Japan Electric 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.

Electrostatic Precipitator Design Optimization for the Removal of Aerosol and Airborne Viruses

In the midst of the COVID-19 pandemic, new requirements for clean air supply are introduced for heating, ventilation, and air conditioning (HVAC) systems. One way for HVAC systems to efficiently remove airborne viruses is by filtering them. Unlike disposable filters that require repeated purchases of consumables, the electrostatic precipitator (ESP) is an alternative option without the drawback of reduced dust collection efficiency in high-efficiency particulate air (HEPA) filters due to dust buildup. The majority of viruses have a diameter ranging from 0.1 μm to 5 μm. This study proposed a two-stage ESP, which charged airborne viruses and particles via positive electrode ionization wire and collected them on a collecting plate with high voltage. Numerical simulations were conducted and revealed a continuous decrease in collection efficiencies between 0.1 μm and 0.5 μm, followed by a consistent increase from 0.5 μm to 1 μm. For particles larger than 1 μm, collection efficiencies exceeding 90% were easily achieved with the equipment used in this study. Previous studies have demonstrated that the collection efficiency of suspended particles is influenced by both the ESP voltage and turbulent flow at this stage. To improve the collection efficiency of aerosols ranging from 0.1 μm to 1 μm, this study used a multi-objective genetic algorithm (MOGA) in combination with numerical simulations to obtain the optimal parameter combination of ionization voltage and flow speed. The particle collection performance of the ESP was examined under the Japan Electrical Manufacturers’ Association (JEMA) standards and showed consistent collection performance throughout the experiment. Moreover, after its design was optimized, the precipitator collected aerosols ranging from 0.1 μm to 3 μm, demonstrating an efficiency of over 95%. With such high collection efficiency, the proposed ESP can effectively filter airborne particles as efficiently as an N95 respirator, eliminating the need to wear a mask in a building and preventing the spread of droplet infectious diseases such as COVID-19 (0.08 μm–0.16 μm).

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.

The cost of war: Impact of sanctions on Russia following the invasion of Ukraine

We use a computable general equilibrium model of world trade to quantify the possible impact of economic sanctions imposed by Western and other countries in response to Russia’s invasion of Ukraine. If sender countries chose 100 % import tariffs and export taxes on trade with Russia, Russia’s GDP would decline by 3–7 % due to the resulting significant reduction in exports. By contrast, the GDP loss for those countries would be 0.2 % for the European economies, but only about 0.05 % for Japan. Although unlikely, the effect of China’s participation in the sanctions would be more significant than that of India. There are concerns about food and energy crises due to economic sanctions against Russia, but the effect on food supplies would not be a serious problem for either senders or third parties. The impact on energy supplies would affect all senders to some extent; for example, there would be a 3% reduction of energy consumption and a 3–4 % rise in electricity and town gas prices in Japan.

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.

Cross-regional effects of renewable power generation on the electricity market: an empirical study on Japan's electricity spot market

ABSTRACT We examined the indirect effects of renewable power generated in Tohoku on the electricity spot market in Tokyo, through the channel of cross-regional interconnection. By adopting a two-step regression analysis framework, we investigated how the renewable power generated in Tohoku affects Tokyo’s spot price and market volatility. Our results indicate that the electricity spot market is affected not only by within-regional renewable power generation but also by cross-regional transmitted renewable power generation through interconnection. The relatively higher price reduction effect of transmitted power can be attributed to higher price elasticity under a tight supply-demand balance. Our results confirm the cross-regional price stabilization effect of renewable power generation in Tohoku. We attribute the negative effect on realized volatility of Tokyo to the relatively stabler renewable electricity supply from Tohoku. Furthermore, we find that renewable power producers – especially solar power producers – are not incentivized by the fixed-price feed-in tariff to meet electricity demand during peak hours.

News From Japan

The history of the use of electricity in Japan began as telecommunication, which started in 1869, one year after the Meiji Restoration. The first electric arc lamp shone in Ginza, Tokyo, in 1882, the same year as the start of operation of Edison's Pearl Street Power Plant in New York. Only four years later (1886), Tokyo Electric Light Company, one of the predecessors of Tokyo Electric Power Company Holdings, started its business as the first electric power company in Japan. Many similar companies started their businesses in the following several years. In these early days, electric power companies in eastern Japan imported 50-Hz generators from Allgemeine Elektricitäts-Gesellschaft (AEG) in Germany, and those in western Japan imported 60-Hz generators from General Electric Company in the United States. From this historical background, AC 50 Hz is still used in eastern Japan and 60 Hz in western Japan.

100% renewable energy in Japan

Low-cost solar photovoltaics and wind offer a reliable and affordable pathway to deep decarbonization of energy, which accounts for three quarters of global emissions. However, large-scale deployment of solar photovoltaics and wind requires space and may be challenging for countries with dense population and high per capita energy consumption. This study investigates the future role of renewable energy in Japan as a case study. A 40-year hourly energy balance model is presented of a hypothetical 100% renewable Japanese electricity system using representative demand data and historical meteorological data. Pumped hydro energy storage, high voltage interconnection and dispatchable capacity (existing hydro and biomass and hydrogen energy produced from curtailed electricity) are included to balance variable generation and demand. Differential evolution is used to find the least-cost solution under various constraints. This study shows that Japan has 14 times more solar and offshore wind resources than needed to supply 100% renewable electricity and vast capacity for off-river pumped hydro energy storage. Assuming significant cost reductions of solar photovoltaics and offshore wind towards global norms in the coming decades driven by large-scale deployment locally and global convergence of renewable generation costs, the levelized cost of electricity is found to be US$86/Megawatt-hour for a solar-dominated system, and US$110/Megawatt-hour for a wind-dominated system. These costs can be compared with 2020 average system prices on the spot market in Japan of US$102/Megawatt-hour. Cost of balancing 100% renewable electricity in Japan ranges between US$20–27/Megawatt-hour for a range of scenarios. In summary, Japan can be self-sufficient for electricity supply at competitive costs, provided that the barriers to the mass deployment of solar photovoltaics and offshore wind in Japan are overcome.

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.

Willingness-to-pay promoted renewable energy diffusion: The case of Japan's electricity market

The present work aims to simulate the willingness-to-pay (WTP) for renewable energy (RE) in Japan under various socioeconomic conditions and further analyzes penetration process of WTP-promoted RE into Japan's energy sector from 2015 to 2050. The analysis was performed by incorporating the simulated WTP for RE into a multiregional optimal generation planning model, applying Japan's mid-century CO2 emissions target. The results show that total WTP in 10 aggregated regions by 2050 would increase to varying degrees than its initial value in 2015 under Japan's unique socioeconomic circumstances of gradual economic growth yet decreasing population. These increasing WTP would create an opportunity for RE expansion into generation mix, allowing increase of share of generation by solar photovoltaic and wind power from 0.7% to 4.3–9.9% by 2050. With integrating WTP-promoted RE into the generation mix, it would promote to achieve the feasible emission reductions by up to 8% of 2015 emission levels and decrease the cost gap for shifting of generation mix from baseline to zero-carbon emission target. All these results indicated the significant roles of WTP for RE penetration into the generation mix in reaching a completely decarbonized power system.

Calibration Scheme of Impulse‐High‐Voltage Measuring System Traceable to National Standards of Japan

It is recommended in IEC 60060‐2:2010 that measuring systems employed in high‐voltage impulse tests of electric apparatuses are calibrated directly or indirectly through comparison with a national standard. Japan High‐voltage Impulse testing Laboratory Liaison has attempted to establish a calibration scheme of impulse‐high‐voltage measuring system traceable to national standards in cooperation with Japan Electric Meters Inspection Corporation. In this paper, the calibration of the working‐standard measuring systems employed in the established calibration scheme are introduced. Nextly, uncertainties of the calibration by the established scheme are explained. Finally, the traceability system of the established calibration scheme is presented. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Impact of Inelastic Seismic Design Using Elastic–Plastic Pipe Supports on Vibration Response of Piping Systems

Abstract This study presents the response mitigation effect of piping systems by inelastic seismic design based on elastic–plastic property of steel pipe supports. The inelastic seismic design to control vibration by absorbing energy using elastic–plastic properties of materials can be one of useful ideas. The design idea to use the elastic–plastic behavior of pipe supports is addressed in Technical Code for Seismic Design of Nuclear Power Plants (JEAC4601) published by the Japan Electric Association in Japan. Here, the component named an elastic–plastic pipe support is proposed as an energy-absorbing element. However, in order to put the inelastic seismic design using the elastic–plastic pipe supports into practical use, it is necessary to accumulate more findings related to the seismic response and the application range. This study aims to investigate the applicability of the inelastic seismic design taking the elastic–plastic pipe supports in the piping systems and to increase the basic findings. In this study, the seismic response analysis using three-dimensional piping system with an elastic–plastic pipe support was conducted. As a result, it was found that the elastic–plastic pipe support affected the seismic response largely. Additionally, the vibration characteristics, the response acceleration, and the load generated in the piping system were discussed relating to the plastic deformation and the plasticity rate of the elastic–plastic pipe support.

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.