to appear by comparing with the season and past forecasted solar power generation. Then, the JEPX spot prices forecasting system was developed considering the appearance of the lowest prices. The system combined these conditions, and the authors developed the JEPX spot prices forecasting system. This system can accurately forecast the appearance of the lowest prices and spot prices.

Synthesizing the market clearing mechanism based on the national power grid using hybrid of deep learning and econometric models: Evidence from the Japan Electric Power Exchange (JEPX) market

In liberalized electricity markets, formulating the techno-economic functions of each market's participant restricts the resulting market clearing mechanism (MCM) into the market equilibrium. This study aims to synthesize the MCM based on a new perspective that correlates the nationwide energy supply and demand equilibrium with the wholesale market's prices. The nationwide energy capacities are collected from all the Japanese transmission and distribution utilities from 2016 to 2022. The demand curve is structured based on estimating the price elasticity of demand (PED) considering regional and hourly cross-sections using two-stage econometrics. The supply curve is structured based on the merit order effect of all existing energy production technologies. A long short-term-memory forecasting model is employed to capture the non-stationarity and -linearity of the prices. The estimated PED varies spatiotemporally from −0.017 to −0.25. From the synthesized MCMs, Kyushu region's price drop to nearly zero is justified by the 93.5% clean supply and the use of regional interconnections. Nationally, nuclear, hydroelectric, and hydro-storage contributions to the supply mix and the balancing market launching have mitigated the price spike of 2022. In addition, the planned share increase of baseload resources in 2030 drives the prices lower with higher stability versus the current electricity mix.

Incentive-based demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer satisfaction on social welfare and profitability

Incentive-based demand response programs (IBDRPs) are voluntary programs that encourage customers (CUs) to cut their electricity usage during peak periods, in return for receiving incentives from the service provider (SP). This research addresses a comprehensive multi-objective modeling approach, based on maximizing the social welfare function of both SPs and CUs involved in the IBDRPs, considering both the economic drivers and the social factors affecting the market actors. The social aspect of the CUs’ participation in the IBDRPs is evaluated with respect to their satisfaction with the intangible benefits, which cannot be measured in monetary terms. To this aim, a Kano model for customer satisfaction is developed to estimate the level of CUs’ satisfaction with participation in the IBDRPs, taking into account the impact of the four attributes of comfort, flexibility, energy security, and environmental protection. The model then analyzes the proposed IBDRP implemented in the Japan Electric Power Exchange (JEPX) market, using real-time wholesale electricity prices and demand loads in the Tokyo residential areas. The results revealed that, the environmental protection attribute could more positively impact the CUs’ satisfaction level; consequently, their welfare is higher with more reduction in electricity from incentive incomes.

Electricity price spike formation and LNG prices effect under gross bidding scheme in JEPX

This study empirically analyzes the price spikes that occurred in the Japan Electric Power Exchange (JEPX) market in January 2021 and draws energy policy implications from the results. The contributions of this research are as follows. First, we propose a novel structural model to capture electricity price spikes based on the buy and sell bid curves of the JEPX order book and incorporate the effect of LNG spot price fluctuations on the price of electricity. Second, the results of our empirical analysis show that the model can adequately capture the price spike in the JEPX market that occurred in January 2021. Third, the estimation of the model parameters shows that the natural gas spot price immediately shifts the price curve upward. As an important energy policy implication from the results, we propose that to manage the risk of electricity price spikes, the LNG effect on the market players’ bidding behavior, rather than the marginal cost of the supply curve, be considered; from this perspective, the LNG spot price, rather than the trend of the LNG price a few months earlier, must be monitored, and the institutional design of the JEPX market should take this into account.

A Hawkes Model Approach to Modeling Price Spikes in the Japanese Electricity Market

The Japan Electric Power Exchange (JEPX) provides a platform for the trading of electric energy in a manner similar to more traditional financial markets. As the number of market agents increase, there is an increasing need for effective price-forecasting models. Electricity price data are observed to exhibit periods of relatively stable, i.e., low-magnitude, low-variance prices interspersed with periods of higher prices accompanied by larger uncertainty. The price data time series therefore exhibits a temporal non-stationarity characteristic that is difficult to capture with typical time series modeling frameworks. In this paper, we implement models for the occurrence of price spike events where spikes are defined as observing prices above a predefined threshold set here at 25 JPY/kWh. This value corresponds to about the 90th percentile of observed prices during peak trading periods. The price spikes time series is observed to be rare events that occur in clusters. We therefore propose to model the data as a Hawkes process whereby the occurrence of a spike event increases the probability of observing more spikes in the period immediately following a price spike event. We test two variations of the classical Hawkes model: the first variation models the change in the magnitude of the underlying intensity as a function of the magnitude of the price spike while the second variation models the change in the decay rate of the underlying intensity as a function of the magnitude of the price spike. An analysis of the performance of the models based on the mean absolute error (MAE) of the spike occurrence probability, a weighted accuracy index, and the Matthews correlation coefficient (MCC) metrics shows the effectiveness of the variable magnitude variation of the Hawkes model in generating short-term forecasts of the occurrence of price spike events. The modified Hawkes model especially outperforms other candidate models as the length of the forecasting horizon increases.

JEPX spot prices forecasting system using GIS

Japan Electric Power Exchange (JEPX) is the only electricity market in Japan that allows transactions for electric power established in 2005. Several commodities are traded there, such as the spot market and the forward market. In particular, the spot market is a major trading market. The price of the spot market changes depending on the relationship between supply and demand. Therefore, it is important to forecast spot market prices to make a supply and demand plan for the next day. This research focused on the factors that determine supply and demand are related to geospatial information—first, deriving the explanatory variables for JEPX spot prices using Geographic Information System (GIS). Then, constructing the spot price forecasting system by machine learning using the derived explanatory variables. By using this system, it is possible to forecast electricity prices with higher accuracy than existing methods.

Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand

This study aims to develop a mathematical modeling approach to maximize the welfare of the price-responsive customers (CUs) in a wholesale electricity market in Tokyo, Japan. The contributions made by this paper in the quest to determine the role of CUs in a demand response market are twofold. First, the aversion of the CUs to the risk of choosing the Demand Response Programs (DPRs) is taken into account by considering their expected utility from consuming electricity. The proposed model is founded on the customer theory in microeconomics, using the concept of the expected utility function to model the behavior of the risk-averse CUs in response to different DPRs. Second, it introduces an hourly-based model for the short-term price elasticity of demand, considering the day-ahead price mechanism defined in the Japan Electric Power Exchange (JEPX) market. The estimated price elasticities are used in a price elasticity matrix of demand (PEMD) to precisely reflect the different response strategies, including flexible, in-flexible forward-shifting, backward shifting, and optimizing responses. An accurate day-ahead hourly load forecasting is performed, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, which is trained on four years of data provided by the Tokyo Electric Power Company (TEPCO), with a mean absolute percentage error (MAPE) of 0.94%. The developed model is used to analyze the CUs’ behaviors with different response strategies in the JEPX market in Tokyo. By applying the Time-of-Use (TOU) and Real-Time-Pricing (RTP) programs, the results reveal a peak reduction potential of 10.7% and 7.3%, respectively, for the flexible CUs. Applying the RTP program to the curtailable loads can achieve a 7.7% reduction in daily peak demand and a 1.6% reduction in daily electricity consumption.

Econometric analysis of pricing and energy policy regulations in Japan electric power exchange spot market

This study aims to provide a quantitative overview of the energy policy regulations concerning the Japan spot market liberalization and analyzes the market clearing price (MCP) and the regional spot prices based on the hourly contribution of the supply stack mix and the merit order effect. In addition, it estimates the regional spot prices interdependency on the MCP. The study acquires an hourly dataset in panel data format from 2016 to 2020 with total observations of 438,240 data records. The developed econometric models include panel data regression using fixed-effect estimator, ordinary least square, and two-stage least square equations. From the results, a one GWh addition from nuclear, hydroelectric, geothermal, biomass, solar, or wind power production technologies decreases spot prices. Contrarily, adding one GWh from thermal, hydroelectric storage, or regional interconnections raises the spot prices by 27.4, 606, 103 ¥/MWh, respectively. The spot prices of Hokuriku and Chugoku tend to offset the price spikes in Kansai, while Tokyo and Tohoku spot prices are significantly related. Adding one ¥/kWh to Tohoku spot price is estimated to raise the MCP by 0.35 ¥/kWh, while the addition of one ¥/kWh to Tokyo spot price is estimated to increase the MCP by 0.04 ¥/kWh. Furthermore, Kyushu and Tohoku, followed by Chubu, have appreciable control over the MCP. The study further highlighted policy implications to constrain the applications of hydroelectric storage as the costliest solution to balance the intermittency of renewable energy sources.

The Impact of Variable Renewable Energy Penetration on Wholesale Electricity Prices in Japan Between FY 2016 and 2019

The merit order effect (MOE), which renewable energy sources can decrease wholesale electricity prices, plays an important role in establishing low-carbon societies. After the liberalization of the electricity market, the trade volume of the Japan Electric Power Exchange (JEPX) day-ahead spot market drastically increased between 2016 and 2019; however, price spikes still occur often. Ordinary least squares and quantile regression analyses were applied in this study to investigate how wind and solar photovoltaics (PV) energy generation affect the JEPX day-ahead spot price by time, price range, and area, and we concluded that the MOE of wind increased between 2016 and 2019 while that of PV decreased during this time. In regard to the high price ranges, although wind generation is not significant in terms of reducing price spikes, PV had this effect in 2016 and 2017 but not during the other years covered. The study area was divided into four regions, and each area followed trends that were different from those of the national analysis. Overall, the key finding of our study is that wind power has more potential to reduce electricity prices than PV.

Simultaneous hedging strategy for price and volume risks in electricity businesses using energy and weather derivatives

In general, electric utilities face intrinsic risks as their revenues depend on high volatility factors including price and volume of sales/procurements. Aiming for an effective strategy to control those risks, we construct a hedging portfolio based on energy and weather derivatives, which can minimize the revenue fluctuations. To this end, we provide unique methods by applying nonparametric regression techniques to synthesize the payoff functions of derivatives that change with time, based on tensor product spline functions. The proposed methodology enables us to incorporate two dimensional smoothing conditions of the underlying asset price and expiration date with a yearly cyclical trend. Moreover, we show that the applied method of Analysis of Variance (ANOVA) decomposition can separate deterministic time trends from the original multivariate payoff functions, and hence, a simultaneous estimation of multiple derivatives payoff functions is achieved. By assuming that revenues have yearly cyclical trends even when viewed at the rate of annual change, we also introduce a spline function with cross variables to consider such a mixed effect. In addition, we propose new standardized derivatives with the square of the temperature prediction error as the underlying asset. Empirical analysis using data from both the Japan Electric Power Exchange (JEPX) and PJM in the U.S. demonstrates the significant hedging effect and supports the versatility of the proposed modeling approach.

Multipurpose Strategy for Energy Storage System Based on Capital Asset Pricing Model With Ensemble Approach

In this paper, we present a multipurpose operation strategy for the efficient distribution of power to multiple power services from a single battery energy storage system (BESS). The proposed operation strategy uses a modern portfolio theory of financial engineering known as the capital asset pricing model to determine the allocation of BESS discharge power. Using this strategy, it is possible to quantitatively evaluate the expected return and risk in BESS operation and achieve the maximum expected return for the selected acceptable risk. Further, the predictability of the expected return is improved by applying the ensemble approach to the estimation model of the expected return. A one-year long performance test was conducted at the Japan Electric Power Exchange using the proposed operation strategy. A mean absolute error of approximately 2.0% between the expected return of the proposed strategy and the actual return was obtained, confirming the accuracy of the proposed strategy.

One-week-ahead electricity price forecasting using weather forecasts, and its application to arbitrage in the forward market: an empirical study of the Japan Electric Power Exchange

Although forecasting one-week-ahead average electricity prices is necessary for decision-making such as evaluating forward contracts, its modeling has not been sufficiently studied. Therefore, to find a suitable forecasting approach, this study constructs and compares multiple parsimonious models using widely published weekly weather forecasts and then applies them to arbitrage trading in the forward market. In particular, we clarify the following empirical results using the data from Japan Electric Power Exchange. First, instead of using forecasted temperature directly as an explanatory variable, the two-step forecasting method using measured temperature as an intermediate variable is more likely to reduce forecast errors. Second, quantile regression has better density forecast accuracy than the generalized autoregressive conditional heteroscedasticity model. Third, the logarithmic conversion for prices tends to improve forecast accuracy. Fourth, one-week-ahead weather forecasts can significantly improve both the price forecast accuracy and the arbitrage profit. The proposed arbitrage strategy can be used by many participants because it can be flexibly changed according to the player’s risk tolerance. In addition, our forecasting/trading method, based on published weather forecasts, has wide applicability in that it can be constructed even in markets where system information is not sufficiently disclosed.

A Speculative Trading Model for the Electricity Market: Based on Japan Electric Power Exchange

Renewable energy sources produce less environmental impact and have little marginal cost. Thus, because of these characteristics, it is desirable to disseminate it for the purpose of economic efficiency. Because of the uncertainty in the supply of renewable energy and the special feature of electricity as a good, such as merit order curve, introducing forward markets is an essential factor in a liberalized market. In European countries, which have already established several mechanisms for managing liquidity including markets with several timelines, the market liquidity invites the investor to perform some speculative action. We present a simple electric power market model to analyze the speculative actions of electricity suppliers and the price effect of such actions. Moreover, we found that the speculative action improves the inelasticity of the demand in electricity market.

The Effect of Renewable Energy Generation on the Electric Power Spot Price of the Japan Electric Power Exchange

This study aims to explore the relationship between renewable energies and the electric power spot price of the Japan Electric Power Exchange (JEPX). By using panel data analysis and proxy modeling, this work attempts to estimate how renewable energies (displayed through the proxies) and other factors influence the electric power spot price in Japan. Based on an analysis of the estimations, some policy implications have been proposed, such as to incorporate weather information into the price forecast, or to provide a guide to more effectively transact on the JEPX.

Illiquidity in the Japan electric power exchange

Historical data of system prices and trading volumes of electric power over the 48 half-hour intra-daily intervals in the Japan Electric Power Exchange (JEPX) from August 2005 to March 2015 are analyzed. The data allow me to compute several representative measures of economic illiquidity, namely, Amihud's price-impact measure, Roll's implied spread cost measure, and an idiosyncratic volatility measure. Based on a dynamic panel regression framework allowed by a panel reinterpretation of the data, I establish that (a) these illiquidity measures comove to some extent, (b) a higher trading volume of electric power does not lower the spread cost, (c) a positive contribution of the price-impact measure to the return is stronger than that of the spread cost, (d) the great earthquake might disturb the risk-return tradeoff, and (e) a negative return-volatility relationship may stem from overlooked upward spikes in electric power prices.

Optimal operation strategy using chance-constrained programming for cooperative multi-area small power systems in remote islands

Due to the depletion of fossil resources and the increase in carbon dioxide emissions, renewable energy systems (RESs) such as photovoltaic (PV) and wind generator (WG) have become an active player in power systems (PSs). In Japan, diesel generators (DGs) are the means of power generation in most isolated islands. Therefore, the introduction of RESs into these islands is of great importance. However, due to the uncertainty of RESs, battery energy storage systems (BESSs), fuel cells (FCs), and exploiting the electric vehicles (EVs) owned by customers are expected as vital power sources for the next generation. Moreover, demand response programs are utilized in this paper. As a result of reforming PSs in recent years, transactions on power markets have also been increasing in Japan. The Japan electric power exchange provides day-ahead market, intraday hour-ahead market, and forward market. It is expected that these markets make it possible to deal with uncertainties related to the generated power fluctuation. This paper presents the optimal operation method for multi-area PSs consisting of DG, PV, WG, BESS, and FC by using chance-constrained programming. The proposed method has achieved reduction of the total operational cost of the multi-area small power systems by using FC, household EVs, and two power markets.

Optimal operation method coping with uncertainty in multi-area small power systems

Japan contains a vast number of isolated islands. Majority of these islands are powered by diesel generators (DGs), which are operationally not economical. Therefore, the introduction of renewable energy systems (RESs) into these area is very much vital. However, the variability of RESs as a result of weather condition as well as load demand , battery energy storage system (BESS) is brought into play. Demand response (DR) programs have also been so attractive in the energy management systems for the past decades. Among them, the real-time pricing (RTP) has been one of the most effective demand response program being utilized. This program encourages the customer to increase or reduce the load consumption by varying the electricity price. Also, due to the increase in power transaction market, Japan electric power exchange (JEPX) has established spot (day-ahead), intraday hour-ahead, and forward market programs. This paper utilizes day-ahead and hour-ahead markets, since these markets can make it possible to deal with uncertainty related to generated power fluctuations. Therefore, this paper presents the optimal operation method coping with the uncertainties of RESs in multi-area small power systems. The proposed method enables flexibility to correspond to the forecasting error by providing two kinds of power markets among multi-area small power systems and trading the shortage and surplus powers. Furthermore, it accomplishes a stable power supply and demand by RTP. Thus, the proposed method was able to reduce operational cost for multi-area small power systems. The process of creating operational plan for RTP, power trading at the markets and the unit commitment of DGs are also presented in this paper. Simulation results corroborate the merit of the proposed program.